Validating Non-Targeted Analysis in Forensic Chemistry: A Roadmap from Foundational Principles to Courtroom Confidence

Abstract

Non-targeted analysis (NTA) using high-resolution mass spectrometry is transforming forensic chemistry by enabling the comprehensive detection of known and unknown chemicals in complex evidence. This article provides a structured guide for researchers and forensic professionals on validating these powerful but complex methods. We explore the foundational principles of NTA, detail advanced methodological workflows for applications like seized drug and fire debris analysis, address critical troubleshooting and optimization challenges, and finally, establish a rigorous framework for validation and comparative assessment to ensure results meet the stringent standards required for legal admissibility.

Beyond Targeted Screening: Foundational Principles and the Power of Non-Targeted Analysis in Forensics

Non-targeted analysis (NTA) represents a paradigm shift in analytical chemistry, enabling comprehensive characterization of chemical mixtures without prior knowledge of their composition. This approach has gained significant traction in forensic chemistry and drug development for its ability to identify novel compounds, unexpected contaminants, and transformation products that traditional targeted methods overlook. By combining high-resolution mass spectrometry with advanced computational tools, NTA provides researchers with a powerful hypothesis-generating tool for forensic evidence examination, exposomics, and pharmaceutical impurity profiling. This guide examines the performance, applications, and methodological considerations of key NTA approaches, providing experimental data and protocols to inform their implementation in forensic research settings.

Non-targeted analysis is broadly defined as an analytical approach that characterizes the chemical composition of a sample without relying on a priori knowledge of its chemical content [1]. Unlike targeted methods that focus on specific predetermined analytes, NTA aims to capture a broader range of compounds present in a given sample, expanding beyond the traditional targeted framework [2]. This methodology is particularly valuable in forensic contexts where sample composition may be unpredictable or complex, such as in the identification of novel psychoactive substances, drug impurities, or trace evidence compounds.

The NTA workflow encompasses several related but distinct approaches: true non-targeted analysis for completely unknown compound discovery, suspect screening analysis (SSA) to identify compounds from predefined suspect lists, and sample classification using detected chemical profiles [1] [3]. The flexibility of NTA makes it indispensable for forensic applications where unknown or unexpected compounds may be present in evidence samples, including biological specimens, seized materials, and environmental forensics [4] [5].

The analytical foundation of NTA relies heavily on high-resolution mass spectrometry (HRMS) platforms, typically coupled with liquid or gas chromatography (LC or GC) separation techniques [2] [3]. These technologies enable the detection and identification of compounds based on accurate mass measurements, isotopic patterns, and fragmentation spectra, providing the data richness necessary for comprehensive chemical characterization.

Key Approaches in Non-Targeted Analysis

Conceptual Framework and Definitions

The NTA landscape encompasses several distinct but complementary approaches, each with specific applications in forensic research:

• True Non-Targeted Analysis: This discovery-focused approach aims to identify completely unknown compounds without using predefined suspect lists. It relies on first principles and careful evaluation of experimental data to postulate chemical identities for unknown features [1] [3]. This approach is particularly valuable for detecting novel compounds or transformation products not previously documented in databases.

• Suspect Screening Analysis (SSA): This approach screens samples against lists of suspected chemicals that may be present based on prior knowledge or hypothesis. While not having quantitative calibration for these suspects, SSA uses available reference data (e.g., spectra, retention time) for identification [2] [1]. SSA strikes a balance between discovery and confirmation, making it highly efficient for monitoring specific classes of compounds relevant to forensic investigations.

• Sample Classification: This approach focuses on using chemical profiles (containing both unknown and identified chemicals) to classify samples based on their origin, authenticity, or other relevant characteristics [1]. This has applications in forensic source attribution, fingerprinting, and comparative analyses.

Comparative Performance of NTA Approaches

The table below summarizes the key characteristics, strengths, and limitations of each primary NTA approach:

Table 1: Comparison of Non-Targeted Analysis Approaches

Approach	Primary Objective	Data Requirements	Identification Confidence	Ideal Forensic Applications
True Non-Targeted	Discover completely unknown chemicals	High-resolution MS/MS data, computational tools for structure elucidation	Variable (levels 3-5) [2]	Novel psychoactive substance identification, transformation product discovery, unknown impurity profiling
Suspect Screening	Identify known chemicals from suspect lists	Suspect lists with chemical information, reference spectra/metadata	Higher (levels 1-3) when reference standards are available [3]	Monitoring emerging contaminants, drug metabolite identification, targeted class-based screening
Sample Classification	Classify samples using chemical profiles	Chemical profiles from multiple samples, chemometric tools	Comparative rather than identificatory	Source attribution, fingerprint age estimation, comparative evidence analysis

The selection of an appropriate NTA approach depends heavily on the research question and available resources. True non-targeted methods offer the greatest potential for discovery but require more sophisticated data interpretation and may yield lower identification confidence. Suspect screening provides a practical middle ground that leverages growing chemical databases to efficiently identify compounds of interest. In practice, many forensic studies employ a hybrid approach, using suspect screening for prioritized compounds while remaining open to unexpected discoveries through true non-targeted methods [3].

Experimental Protocols and Methodologies

Standardized NTA Workflow for Forensic Applications

A generalized NTA workflow for forensic applications involves multiple critical stages from sample collection to data interpretation. The following diagram illustrates this multi-step process:

NTA Workflow for Forensic Analysis

Detailed Protocol: NTA for Psychoactive Substances in Blood

The following protocol, adapted from a validated method for screening psychoactive, analgesic, and anesthetic drugs in whole blood and plasma, demonstrates a robust approach to forensic NTA [6]:

Sample Preparation:

• Use 100 μL of whole blood or plasma sample
• Deproteinize with methanol/acetonitrile (combined for better recovery) and zinc sulfate
• Vortex mix and centrifuge to precipitate proteins
• Transfer supernatant for analysis
• This minimal sample volume is advantageous when limited sample is available (e.g., pediatric cases)

Instrumental Analysis:

• LC System: Ultra-high performance liquid chromatography
• Column: Accucore Phenyl Hexyl UPLC (100 × 2.1 mm, 2.6 μm)
• Mobile Phase: A: water + 0.1% formic acid; B: acetonitrile + 0.1% formic acid
• Gradient: Optimized for separation of diverse chemical structures
• MS System: High-resolution mass spectrometer with data-dependent acquisition (DDA)
• Ionization: Electrospray ionization (ESI) in both positive and negative modes

Data Acquisition:

• Full-scan MS1 at high resolution (typically >50,000 FWHM)
• Data-dependent MS/MS on most intense ions
• Dynamic exclusion to maximize compound coverage

This method was validated for 53 compounds selected based on diverse chemical structures, therapeutic families, mass-to-charge distribution, ionization modes, and elution times. The validation demonstrated high selectivity and specificity with identification limits at sub-therapeutic or therapeutic concentrations, making it suitable for forensic toxicology applications [6].

Quantitative NTA (qNTA) Performance Metrics

While traditionally qualitative, quantitative NTA approaches are emerging but present distinct performance characteristics compared to targeted analysis:

Table 2: Quantitative Performance Comparison: Targeted vs. Non-Targeted Analysis

Performance Metric	Targeted Analysis	Quantitative NTA (Global Surrogates)	Quantitative NTA (Expert-Selected Surrogates)
Predictive Accuracy	Benchmark	~4× decrease vs. targeted [7]	~1.5× improvement vs. global surrogates [7]
Uncertainty	Benchmark	~1000× increase vs. targeted [7]	~70× improvement vs. global surrogates [7]
Reliability	Benchmark	~5% decrease vs. targeted [7]	Further ~5% decrease vs. global surrogates [7]
Calibration Approach	Compound-specific curves with internal standards	Surrogate calibration using all available chemicals	Surrogate calibration using 3 structurally similar chemicals
Internal Standard Use	Matched stable isotope-labeled standards	Not typically available for unknowns	Not typically available for unknowns

These performance differences highlight the current trade-offs between comprehensive compound detection and quantitative precision in NTA methods. The use of expert-selected surrogates based on structural similarity can improve accuracy but requires more chemical knowledge and may reduce reliability [7].

Essential Research Tools for NTA

The Scientist's Toolkit: Key Research Reagents and Materials

Successful implementation of NTA requires specific reagents, materials, and computational resources:

Table 3: Essential Research Toolkit for Non-Targeted Analysis

Tool Category	Specific Examples	Function in NTA Workflow
Sample Preparation	Solid-phase extraction (SPE) cartridges [8], QuEChERS kits [8], protein precipitation reagents (methanol/acetonitrile) [6]	Extract and concentrate analytes while removing matrix interferents
Chromatography	UPLC/HPLC systems, C18/phenyl-hexyl columns [6], HILIC columns, mobile phase additives (formic acid, ammonium acetate) [6]	Separate complex mixtures to reduce ionization suppression and co-elution
Mass Spectrometry	High-resolution mass spectrometers (Orbitrap, TOF, Q-TOF), electrospray ionization sources [3], electron ionization sources [3]	Detect compounds with high mass accuracy and generate fragmentation data
Reference Standards	Commercially available analytical standards, internal standards (stable isotope-labeled) [9]	Confirm compound identities and support quantitative estimates
Data Processing	Compound Discoverer [6] [3], XCMS, MZmine [3], MS-DIAL [3]	Detect features, align peaks across samples, and perform compound annotation
Chemical Databases	NIST Mass Spectral Library [3], mzCloud [6], EPA's CompTox Chemicals Dashboard [8], in-house spectral libraries	Compare experimental spectra to reference data for compound identification

Bioinformatics and Data Processing Tools

The complex data generated in NTA studies requires sophisticated bioinformatics tools for meaningful interpretation. Both commercial and open-source platforms are available, each with distinct advantages:

Commercial Software:

• Compound Discoverer: Widely used platform that enables automated data processing, compound identification, and statistical analysis [6] [3]
• MassHunter: Provides similar functionality for Agilent instrumentation users [3]

Open-Source Alternatives:

• MzMine: Flexible platform for mass spectrometry data processing, particularly strong in chromatographic peak detection and alignment [3]
• MS-DIAL: Specialized tool for lipidomics and metabolomics with integrated retention time prediction [3]
• XCMS: R-based package for statistical analysis of mass spectrometry data [3]

The selection of data processing tools significantly impacts the detectable chemical space and identification confidence in NTA studies. Most current research (approximately 57% of studies) relies on vendor software, while only about 7% use open-source alternatives, indicating a significant gap in available open-source tools for comprehensive NTA workflows [3].

Applications in Forensic Chemistry and Beyond

Non-targeted analysis has demonstrated significant utility across diverse forensic applications:

Novel Psychoactive Substance Identification:

• LC-ESI-MS/MS with fragmentation profiling enables characterization of nitazene analogs and other emerging synthetic drugs [4]
• Proposed fragmentation mechanisms yield diagnostic product ions that aid in distinguishing between structurally similar analogs [4]

Fingerprint Age Estimation:

• Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOF-MS) uncovers time-dependent chemical changes in fingerprints [4]
• Enables fingerprint age estimation through chemometric modeling, advancing forensic timelines beyond conventional ridge pattern analysis [4]

Postmortem Interval Estimation:

• UHPLC-QTOF-MS combined with machine learning aids postmortem interval estimation through metabolite profiling in tissues [4] [1]
• The Forens-OMICS approach integrates metabolomics, proteomics, and metabarcoding for more accurate time-since-death determinations [4]

Explosives and Gunshot Residue Analysis:

• Multidimensional liquid chromatography techniques analyze traces of explosives with high sensitivity [4]
• Filter-and-shoot methods coupled with regression models enable estimation of residue levels from smokeless powder handling [4]

Environmental Forensics:

• Solid-phase microextraction (SPME) coupled with GC-MS effectively distinguishes crude oil sources for contamination attribution [4]
• Non-targeted screening of soil and sediment identifies anthropogenic compounds and their transformation products [3]

Challenges and Future Directions

Despite its powerful capabilities, NTA faces several significant challenges that impact its implementation in forensic chemistry:

Confidence in Compound Identification:

• Without reference standards, definitive identification of unknown compounds remains challenging
• Isomeric compounds (e.g., 6-monoacetylmorphine and naloxone) require sophisticated separation and fragmentation for distinction [6]
• Reporting standards have been proposed with confidence levels ranging from level 1 (confirmed with reference standard) to level 5 (exact mass only) [2]

Quantitative Limitations:

• As highlighted in Table 2, quantitative NTA approaches show significantly higher uncertainty compared to targeted methods [7]
• Only relative quantification among samples is typically possible with untargeted methods, while absolute quantification requires reference standards [2]

Method Reproducibility:

• Variability in NTA results can be artifacts driven by selected preparation techniques, instrumentation, software, and user settings [1]
• Without generally accepted community-wide QA/QC guidelines, comparing results between laboratories remains challenging [1]

Data Complexity and Interpretation:

• The huge amounts of complex data generated in NTA studies require advanced bioinformatics tools and skilled personnel for interpretation [2] [10]
• False positive and false negative rates are difficult to determine in the absence of analytical standards [2]

To address these challenges, the Benchmarking and Publications for Non-Targeted Analysis (BP4NTA) Working Group has formed to establish consensus definitions, harmonize reporting practices, and develop performance assessment methodologies [1]. Future directions include improved instrument sensitivity, expanded chemical databases, more sophisticated prediction algorithms for compound identification, and standardized protocols to enhance reproducibility across laboratories.

Non-targeted analysis represents a transformative approach in forensic chemistry, enabling comprehensive characterization of complex samples beyond the limitations of traditional targeted methods. The complementary approaches of true non-targeted analysis, suspect screening, and sample classification provide flexible frameworks for addressing diverse forensic questions, from novel drug identification to evidence source attribution.

While challenges remain in compound identification confidence, quantification uncertainty, and method reproducibility, ongoing harmonization efforts and technological advancements continue to enhance the capabilities and reliability of NTA methods. By understanding the comparative performance, appropriate applications, and current limitations of different NTA approaches, forensic researchers can effectively leverage these powerful tools to advance chemical characterization in support of legal and investigative processes.

As the field continues to evolve, the integration of NTA with emerging technologies such as machine learning, improved separation techniques, and more comprehensive chemical databases will further expand its utility in forensic investigations, ultimately contributing to more robust and scientifically-defensible analytical outcomes.

Non-targeted analysis (NTA) represents a paradigm shift in analytical chemistry, enabling the comprehensive detection and identification of known and unknown chemicals in complex samples without predefined targets. For forensic chemistry research, where uncovering the complete chemical signature of evidence is paramount, validating robust NTA approaches is critical. This comparison guide examines the core instrumental components of NTA workflows, focusing on the interplay between high-resolution mass spectrometry (HRMS), chromatographic separation, and data acquisition techniques. We objectively evaluate the performance of Data-Dependent Acquisition (DDA), Data-Independent Acquisition (DIA), and the newer AcquireX platform, providing supporting experimental data to guide researchers in selecting fit-for-purpose methodologies for forensic applications.

Experimental Protocols for Instrument Comparison

To generate comparable performance data for different acquisition modes, researchers typically employ structured experimental designs. The following protocol, based on recent comparative studies, outlines a standardized approach for evaluating DDA, DIA, and AcquireX.

Sample Preparation and System Suitability

• Complex Matrix Selection: Studies utilize complex biological matrices such as bovine liver total lipid extract (TLE) to simulate challenging real-world samples. The matrix is spiked with decreasing concentrations (e.g., 10–0.01 ng/mL) of a standard mixture of eicosanoids or other relevant analytes to assess detection power and sensitivity [11].
• System Suitability Test (SST): A test based on 14 eicosanoid standards is implemented before untargeted analyses to verify instrumental performance and monitor long-term system stability [11].
• Chromatographic Separation: Analysis is typically performed using reversed-phase chromatography (e.g., C18-Kinetex Core-Shell column) with HPLC systems to separate the complex mixture of metabolites [11].

Instrumental Setup and Data Acquisition

• HRMS Platform: Data is acquired using an Orbitrap Exploris 480 or similar high-resolution accurate mass spectrometer to ensure high mass accuracy and resolution [11].
• Acquisition Parameter Comparison:
  • DDA: The mass spectrometer cycles between a full scan and data-dependent MS2 scans. Precursor selection is based on user-defined criteria like intensity threshold and a top-N rule (selecting the N most intense ions), typically with narrow isolation windows (1–1.5 m/z) [11].
  • DIA/vDIA: The full scan is followed by MS2 acquisition using user-defined, variable isolation windows that fragment all ions within those windows, independent of precursor ion intensity [11] [12].
  • AcquireX: This automated workflow creates an exclusion list from blank samples and an inclusion list from a pooled sample. It then performs iterative DDA analyses on the pooled sample, dynamically updating inclusion and exclusion lists to prioritize new ions in subsequent runs [11].

Data Processing and Performance Metrics

• Reproducibility Assessment: Analyses are conducted over multiple independent measurements (e.g., triplicates spaced one week apart) to determine inter-day reproducibility, reported as the coefficient of variance (CV) across detected compounds [11].
• Feature and Identification Consistency: The total number of metabolic features, the overlap of identified compounds between different days, and the consistency of fragmentation spectra are quantitatively compared [11].
• Detection Power: The ability of each mode to detect and identify spiked standards at decreasing concentration levels in the complex matrix is evaluated to determine sensitivity limits [11].

Performance Comparison of Data Acquisition Modes

The following tables summarize key quantitative findings from comparative studies, highlighting the strengths and weaknesses of each acquisition mode.

Table 1: Overall Performance Metrics of DDA, DIA, and AcquireX in Untargeted Metabolomics

Performance Metric	DDA	DIA	AcquireX
Number of Metabolic Features	18% fewer than DIA [11]	Highest (Avg. 1036 over 3 runs) [11]	37% fewer than DIA [11]
Reproducibility (CV)	17% [11]	10% (Best) [11]	15% [11]
Identification Consistency (Inter-day Overlap)	43% [11]	61% (Best) [11]	50% [11]
MS/MS Spectral Quality	High (Clean spectra from isolated precursors) [11] [13]	Moderate (Requires deconvolution) [11] [13]	High (Similar to DDA) [11]
Best Use Case	Targeted verification, high-quality library spectra [12]	Comprehensive biomarker discovery, high reproducibility [11]	Exhaustive ion fragmentation for deep mining [11]

Table 2: Detection Power for Low-Abundance Analytes in a Complex Matrix

Spiking Level (ng/mL)	DDA Performance	DIA Performance	AcquireX Performance
10 and 1 ng/mL	Good detection	Best detection power [11]	Good detection
0.1 and 0.01 ng/mL	Limited/Not detected	Limited/Not detected	Limited/Not detected
Physiological Relevance	Frequently omitted in routine untargeted analysis due to detection limits [11]	Frequently omitted in routine untargeted analysis due to detection limits [11]	Frequently omitted in routine untargeted analysis due to detection limits [11]

Technical Workflow and Considerations

The choice of data acquisition mode significantly influences the NTA workflow, from initial setup to final data interpretation. The following diagram illustrates the logical relationship between the core instrumentation components and the resulting data characteristics.

Diagram: Core NTA Instrumentation Workflow and Output. HRMS and chromatography are foundational. The acquisition mode (DDA, DIA, AcquireX) directly determines the characteristics of the MS/MS data generated.

Key Technical Differentiators

• Fragmentation Strategy: The fundamental difference lies in how precursor ions are selected for fragmentation. DDA uses intensity-dependent selection, leading to high-quality MS/MS spectra but potential bias against low-abundance ions. In contrast, DIA fragments all ions within sequential, wide isolation windows, providing more comprehensive coverage but generating complex spectra that require advanced bioinformatic deconvolution [11] [13] [12]. AcquireX attempts to bridge this gap by using an iterative DDA approach on a pooled sample to systematically cover more ions over multiple injections [11].

• Chromatographic Reproducibility Dependence: The performance of AcquireX is highly dependent on precise and reproducible chromatographic separation. Inclusion and exclusion lists are built using precursor m/z and retention time from different samples. Retention time shifts (e.g., ≥1%) can reduce the selectivity of these lists and cause closely eluting isomers to be missed [11].

• Bias and Comprehensiveness: DDA is inherently biased towards the most abundant ions, which can be a limitation for detecting trace-level contaminants in forensic samples [13]. DIA is more unbiased, as it does not rely on precursor intensity, making it better suited for discovering unexpected or low-abundance compounds [11] [13].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Key Reagents and Materials for Core NTA Experiments

Item	Function in NTA Workflow	Example Use Case
Complex Biological Matrix	Provides a challenging, realistic medium for method validation.	Bovine Liver Total Lipid Extract (TLE) for spiking studies [11].
System Suitability Standard Mix	Evaluates instrument performance and monitors long-term stability.	Eicosanoid standard mix (14 compounds) [11].
Polymer Additive Reference Standards	Aids in accurate identification/quantification and covers diverse chemical space.	A set of 106 polymer additives with varied properties [9].
Internal Standards & Quality Controls	Corrects for matrix effects, monitors analytical performance, and ensures data quality.	Stable isotope-labeled analogs of target analytes; pooled quality control (QC) samples [9] [14].
Solid Phase Extraction (SPE) Cartridges	Enriches analytes and reduces matrix interference from complex samples.	Multi-sorbent strategies (e.g., Oasis HLB + ISOLUTE ENV+) for broad-range extraction [15].

Implications for Forensic Chemistry Research

The choice of acquisition mode directly impacts the scope and confidence of findings in forensic NTA.

• DIA for Comprehensive Screening: For general unknown screening in forensic casework, where the goal is to build the most complete chemical profile of a sample, DIA is the superior choice. Its higher feature detection, excellent reproducibility, and unbiased nature make it ideal for generating a reliable chemical inventory for retrospective analysis [11].
• DDA for Confirmation and Targeted Workflows: When the investigation requires high-confidence identification of specific, suspected compounds, DDA provides cleaner, more interpretable MS/MS spectra that are easier to match against spectral libraries. This is valuable for confirming the presence of a specific drug or metabolite [12].
• AcquireX for Deep Discovery in Stable Matrices: In research-driven forensic projects focused on exhaustive characterization of a stable, well-defined sample (e.g., a novel psychoactive substance), AcquireX can provide deeper coverage through its iterative fragmentation, though at the cost of significant instrument time and stringent requirements for chromatographic stability [11].

A critical finding for forensic validation is that none of the current acquisition modes may be sufficiently sensitive to detect very low-abundance but potentially toxicologically relevant compounds (e.g., certain eicosanoids at pg/mL levels) without specialized, targeted methods [11]. This underscores the importance of defining the analytical scope and limits of any NTA method used for forensic purposes.

The core instrumentation of HRMS and chromatography, coupled with the choice of data acquisition mode, forms the backbone of reliable NTA. Performance data clearly indicates that DIA provides the most comprehensive and reproducible data for broad forensic screening applications, while DDA remains valuable for targeted confirmation due to its high-quality spectral output. The AcquireX platform offers a powerful but resource-intensive middle ground. For forensic chemistry research, validating an NTA approach requires careful matching of the acquisition strategy to the specific question at hand, with a clear understanding of the performance trade-offs outlined in this guide.

Non-targeted analysis (NTA) represents a paradigm shift in forensic chemical analysis, moving beyond the traditional targeted approach that searches for a predefined set of compounds. In forensic science, NTA is an analytical methodology that can characterize the complete chemical composition of a sample without prior knowledge of its chemical content, enabling the detection and identification of both expected and unexpected compounds [1] [2]. This capability is particularly valuable for forensic chemistry research, where complex and evolving illicit drug mixtures increasingly contain novel psychoactive substances and excipients that evade conventional targeted methods [16].

The forensic science community faces critical challenges with evidence backlogs that delay justice and compromise public safety. Crime labs nationwide are "drowning in evidence" from rape kits to drug samples, with delays stretching to 570 days for sexual assault kit processing in some jurisdictions [17]. These backlogs stem from years of underinvestment, staffing shortages, and the limitations of traditional forensic techniques that struggle to keep pace with emerging substances [17]. Within this context, NTA offers a transformative approach that can accelerate justice through more comprehensive and efficient chemical analysis, potentially identifying multiple compounds in a single analytical run rather than requiring separate tests for each suspected substance [16].

NTA Versus Traditional Targeted Approaches: A Comparative Analysis

Fundamental Differences in Methodology

Traditional targeted analysis in forensic chemistry relies on searching for specific, predefined compounds using validated methods with known performance characteristics for those particular analytes. This approach depends heavily on reference standards and prior knowledge of what compounds to expect in evidence samples. While highly reliable for known substances, targeted methods cannot detect compounds outside their predefined scope, creating significant blind spots as new synthetic drugs and complex mixtures emerge [2].

In contrast, NTA employs a hypothesis-free approach that systematically characterizes the chemical composition of a sample without a priori knowledge. Using high-resolution mass spectrometry (HR-MS) and advanced data processing techniques, NTA can detect both predicted and unexpected compounds, making it particularly valuable for identifying novel psychoactive substances, transformation products, and complex mixture components that would escape detection with targeted methods [18] [2]. The fundamental distinction lies in NTA's capability to cast a wider analytical net, capturing a more comprehensive chemical profile of forensic evidence.

Performance Comparison in Forensic Applications

Table 1: Comparative Performance of Targeted Analysis vs. Non-Targeted Analysis in Forensic Applications

Performance Characteristic	Traditional Targeted Analysis	Non-Targeted Analysis (NTA)
Scope of Detection	Limited to predefined compounds	Comprehensive, capable of detecting known and unknown compounds
Novel Compound Identification	Limited capability	Excellent for novel psychoactive substances and metabolites
Quantitation Capability	Absolute quantification possible	Typically limited to relative quantification between samples
Method Validation	Straightforward with established protocols	Complex due to unlimited chemical space
Sample Throughput	High for targeted compounds	Initially slower, but provides more information per analysis
Data Complexity	Manageable, focused data	Highly complex datasets requiring advanced bioinformatics
Ideal Application	Routine confirmation of known substances	Discovery, complex mixture characterization, emerging threats

Forensic studies demonstrate NTA's superior performance for complex mixture characterization. A validated forensic workflow for illicit drug and excipient screening demonstrated that NTA using LC-HRMS identified all organic components in simulated and unknown mixtures, outperforming targeted methods that missed novel compounds [16]. This comprehensive profiling is particularly valuable for modern illicit drug preparations, which often contain complex mixtures of active pharmaceutical ingredients, excipients, cutting agents, and impurities that collectively provide valuable intelligence about manufacturing sources and distribution networks [16].

Experimental Validation: NTA Workflows for Forensic Chemistry

Validated NTA Workflow for Illicit Drug Analysis

A rigorous experimental protocol was developed and validated to demonstrate NTA's applicability to forensic casework [16]. The workflow was designed to meet forensic admissibility standards while expanding analytical capabilities beyond traditional targeted approaches:

• Sample Preparation: Incorporates protein precipitation, liquid-liquid extraction, and solid-phase extraction techniques optimized for broad chemical coverage. The key challenge lies in achieving sufficient extraction efficiency across diverse chemical classes while minimizing matrix effects [2].

• Instrumental Analysis: Utilizes multiple analytical techniques organized according to SWGDRUG guidelines categories. The workflow incorporates gas chromatography-mass spectrometry (GC-MS) for volatile compounds, liquid chromatography-high resolution mass spectrometry (LC-HRMS) for non-volatile compounds, and Fourier-transform infrared spectroscopy (FTIR) for structural elucidation [16].

• Data Acquisition: HRMS data is acquired using full-scan MS with data-dependent MS/MS fragmentation to provide both accurate mass measurements and structural information for compound identification [16].

• Compound Identification: Combines multiple lines of evidence for confident identification, including retention time alignment, accurate mass matching (typically <5 ppm mass error), isotopic pattern matching, MS/MS spectral library matching (using databases such as MzCloud), and when available, comparison to reference standards [16].

This validated workflow successfully characterized counterfeit benzodiazepine preparations, identifying not only the active pharmaceutical ingredients but also multiple excipients and impurities that provided additional intelligence about the manufacturing process and product authenticity [16].

NTA for Biotransformation Product Identification

Further demonstrating NTA's forensic utility, a study investigating the biotransformation of perfluorohexanesulfonamide (PFHxSA) in rat plasma and liver revealed both predicted and unexpected metabolites [18]. The experimental protocol included:

• Chromatographic Separation: Reversed-phase liquid chromatography optimized for separating relatively polar transformation products using aqueous and organic mobile phases [18].

• Mass Spectrometric Detection: High-resolution accurate mass spectrometry enabling detection of both expected biotransformation products (perfluorohexane sulfonic acid) and previously unreported metabolites (perfluorohexanesulfinic acid) [18].

• Data Processing: Untargeted data analysis using computational tools to detect chromatographic features and identify potential biotransformation products through mass defect filtering, isotope pattern matching, and fragment ion analysis [18].

This application highlights NTA's value in toxicology and forensic science for comprehensively characterizing metabolic pathways of emerging substances, which is crucial for understanding toxicity, persistence, and biomonitoring strategies [18].

Visualizing NTA Workflows: From Evidence to Intelligence

The following diagram illustrates the generalized NTA workflow applicable to forensic evidence analysis, highlighting steps where efficiency gains can help address backlog challenges:

NTA Forensic Analysis Workflow

This workflow demonstrates how NTA generates comprehensive chemical intelligence from evidence samples. The process begins with sample preparation optimized for broad chemical coverage, followed by high-resolution mass spectrometric analysis that captures data on thousands of chemical features simultaneously [1] [2]. Bioinformatics tools then process this complex data to detect chromatographic features, identify compounds through database matching, and interpret patterns relevant to forensic investigations [2] [16]. The efficiency of this approach stems from its ability to generate multiple lines of evidence from a single analytical run, potentially reducing the need for repeated testing with different targeted methods.

Essential Research Reagents and Materials for NTA

Implementing NTA in forensic research requires specific reagents, reference materials, and computational tools. The following table details key components of the "NTA Toolkit" for forensic chemistry applications:

Table 2: Essential Research Reagent Solutions for Forensic NTA

Reagent/Material	Function in NTA Workflow	Application Example
LC-MS Grade Solvents	Ensure minimal background interference during chromatographic separation and mass spectrometric detection	Methanol, acetonitrile, water for mobile phase preparation and sample reconstitution
Solid Phase Extraction Cartridges	Isolate and concentrate analytes from complex forensic matrices while reducing interfering substances	Mixed-mode cation/anion exchange sorbents for broad-spectrum drug extraction from biological samples
Chemical Reference Standards	Provide retention time, mass spectral, and fragmentation data for compound identification and confirmation	Certified reference materials for emerging psychoactive substances and pharmaceutical compounds
Stable Isotope-Labeled Internal Standards	Correct for matrix effects and analytical variability in mass spectrometric analysis	Deuterated analogs of target compounds for retention time alignment and semi-quantitation
High-Resolution Mass Spectral Databases	Enable compound identification through accurate mass and fragmentation pattern matching	Commercial and open-source databases (e.g., MzCloud, NIST) with MS/MS spectra
Data Processing Software	Extract meaningful chemical information from complex HRMS datasets	Platforms for chromatographic alignment, peak picking, and compound identification

The selection of appropriate reagents and materials is critical for successful NTA implementation. For example, sample preparation techniques must balance comprehensive compound extraction with sufficient matrix clean-up to minimize ion suppression in mass spectrometric analysis [2]. The growing commercial availability of reference standards for emerging drugs and the expansion of high-resolution mass spectral libraries are significantly enhancing NTA's capabilities in forensic chemistry research [16].

Non-targeted analysis represents a significant advancement in forensic chemistry's ability to address the complex challenges of modern evidence analysis. By enabling comprehensive chemical characterization of forensic evidence in a single analytical run, NTA offers a pathway to reduce testing backlogs, provide more intelligence from limited evidence, and stay ahead of emerging synthetic drugs designed to evade traditional detection methods [16] [17].

The validation of NTA workflows for forensic applications demonstrates that this approach can meet the rigorous standards required for admissibility while expanding analytical capabilities beyond the limitations of targeted methods [16]. As forensic laboratories nationwide struggle with overwhelming case backlogs that delay justice for victims and defendants alike [17], the implementation of efficient, comprehensive analytical approaches like NTA becomes increasingly imperative.

For the forensic research community, continued development of NTA methodologies, expansion of reference databases, and establishment of standardized reporting frameworks through initiatives like the Benchmarking and Publications for Non-Targeted Analysis Working Group (BP4NTA) will be essential for advancing this transformative technology [1]. By embracing NTA alongside traditional targeted methods, forensic chemistry can accelerate justice through more efficient evidence analysis while maintaining the scientific rigor that underpins our legal system.

Non-targeted analysis (NTA) has emerged as a powerful approach in forensic chemistry and environmental science for identifying unknown chemicals without predefined targets [2]. Unlike traditional targeted methods that focus on specific compounds, NTA employs high-resolution mass spectrometry (HRMS) to screen for a broad range of known and unknown substances present in complex samples [19]. This capability is particularly valuable for forensic applications such as seized drug analysis, toxicological screening, and chemical characterization of complex matrices where the full chemical composition is unpredictable [20] [21].

Despite its potential, the implementation of NTA in regulated forensic chemistry environments faces three interconnected fundamental challenges: matrix effects that compromise quantitative accuracy, immense data complexity that strains interpretation capabilities, and a critical lack of standardized protocols [22] [2] [20]. These challenges collectively hinder the reproducibility, reliability, and admissible use of NTA data in legal contexts. This review systematically examines these limitations, presents comparative experimental data, and discusses emerging strategies to validate NTA approaches for forensic applications.

Matrix Effects in Non-Targeted Analysis

Understanding Matrix Effects

Matrix effects represent a significant challenge in liquid chromatography-mass spectrometry (LC-MS) based non-targeted analysis, manifesting as ion suppression or enhancement of target analytes due to co-eluting compounds from the sample matrix [22]. These effects occur when matrix components alter ionization efficiency in the mass spectrometer source, leading to compromised data quality through inaccurate quantification, reduced analytical sensitivity, and potential misidentification of compounds [22] [23].

In forensic contexts, matrix effects are particularly problematic due to the complex and variable nature of evidence samples. Biological specimens (hair, urine, blood), seized drug compositions, and environmental forensic samples contain numerous interfering substances that can significantly impact analytical results [22] [24]. For example, a study analyzing γ-hydroxybutyric acid (GHB) in hair documented substantial signal suppression and matrix-dependent variations in recovery rates, complicating accurate quantification of this drug-facilitated crime marker [24].

Experimental Assessment and Mitigation Strategies

Robust assessment of matrix effects follows established experimental approaches. One widely adopted strategy involves comparing analyte responses in neat standard solutions to responses in spiked sample matrices [22]. The matrix effect (ME) is typically calculated as: ME (%) = (B/A - 1) × 100, where A represents the peak area of the analyte in neat solution and B represents the peak area in spiked matrix. Significant deviations from zero indicate either ion enhancement (positive values) or suppression (negative values).

Forensic laboratories have developed multiple effective strategies to overcome matrix effects:

Sample Preparation Optimization: Techniques including solid-phase extraction (SPE), QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe), and liquid-liquid extraction can selectively isolate target analytes while removing interfering matrix components [2] [14] [8]. The effectiveness of different sample preparation techniques varies significantly across forensic matrices, as illustrated in Table 1.

Table 1: Comparison of Sample Preparation Techniques for Forensic Matrices

Matrix Type	Preparation Technique	Key Advantages	Limitations	Reported Efficiency (%)
Hair	NaOH Digestion	Effective for difficult matrices	Potential analyte degradation	~70% recovery for various drugs [24]
Hair	M3 Reagent	Higher reliability for certain drugs	Matrix-dependent variations (~30% recovery for GHB)	~30% recovery for GHB [24]
Biological Fluids	Protein Precipitation	Simple, rapid	Less selective, may not remove all interferents	Variable [22]
Soil/Dust	Pressurized Liquid Extraction	Comprehensive extraction	Requires specialized equipment	71% method sensitivity in NTA workflow [8]
Food/Urine	QuEChERS	Effective for diverse analytes	May require optimization for specific matrices	Successfully applied in multi-matrix study [8]

Chromatographic Separation Enhancement: Improved separation through ultra-high-performance liquid chromatography (UHPLC), specialized columns (HILIC), or optimized gradient methods reduces co-elution of analytes with matrix components [22] [24]. For GHB analysis in hair, implementing HILIC chromatography effectively enhanced separation capacity and mitigated matrix interference [24].

Internal Standardization: Stable isotope-labeled internal standards (SIL-IS) compensate for ionization variations by experiencing nearly identical matrix effects as their corresponding analytes [22]. This approach significantly improves quantification accuracy in complex forensic matrices.

Dilution Methods: Strategic sample dilution reduces matrix component concentrations while maintaining detectable analyte levels, potentially minimizing ionization competition [22]. However, this approach must be balanced against potential losses in sensitivity.

Data Complexity and Interpretation Challenges

The Data Deluge in NTA

Non-targeted analysis generates extraordinarily complex datasets that present significant interpretation challenges. A single HRMS analysis can detect thousands of chemical features across numerous samples, creating high-dimensional data that exceeds manual processing capabilities [2] [14]. This data complexity stems from several factors: the comprehensive nature of signal acquisition capturing all ionizable compounds, presence of multiple peaks for a single compound (including adducts, fragments, and isotopes), and the need to distinguish meaningful signals from chemical noise and background interferences [2].

In forensic applications, this complexity is compounded by the need for confident compound identification, unknown structural elucidation, and differentiation of isomeric compounds that may have different legal implications [20]. For example, rapid GC-MS validation studies have demonstrated limitations in differentiating certain positional isomers and isobaric compounds, which is crucial for accurate forensic identification [20].

Computational Strategies and Machine Learning Integration

Advanced computational approaches are essential for extracting meaningful information from NTA datasets. The typical workflow for managing NTA data complexity involves multiple processing stages, as illustrated in the following workflow:

Machine learning (ML) algorithms have dramatically enhanced the capability to identify patterns and source signatures within complex NTA data [14]. Supervised ML models, including Random Forest and Support Vector Classifier, can be trained on labeled datasets to classify contamination sources or identify novel psychoactive substances based on chemical fingerprints [14]. For example, in one study, ML classifiers successfully screened 222 targeted and suspect per- and polyfluoroalkyl substances across 92 samples with classification balanced accuracy ranging from 85.5% to 99.5% across different sources [14].

Unsupervised learning methods such as principal component analysis (PCA) and hierarchical clustering enable exploratory data analysis without prior knowledge of sample groups, revealing intrinsic patterns and potential outliers [14]. These approaches are particularly valuable in forensic intelligence for identifying new emerging drugs or connecting related exhibits through chemical profiling.

Table 2: Bioinformatics Tools for NTA Data Processing in Forensic Chemistry

Tool Category	Specific Software/Platform	Primary Function	Application in Forensic Chemistry
Spectral Libraries	NIST Mass Spectral Library, mzCloud	Compound identification via spectral matching	Identification of known drugs, metabolites, and impurities [20]
Computational Platforms	EPA CompTox Chemicals Dashboard, XCMS	Chemical structure mapping, peak alignment	Linking observed analytes to registered substances, batch processing [21]
Statistical Analysis	R packages, Python SciKit Learn	Multivariate statistics, machine learning	Pattern recognition, source attribution, classification of unknown samples [14]
Identification Confidence Framework	Schymanski Level System	Standardized identification confidence scoring	Reporting standards for forensic evidence, with Level 1 as highest confidence [2]

Standardization Gaps in NTA Workflows

Current Lack of Standardized Protocols

The absence of standardized protocols represents a critical barrier to the implementation of non-targeted analysis in forensic chemistry [20] [9]. Unlike targeted analytical methods with well-established validation criteria, NTA workflows lack consensus on performance evaluation, quality control procedures, and acceptance criteria [20]. This standardization gap leads to significant challenges in method reproducibility, data comparability between laboratories, and admissibility of NTA data in legal proceedings [20] [9].

Forensic chemistry particularly suffers from this standardization deficit. A comprehensive review of rapid GC-MS applications noted "the lack of standardized validation protocols across the forensic chemistry community" and highlighted that "validation of instrumentation can be a challenging and time-consuming task" in the absence of established guidelines [20]. This challenge is especially pronounced for emerging technologies being implemented in forensic laboratories, where validation templates are not yet available [20].

Towards Quantitative NTA and Standardized Frameworks

Significant efforts are underway to bridge the quantitative and standardization gaps in NTA. Recent research has focused on developing approaches for quantitative NTA (qNTA) that can provide concentration estimates essential for risk assessment and forensic decision-making [21]. These approaches include:

Internal Standard-Based Quantification: Using a diverse set of internal standards to cover different chemical classes and ionization responses [21] [9]. The relative response factor (RRF) serves as a correction factor that accounts for variations in signal response between analytes and reference standards, improving quantification accuracy [9].

Uncertainty Factor Determination: Standardized procedures for calculating uncertainty factors (UF) that address analytical variability in screening methods, as outlined in ISO 10993-18:2020 for chemical characterization of medical devices [9]. The UF is calculated as: UF = 1/(1 - RSD), where RSD is the relative standard deviation of response factors from reference standard databases.

Reference Standard Databases: Developing comprehensive reference standard sets that cover diverse chemical spaces, as demonstrated in a study that compiled 106 reference standards of polymer additives with wide physicochemical properties and toxicological coverage [9]. Such databases enhance consistency in chemical analysis across different laboratories.

The following diagram illustrates a proposed standardization framework for quantitative NTA in forensic applications:

Experimental Protocols for NTA Validation

Comprehensive Validation Framework

Implementing non-targeted analysis in forensic chemistry requires rigorous validation to ensure reliable, defensible results. Based on established forensic validation approaches and emerging NTA standards, a comprehensive validation framework should address the following components [20] [8]:

Selectivity/Specificity: Assessment of the method's ability to differentiate target analytes from matrix components and isomeric compounds. This includes evaluation of retention time stability and mass spectral differentiation capabilities [20].

Precision: Determination of repeatability (intra-day precision) and intermediate precision (inter-day, inter-operator, inter-instrument precision) using relative standard deviation (%RSD) of retention times and mass spectral search scores, with acceptance criteria generally set at ≤10% RSD [20].

Accuracy: Evaluation through analysis of certified reference materials (CRMs), spiked recovery studies, or comparison with validated methods where available [20] [8].

Matrix Effects: Systematic investigation of ion suppression/enhancement using the post-column infusion method or post-extraction spiking approach across different lots of matrix [22] [20].

Sensitivity: Determination of limits of detection (LOD) and quantification (LOQ) for key representative compounds, considering the analytical evaluation threshold (AET) concept from ISO 10993-18 when applicable [9].

Carryover/Contamination: Assessment of sample-to-sample carryover and background contamination to ensure result integrity [20].

Robustness and Ruggedness: Evaluation of method performance under deliberate variations in analytical parameters and across different instruments, operators, or laboratories [20].

Stability: Examination of analyte stability in sample solutions under various storage conditions and timeframes [20].

Performance Benchmarks in NTA Methods

Recent studies have established performance benchmarks for NTA methods across different matrices. A comprehensive evaluation of NTA for chemical characterization of organic contaminants in matrices relevant to children's exposure (dust, soil, urine, food, and water) reported method accuracy of 98.2%, precision of 20.3% RSD, selectivity of 98.4%, and sensitivity of 71.1% [8]. The study successfully annotated 30, 78, 103, 20, and 265 features that were frequently identified (detection frequency >80%) in food, dust, soil, water, and urine samples, respectively [8].

In forensic drug analysis, validation of rapid GC-MS systems demonstrated retention time and mass spectral search score %RSDs of ≤10% for precision and robustness studies, meeting designated acceptance criteria for most validation components [20]. However, limitations were identified in differentiating some isomeric compounds, highlighting the need for complementary techniques in forensic applications [20].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents and Materials for NTA in Forensic Chemistry

Category	Specific Reagents/Materials	Function in NTA Workflow	Application Examples
Reference Standards	Custom 14-compound test solution (Cayman Chemical)	Method performance assessment, retention time calibration	Seized drug screening, instrument qualification [20]
Internal Standards	Stable isotope-labeled analogs (e.g., deuterated compounds)	Quantification calibration, matrix effect compensation	Improving quantification accuracy in complex matrices [22] [9]
Sample Preparation	M3 reagent, NaOH, methanol, acetonitrile, solid-phase extraction cartridges (Oasis HLB, ISOLUTE ENV+)	Matrix component removal, analyte extraction/enrichment	Hair digestion (M3), pesticide extraction (SPE), multi-residue analysis [24] [8]
Quality Control Materials	Certified reference materials (CRMs), in-house quality control samples	Method validation, ongoing performance verification	Quality assurance in analytical batches [14] [8]
Chromatographic Materials	C18 columns, HILIC columns, GC capillary columns	Compound separation, reduced matrix effects	GHB analysis (HILIC), multi-analyte screening (C18) [24]
Data Processing Tools	Compound Discoverer, XCMS, EPA CompTox Dashboard	Feature detection, alignment, compound identification	Non-targeted screening, unknown identification [21] [8]

Matrix effects, data complexity, and standardization challenges represent significant but addressable hurdles in the implementation of non-targeted analysis for forensic chemistry research. Through strategic sample preparation techniques, advanced computational tools, and emerging standardization frameworks, the forensic science community is progressively overcoming these limitations. The ongoing development of quantitative NTA approaches, comprehensive validation protocols, and standardized reference materials will further enhance the reliability and admissible use of NTA data in legal contexts. As these methodologies continue to mature, non-targeted analysis holds immense promise for expanding forensic capabilities in identifying novel psychoactive substances, profiling complex exhibits, and addressing challenging analytical scenarios where traditional targeted methods fall short.

From Theory to Crime Lab: Methodological Workflows and Real-World Applications of NTA

Within forensic chemistry and drug development, the accuracy of any analytical result is fundamentally dependent on the steps taken before the sample even enters the instrument. Complex biological matrices such as blood, plasma, and oral fluid contain a plethora of interferents, including proteins, lipids, and salts, that can obstruct detection and compromise data integrity. Effective sample preparation is, therefore, not merely a preliminary step but a critical component of the analytical workflow. It serves to isolate analytes, reduce matrix effects, and preconcentrate targets to detectable levels, ensuring that subsequent analysis—whether targeted or non-targeted—is both reliable and reproducible.

This guide objectively compares contemporary sample preparation strategies, focusing on deproteinization, precipitation, and modern microextraction techniques. The evaluation is framed within the rigorous demands of validating non-targeted analysis (NTA) approaches for forensic research. NTA aims to comprehensively characterize sample compositions without prior knowledge of their content, a powerful paradigm for discovering novel psychoactive substances or unknown contaminants [25]. However, the transition of NTA from qualitative discovery to quantitative risk assessment hinges on the ability to generate concentration data with low uncertainty [25]. The selection of a sample preparation method directly influences this uncertainty by controlling parameters such as recovery, matrix effect, and reproducibility. This guide provides forensic researchers and drug development professionals with a detailed comparison of techniques, supported by experimental data and protocols, to make informed decisions that bolster the validity of their analytical findings.

Comparative Analysis of Sample Preparation Techniques

The following sections provide a detailed examination of three broad categories of sample preparation methods. A summary of their comparative performance, based on published data, is provided in Table 1.

Deproteinization and Protein Precipitation

Principle and Workflow: Deproteinization is a fundamental cleaning step used to disrupt the binding of analytes to proteins and to remove proteins that can cause interference, fouling, or instability in chromatographic systems. Protein precipitation (PP) is the most common approach, achieved by adding an organic solvent (e.g., acetonitrile, methanol), an acid (e.g., trichloroacetic acid, perchloric acid), or a salt to the sample. This alters the solvent environment, denaturing proteins and causing them to aggregate and pellet upon centrifugation, leaving the analytes of interest in the supernatant.

Forensic Application and Experimental Data: This technique is often employed as a rapid first step for blood and plasma analysis. Its critical influence on quantitative analysis was demonstrated in a method for determining fentanyl in human plasma using head-space solid-phase microextraction (HS-SPME) and GC-MS. The study systematically evaluated different deproteinization strategies, finding that precipitation with trichloroacetic acid provided superior extraction efficiency. The research underscored that deproteinization is necessary for determining the total concentration of an analyte in plasma, as protein binding can significantly reduce the amount available for extraction and detection [26].

Advantages and Limitations: The primary advantage of PP is its simplicity and speed, requiring minimal specialized equipment. It effectively handles high-protein matrices. However, it is a relatively non-selective process. While it removes proteins, other matrix components may remain, and the resulting supernatant can be dilute, potentially requiring an additional evaporation and reconstitution step to achieve adequate sensitivity [26]. This can introduce additional sources of error and may not be compatible with the high-throughput demands of modern NTA workflows.

Dispersive Liquid-Liquid Microextraction (DLLME)

Principle and Workflow: DLLME is a miniaturized, solvent-based extraction technique known for its high enrichment capabilities. It involves the rapid injection of a mixture of an extraction solvent (a high-density, water-immiscible organic solvent) and a disperser solvent (a water-miscible solvent like acetonitrile or acetone) into an aqueous sample. This forms a cloudy solution comprising fine droplets of the extraction solvent, which provides a vast surface area for the rapid partitioning of analytes from the aqueous sample. The mixture is then centrifuged, the extraction solvent droplets settle, and the sedimented phase is collected for analysis [27].

Forensic Application and Experimental Data: DLLME has been successfully optimized for extracting six synthetic cannabinoids (e.g., JWH-018, JWH-073, AM-694) from oral fluid. The protocol specified using 0.5 mL of oral fluid, with acetonitrile (1 mL) serving a dual role as a protein precipitant and the disperser solvent. The optimal extraction solvent was dichloromethane (100 µL), with vortexing for 2 minutes. This method achieved impressive recoveries ranging from 73% to 101%, with limits of detection (LOD) between 2 and 18 ng/mL. The method was validated as rapid, simple, inexpensive, and efficient, offering high throughput for clinical or forensic screening [27].

Advantages and Limitations: DLLME offers exceptional high enrichment factors and rapid extraction due to the large surface area between the extraction solvent and the sample. It is also low-cost and has minimal solvent consumption. A key limitation is the dependence on the density of the extraction solvent for easy retrieval. Furthermore, the disperser solvent can co-disperse some matrix components, potentially reducing selectivity, and the choice of solvents must be carefully optimized for each application to ensure high recovery and cleanliness [27].

Solid-Phase Microextraction (SPME) and Related Sorbent-Based Techniques

Principle and Workflow: SPME is a non-exhaustive, solvent-free technique that integrates sampling, extraction, and concentration into a single step. It utilizes a fiber coated with a sorbent material (e.g., polydimethylsiloxane, divinylbenzene) that is exposed to the sample (via direct immersion or headspace). Analytes partition from the sample matrix into the coating. After a predetermined extraction time, the fiber is retracted and introduced into a chromatographic inlet for thermal desorption and analysis. Related techniques like Microextraction by Packed Sorbent (MEPS) and Stir Bar Sorptive Extraction (SBSE) operate on similar principles but with different geometries and sorbent capacities [28] [29].

Forensic Application and Experimental Data: SPME is highly versatile. The fentanyl in plasma study utilized a home-made sol-gel SPME fiber in headspace (HS) mode, with optimized parameters including extraction temperature and time, pH, and ionic strength. The resulting method was highly sensitive, with a LOD of 0.03 ng/mL and an inter-day precision of less than 5% [26]. Beyond targeted analysis, SPME's ability to provide clean extracts makes it highly valuable for NTA. Its principles align with the modern push for green and sustainable chemistry, as it minimizes or eliminates organic solvent waste [29] [30]. Furthermore, new paradigms like White Analytical Chemistry (WAC) are being used to holistically evaluate such methods. WAC assesses the red (analytical performance), green (environmental impact), and blue (practical & economic) principles. When 23 methods for benzodiazepine analysis were evaluated using the WAC-based RGB12 model, several microextraction techniques (SPME, MEPS) demonstrated a strong balance of high whiteness—meaning they successfully combined functional efficiency with sustainability and practicality [28].

Advantages and Limitations: The primary advantages of SPME are its simplicity, solvent-free operation, and ability to analyze both volatile and non-volatile compounds (via HS or DI mode). It is easily automated and can be used for in-vivo sampling. Its main limitation is the non-exhaustive nature of the extraction, meaning the amount extracted depends on the partition coefficient and can be influenced by matrix effects, making absolute quantitation challenging without careful calibration (e.g., using internal standards). The fiber coating can also be fragile and has a limited lifespan [28] [26].

Table 1: Comparison of Sample Preparation Techniques for Complex Matrices

Technique	Principle	Optimal Recovery (%)	Typical LOD	Key Advantage	Primary Limitation	Suitability for NTA
Protein Precipitation	Denaturation and removal of proteins via organic solvent/acid.	N/A (Cleaning step)	Varies	Simplicity and speed; high sample throughput.	Low selectivity; dilute extract; may not remove all interferents.	Low (Limited clean-up and enrichment)
DLLME	High-surface-area extraction into dispersed solvent droplets.	73 - 101 [27]	2-18 ng/mL [27]	High enrichment factor; very fast; low cost.	Requires dense extraction solvent; potential for matrix co-dispersion.	Medium (Good enrichment, but selectivity can be variable)
SPME	Partitioning of analytes onto a solid sorbent coating.	N/A (Non-exhaustive)	0.03 ng/mL [26]	Solvent-free; simple; integrates sampling/extraction/ concentration.	Fiber cost and fragility; sensitive to matrix effects; requires calibration.	High (Excellent for clean extracts and broad chemical coverage)
MEPS	Miniaturized solid-phase extraction packed in a syringe.	High (varies by method)	Sub-ng/mL [28]	Low solvent volumes; high recovery; easily automated.	Potential for carryover; sorbent can be clogged by dirty samples.	High (Good for automation in high-throughput labs)

Experimental Protocols for Key Techniques

• 1. Sample Preparation: Add 500 µL of human plasma sample into a headspace vial.
• 2. Deproteinization: Add 100 µL of hydrochloric acid (2 M) and 100 µL of trichloroacetic acid (50% w/v) to the plasma. Vortex mix thoroughly.
• 3. SPME Extraction: Place the vial on a heated agitator. Condition and expose a suitable SPME fiber (e.g., sol-gel-based coating) to the headspace of the sample. Extract for a predetermined time (e.g., 20 min) at an optimized temperature (e.g., 80°C) with continuous agitation.
• 4. Desorption and Analysis: Retract the fiber and immediately introduce it into the GC-MS injection port for thermal desorption (e.g., 270°C for 5 min). Analyze via GC-MS using selected ion monitoring (SIM).

• 1. Sample Collection: Obtain 0.5 mL of oral fluid sample.
• 2. Protein Precipitation & Dispersion: Add 1 mL of acetonitrile (acting as both disperser and precipitating solvent) to the sample. Vortex mix vigorously.
• 3. DLLME Extraction: Rapidly inject a mixture containing 100 µL of dichloromethane (extraction solvent) dispersed in the acetonitrile-treated sample. Vortex the entire mixture for 2 minutes to form a cloudy solution.
• 4. Phase Separation: Centrifuge the mixture at high speed (e.g., 5000 rpm) for 5 minutes to sediment the dense dichloromethane droplets.
• 5. Analysis: Carefully collect the sedimented organic phase with a micro-syringe. Inject an aliquot directly into an HPLC-MS/MS system for analysis.

Workflow Integration and Strategic Selection

The choice of a sample preparation strategy must be driven by the analytical goals. For non-targeted analysis in forensic chemistry, the ideal technique should provide broad chemical coverage, minimize matrix effects that complicate data interpretation, and be reproducible. The following diagram illustrates a decision workflow for selecting a sample preparation method based on key analytical requirements.

Sample Preparation Strategy Selection

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 2: Key Reagents and Materials for Sample Preparation

Item	Function in Sample Preparation	Example Application
Trichloroacetic Acid	Strong acid used for protein precipitation by denaturation.	Deproteinization of plasma for fentanyl analysis [26].
Acetonitrile	Organic solvent used for protein precipitation and as a disperser solvent in DLLME.	Precipitating agent and disperser for synthetic cannabinoids in oral fluid [27].
Dichloromethane	Water-immiscible organic solvent with high density, used as an extraction solvent in DLLME.	Extraction of synthetic cannabinoids from the aqueous phase in DLLME [27].
SPME Fiber	Fused silica fiber with a polymeric coating (e.g., PDMS, DVB) that absorbs/adsorbs analytes.	Extraction of fentanyl from the headspace of prepared plasma samples [26].
MEPS Sorbent	Miniaturized cartridge packed with a sorbent (e.g., C8, C18, MIP) integrated into a syringe barrel.	Extraction and clean-up of benzodiazepines from biological samples [28].
Deep Eutectic Solvents (DES)	Green solvents formed from a mixture of compounds, used as extraction phases in microextraction.	Emerging as a green alternative to traditional organic solvents in DLLME [30].

The journey toward robust validation of non-targeted analysis in forensic chemistry is inextricably linked to the initial sample preparation strategy. As demonstrated, techniques range from the simple but limited protein precipitation to the highly efficient and green microextraction methodologies. While techniques like DLLME offer impressive enrichment factors, the movement in forensic science is increasingly towards sustainable, automatable, and comprehensive techniques like SPME and MEPS. These methods, evaluated under modern frameworks like White Analytical Chemistry, demonstrate that it is possible to achieve high analytical performance while adhering to green and practical principles [28]. The choice of method is a critical determinant of the quality, reliability, and defensibility of the final analytical data, forming the bedrock upon which confident non-targeted screening and quantitative risk characterization are built.

In forensic chemistry, the dual demands of analytical precision and operational speed are paramount. The ability to rapidly and reliably identify unknown substances in complex matrices directly impacts criminal investigations and public health responses. Within this framework, non-targeted screening has emerged as a critical approach for the comprehensive detection of analytes, including unexpected or novel compounds, without prior selection [31]. This article examines the optimized application of two powerful chromatographic techniques—Ultra-Performance Liquid Chromatography (UPLC) and Rapid Gas Chromatography-Mass Spectrometry (Rapid GC-MS)—within a validation-focused paradigm for forensic research. The core thesis is that while both techniques offer significant advantages in speed and sensitivity, their optimal implementation requires a thorough understanding of their complementary strengths, limitations, and the rigorous validation protocols necessary for forensic admissibility.

Analytical Principles and Technological Advancements

Ultra-Performance Liquid Chromatography (UPLC)

UPLC represents a significant evolution from traditional High-Performance Liquid Chromatography (HPLC). Its enhanced performance stems from the use of sub-2-micron particles in the chromatographic column, coupled with instrumentation designed to withstand high back-pressures. This combination facilitates superior resolution, increased sensitivity, and a dramatic reduction in analysis time. The primary separation mechanism involves the differential partitioning of analytes between a pressurized liquid mobile phase and the stationary phase.

A key forensic application of UPLC is its coupling with high-resolution mass spectrometry (HRMS) for non-targeted screening. This powerful combination allows for the accurate mass measurement of any ionizable component in a sample, enabling the identification of unknowns through the determination of their elemental composition [32]. The high resolution provided by UPLC minimizes component co-elution, which is critical when analyzing complex biological matrices like blood or urine [31].

Rapid Gas Chromatography-Mass Spectrometry (Rapid GC-MS)

Rapid GC-MS builds upon the established principles of traditional GC-MS but incorporates specific modifications to drastically reduce analysis time. These modifications include using columns with a smaller bore size, more efficient capillary columns, rapid heating rate ovens, and high-pressure carrier gas control [33]. Such innovations can reduce chromatographic run times by as much as two-thirds while maintaining acceptable analyte resolution [20].

This technology is particularly suited for the screening of volatile and semi-volatile organic compounds. In seized drug analysis, for instance, rapid GC-MS can provide informative screening with chromatography times of less than two minutes per injection [20]. Its compatibility with traditional Electron Ionization (EI) sources ensures that generated mass spectra are reproducible and can be searched against standard reference libraries—a cornerstone of forensic chemistry.

Direct Performance Comparison: UPLC-MS vs. Rapid GC-MS

The choice between UPLC and Rapid GC-MS is not a matter of superiority, but of selecting the right tool for the specific analytical question, sample matrix, and target analytes. The table below summarizes their core performance characteristics.

Table 1: Performance Comparison of UPLC-MS and Rapid GC-MS in Forensic Applications

Performance Characteristic	UPLC-MS (ESI/MS/MS)	Rapid GC-MS (EI)
Typical Analysis Time	~1 - 15 minutes [34] [31] [35]	< 2 minutes [20]
Ideal Analyte Polarity	Polar, non-volatile, thermally labile	Volatile, semi-volatile, thermally stable
Sample Preparation	Often simpler (e.g., protein precipitation) [36]	May require derivatization for many drugs [37]
Ionization Method	Electrospray Ionization (ESI) - soft ionization	Electron Ionization (EI) - hard ionization (70 eV)
Mass Spectral Libraries	Limited; spectra less reproducible [32]	Extensive, reproducible EI libraries (e.g., NIST) [32]
Limit of Quantification (LOQ)	Very low (e.g., 0.005–0.01 μg L⁻¹ for NPS in urine) [36]	~1-10 ng/mL (GC-QMS) [33]
Key Forensic Strength	Broad, non-targeted screening; metabolite identification	High-confidence library matching; isomer differentiation

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Interpreting the Comparison Data

• Analysis Time: While rapid GC-MS boasts faster chromatographic separation, total analysis time must account for sample preparation. UPLC-MS often requires minimal preparation (e.g., dilution or protein precipitation), whereas GC-MS often necessitates a more time-consuming derivatization step to make analytes volatile [37].
• Analyte Scope: UPLC-MS is unrivaled for polar, non-volatile, or thermally unstable molecules, including many pharmaceuticals, metabolites, and new psychoactive substances (NPS) [35]. Rapid GC-MS is ideal for volatile compounds, such as ignitable liquid residues, controlled substances, and other organics amenable to gas-phase separation.
• Identification Confidence: GC-MS with EI provides a "fingerprint" mass spectrum that is highly reproducible across instruments, allowing for confident identification against large libraries [33] [32]. UPLC-MS typically uses tandem mass spectrometry (MS/MS) for identification. While highly specific, the lack of standardized, transferable MS/MS libraries can be a challenge, though high-resolution accurate mass (HRAM) data helps mitigate this by providing elemental compositions [31].

Experimental Protocols for Validated Non-Targeted Screening

Protocol 1: UPLC-HRMS for Non-Targeted Screening in Biological Fluids

This protocol, adapted from validated methods in clinical and forensic toxicology, is designed for the broad detection of drugs and metabolites in blood or plasma [31] [35].

• Sample Preparation (Protein Precipitation):

  • Pipette 100 µL of whole blood or plasma into a microcentrifuge tube.
  • Add 300 µL of a precipitation solvent (e.g., a mixture of methanol and acetonitrile, potentially with zinc sulphate to improve recovery for some analytes in blood).
  • Vortex mix vigorously for 60 seconds.
  • Centrifuge at >14,000 x g for 10 minutes.
  • Transfer the clear supernatant to an autosampler vial for analysis.

• Chromatographic Conditions (UPLC):

  • Column: Accucore Phenyl Hexyl UPLC (100 x 2.1 mm, 2.6 µm) or similar [31] [35].
  • Mobile Phase A: 2 mM Ammonium Formate in water, 0.1% Formic Acid.
  • Mobile Phase B: 2 mM Ammonium Formate in Methanol/Acetonitrile (50:50, v/v), 0.1% Formic Acid.
  • Gradient: Optimized linear gradient (e.g., 5-100% B over 10-15 minutes).
  • Flow Rate: 0.3 - 0.7 mL/min.
  • Column Temperature: Room temperature to 40°C.

• Mass Spectrometry (HRMS):

  • Instrument: High-Resolution Mass Spectrometer (e.g., Q-TOF or Orbitrap).
  • Ionization: Electrospray Ionization (ESI), positive and/or negative mode.
  • Acquisition Mode: Full-scan data-dependent MS2 (ddMS2). A full scan (e.g., m/z 100-1000) is performed, followed by MS/MS fragmentation of the most intense ions.
  • Mass Accuracy: Typically required to be < 5 ppm for confident elemental composition assignment [32].

• Data Processing and Validation:

  • Use deconvolution software to manage the complex dataset.
  • Compare experimental accurate masses and MS/MS spectra against a home-made theoretical library of relevant toxic compounds (which can contain hundreds to over a thousand entries) [32] [31].
  • Identification criteria should include high-resolution precursor mass, fragment ions, isotopic pattern, and retention time match to a standard [31].

Protocol 2: Rapid GC-MS for Seized Drug Screening

This protocol outlines a validated method for the rapid screening of controlled substances in seized drug samples, emphasizing speed and ruggedness [20].

• Sample Preparation (Minimal):

  • A small amount of suspected drug material is dissolved in a suitable solvent (e.g., methanol or acetonitrile) at an appropriate concentration (e.g., 0.25-1 mg/mL).
  • The sample is vortexed and centrifuged, if necessary, and the supernatant is transferred to an autosampler vial. No derivatization is required for this screening step, which is a key factor in speed.

• Chromatographic Conditions (Rapid GC):

  • Column: Short, narrow-bore GC column (specifics may vary by instrument).
  • Oven Program: A fast temperature ramp rate is critical (often >50°C/min) to achieve the short runtime.
  • Carrier Gas: Helium or Hydrogen, with high-pressure electronic control.
  • Run Time: < 2 minutes.

• Mass Spectrometry:

  • Instrument: Single Quadrupole Mass Spectrometer.
  • Ionization: Electron Ionization (EI) at 70 eV.
  • Acquisition Mode: Total Ion Chromatogram (TIC) in full scan mode (e.g., m/z 40-550).

• Validation and Data Analysis:

  • Identification: Based on retention time and mass spectral search score against a commercial EI library (e.g., NIST). In validation, % RSD for retention time and spectral score should generally be ≤10% [20].
  • Selectivity: The method must be able to differentiate between common isomeric substances (e.g., positional isomers of synthetic cathinones), which is a known limitation for some compound pairs [20].

Visualizing the Analytical Workflows

The following diagrams illustrate the logical flow and critical decision points for the two optimized methodologies, highlighting their parallel paths toward the common goal of forensic identification.

Diagram Title: Comparative Workflows for UPLC-HRMS and Rapid GC-MS

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful implementation of these optimized methods relies on the use of specific, high-quality materials. The following table details key reagents and their functions in the experimental protocols.

Table 2: Essential Research Reagent Solutions for UPLC and Rapid GC-MS Methods

Reagent/Material	Function/Application	Key Characteristics
Accucore Phenyl Hexyl UPLC Column	Stationary phase for UPLC separation of NPS and drugs [31] [35].	Core-shell particle technology (2.6 µm); provides high efficiency and fast separation under UPLC pressures.
Methanol & Acetonitrile (UPLC/MS Grade)	Mobile phase components and protein precipitation solvents [31] [36].	Low UV absorbance; free from impurities that cause high background noise or ion suppression in MS.
Ammonium Formate & Formic Acid	Mobile phase additives for UPLC-MS [35] [36].	Volatile buffers and pH modifiers that enhance ionization efficiency and are compatible with MS detection.
HP-5MS UI GC Column	Low-bleed, stationary phase for rapid GC-MS separation [35].	(5%-Phenyl)-methylpolysiloxane phase; thermostable for fast temperature programming.
Deuterated Internal Standards	Internal standards for quantitative LC-MS and GC-MS [33] [37].	Chemically identical to analyte but with different mass; corrects for losses in sample prep and matrix effects.
NIST MS Library	Reference spectral library for GC-MS identification [20].	Contains reproducible EI mass spectra of hundreds of thousands of compounds; essential for confident ID.

The optimization of UPLC and Rapid GC-MS presents a powerful dual-strategy for modern forensic laboratories. UPLC-HRMS excels in the broad, non-targeted screening of polar and labile substances with high sensitivity and speed, making it indispensable for toxicology and the detection of emerging NPS. Rapid GC-MS provides a rugged, high-confidence screening tool for volatile compounds, leveraging robust, standardized libraries for definitive identification. The adoption of these techniques, however, must be grounded in rigorous validation as outlined in the provided protocols. This includes establishing selectivity, precision, accuracy, and identification limits, while also understanding inherent limitations, such as the difficulty in differentiating some isomers [20]. As forensic science continues to evolve, the complementary application of these advanced separation techniques, supported by robust experimental data and validated protocols, will be crucial for delivering the speed and precision required by both science and the legal system.

Leveraging Molecular Networking and Chemometrics for Structural Elucidation

In the evolving landscape of forensic chemistry and drug development, the identification of unknown compounds in complex mixtures presents a significant analytical challenge. Traditional targeted approaches often fail to detect novel or unexpected substances, creating a critical need for advanced non-targeted analysis (NTA) strategies. Molecular networking (MN) has emerged as a powerful computational methodology for visualizing complex mass spectrometry data and identifying structurally related molecules, while chemometrics provides the statistical framework for extracting meaningful patterns from chemical information. This review compares the performance of current molecular networking and chemometric approaches within the context of validating non-targeted analysis for forensic chemistry research, providing experimental data and methodologies to guide researchers in selecting appropriate techniques for structural elucidation.

Molecular Networking Approaches: Principles and Comparative Performance

Molecular networking represents a paradigm shift in how mass spectrometry data is processed and interpreted. Based on the principle that structurally similar molecules exhibit similar fragmentation patterns, MN transforms complex tandem MS (MS2) data into visual networks where related compounds cluster together [38]. The foundational technology, Classical Molecular Networking, introduced in 2012 through the Global Natural Products Social Molecular Networking (GNPS) platform, groups molecules based on the cosine similarity of their MS2 spectra, creating molecular families that facilitate drug discovery [38]. This approach has demonstrated particular utility in natural product discovery, where it enables targeted isolation of novel compounds based on their spectral proximity to known molecules.

As the field has advanced, several specialized MN tools have been developed to address specific analytical challenges. Feature-Based Molecular Networking (FBMN) integrates chromatographic information alongside spectral data, improving peak detection and reducing redundancy [38]. Ion Identity Molecular Networking (IIMN) addresses the critical bottleneck of multiple ion species (e.g., [M+H]+, [M+Na]+) originating from the same compound, which traditionally created disconnected subnetworks [39]. By incorporating chromatographic peak shape correlation analysis, IIMN connects and collapses different ion species of the same molecule, enhancing network connectivity and annotation propagation [39]. Validation studies using post-column infusion of salt solutions demonstrated IIMN successfully connects ion identities and can reduce network complexity by up to 56% [39].

Other specialized approaches include Bioactive Molecular Networking (BMN) and Activity Labelled Molecular Networking (ALMN), which integrate bioactivity data directly into molecular networks, and Chemical-Classification-Driven Molecular Networking (CCMN), which incorporates automated chemical class predictions [38]. The Integrated Molecular Networking Workflow for NP Dereplication (IMN4NPD) represents a comprehensive approach for efficiently identifying known compounds and prioritizing novel ones [38].

Table 1: Comparison of Molecular Networking Tools for Structural Elucidation

Network Type	Key Features	Optimal Use Cases	Performance Metrics
Classical MN	Groups molecules by MS2 spectral similarity; Basic visualization	Preliminary exploration of chemical space; Natural product discovery	Foundation for all other MN types; Limited by disconnected ion species
Feature-Based MN (FBMN)	Incorporates chromatographic data; Improved peak detection	Complex mixture analysis; Reducing feature redundancy	Enhanced quantification capabilities; Better separation of isomeric compounds
Ion Identity MN (IIMN)	Links different ion adducts of same molecule; Peak shape correlation	Comprehensive profiling; Overcoming adduct fragmentation differences	Reduces network size by up to 56%; Connects [M+H]+ and [M+Na]+ species
Bioactive MN (BMN)	Integrates bioassay results; Activity-anchored discovery	Bioactivity-guided isolation; Drug discovery pipelines	Direct structure-activity relationship visualization
Chemical Classification MN	Adds automated class predictions; Class-based networking	Chemical ecology studies; Metabolic pathway analysis	Enhances annotation of unknown nodes; Class-based pattern recognition

Chemometric Integration with Molecular Networking

Chemometrics—the application of mathematical and statistical methods to chemical data—complements molecular networking by providing rigorous frameworks for data interpretation, classification, and validation [40]. In forensic chemistry, chemometric techniques are increasingly applied to diverse areas including drug profiling, arson debris analysis, spectral imaging, and age determination of evidence [40].

The integration of chemometrics with molecular networking creates a powerful synergy for non-targeted analysis. While MN provides visual organization of complex MS data, chemometrics offers quantitative validation of the patterns observed. This integration is particularly valuable in forensic intelligence, where chemical profiling of illicit drugs can reveal manufacturing batches, trafficking routes, and distribution networks [40]. Current applications cover both low-dimensional data (e.g., drug impurity profiles) and high-dimensional data (e.g., Infrared and Raman spectra) [40].

Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) are among the most frequently employed chemometric techniques in forensic chemistry. PCA enables dimensionality reduction and visualization of multivariate data, allowing researchers to identify outliers, clusters, and trends within complex datasets. PLS-DA provides supervised classification, building predictive models for sample categorization based on known classes. These techniques enhance the interpretability of molecular networks by providing statistical validation of the observed clustering patterns.

Table 2: Chemometric Methods in Forensic Chemistry and Their Applications

Chemometric Method	Primary Function	Forensic Applications	Compatibility with MN
Principal Component Analysis (PCA)	Dimensionality reduction; Unsupervised pattern recognition	Drug profiling; Sample classification; Batch comparison	Validates network clustering; Identifies outliers in datasets
Partial Least Squares Discriminant Analysis (PLS-DA)	Supervised classification; Feature selection	Source identification; Age determination of evidence	Enhances annotation confidence; Supports activity-based networking
Hierarchical Cluster Analysis (HCA)	Sample grouping based on similarity measures	Geographic origin tracing; Manufacturing batch linking	Complementary visualization to MN; Confirms molecular families
Multivariate Curve Resolution (MCR)	Decomposition of complex mixtures	Arson analysis; Multi-component quantification	Aids in deconvoluting co-eluting compounds in LC-MS data

Experimental Validation of Non-Targeted Workflows

Analytical Methodologies and Protocols

Robust validation of non-targeted approaches requires carefully designed experimental protocols. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) has become the cornerstone technology for NTA, with Orbitrap instruments providing the high mass accuracy and resolution necessary for confident compound identification [31] [41]. Typical analytical workflows employ data-dependent acquisition (DDA) or data-independent acquisition (DIA) modes. DDA selects the most intense precursor ions for fragmentation, producing high-quality MS2 spectra ideal for molecular networking, while DIA (including SWATH - Sequential Window Acquisition of All Theoretical Fragment-Ion Spectra) fragments all ions within sequential mass windows, providing comprehensive coverage [42].

Sample preparation represents a critical step in ensuring reproducible results. For blood and plasma analysis, efficient protein precipitation and extraction are essential. Recent methodologies have optimized QuEChERS (Quick, Easy, Cheap, Effective, Rugged, Safe) salts for toxicological analysis, providing high recovery for a broad range of compounds [41]. A validated protocol for plasma analysis uses 200 μL of sample with acetonitrile and QuEChERS salts, followed by centrifugation and analysis of the supernatant [41]. This approach demonstrates that simple and rapid extraction methods can yield comprehensive screening results when coupled with advanced instrumentation.

Chromatographic separation typically utilizes reversed-phase (RP) columns, with the Accucore Phenyl Hexyl column showing optimal peak shape for diverse compounds [31]. Mobile phases often consist of water with 0.1% formic acid (mobile phase A) and acetonitrile with 0.1% formic acid (mobile phase B), with gradient elution over 15-20 minutes [31]. The inclusion of ammonium acetate or formate can enhance signal strength for certain compounds [31].

Validation Metrics and Performance Data

Rigorous validation of non-targeted methods requires assessing multiple performance parameters. For the screening component, limits of identification (LOI) and detection (LOD) are critical metrics. A validated HRMS method for toxicological analysis demonstrated a mean LOI of 8.8 ng/mL (range: 0.05-500 ng/mL) and mean LOD of 0.25 ng/mL (range: 0.05-5 ng/mL) across 132 compounds [41]. For quantitative performance, linearity, accuracy, and precision are essential. The same method showed linearity in the 5-500 ng/mL range (0.5-50 ng/mL for cannabinoids) with correlation coefficients >0.99, and intra- and inter-day accuracy and precision <15% for all compounds [41].

Selectivity and specificity must be established through analysis of blank matrix samples from multiple sources. Interference studies should confirm the absence of signal in blank samples at the retention times of analytes [31]. For isobaric compounds (e.g., amitriptyline/maprotiline; clobazam/temazepam), chromatographic separation is necessary for distinction, highlighting the importance of orthogonal separation to mass analysis [41].

The following experimental workflow diagram illustrates the integrated process of sample preparation, data acquisition, and computational analysis for molecular networking and chemometrics:

Structural Annotation Tools and Spectral Libraries

The identification power of molecular networking heavily depends on the quality and comprehensiveness of structural annotation tools and spectral libraries. The GNPS platform integrates multiple in silico annotation algorithms that operate through different approaches [38]. DEREPLICATOR and DEREPLICATOR+ utilize database searching to identify peptidic natural products, while Network Annotation Propagation (NAP) transfers annotations from known to unknown nodes based on spectral similarity [38]. MS2LDA discovers conserved fragmentation patterns across molecules, and MolNetEnhancer integrates outputs from multiple in silico tools to provide comprehensive chemical classifications [38].

Three primary strategies exist for structural annotation of mass spectra: (1) comparison with authentic standard compounds (providing Level 1 confident annotations); (2) searching public/commercial reference spectral libraries (Level 2 probable structures); and (3) in silico prediction (Level 3 tentative candidates) [42]. Each approach offers distinct advantages and limitations in coverage and confidence.

Reference spectral libraries play a crucial role in annotation confidence. Commercial and public libraries such as those in GNPS, MassBank, and NIST contain hundreds of thousands of spectra, though coverage remains incomplete compared to the chemical diversity in complex samples [42]. For forensic applications, targeted expansion of libraries with relevant compounds is often necessary. One approach involves creating a customized library of 106 reference standards covering diverse polymer additives for medical device analysis, ensuring comprehensive toxicological coverage [9].

Table 3: Structural Annotation Tools Compatible with Molecular Networking

Tool Name	Annotation Approach	Strengths	Confidence Level
DEREPLICATOR+	Database search for peptides	High accuracy for peptidic natural products	Level 1-2 (when matched to standards)
Network Annotation Propagation (NAP)	Spectral similarity propagation	Extends annotations to unknown analogs	Level 2-3 (dependent on library coverage)
MS2LDA	Fragmentation pattern discovery	Identifies conserved substructures without libraries	Level 3-4 (putative compound classes)
SIRIUS	In silico fragmentation prediction	Comprehensive formula and structure identification	Level 3-4 (tentative candidates)
MolNetEnhancer	Integrated multi-tool workflow	Combines outputs from various annotation tools	Level 2-3 (enhanced classification)

Essential Research Reagents and Computational Tools

Successful implementation of molecular networking and chemometrics requires specific research reagents and computational resources. The following table details key solutions essential for conducting experiments in this field:

Table 4: Essential Research Reagents and Computational Tools for Molecular Networking

Resource Category	Specific Tools/Reagents	Function/Purpose	Availability
MS Data Platforms	GNPS (Global Natural Products Social Molecular Networking)	Web-based platform for molecular networking and spectral analysis	Freely available at https://gnps.ucsd.edu
Feature Finding Software	MZmine, XCMS, MS-DIAL	LC-MS data processing; feature detection; ion identity grouping	Open source
Chemometric Software	R packages (ropls, mixOmics), SIMCA, Matlab	Multivariate statistical analysis; classification; regression	Commercial and open source
Reference Libraries	GNPS libraries, NIST, MassBank, HMDB	Spectral reference for compound identification	Commercial and public
Sample Preparation	QuEChERS kits, organic solvents (ACN, MeOH), buffers	Matrix cleanup; compound extraction; protein precipitation	Commercial suppliers
LC Columns	Accucore Phenyl Hexyl, HSS T3, BEH C18	Compound separation; isomer resolution	Commercial suppliers
Internal Standards	Stable isotope-labeled analogs	Quantitation; process monitoring; normalization	Commercial suppliers

Molecular networking and chemometrics represent complementary pillars of modern non-targeted analysis for structural elucidation in forensic chemistry and drug development. Molecular networking provides powerful visualization and organization of complex mass spectrometry data, with specialized approaches like IIMN and FBMN addressing specific analytical challenges. Chemometrics adds statistical rigor and validation, enabling confident pattern recognition and classification. The integration of these approaches, supported by robust experimental protocols and comprehensive spectral libraries, creates a validated framework for identifying known compounds, discovering novel substances, and elucidating structural relationships in complex mixtures. As these technologies continue to evolve, they promise to further enhance our capability to navigate the complex chemical space encountered in forensic investigations and natural product discovery.

The paradigm in forensic chemistry is progressively shifting from targeted confirmation to non-targeted analysis (NTA), driven by the need to identify unknown and emerging threats. Non-targeted approaches, particularly those utilizing high-resolution mass spectrometry (HRMS), enable the detection and identification of compounds without prior knowledge of their presence [31] [43]. The validation of these NTA approaches forms a critical thesis in modern forensic science, ensuring that methods are reliable, defensible, and capable of providing actionable intelligence across diverse sample types. This guide objectively compares the application of various analytical techniques and workflows, highlighting how validated NTA methods are integral to advancing forensic capabilities in key domains such as seized drugs, fire debris, homemade explosives, and toxicology.

Seized Drug Analysis

The identification and profiling of illicit substances are fundamental to forensic chemistry. While traditional targeted methods remain the mainstay, NTA workflows are increasingly vital for characterizing novel psychoactive substances (NPS) and complex mixtures.

Analytical Workflows and Performance Comparison

Forensic laboratories often employ different analytical workflows, balancing time, cost, and informational yield. A comparative study of two distinct workflows for analyzing synthetic cannabinoids, cathinones, and opioids provides quantitative performance data [44].

Table 1: Comparison of Analytical Workflows for Seized Drug Analysis

Workflow Component	Traditional Workflow	Experimental Workflow	Comparative Outcome
Screening Technique	Color tests	Direct Analysis in Real Time Mass Spectrometry (DART-MS)	DART-MS required the same time but yielded "significantly more information."
Confirmation Technique	General-purpose GC-FID and GC-MS	Targeted GC-MS methods	Targeted GC-MS reduced instrument and data interpretation time by more than half.
Data Interpretation Time	Higher	Simplified	Targeted methods "simplified data interpretation."
Overall Workflow Efficiency	Lower; faced analytical challenges preventing confirmation in some samples	Higher; addressed "almost all the limitations" of general-purpose methods	The experimental workflow reduces turnaround times and backlogs.

Method Validation and Uncertainty in Targeted GC-MS

Gas Chromatography-Mass Spectrometry (GC-MS) is a gold standard in seized drug analysis. A critical study assessing the uncertainty of measurement for GC-MS data provides evidence that existing acceptance criteria may be overly conservative, potentially increasing the risk of false positives [45].

• Retention Time Uncertainty: Measured expanded uncertainty (2σ) for GC retention times was found to be approximately ±0.20%, which is significantly smaller than the ±2% criterion recommended by many agencies [45].
• Relative Ion Abundance Uncertainty: The measured uncertainties for relative ion abundances were similar to recommended criteria (e.g., ±20-30%). However, the study revealed that ion abundances within a spectrum are not independently variable, with some ions showing high correlation (R² > 0.9). This indicates that current criteria, while conservative, do not fully account for the spectral structure [45].

Table 2: Measured vs. Recommended Acceptance Criteria for GC-MS in Drug Analysis

Measurement Parameter	Recommended Criteria (Various Agencies)	Measured Uncertainty (2σ)	Implication for Validation
Retention Time	±2% or ±0.1 min	±0.20%	Overly wide criteria can elevate the risk of false-positive identifications (Type I errors).
Relative Ion Abundance	±20% to ±30% (absolute or relative)	Similar to recommendations, but ions are correlated	Correlation among ions is not considered in traditional criteria, requiring a more nuanced validation approach.

Drug Profiling and Isotopic Analysis

Drug profiling, or chemical fingerprinting, goes beyond identification to link seizures and determine origin. Advanced techniques include:

• Isotope-Ratio Mass Spectrometry (IRMS): Analyzes stable isotopes to provide information on a drug's geographic origin, especially for plant-derived exhibits like cocaine and marijuana [46].
• Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Used for inorganic profiling to trace elemental impurities back to specific catalysts or synthetic routes [46].

Fire Debris Analysis

Fire debris analysis aims to detect and identify ignitable liquid residues (ILRs) within burnt materials. The complex sample matrix, resulting from pyrolysis of background materials, makes this a prime application for robust and validated analytical methods.

Standard Analytical Methodology

The standard practice involves extracting volatile residues from debris and concentrating them for analysis by Gas Chromatography-Mass Spectrometry (GC-MS) [47] [48]. The interpretation relies on comparing the resulting chromatographic pattern to known ignitable liquid standards, using guidelines such as ASTM E1618 [47] [48].

Advancements with Machine Learning and Chemometrics

The interpretation of GC-MS data from fire debris is complicated by background interference. Machine learning (ML) models are being validated to automate and improve this classification.

• A study comparing the Naïve Bayes (NB) and Classification and Regression Tree (CART) algorithms on GC-MS data of fire debris found that NB consistently outperformed CART [48]. NB achieved prediction accuracies above 70% and demonstrated more consistent performance and a lower risk of overfitting across different validation data splits [48].
• These ML models assist in classifying complex total ion chromatograms (TICs), providing a complementary, data-driven approach to traditional manual interpretation [48].

Diagram 1: Fire Debris Analysis Workflow with ML Integration. The standard ASTM method can be supplemented by Machine Learning classification of GC-MS data.

Homemade Explosives (HME)

The threat landscape of homemade explosives (HMEs) is dynamic, requiring continuous characterization of emerging threats to develop effective countermeasures.

The HEIDM Program and Detection Development

The Homemade Explosives Identification, Detection and Mitigation (HEIDM) Program is a key initiative that focuses on the chemical and physical characterization of HMEs [49]. The program's work directly supports:

• Improving Detection Technology: Providing data on explosives of interest to update detection standards and algorithms for security equipment like X-ray and Advanced Imaging Technology [49].
• Threat Assessment and Mitigation: Developing decision-support tools for first responders to safely mitigate HME incidents [49]. A core challenge is that HMEs are often made from common commercial products, making it difficult to distinguish threats from innocent materials. Validation of NTA methods is therefore critical for developing reliable detection systems [49].

Toxicological Screening

Toxicological screening in clinical and forensic contexts requires broad-panel methods capable of detecting a vast range of xenobiotics at low concentrations.

Validation of an LC-HRMS Non-Targeted Screening Method

Liquid Chromatography coupled to High-Resolution Mass Spectrometry (LC-HRMS) is a powerful platform for NTA. A validated method for screening 53 compounds in whole blood and plasma demonstrates the rigorous application of NTA principles [31].

• Sample Preparation: A simple deproteinization step using only 100 µL of sample with methanol/acetonitrile and zinc sulphate was optimized, offering an advantage in cases with limited sample volume [31].
• Data Acquisition and Processing: The method uses data-dependent acquisition (DDA), where the most intense ions are automatically selected for fragmentation. The primary identification criteria were the presence of the high-resolution precursor ion and fragment ions, with a match to a library spectrum. This overcomes limitations in distinguishing isomeric compounds [31].
• Performance: The method demonstrated high selectivity and specificity, with limits of identification established at sub-therapeutic or therapeutic concentrations. It was successfully applied to routine clinical and forensic samples [31].

Diagram 2: Non-Targeted Toxicological Screening Workflow. The LC-HRMS method uses a small sample volume and relies on high-resolution mass and fragment matching for confident identification.

The Role of Machine Learning in NTA

Machine learning is poised to significantly enhance NTA workflows for emerging environmental contaminants, with direct applicability to toxicological screening. ML models can optimize data processing, improve chemical structure identification, and even advance quantification and toxicity prediction capabilities [43]. Integrating these computational tools addresses the challenge of interpreting the vast, complex datasets generated by HRMS.

The Scientist's Toolkit: Key Research Reagents and Materials

The following table details essential materials and their functions in the experiments and fields discussed in this guide.

Table 3: Essential Research Reagents and Materials for Forensic Chemistry Applications

Material/Reagent	Function/Application	Field of Use
Drug Standards (e.g., Methamphetamine, Cocaine)	Used as reference materials for method calibration, quantification, and determining measurement uncertainty.	Seized Drug Analysis [45] [50]
SPME Fibers (e.g., 65 μm PDMS/DVB)	Extracts and concentrates volatile ignitable liquid residues from the headspace of fire debris samples.	Fire Debris Analysis [48]
LC-HRMS Library (>1400 compounds)	A curated database of accurate mass spectra used for the automated identification of unknown compounds in non-targeted screening.	Toxicological Screening [31]
Organic Solvents (Methanol, Acetonitrile)	Used for sample preparation, including deproteinization of biological samples and liquid-liquid extraction.	Toxicological Screening, Seized Drug Analysis [31] [50]
GC-MS Capillary Columns (e.g., Elite-5MS)	Separates complex mixtures of compounds based on their volatility and interaction with the stationary phase.	Seized Drug Analysis, Fire Debris Analysis [50] [48]

The validation of non-targeted analysis is a cornerstone of modern forensic chemistry, enabling laboratories to adapt to evolving threats with scientific rigor. The experimental data and comparisons presented in this guide demonstrate that:

• In seized drug analysis, re-evaluating traditional acceptance criteria and adopting efficient workflows can minimize errors and improve throughput.
• For fire debris, integrating machine learning models like Naïve Bayes with standard GC-MS methods enhances the objectivity and reliability of data interpretation.
• Programs like HEIDM are crucial for systematically characterizing HME threats, providing the foundational data needed to validate detection methods.
• In toxicological screening, validated LC-HRMS methods represent the state of the art, offering sensitive, broad-panel detection capable of identifying unknowns.

The continuous refinement of these analytical approaches, supported by robust validation and the integration of computational tools, ensures that forensic chemistry remains at the forefront of public safety and scientific innovation.

Navigating Complexities: Troubleshooting and Optimizing Your Forensic NTA Workflow

In forensic chemistry research, the shift from targeted to non-targeted analysis (NTA) has created a paradigm similar to that faced by product managers: an overwhelming abundance of data points—or "features"—that must be systematically winnowed down to the most scientifically actionable leads. For scientists identifying unknown compounds in complex matrices, this process is not merely about convenience but about accurately identifying toxicologically relevant substances amid thousands of potential signals. The prioritization strategies employed can determine the success of identifying a novel psychoactive substance in a forensic sample or a toxic leachable in a medical device. This guide compares established methodological frameworks for prioritizing analytical features, providing researchers with structured approaches to enhance the efficiency and reliability of their non-targeted screening protocols.

Core Prioritization Frameworks for Analytical Research

Various structured frameworks can be adapted to manage the complexity of NTA data. The table below summarizes the core principles, advantages, and challenges of several prominent methodologies.

Framework	Core Principle	Best Use-Case in Research	Key Advantages	Key Limitations
Value vs. Complexity [51] [52] [53]	Plots potential features on a 2D matrix based on their value (e.g., toxicological significance) versus the complexity (e.g., analytical effort) to implement.	Initial triage of detected features for a new screening method.	Provides a quick, visual overview; facilitates collaborative team discussions [52].	"Value" can be subjective without clear criteria; effort estimates may be inaccurate [53].
Weighted Scoring [51]	Assigns scores to features based on multiple weighted criteria (e.g., peak intensity, toxicological concern, confidence in identification) for an objective ranking.	Prioritizing features for definitive identification when multiple relevant signals are detected.	Introduces objectivity and mitigates bias by using a predefined scoring model [51].	Requires upfront agreement on criteria and their weights; can be time-consuming to set up.
Kano Model [51] [54] [52]	Categorizes features based on their impact on the overall analytical goal: Basic (must identify), Performance (linear improvement), and Delighters (unexpected high-value hits).	Evaluating and communicating the strategic role of different identifications to stakeholders.	Focuses on the impact of findings, helping to balance routine monitoring with innovative discovery [54].	Customer satisfaction is the original metric; requires adaptation for scientific context. Categorization can be subjective [52].
RICE Scoring [54] [52] [53]	Ranks features by Reach (how many samples/show up), Impact (toxicological risk), Confidence (in identification), and Effort (to confirm).	Detailed portfolio management of a long list of potential analytes across multiple projects.	Comprehensive and data-driven; confidence metric helps de-prioritize uncertain but loud signals [52].	Can be time-consuming; requires good data for accurate scoring; may be overkill for small projects [53].
MoSCoW Method [54] [52] [53]	Classifies features into four buckets: Must have, Should have, Could have, and Won't have (for this reporting cycle).	Defining the scope for a specific phase of method validation or a time-bound project report.	Simple to understand and implement; excellent for setting clear boundaries and expectations [53].	Can lead to too many "Must-haves"; lacks granularity for ranking within categories [52].

Experimental Protocols for Framework Validation

To validate the effectiveness of a prioritization strategy in a research environment, a rigorous comparative experiment must be designed. The following protocol outlines a quasi-experimental approach suitable for this purpose.

Study Design: Comparative Method Evaluation

This design describes a structured approach to evaluate and compare the performance of different prioritization frameworks within a controlled setting [55].

• Aim: To assess the degree of agreement and performance between a new prioritization framework (the comparison method) and an established benchmark (the reference method) in the context of NTA.
• Sample Selection: A set of 40-100 authentic or spiked samples should be used, carefully selected to cover the entire clinically or forensically meaningful measurement range [55].
• Experimental Procedure:
  • Sample Analysis: Analyze all samples using the standard NTA platform (e.g., LC-HRMS) to generate a raw list of detected features [31].
  • Parallel Prioritization: Process the identical raw feature list through the different prioritization frameworks being compared (e.g., Weighted Scoring vs. RICE).
  • Outcome Measurement: The final output of each framework is a ranked list of features recommended for identification. The key metric is the "time-to-actionable-lead"—the analyst effort required to confirm the identity of a toxicologically relevant substance from the top recommendations of each list.
• Data Analysis:
  • Bland-Altman Analysis: Plot the difference in "time-to-actionable-lead" between the two methods against their average to visualize bias and agreement limits [55].
  • Passing-Bablok Regression: A non-parametric regression used to assess the constant and proportional bias between the ranked results of two methods, as it makes no assumptions about the distribution of errors [55].

Experimental Workflow for Framework Comparison

The Scientist's Toolkit: Essential Reagents & Materials for NTA

The successful implementation of an NTA workflow, and by extension the features that will be prioritized, relies on a foundation of high-quality materials and reagents.

Item	Function in Non-Targeted Analysis
Reference Standards [31] [9]	Pure chemical substances used to build spectral libraries for compound identification based on retention time and fragmentation pattern. Crucial for determining Relative Response Factors (RRF) [9].
LC-MS Grade Solvents (e.g., Methanol, Acetonitrile) [31]	High-purity solvents used for mobile phases and sample preparation to minimize background noise and ion suppression, ensuring optimal instrument sensitivity.
Internal Standards (stable isotope-labeled) [31]	Compounds added to all samples to monitor and correct for variability in sample preparation, injection, and ionization efficiency during mass spectrometry.
Quality Control (QC) Materials [55]	Pooled samples or reference materials analyzed repeatedly throughout a batch to monitor instrument stability, performance, and data reproducibility over time.
Sample Preparation Kits (e.g., Deproteinization kits) [31]	Standardized kits for procedures like protein precipitation using solvents (methanol/acetonitrile) and salts (zinc sulphate) to clean up complex biological matrices like blood or plasma.

Visualization of a Prioritization Workflow in Forensic NTA

The following diagram maps a generalized logical workflow for applying prioritization strategies to filter thousands of analytical features down to a shortlist of actionable leads, integrating multiple frameworks into a cohesive process.

Logical Flow for Feature Prioritization

The transition from data-rich non-targeted analyses to scientifically defensible conclusions requires robust, transparent, and systematic prioritization strategies. By adopting and validating frameworks like Weighted Scoring, RICE, and the Kano Model, forensic chemists and researchers can move beyond intuitive, and potentially biased, feature selection. Integrating these methodologies into a standardized workflow, supported by a well-characterized toolkit of reagents and reference materials, ensures that resources are focused on the most toxicologically relevant substances, thereby strengthening the validity of chemical risk assessments and accelerating the pace of analytical discovery.

Overcoming Matrix Effects and Interference in Biological and Post-Blast Residues

Matrix effects and interference present significant challenges in forensic chemistry, potentially compromising the sensitivity, selectivity, and reliability of analytical results. These phenomena occur when co-eluting components from complex sample matrices alter the detector response for target analytes, leading to signal suppression or enhancement. In forensic contexts, where evidentiary value depends on analytical precision, overcoming these effects is paramount for accurate identification and quantification of compounds in biological specimens and post-blast residues [56].

This guide objectively compares contemporary analytical approaches for managing matrix effects across different forensic sample types, focusing on methodological innovations that enhance the validity of non-targeted analysis (NTA). As forensic chemistry increasingly adopts NTA for comprehensive chemical screening, establishing robust protocols for matrix effect compensation has become essential for research and method validation [43] [57].

Analytical Techniques Comparison

The table below summarizes key analytical techniques, their applications to different forensic matrices, and their performance in overcoming matrix effects:

Table 1: Comparison of Analytical Techniques for Managing Matrix Effects

Analytical Technique	Target Matrices	Key Strategy for Matrix Effects	Performance Data	Limitations
Dilute-and-Shoot LC-MS/MS [58]	Biological (Urine)	Sample dilution with organic solvents	LOD/LOQ: 0.01-1.5/0.05-5 ng/mL; Validated for 115 drugs/metabolites	Limited for highly complex matrices
Individual Sample-Matched IS (IS-MIS) [59]	Environmental (Urban Runoff)	Sample-specific internal standard matching	<20% RSD for 80% of features (vs. 70% with pooled standards)	Requires 59% more analysis time
Direct Analysis in Real Time (DART-MS) [60]	Post-blast Explosive Residues	Ambient ionization, minimal sample preparation	Detected TATP, HMTD, MEKP from genuine post-blast fragments	Requires high-resolution MS for confident ID of small molecules
Gas Chromatography-Vacuum UV (GC-VUV) [61]	Post-blast Explosive Residues	Spectral uniqueness in VUV region	Detection in low ppm range; picogram sensitivity	Sensitivity may be insufficient for trace ppb-level residues
Ion Chromatography (IC) [62]	Post-blast Soil (Nitrate Ions)	High selectivity for ionic species	Quantified nitrate ions in rainy conditions (8.4-38.2 ppm in spiked soils)	High solubility of analytes increases leaching risk

Experimental Protocols for Matrix Effect Mitigation

Dilute-and-Shoot LC-MS/MS for Biological Samples

This protocol enables screening of 115 drugs and metabolites in urine with minimal sample preparation, effectively reducing matrix effects through optimized dilution [58].

• Sample Preparation:
  • Combine 100 µL of urine sample with 200 µL of methanol:acetonitrile (3:1, v/v) mixture.
  • Vortex the mixture thoroughly to ensure complete precipitation of proteins and other macromolecular interferents.
  • Centrifuge the sample to separate the supernatant from the precipitate.
  • Directly inject the supernatant into the LC-MS/MS system without further processing.
• Instrumental Analysis:
  • Chromatography: Utilize a C18 column with a gradient elution program optimized for a rapid 7.5-minute run time.
  • Mass Spectrometry: Employ tandem mass spectrometry (MS/MS) in multiple reaction monitoring (MRM) mode for high selectivity.
• Validation Data: The method demonstrated excellent sensitivity with limits of detection (LOD) and quantification (LOQ) ranging from 0.01 to 1.5 ng/mL and 0.05 to 5 ng/mL, respectively. It successfully applied to monitoring individuals in rehabilitation programs [58].

Post-Blast Soil Analysis for Nitrate Ions by Ion Chromatography

This method addresses the challenge of detecting highly soluble nitrate ions in post-blast soils, even after rainfall, which can dilute or redistribute residues [62].

• Experimental Design:
  • Construct simulated post-blast pits on sloped terrain, spiking with potassium nitrate (1 g KNO₃) to mimic explosive residue.
  • Expose pits to natural rainfall for one week to study leaching effects.
  • Collect soil samples from distinct surface and subsurface layers after drying.
• Sample Preparation:
  • Extract nitrate ions from soil samples using demineralized water.
  • Filter the extracts to remove particulate matter.
  • Evaporate the filtrate to dryness and reconstitute in a smaller volume of demineralized water to pre-concentrate analytes.
  • Perform a ten-fold dilution followed by syringe filtration prior to IC analysis.
• Instrumental Analysis:
  • System: Metrohm Advanced Ion Chromatography system.
  • Column: Metrosep A Supp 19 anion exchange column (150 × 4.0 mm).
  • Eluent: 8.0 mM Sodium Carbonate (Na₂CO₃) + 0.25 mM Sodium Bicarbonate (NaHCO₃).
  • Flow Rate: 0.7 mL/min.
• Key Findings: IC successfully quantified nitrate ions despite rainfall exposure, with concentrations higher in the simulated pit (SPT: 38.2 ppm, SPB: 25.0 ppm) compared to the control pit (CPT: 14.3 ppm, CPB: 8.4 ppm), demonstrating its resilience for forensic analysis of water-soluble residues [62].

Workflow Visualization

Dilute-and-Shoot LC-MS/MS Workflow

Dilute-and-Shoot LC-MS/MS Workflow for Urine Analysis

Post-Blast Soil Analysis Workflow

Post-Blast Soil Analysis Workflow for Nitrate Ion Detection

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 2: Key Research Reagent Solutions for Forensic Analysis

Item	Function/Application	Example from Protocols
Methanol:Acetonitrile (3:1, v/v)	Protein precipitation and sample dilution in "dilute-and-shoot" methods	Urine sample preparation for LC-MS/MS [58]
Isotopically Labeled Internal Standards	Correction for signal suppression/enhancement and instrumental drift	IS-MIS strategy for urban runoff analysis [59]
Anion Exchange IC Column	Separation of inorganic ions in complex matrices	Metrosep A Supp 19 column for nitrate analysis in soil [62]
Carbonate/Bicarbonate Eluent	Mobile phase for anion separation in Ion Chromatography	8.0 mM Na₂CO₃ / 0.25 mM NaHCO₃ for nitrate analysis [62]
Solid-Phase Extraction (SPE) Sorbents	Sample clean-up and pre-concentration for complex matrices	Multilayer SPE (HLB, ENV+) for urban runoff [59]
High-Purity Water & Solvents	Preparation of blanks, standards, and sample reconstitution	Demineralized water for soil extraction and IC analysis [62]

Effectively overcoming matrix effects requires a strategic combination of sample preparation, instrumental analysis, and data processing techniques tailored to specific forensic matrices. For biological fluids like urine, efficient "dilute-and-shoot" approaches paired with LC-MS/MS provide robust, high-throughput solutions. In contrast, analysis of post-blast residues demands specialized techniques such as IC for inorganic ions or DART-MS and GC-VUV for organic explosives, which maintain sensitivity and specificity despite environmental challenges and complex interferents.

The ongoing validation and adoption of advanced strategies like individual sample-matched internal standards and non-targeted screening workflows are critical for enhancing the reliability and defensibility of forensic chemical analysis. As the field progresses, the development of standardized protocols and shared databases will be instrumental in advancing forensic research and practice [57].

In the evolving field of forensic chemistry, the adoption of non-targeted analysis (NTA) represents a paradigm shift from traditional targeted methods. NTA employs high-resolution mass spectrometry (HRMS) to screen for a vast array of unknown or unexpected chemical compounds without prior knowledge of their presence [2]. This approach is particularly valuable for forensic toxicology, drug detection, and identifying novel psychoactive substances (NPS) that continually emerge in the drug market [41] [63]. However, implementing NTA comes with significant analytical challenges, including poor chromatographic resolution, low analytical sensitivity, and complete method failure. This guide objectively compares troubleshooting approaches and provides validated experimental protocols to overcome these hurdles, ensuring reliable data for forensic casework and research.

Analytical Fundamentals: NTA Workflow and Key Performance Indicators

Understanding the complete NTA workflow is essential for effective troubleshooting. Each stage, from sample preparation to data interpretation, introduces potential variables that can impact overall method performance and contribute to the pitfalls discussed in subsequent sections.

Figure 1: Non-Targeted Analysis Workflow. The process flow highlights critical stages where analytical pitfalls commonly occur, from sample preparation through final reporting. Key troubleshooting points are indicated at each transition.

The success of an NTA method is quantified through specific performance indicators. Resolution refers to the ability to separate analytes chromatographically and mass spectrometrically. Sensitivity is the method's lowest reliably detectable amount of an analyte, often defined by the limit of identification (LOI) or detection (LOD). Method robustness encompasses the reliability and reproducibility of the entire analytical process across variations in reagents, instruments, and operators [41] [15].

Troubleshooting Poor Resolution

Poor resolution in NTA manifests as co-eluting compounds in chromatography or overlapping masses in spectrometry, leading to misidentifications and inaccurate quantification.

Root Causes and Experimental Evidence

Chromatographic resolution failures often stem from column selection, gradient optimization, or matrix effects. Mass resolution issues typically relate to instrument calibration and settings. A validated forensic method for toxicological screening achieved excellent separation of 29 compounds including cannabinoids and opioids using a C18 column with a water/acetonitrile gradient containing formic acid, demonstrating that optimized conditions can resolve complex mixtures [41].

Protocols for Resolution Enhancement

• Column Re-equilibration Verification: After gradient runs, ensure 10-15 column volumes of starting conditions for consistency [15].
• Alternative Column Chemistry Testing: Compare performance between C18, phenyl-hexyl, and HILIC columns for challenging separations.
• Mobile Phase pH Optimization: Test formate, acetate, and ammonium bicarbonate buffers at pH 3-8 to improve peak shape for ionizable compounds.
• Mass Resolution Calibration: Daily calibrate HRMS using reference standards per manufacturer protocols to maintain mass accuracy <5 ppm [15].

Comparative Performance Data

Table 1: Resolution Improvement Strategies with Experimental Outcomes

Strategy	Protocol Parameters	Performance Metrics	Limitations
Extended Gradient	Increase from 10min to 20min; 5-95% ACN	40% increase in peak capacity; Resolution >1.5 for critical pairs	100% longer analysis time
Column Temperature Control	30°C vs 45°C; C18 column	Better shape for bases at 45°C; 15% efficiency gain	Potential analyte degradation at higher temperatures
UPLC Implementation	Sub-2μm particles; >600 bar pressure	2x resolution power; 3x speed	System compatibility; clogging risk

Addressing Low Sensitivity

Low sensitivity results in undetected analytes at toxicologically relevant concentrations, compromising the screening comprehensiveness that defines NTA.

Root Causes and Experimental Evidence

Sensitivity limitations often originate from suboptimal extraction, ion suppression, or instrument detection parameters. In a plasma screening method, a simple QuEChERS-based extraction (acetonitrile with salt purification) provided sufficient recovery for 132 toxicologically relevant compounds at mean LOI of 8.8 ng/mL, demonstrating that efficient sample cleanup is crucial for sensitivity [41]. Ion suppression from co-eluting matrix components can reduce signal intensity by over 50% in complex biological samples [2].

Protocols for Sensitivity Improvement

• Extraction Efficiency Assessment: Spike blank matrix with reference standards at low concentrations to calculate absolute recovery and optimize extraction solvents and procedures [64].
• Ion Suppression Mapping: Infuse analyte standards post-column while injecting extracted blanks to identify chromatographic regions with signal suppression.
• Source Parameter Optimization: Systematically adjust desolvation temperature, gas flows, and spray position using experimental design (DoE) approaches.
• Data Acquisition Mode Selection: Compare full scan (60,000 FWHM) versus data-dependent MS/MS modes for optimal detection of low-abundance compounds [41].

Comparative Performance Data

Table 2: Sensitivity Enhancement Techniques with Experimental Results

Technique	Experimental Implementation	Sensitivity Gain	Trade-offs
SPE Concentration	Oasis HLB; 100mL to 1mL (100x)	10-50x lower LOD for pharmaceuticals	Selective compound recovery; additional steps
In-source Fragmentation Reduction	Lower cone voltage; soft ionization	3-5x signal increase for molecular ions	Reduced structural information
Ion Mobility Integration	LC-IM-HRMS; separation in gas phase	2-4x S/N in complex matrix	Instrument complexity; data handling challenges

Preventing Method Failure

Complete method failure represents the most severe pitfall, where the analytical process yields unreliable or non-reproducible results, invalidating the analysis.

Root Causes and Experimental Evidence

Method failures typically stem from inadequate method development, improper validation, or unexpected sample matrix effects. The NORMAN Network emphasizes that NTA methods must undergo rigorous validation and quality assurance procedures to ensure reliability, especially when used in regulatory or forensic contexts [15]. A key challenge is the lack of standardized protocols, with different laboratories employing varying approaches to sample preparation, instrumentation, and data processing [2] [15].

Protocols for Method Robustness

• Comprehensive Quality Control: Include procedural blanks, fortified samples, and reference materials in each batch to monitor performance [15].
• Internal Standard Selection: Use isotopically labeled analogs or chemical analogues as internal standards across different compound classes to correct for variability.
• Reference Standard Database Development: Maintain a curated set of reference standards covering diverse chemical space (e.g., 106 polymer additives for medical device analysis) to improve identification confidence and quantitative accuracy [9].
• Cross-Platform Verification: Confirm identifications using orthogonal techniques (e.g., both LC-MS and GC-MS) when possible, especially for novel compounds [63].

System Suitability Testing Framework

Implement daily system suitability tests including:

• Reference standard mixture at specified concentrations
• Retention time stability (<0.1 min deviation)
• Mass accuracy (<5 ppm error)
• Intensity response (>minimum threshold)
• Peak shape (asymmetry factor 0.8-1.5)

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful NTA implementation requires carefully selected reagents and materials to ensure method reliability and reproducibility.

Table 3: Essential Research Reagents for Forensic NTA

Reagent/Material	Function	Application Example	Performance Considerations
QuEChERS Extraction Salts	Rapid sample cleanup; protein precipitation	Plasma toxicology screening [41]	High recovery for broad compound classes; minimal matrix effects
Solid Phase Extraction (SPE) Cartridges	Sample concentration and cleanup	Water analysis for environmental forensics [15]	Mixed-mode phases for broad chemical coverage
LC-MS Grade Solvents	Mobile phase preparation; sample reconstitution	All LC-HRMS applications	Low background; minimal ion suppression
Stable Isotope-Labeled Standards	Quantitation and process monitoring	Internal standards for quality control	Cover diverse physicochemical properties
Reference Standard Libraries	Compound identification and confirmation	Suspect screening confirmation [9]	100+ compounds with diverse structures and toxicities

Method Validation and Quality Assurance

Implementing rigorous validation protocols is essential for generating defensible NTA data in forensic research. The Red Analytical Performance Index (RAPI) provides a recently developed tool to systematically assess analytical methods across ten predefined criteria, offering a quantitative score (0-100) for overall method performance [65]. This tool complements existing green chemistry metrics and helps standardize method evaluation across laboratories.

Key validation parameters for NTA methods include:

• Linearity and Range: Demonstrate correlation coefficients >0.99 across the calibrated range (e.g., 5-500 ng/mL for most compounds) [41].
• Intra- and Inter-day Precision: Achieve accuracy and precision <15% for all validated compounds [41].
• Limit of Identification (LOI): Establish mean LOI <10 ng/mL for comprehensive screening [41].
• Uncertainty Factors: Calculate appropriate uncertainty factors (UF) for quantitative NTA based on the relative standard deviation of response factors from reference standards [9].

Advanced Troubleshooting: Integrated Approaches

For persistent challenges, integrated troubleshooting approaches combining multiple strategies often prove most effective. The following diagram illustrates a decision pathway for systematic problem resolution in NTA methods.

Figure 2: Systematic Troubleshooting Pathway. A decision workflow for diagnosing and addressing common NTA failures, from initial problem assessment through targeted solution implementation.

Troubleshooting poor resolution, low sensitivity, and method failure in non-targeted analysis requires systematic investigation of each methodological component. The protocols and comparative data presented here, drawn from validated forensic applications, provide actionable strategies for overcoming these challenges. As the field advances, increased standardization, expanded reference databases, and harmonized reporting frameworks will further enhance the reliability of NTA for forensic chemistry research. By implementing these evidence-based troubleshooting approaches, researchers can robustly validate NTA methods to meet the rigorous demands of forensic science and public health protection.

In forensic chemistry, the rise of complex synthetic drug markets necessitates a shift from targeted analyses to non-targeted analysis (NTA) for comprehensive evidence characterization. The central challenge lies in optimizing data processing workflows to maximize true positive identification while minimizing false positives and false negatives, which is critical for the admissibility of evidence in legal proceedings. This guide compares advanced analytical approaches, evaluating their performance data and methodologies within a rigorous validation framework.

Comparative Performance of Analytical Techniques

The following table summarizes the core performance metrics of different techniques used for the identification of unknown compounds, such as New Psychoactive Substances (NPS).

Table 1: Performance Comparison of Forensic Identification Techniques

Analytical Technique	Key Performance Metric	Result / Capability	Key Factor Influencing Accuracy
Benchtop NMR with Database Search [66]	Positive Detection Rate (Single Component)	100% (40/40 samples correctly identified with first hit) [66]	Chemical shift correction & removal of solvent peaks [66]
Machine Learning (ML) Classification [67]	Cross-Validation Balanced Accuracy	92-100% for various chemical sources [67]	Selection of top diagnostic chemical features (e.g., 10, 25, 50, 100) [67]
Non-Targeted Analysis (LC-HRMS) [8]	Workflow Performance (Average)	Sensitivity: 71.1%; Selectivity: 98.4% [8]	Sample preparation optimization and data post-processing [8]
Automated Structure Verification (ASV) [68]	Total Error Reduction (vs. manual)	~78% reduction in human error; ~5% total system error [68]	Use of combined NMR and LC-MS data for verification [68]

Detailed Experimental Protocols

Automated NPS Screening via Benchtop NMR and Spectral Database Search

This protocol is designed for the rapid and automated screening of seized materials using benchtop NMR technology [66].

• Instrumentation: 80 MHz benchtop NMR spectrometer.
• Database Construction:
  • A spectral database is constructed using 360 NPS reference standards, encompassing synthetic cannabinoids, cathinones, phenethylamines, and fentanyl analogues.
  • `H NMR spectra are collected for all standards.
  • Spectral processing is performed, including baseline correction, phase correction, chemical shift correction, and peak picking.
  • Processed spectra and their corresponding chemical structures are compiled into a searchable database using software such as MestReNova.
• Sample Analysis:
  • Seized samples are dissolved in a deuterated solvent (e.g., DMSO-d6).
  • The `H NMR spectrum of the sample is acquired using the same parameters as the database standards.
  • The sample spectrum undergoes the same processing steps, with emphasis on chemical shift correction and identification/removal of solvent peaks.
  • The processed sample spectrum is queried against the NPS database using a search algorithm (e.g., Peak Purity Search).
• Identification:
  • The software returns a list of potential matches ranked by a similarity score.
  • A correct identification with the first hit is considered a true positive. For mixtures, the major component is identified, while minor components may require further investigation.

Quantitative Source Tracking with Machine Learning

This workflow uses non-targeted chemical data and machine learning to attribute environmental samples to specific sources with a quantifiable probability [67].

• Sample Collection: Grab samples (e.g., n = 51) are collected from multiple chemical sources, such as agricultural runoff, municipal wastewater, and livestock manure.
• Chemical Analysis: Samples are analyzed using Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) to generate comprehensive chemical profiles.
• Data Processing and Feature Selection:
  • LC-HRMS data is processed to identify all detectable chemical features.
  • A machine learning model, such as Support Vector Classification, is trained on this data.
  • The model selects the top n diagnostic features (e.g., 10, 25, 50, 100) that best discriminate each source from all others.
• Model Validation and Application:
  • The model's performance is validated through iterative cross-validation (e.g., n = 1000 iterations) to report balanced accuracy.
  • The trained model can then screen unknown samples, outputting a probability for the presence of each known source.
  • The model's sensitivity is tested using in silico mixtures to determine detection limits at high dilutions.

Workflow Visualization

The following diagram illustrates the logical flow of a generalized non-targeted analysis workflow, integrating the elements of the protocols described above.

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful implementation of optimized NTA workflows relies on specific materials and software.

Table 2: Essential Research Reagents and Solutions for NTA

Item	Function in the Workflow
NPS Reference Standards [66]	Critical for building validated spectral databases for the identification of unknown compounds via NMR or LC-MS.
Deuterated Solvents (e.g., DMSO-d6) [66]	Used for preparing samples for NMR analysis without introducing interfering signals in the spectrum.
Solid Phase Extraction (SPE) Cartridges [69]	Used for cleaning up complex samples and pre-concentrating analytes, which reduces matrix effects and improves sensitivity.
LC-HRMS Quality Control Standards [8]	Used to monitor and ensure the accuracy, precision, and sensitivity of the mass spectrometer throughout the analytical run.
Database & Search Software (e.g., MestReNova) [66]	Enables automated and rapid comparison of acquired sample spectra against a library of reference spectra for identification.
Compound Discoverer/Similar Software [8]	A key data post-processing platform for LC-HRMS-based NTA, used for peak alignment, compound identification, and statistical analysis.

The data confirms that integrating complementary techniques is the most robust path to optimizing data processing. While benchtop NMR offers exceptional accuracy for single-component identification, LC-HRMS coupled with machine learning provides unparalleled breadth for complex mixtures. The future of forensic chemistry validation lies in leveraging these advanced workflows, supported by rigorous protocols and quality control measures, to deliver definitive, court-ready results.

Ensuring Courtroom Admissibility: Validation Frameworks and Comparative Performance Metrics for Forensic NTA

In forensic chemistry, the reliability of analytical results is paramount, as they directly impact legal outcomes and public safety. Establishing robust validation protocols is therefore a cornerstone of the discipline, ensuring that methods consistently produce accurate, reproducible, and defensible data. This is especially critical for non-targeted analysis (NTA), an approach designed to detect and identify unknown chemical entities in complex samples without prior knowledge of their structure [2]. The move from traditional targeted methods to NTA presents unique challenges for validation, requiring a structured framework to assess key performance parameters. This guide provides a comparative overview of validation protocols, focusing on selectivity, sensitivity, and reproducibility, and frames them within the context of validating non-targeted approaches for forensic chemistry research.

Core Validation Parameters in Forensic Chemistry

Validation is a formal, systematic process to demonstrate that an analytical method is fit for its intended purpose. For forensic applications, including seized drug analysis and toxicology, several core parameters must be evaluated to understand a method's capabilities and limitations [20] [70].

• Selectivity and Specificity: Selectivity refers to a method's ability to distinguish the analyte from other components in the sample, such as isomers, matrix interferences, or co-eluting compounds. In chromatographic methods, this is assessed using both retention time and mass spectral data. A comprehensive validation will test the method against a range of potential interferents. A key limitation, even for advanced techniques like rapid GC-MS, is the inability to differentiate some isomers that have nearly identical mass spectra and similar chromatographic behavior [20]. For NTA, selectivity is further challenged by the need to identify completely unknown compounds in complex matrices like biological fluids or environmental samples [2].

• Sensitivity: LOD and LOQ: Sensitivity defines the lowest amount of an analyte that can be reliably detected (Limit of Detection, LOD) and quantified (Limit of Quantification, LOQ). These parameters are vital for detecting low-dosage drugs like LSD in biological matrices, where concentrations can be at ultra-trace levels [70]. Improvements in analytical techniques can yield significant gains; for instance, an optimized rapid GC-MS method demonstrated a 50% improvement in LOD for cocaine, achieving 1 μg/mL compared to 2.5 μg/mL with a conventional method [71].

• Precision and Reproducibility: Precision, measured as the relative standard deviation (% RSD) of repeated analyses, expresses the closeness of agreement between independent results obtained under stipulated conditions. It encompasses repeatability (same conditions, short time) and intermediate precision (different days, different analysts). Acceptance criteria often set a threshold of ≤10% RSD for parameters like retention time, aligning with the standards of many accredited forensic laboratories [20] [71]. Reproducibility extends this concept to results between different laboratories.

Table 1: Key Validation Parameters and Acceptance Criteria for Forensic Methods

Validation Parameter	Definition	Typical Acceptance Criteria	Common Assessment Methods
Selectivity	Ability to distinguish analyte from interferents	No interference from blanks or matrix; differentiation of isomers where possible [20].	Analysis of blank samples, certified reference materials, and known interferents [20].
Sensitivity (LOD/LOQ)	Lowest detectable/quantifiable analyte amount	Signal-to-noise ratio (e.g., 3:1 for LOD, 10:1 for LOQ); LOD as low as 1 μg/mL for seized drugs [71].	Analysis of serial dilutions; statistical calculations based on signal and noise [71].
Precision	Closeness of agreement between repeated measurements	% RSD ≤ 10% for retention time and spectral matching [20] [71].	Multiple injections of a homogeneous sample (e.g., n=6) over short and long-term periods [20].
Accuracy	Closeness of result to true or accepted value	Quantitative recovery of 80-120% from fortified samples [20].	Analysis of certified reference materials (CRMs) or spiked samples [20].
Robustness/Ruggedness	Resilience to small, deliberate changes in parameters	% RSD ≤ 10% despite variations [20].	Deliberate changes to operational parameters (e.g., flow rate, temperature) or using a different analyst [20].

Comparative Analysis of Analytical Techniques

The choice of analytical platform significantly influences the validation strategy and the method's ultimate performance. While GC-MS is a mainstay in forensic labs, LC-MS/MS and high-resolution mass spectrometry (HR-MS) are increasingly important, especially for non-targeted work.

• Rapid GC-MS vs. Conventional GC-MS: Rapid GC-MS has emerged as a powerful screening tool that shortens analysis times dramatically—from 30 minutes to as little as 10 minutes or less—while maintaining a high degree of accuracy [20] [71]. It utilizes the same MS detectors as conventional GC-MS but with optimized, faster chromatography. Validation studies show it maintains excellent precision (RSDs <0.25% for stable compounds) and can improve detection limits [71]. Its primary limitation, shared with conventional GC-MS, is difficulty differentiating some isomeric compounds [20].

• LC-MS/MS vs. HR-MS for Non-Targeted Analysis: For NTA, LC-MS/MS is often the most sensitive and widely validated technique, particularly in forensic toxicology for substances like LSD [70]. However, HR-MS is considered the most successful and reliable technology for NTA because it can obtain accurate mass measurements, which are crucial for determining the elemental composition of unknown compounds [2]. The trade-off often involves sensitivity versus the breadth of chemical information.

Table 2: Comparison of Analytical Techniques for Forensic Validation

Technique	Key Strengths	Key Limitations	Best Suited For
Rapid GC-MS	Very fast analysis (<2 min/injection); high specificity; improved LOD for some drugs [20] [71].	Limited isomer differentiation; requires sample volatility and thermal stability [20].	High-throughput screening of seized drugs; fire debris analysis [20].
LC-MS/MS	High sensitivity and specificity; does not require volatile analytes; ideal for biological matrices [70].	Limited to targeted or "suspect" lists; less effective for true unknowns compared to HR-MS [2].	Targeted quantification of drugs and metabolites in blood, urine, etc. [70].
HR-MS (e.g., LC-HRMS)	Accurate mass data for unknown ID; broad, untargeted screening capability; high resolution [2].	Higher instrument cost; complex data interpretation; can be less sensitive than MS/MS [2].	Non-targeted analysis; metabolomics; discovery of novel psychoactive substances (NPS) [2].

Experimental Protocols for Validation

A validation protocol must be a detailed, pre-defined plan. The following examples, drawn from recent research, illustrate how key experiments are designed and executed.

Protocol for Assessing Selectivity and Specificity

A standard protocol for evaluating selectivity in seized drug analysis using GC-MS involves analyzing a range of samples to confirm the method's distinguishing power [20].

• Materials: Certified reference materials of target analytes (e.g., methamphetamine, cocaine, opioids); isomeric compounds (e.g., pentylone and its isomers); blank biological or seizure matrices; internal standards.
• Procedure:
  • Inject and analyze a blank matrix (e.g., drug-free serum or a clean solvent) to confirm no interferences co-elute with the target analytes.
  • Analyze each target analyte and its potential isomers individually to establish their baseline retention times and mass spectra.
  • Analyze a mixture containing all target analytes and isomers to confirm resolution.
  • Fortify a blank matrix with a known concentration of the target analytes and process to check for matrix-derived interferences.
• Data Analysis: Selectivity is confirmed if (a) the blank shows no peaks >20% of the LOD at the analyte's retention time, (b) all target analytes are resolved from their nearest potential interferent (resolution factor >1.5), and (c) mass spectral purity scores meet pre-defined criteria (e.g., >90% match) [20].

Protocol for Determining LOD and LOQ

This protocol outlines the determination of LOD and LOQ for a rapid GC-MS method screening seized drugs, as performed in a recent study [71].

• Materials: Stock solutions of target drugs (e.g., cocaine, heroin, MDMA) in methanol at a known concentration (e.g., 1 mg/mL); HPLC-grade methanol for serial dilution.
• Procedure:
  • Prepare a series of dilutions from the stock solution to create standards at concentrations expected to be near the detection limit.
  • Analyze each dilution in triplicate using the optimized rapid GC-MS method.
  • For each analyte, identify the concentration that yields a signal-to-noise ratio (S/N) of approximately 3:1 for LOD and 10:1 for LOQ.
• Data Analysis: The LOD and LOQ are determined directly from the S/N ratios. The results should be documented with chromatograms. For instance, a study might report an LOD for cocaine of 1 μg/mL with the new rapid method, a significant improvement over the 2.5 μg/mL LOD of the conventional method [71].

Protocol for Evaluating Precision and Reproducibility

A comprehensive precision study assesses variability under different conditions, encompassing both repeatability and intermediate precision (ruggedness) [20].

• Materials: A single, homogeneous test solution containing multiple target compounds at a mid-range concentration (e.g., 0.25 mg/mL per compound) [20].
• Procedure:
  • Repeatability: A single analyst performs six consecutive injections of the test solution in one day on the same instrument.
  • Intermediate Precision (Ruggedness): A second analyst repeats the experiment on a different day, or using a different instrument of the same model.
• Data Analysis: Calculate the % RSD for the retention times and mass spectral search scores (or peak areas for quantitative work) for both the intra-day and inter-day studies. The method is considered acceptable if the % RSDs are within the pre-defined criteria (e.g., ≤10%) for both studies [20].

Workflow Visualization for Method Validation

The following diagram illustrates the logical sequence and key decision points in a systematic method validation workflow for forensic chemistry.

Systematic Method Validation Workflow

The Non-Targeted Analysis (NTA) Workflow

Non-targeted analysis requires a distinct workflow from targeted methods, emphasizing comprehensive data acquisition and sophisticated data processing to handle unknowns.

Non-Targeted Analysis Workflow

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful method development and validation rely on high-quality, well-characterized materials. The following table details key resources for forensic chemistry research.

Table 3: Essential Research Reagents and Materials for Forensic Method Validation

Item	Function	Example in Use
Certified Reference Materials (CRMs)	Provide a traceable and definitive standard for method calibration, identification, and assessing accuracy [20] [71].	Used as the primary standard for quantifying cocaine or heroin in a seized drug sample [71].
Custom Multi-Compound Test Solutions	Allow for efficient evaluation of method performance (e.g., selectivity, precision) across a wide range of analytes simultaneously [20].	A 14-compound mixture used to assess precision and robustness in a rapid GC-MS validation study [20].
Certified Spectral Libraries	Enable reliable compound identification by comparing the acquired mass spectrum against a database of known compounds [71].	Using the Wiley or Cayman Spectral Library to identify a synthetic cannabinoid like MDMB-INACA [71].
Blank Matrices	Critical for assessing selectivity/specificity and determining matrix effects by providing a baseline free of target analytes [20] [70].	Using drug-free blood or urine to validate a toxicology method for LSD detection [70].
High-Purity Solvents	Act as the medium for preparing standards and samples; purity is essential to prevent contamination and background interference [71].	HPLC-grade methanol used for preparing stock solutions and extracting solid seized drug samples [71].

The establishment of rigorous validation protocols is non-negotiable for producing reliable data in forensic chemistry. As the field evolves to embrace non-targeted analysis for tackling emerging threats like novel psychoactive substances, the fundamental principles of validation—assessing selectivity, sensitivity, and reproducibility—remain as relevant as ever. However, they must be adapted to address the unique challenges of NTA, such as the need for high-resolution mass spectrometry and advanced bioinformatics tools. By adhering to structured validation frameworks, leveraging appropriate analytical technologies, and using well-characterized materials, researchers and forensic professionals can ensure their methods are robust, defensible, and capable of supporting the critical demands of the justice system and public health.

Non-targeted analysis (NTA) represents a rapidly evolving set of mass spectrometry techniques aimed at characterizing the chemical composition of complex samples without prior knowledge of their chemical content [1]. Also referred to as "non-target screening" or "untargeted screening," NTA enables the discovery of unknown chemicals, identification of known chemicals using suspect lists, and classification of samples using detected chemical profiles [1]. The field has seen substantial growth over the past two decades, driven by advances in high-resolution mass spectrometry (HRMS) instrumentation and computational tools for data analysis [1] [72]. This growth has created an urgent need for community-wide standards and benchmarking approaches to ensure data quality, reproducibility, and interoperability across laboratories and applications.

In forensic chemistry specifically, NTA methods have emerged as powerful tools for toxicological screening, identification of novel psychoactive substances, and characterization of complex mixtures in biological samples [41] [31] [73]. Unlike traditional targeted methods that focus on specific analytes, NTA methods must be capable of detecting and identifying unexpected or unknown compounds while maintaining sufficient rigor to withstand legal scrutiny. The inherent unpredictability of forensic samples—from street drug mixtures to biological specimens—presents unique challenges for method validation and performance assessment [63]. This article examines current initiatives, particularly the Benchmarking and Publications for Non-Targeted Analysis (BP4NTA) Working Group, that aim to address these challenges through community-driven standardization efforts.

The BP4NTA Initiative: Goals, Structure, and Outputs

Formation and Objectives

The Benchmarking and Publications for Non-Targeted Analysis (BP4NTA) Working Group was established in August 2018 following a meeting convened by the U.S. Environmental Protection Agency to discuss interim results from the Non-Targeted Analysis Collaborative Trial (ENTACT) [1]. Recognizing common challenges in publishing and proficiency testing for NTA methods, participants formed an ongoing collaborative group that has since grown to approximately 100 members from North America and Europe [1]. The membership represents academia (25%), industry (25%), and government (50%), creating a balanced partnership across sectors [1].

BP4NTA's overarching goals directly address critical needs in the NTA community [1]:

• Harmonizing and standardizing approaches and reporting practices
• Improving determination, calculation, and communication of performance metrics
• Sharing best practices for quality assurance/quality control (QA/QC)
• Enhancing transparency and reproducibility of peer-reviewed NTA studies

Organizational Structure and Ongoing Initiatives

BP4NTA has evolved into an organized community with specialized subcommittees addressing specific challenges in NTA research. These subcommittees provide focused expertise and drive progress in key areas [74]:

• Educational Subcommittee: Develops and curates resources for NTA education, including literature references and upper-level college course materials
• Study Planning Tool (SPT) Committee: Creates tools and guidance for designing high-quality NTA studies and developing standard operating procedures
• PFAS Subcommittee: Addresses challenges specific to per- and polyfluoroalkyl substances analysis using NTA methods
• GC NTA Subcommittee: Focuses on gas chromatography applications within non-targeted analysis

The group maintains a public website (nontargetedanalysis.org) that serves as a central repository for resources, including consensus definitions, reference content, and available tools [74]. Monthly meetings, forums for discussion, and conference presentations facilitate ongoing collaboration and knowledge sharing within the community [74].

BP4NTA has produced several significant resources to support harmonization in NTA research [1]:

• NTA Glossary: A comprehensive collection of 34 harmonized definitions for key terms spanning all aspects of NTA workflows, from sample preparation to performance assessment
• Reference Content: Supporting materials that elaborate on concepts mentioned in the definitions
• Study Reporting Tool (SRT): An interactive resource to aid researchers in consistently and transparently reporting NTA methods and results
• Public Website: A centralized access point for BP4NTA products and emerging NTA concepts

These deliverables represent dynamic resources designed to evolve with technological advances and community feedback [1]. The SRT in particular has gained traction as a framework for improving research transparency and reproducibility, with adoption encouraged by scientific journals [75].

Comparative Analysis of NTA Performance in Forensic Applications

Method Performance Across Forensic Toxicology Applications

Forensic toxicology laboratories have pioneered the implementation of NTA methods for comprehensive drug screening and toxicological analysis. The table below compares performance characteristics from recent validation studies of HRMS-based NTA methods in biological matrices:

Table 1: Performance Comparison of NTA Methods in Forensic Toxicology

Study & Matrix	Number of Compounds Validated	Sample Volume	Sample Preparation	Limit of Identification (LOI)	Key Performance Metrics
Becam et al. (2023) - Plasma [41]	132 compounds for screening; 29 quantified	200 μL	QuEChERS salts with acetonitrile	Mean LOI: 8.8 ng/mL (range: 0.05-500 ng/mL)	Linear range: 5-500 ng/mL (0.5-50 ng/mL for cannabinoids); accuracy/precision: <15%
LC-HRMS Method (2023) - Plasma & Whole Blood [31]	53 compounds for full validation; 179 for identification limits	100 μL	Deproteinization with methanol/acetonitrile and zinc sulfate	Compound-dependent; established for 179 compounds	High selectivity/specificity; optimal process efficiency; sub-therapeutic/therapeutic sensitivity
Helfer et al. (2017) - Plasma [31]	36 compounds	250 μL	Precipitation with/without TurboFlow online purification	Not specified	Comprehensive screening; applied to 418 authentic samples

Analytical Techniques and Their Applications in Forensic NTA

Different mass spectrometry approaches offer complementary advantages for NTA in forensic applications. The following table compares these techniques:

Table 2: Comparison of Mass Spectrometry Techniques for Forensic NTA

Technique	Mass Resolution	Acquisition Mode	Forensic Applications	Strengths	Limitations
Orbitrap-based LC-HRMS [41] [31]	60,000-140,000 FWHM	Full scan + DDA (Data Dependent Analysis)	General unknown screening, quantitative confirmation	High mass accuracy, compatibility with LC separation, reliable identification via fragmentation	Limited dynamic range, higher instrumentation costs
QTOF-based LC-HRMS [72]	20,000-80,000 FWHM	Full scan + DDA or DIA (Data Independent Acquisition)	Suspect screening, metabolomics, exposomics	Fast acquisition, high sensitivity, well-suited for unknown identification	Requires careful calibration for mass accuracy
GC-Orbitrap HRMS [9]	60,000-120,000 FWHM	Full scan with optional MS/MS	Volatile and semi-volatile compounds, polymer additives, fire debris analysis	Excellent chromatographic resolution, reproducible retention times, robust libraries	Limited to volatile/derivatized compounds, thermal degradation concerns
Ambient Ionization MS [63]	Variable by platform	Direct analysis without chromatography	Rapid screening of seized drugs, surface analysis, contraband detection	Minimal sample preparation, high throughput (seconds per sample)	Limited separation of isobars, matrix effects, less definitive identification

Implementation Challenges Across Laboratory Types

The adoption of NTA methods faces different challenges depending on laboratory context and resources:

Table 3: NTA Implementation Challenges Across Forensic Settings

Laboratory Type	Primary NTA Applications	Key Challenges	BP4NTA Resources Addressing Challenges
Public Health & Forensic Laboratories [41] [31]	General unknown screening, emerging drug threats	Method validation requirements, reference standard availability, data interpretation complexity	Study Planning Tool, validation frameworks, educational resources
Research Institutions [1] [72]	Exposome characterization, suspect screening, method development	Reproducibility between platforms, data sharing standards, confidence in identifications	Study Reporting Tool, harmonized definitions, confidence assessment frameworks
Field & Point-of-Care Settings [63]	Rapid screening, evidence triage, harm reduction services	Technology portability, sensitivity in complex matrices, operator training	Method validation packages, spectral libraries, implementation guides

Experimental Protocols for NTA Method Validation

Protocol for Comprehensive NTA Validation in Biological Matrices

Based on published methodologies for forensic toxicology applications [41] [31], a robust validation protocol for NTA methods includes these critical steps:

Sample Preparation Optimization

• Test multiple extraction approaches (e.g., QuEChERS, protein precipitation, liquid-liquid extraction) with various solvent systems
• Evaluate recovery for compounds spanning diverse physicochemical properties
• Optimize for minimal sample volume (100-200 μL) to accommodate limited forensic specimens
• Incorporate appropriate internal standards for process monitoring

Chromatographic Separation

• Select columns based on peak shape and retention for a representative mixture of compounds
• Test different mobile phase compositions (e.g., with/without ammonium acetate) to optimize signal intensity
• Develop gradient elution programs that provide sufficient separation of isomeric compounds
• Establish retention time stability across multiple batches and instruments

Mass Spectrometric Analysis

• Configure full-scan acquisition at high resolution (≥60,000 FWHM) for accurate mass measurement
• Implement data-dependent acquisition (DDA) to collect fragmentation spectra for identification
• Establish mass calibration procedures to maintain mass accuracy <5 ppm
• Optimize source parameters for broad compound coverage

Data Processing and Compound Identification

• Create and curate comprehensive spectral libraries with accurate mass, retention time, and fragmentation data
• Establish identification criteria (e.g., mass accuracy, isotopic pattern, retention time, fragment match)
• Implement approaches to distinguish isomeric compounds through chromatographic separation or fragmentation patterns
• Validate identification confidence using established frameworks (e.g., Schymanski levels [72])

Protocol for Reference Standard Selection and Uncertainty Factor Calculation

For quantitative NTA applications, particularly in medical device extractables and leachables testing [9], the selection of appropriate reference standards follows a systematic protocol:

Reference Standard Selection Criteria

• Commercial Availability: Prioritize widely available chemical standards to ensure method transferability
• Chemical Relevance: Select standards representing expected chemical classes in the sample matrix
• Structural Diversity: Include compounds spanning a range of physicochemical properties (molecular weight, log P, polarity)
• Toxicological Significance: Incorporate compounds with known toxicological concerns relevant to the application
• Analytical Compatibility: Ensure detectability across GC-MS and LC-MS platforms

Uncertainty Factor Determination

• Analyze reference standards at multiple concentrations across the quantitative range
• Calculate Relative Response Factors (RRF) for each compound relative to internal standards
• Determine the relative standard deviation (RSD) of RRFs across the standard set
• Compute the Uncertainty Factor (UF) using the formula: UF = 1/(1-RSD) [9]
• Apply UF when establishing Analytical Evaluation Thresholds (AET) for reporting limits

This protocol ensures that quantitative estimates for unknown compounds identified through NTA appropriately account for methodological variability in detector response.

Visualizing NTA Workflows and BP4NTA Structure

NTA Experimental Workflow

The following diagram illustrates the generalized workflow for non-targeted analysis in forensic applications, incorporating key quality assurance steps:

BP4NTA Organizational Structure

The BP4NTA working group operates through specialized subcommittees that address specific challenges in NTA standardization:

Essential Research Reagent Solutions for NTA Implementation

Successful implementation of NTA methods in forensic chemistry requires carefully selected reagents and reference materials. The following table details essential research reagent solutions:

Table 4: Essential Research Reagent Solutions for Forensic NTA

Reagent Category	Specific Examples	Function in NTA Workflow	Selection Criteria
Sample Preparation	QuEChERS salts, acetonitrile, methanol, zinc sulfate, carbonate buffers	Extraction and cleanup of target analytes from complex matrices	Compatibility with broad chemical space, minimal bias, high recovery [41] [31]
Reference Standards	106 polymer additives [9], 53 toxicologically relevant compounds [31], 29 drugs of abuse [41]	Method development, identification confidence, quantitative calibration	Structural diversity, commercial availability, toxicological relevance [9]
Internal Standards	Stable isotope-labeled analogs, retention time markers, injection standards	Process monitoring, normalization, quality control	Non-endogenous, not found in samples, covers analytical range [31]
Mobile Phase Additives	Formic acid, ammonium acetate, ammonium formate	Modulate ionization efficiency, improve chromatographic separation	MS-compatibility, volatility, reproducible performance [31]
Quality Control Materials	Pooled human plasma, certified reference materials, in-house quality controls	Method validation, ongoing performance assessment, interlaboratory comparison	Matrix-matched, well-characterized, stable [41] [31]

The BP4NTA initiative represents a critical community-driven response to the standardization challenges in non-targeted analysis. Through the development of harmonized terminology, reporting standards, and validation frameworks, BP4NTA has established essential infrastructure for advancing NTA applications in forensic chemistry and beyond. The comparative data presented in this review demonstrates that while NTA methods show tremendous promise for comprehensive chemical analysis in forensic contexts, consistent performance benchmarking remains challenging without unified protocols.

The experimental workflows, visualization tools, and reagent solutions outlined here provide practical guidance for implementing NTA methods in forensic laboratories. As the field continues to evolve, ongoing collaboration through initiatives like BP4NTA will be essential for establishing measurement confidence, enabling data comparability across laboratories, and ultimately translating NTA approaches from research tools into validated forensic methods capable of meeting the rigorous standards of the justice system.

In forensic chemistry research, the identification of unknown chemical substances presents a significant analytical challenge. The paradigm is shifting from traditional targeted methods to advanced non-targeted analysis (NTA) approaches, each with distinct capabilities and limitations. Targeted methods such as gas chromatography-mass spectrometry (GC-MS) and immunoassays provide highly specific quantification of predefined analytes, while non-targeted analysis (NTA) employs high-resolution mass spectrometry (HRMS) to characterize samples without prior knowledge of their chemical composition [76]. This comparative guide objectively examines the performance characteristics, applications, and experimental requirements of these complementary approaches within the context of validating analytical methods for forensic research.

Fundamental Conceptual Differences

The core distinction between these analytical approaches lies in their fundamental goals and application domains. The following diagram illustrates the conceptual relationship between them based on the "knowns and unknowns" framework:

Targeted analysis qualifies as "known-knowns" – analyzing for specific, predefined compounds with available reference standards [76]. Suspect screening occupies the middle ground of "known-unknowns" – screening for compounds that are expected or suspected to be present without definitive confirmation [76]. True NTA addresses "unknown-unknowns" – discovering and identifying compounds without any prior knowledge of the sample composition [76]. This fundamental difference in scope directly influences each method's implementation, validation requirements, and application suitability in forensic contexts.

Comparative Performance Characteristics

The technical performance of NTA versus traditional targeted methods varies significantly across multiple parameters critical to forensic research.

Table 1: Direct Comparison of Analytical Method Characteristics

Parameter	Non-Targeted Analysis (NTA)	Targeted GC-MS	Immunoassays
Primary Question	Which compounds are in the sample? [76]	Is compound X in the sample? At what concentration? [76]	Is compound X in the sample? At what concentration? [77]
Scope	Unknown-unknowns [76]	Known-knowns [76]	Known-knowns [77]
Instrumentation	HRMS (LC or GC coupled) [3]	MS/MS or HRMS [76]	ELISA, CLIA [77]
Quantitation Capability	Emerging with uncertainty [25] [78]	Highly accurate and precise [78] [76]	Highly accurate and precise [77]
Sensitivity	Varies widely; typically less sensitive than targeted methods [76]	High sensitivity with defined LODs [78]	High sensitivity with defined LODs [77]
Selectivity	Broad, untargeted [1]	Highly selective for predefined analytes [78]	Highly selective for predefined analytes [77]
Standard Requirements	Not required for identification [76]	Required for quantification [76]	Required for quantification [77]
Method Development	Complex, requires multiple data processing streams [79]	Compound-specific optimization [76]	Compound-specific development [77]
Data Reprocessing	Possible as new suspects are identified [76]	Not possible without reanalysis [76]	Not possible without reanalysis

Key Performance Differentiators

• Uncertainty Profile: NTA generates inherently uncertain data where reported chemical presences may be false positives/negatives and concentrations may have significant error margins [78]. Conversely, targeted methods provide definitive identification and quantification when compounds are present above detection limits [78].

• Confidence Assessment: NTA utilizes confidence level frameworks (e.g., Schymanski scale) to communicate identification certainty, with Level 1 representing confirmed structure and Level 5 representing unequivocal molecular formula [79]. Targeted methods typically provide binary presence/absence confirmation.

• Coverage vs. Certainty Tradeoff: NTA sacrifices certainty for broad chemical space coverage, while targeted methods sacrifice coverage for high certainty on specific analytes [1] [78].

Experimental Protocols and Methodologies

Non-Targeted Analysis Workflow

A validated forensic NTA workflow for illicit drug and excipient profiling incorporates multiple analytical techniques organized according to SWGDRUG guidelines [16]:

Sample Preparation: Complex samples undergo appropriate extraction (solid-phase, liquid-liquid) and cleanup procedures to maximize detectable chemical space while minimizing matrix effects [3].

Instrumental Analysis: Employ both LC-HRMS and GC-HRMS to expand coverage of chemical space [3]. LC-HRMS preferentially detects semi-polar to polar compounds, while GC-HRMS covers volatile and thermally stable compounds [3] [80].

Data Processing: Utilize peak picking algorithms to detect molecular features (unique m/z-retention time pairs) [1] [76]. Process data through multiple streams (vendor software, open-source tools like MZmine, MS-DIAL) to maximize compound identification [3] [79].

Compound Identification: Apply tiered identification approach:

• Suspect Screening: Compare experimental data against chemical databases (NORMAN, EPA CompTox) and spectral libraries (mzCloud, NIST) [3] [76]
• True NTA: Use computational approaches (Kendrick mass defect, isotope pattern matching, fragmentation tree analysis) for unknown compounds [3]
• Confidence Ranking: Assign identification confidence levels (1-5) based on available evidence [79]

Validation: Demonstrate method performance through analysis of mock scenarios simulating real-world forensic challenges (illicit drug mixtures, chemical threats) [16] [79].

Targeted Method Protocols

GC-MS Method Validation:

• Establish calibration curves using analytical standards across expected concentration range
• Determine limits of detection (LOD) and quantification (LOQ) using signal-to-noise criteria [78]
• Assess precision and accuracy through replicate analyses [78]
• Evaluate selectivity through analysis of potential interferents [78]

Immunoassay Comparative Protocol: A standardized approach for comparing immunoassay performance, as demonstrated for anti-TNF-α therapeutic drug monitoring [77]:

Table 2: Experimental Design for Immunoassay Comparison

Parameter	Methodology
Sample Types	50 sera from patients treated with infliximab; 49 sera from patients treated with adalimumab [77]
Compared Assays	Promonitor (ELISA), i-Track10 (CLIA), ez-Track1 (POC) vs. Lisa Tracker (gold standard ELISA) [77]
Statistical Analysis	Cohen's kappa for qualitative concordance; Passing-Bablok and Bland-Altman for quantitative agreement [77]
Performance Metrics	Measurement range, interference testing, clinical concordance categorization [77]

Quantitative Comparative Data

Immunoassay Performance Comparison

Experimental data from comparative studies provides direct performance comparisons between targeted methods:

Table 3: Quantitative Comparison of Immunoassay Performance [77]

Analyte	Assay	Cohen's Kappa (Qualitative)	Concordance Interpretation	Lin's Concordance Coefficient (Quantitative)
Infliximab	Promonitor	0.92	Almost perfect	~0.80
	i-Track10	0.58	Moderate	~0.80
	ez-Track1	0.75	Substantial	~0.80
Anti-Infliximab	Promonitor	0.88	Almost perfect	N/R
	i-Track10	0.31	Fair	N/R
	ez-Track1	0.65	Substantial	N/R
Adalimumab	All three assays	~0.58	Moderate	~0.80
Anti-Adalimumab	All three assays	~0.88	Almost perfect	N/R

NTA Performance Metrics

Unlike targeted methods with standardized performance criteria, NTA performance assessment must align with study objectives. Three primary NTA objective categories have been defined with corresponding assessment approaches [78]:

• Sample Classification: Assess using confusion matrices, calculating accuracy, precision, recall despite challenges with unbounded sample classes [78]

• Chemical Identification: Evaluate using identification confidence scales, though traditional performance metrics like false positive/negative rates are difficult to calculate [78]

• Chemical Quantitation: Apply targeted method principles (accuracy, precision) while acknowledging additional uncontrolled error sources [78]

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful implementation of these analytical approaches requires specific reagents and computational resources:

Table 4: Essential Research Materials for Analytical Method Implementation

Resource Type	Specific Examples	Function/Purpose
Reference Standards	Certified analytical standards (e.g., infliximab, adalimumab) [77]	Targeted method quantification and quality control
Chemical Databases	NORMAN Suspect Exchange, EPA CompTox Chemical Dashboard [76]	Compound identification in suspect screening and NTA
Spectral Libraries	NIST MS Library, mzCloud, in-house MS/MS databases [3] [76]	Spectral matching for compound identification
Quality Control Materials	Internal standards (isotope-labeled), proficiency testing samples [77] [78]	Method performance assessment and quality assurance
Data Processing Tools	Compound Discoverer, MZmine, MS-DIAL, NTA WebApp [3] [79]	Data extraction, processing, and compound identification
Confidence Assessment Frameworks	Schymanski identification confidence scale, BP4NTA reporting guidelines [1] [76]	Standardized reporting and confidence communication

Application in Forensic Chemistry Research

The selection between NTA and targeted methods in forensic chemistry depends on the specific research question and available knowledge about sample composition.

Targeted Methods Are Preferred When:

• Analyzing for specific, known compounds with available standards [76]
• Regulatory or clinical decisions require definitive quantification [77] [78]
• High-throughput analysis of predefined compound panels is needed [76]

NTA Is Essential When:

• Characterizing completely unknown samples or novel psychoactive substances [16]
• Identifying transformation products or unexpected compounds in complex mixtures [16] [76]
• Conducting retrospective analysis as new chemical threats are identified [79]

Hybrid Approaches: Many forensic applications benefit from sequential analysis, starting with targeted methods to rule out known compounds, followed by suspect screening and finally true NTA for complete characterization [76]. Recent advances have demonstrated focused NTA methods that provide confident chemical identifications within 24-72 hours, making them viable for rapid response scenarios [79].

The comparative analysis of NTA versus traditional targeted methods reveals complementary rather than competing roles in forensic chemistry research. Targeted GC-MS and immunoassays provide definitive quantification of known analytes with well-established validation protocols, making them indispensable for compliance monitoring and clinical decision-making. Conversely, NTA offers unparalleled capability for discovering unknown compounds and characterizing complex mixtures, albeit with higher uncertainty and more complex validation requirements. The evolving framework for NTA performance assessment, standardized reporting guidelines from initiatives like BP4NTA, and emerging tools like the ChemSpace Tool [80] are addressing current limitations and facilitating broader adoption. Forensic chemistry researchers should select analytical approaches based on clearly defined study objectives, recognizing that a combined targeted and non-targeted strategy often provides the most comprehensive analytical solution for addressing both known and unknown chemical challenges.

Forensic science is a critical pillar of modern criminal justice, relying on physical evidence to reconstruct events and establish links between people, places, and objects. However, traditional forensic methods often depend on visual comparisons and expert judgment, approaches now recognized as vulnerable to human bias and subjective error. Institutional reports from organizations including the U.S. National Academy of Sciences and the U.K.'s Forensic Science Regulator have consistently called for more reliable, objective methods of evidence interpretation [81]. In response to these concerns, chemometrics—the chemical discipline that uses mathematical and statistical methods to design optimal measurement procedures and extract maximum relevant chemical information from data—has emerged as a powerful solution [82]. This analytical approach applies statistical tools to interpret complex chemical data, offering statistically validated methods that enhance the accuracy and reliability of forensic conclusions while mitigating the influence of human cognitive and social biases [81].

The fundamental distinction between classical and chemometric approaches lies in their philosophical framework. The classical approach is reductionist, examining one factor at a time with effects separated as much as possible, aiming to understand causal relationships and discover new natural laws. In contrast, the chemometric approach is multivariate, considering all variables simultaneously to create models optimized for prediction, pattern recognition, and classification, even when full causal understanding remains elusive [82]. This paradigm shift enables forensic scientists to move beyond subjective visual analysis toward data-driven interpretations using robust statistical models, thereby strengthening the credibility of forensic evidence presented in legal contexts [81].

Chemometrics vs. Traditional Methods: A Comparative Analysis

The integration of chemometrics represents a fundamental shift in forensic evidence analysis. The table below provides a systematic comparison between traditional forensic methods and modern chemometric approaches across key dimensions of forensic science practice.

Table 1: Comparative Analysis of Traditional Forensic Methods versus Chemometric Approaches

Analysis Dimension	Traditional Methods	Chemometric Approaches
Primary Basis	Expert judgment, visual comparison	Statistical models, quantitative data
Data Handling	Univariate (one variable at a time)	Multivariate (all variables simultaneously)
Vulnerability to Bias	Higher (cognitive and social biases)	Lower (objective, algorithmic processing)
Statistical Foundation	Often qualitative or semi-quantitative	Rigorous statistical validation
Evidence Interpretation	Subjective, experience-based	Objective, data-driven with probability measures
Typical Output	Categorical matches/non-matches	Quantitative similarity measures, probability statements
Throughput Efficiency	Lower (manual, labor-intensive)	Higher (automated, rapid analysis)

Traditional forensic analysis often relies on univariate approaches that examine individual variables in isolation, which fails to capture the complex interactions between multiple variables in evidentiary samples [82]. This limitation becomes particularly problematic when analyzing trace evidence such as fibers, paints, or explosives, where the multivariate chemical signature contains crucial discriminatory information. Chemometric methods leverage techniques like principal component analysis (PCA) and linear discriminant analysis (LDA) to simplify complex datasets while preserving the essential chemical patterns that differentiate materials or sources [81]. By utilizing the complete chemical profile rather than selectively chosen features, chemometrics provides a more comprehensive and objective basis for forensic comparisons.

A key advantage of chemometric approaches is their capacity to provide quantitative similarity measures between samples from a crime scene and a suspect [81]. Whereas traditional methods might result in categorical statements of association, chemometric models generate statistically robust measures of similarity that can be expressed probabilistically. This quantitative framework enhances transparency in reporting and allows forensic practitioners to communicate the strength of evidence more accurately in legal proceedings. Furthermore, the automation potential of chemometric workflows significantly increases laboratory throughput while reducing the labor-intensive manual examination traditionally required for evidence analysis [81].

Core Chemometric Techniques and Their Forensic Applications

Chemometrics encompasses a diverse toolkit of statistical methods tailored to extract meaningful information from complex chemical data. These techniques have demonstrated utility across multiple forensic disciplines, from toxicology to trace evidence analysis. The table below summarizes the primary chemometric methods, their underlying principles, and specific forensic applications.

Table 2: Core Chemometric Techniques and Their Forensic Applications

Technique	Statistical Principle	Primary Function	Forensic Application Examples
Principal Component Analysis (PCA)	Dimensionality reduction	Identifies patterns, highlights similarities/differences	Exploratory analysis of spectral data from fibers, paints
Linear Discriminant Analysis (LDA)	Maximum class separation	Classifies samples into predefined categories	Drug classification, tissue fluid identification
Partial Least Squares-Discriminant Analysis (PLS-DA)	Covariance maximization	Classification, handles correlated variables	Explosives residue typing, soil comparison
Support Vector Machines (SVM)	Optimal hyperplane creation	Non-linear classification, regression	Differentiation of glass sources, fire debris analysis
Artificial Neural Networks (ANNs)	Neural inspiration	Pattern recognition, modeling complex relationships	Ink age determination, questioned document analysis

Principal Component Analysis (PCA) serves as a fundamental exploratory tool that reduces the dimensionality of complex datasets while preserving the maximum amount of variance [81]. This technique transforms original variables into a new set of uncorrelated variables called principal components, allowing forensic analysts to visualize clustering patterns and identify outliers in high-dimensional data. For example, PCA can reveal natural groupings in spectroscopic data from paint chips collected at different crime scenes, potentially linking multiple incidents to a common source [81].

Classification techniques such as LDA and PLS-DA build predictive models that assign unknown samples to predefined categories based on their chemical profiles [81]. In forensic toxicology, these methods can differentiate between closely related drug analogs with similar chemical structures but different legal statuses. Similarly, advanced pattern recognition methods including Support Vector Machines and Artificial Neural Networks excel at handling non-linear relationships in complex datasets, making them particularly valuable for analyzing mixed or degraded evidence samples where traditional methods may struggle [81] [83]. These techniques have demonstrated particular utility in forensic arson investigation, where they can differentiate between accelerants and background pyrolysis products in fire debris samples [81].

Experimental Validation Frameworks for Chemometric Models

The rigorous validation of chemometric models is paramount to their admissibility and reliability in forensic applications. Proper validation ensures that models generate accurate, reproducible results that withstand scientific and legal scrutiny. The validation process must address both numerical performance and conceptual soundness, confirming that the model adequately represents the underlying chemical phenomena [84].

A critical consideration in chemometric validation is the appropriate selection of calibration and test sets. The commonly applied practice of randomly dividing a dataset into calibration and test subsets can only be justified when no systematic stratification exists that might influence model outcomes [84] [85]. When such stratification is present—such as samples collected across different time periods, geographical locations, or instrument configurations—validation must account for these factors to ensure model robustness. The various levels of validation typically include assessments of repeatability (same operator, same conditions), reproducibility (different operators, different conditions), and variations introduced by instruments and raw materials [84]. For forensic applications specifically, validation must also establish error rates and reliability metrics under conditions representative of real evidence analysis [81].

Table 3: Validation Methods for Chemometric Models in Forensic Applications

Validation Method	Procedure	Key Outputs	Advantages	Limitations
Test Set Validation	Split data into separate calibration and test sets	Prediction error, classification accuracy	Unbiased error estimation	Requires large sample sizes
Cross-Validation	Systematic data splitting (e.g., k-fold, leave-one-out)	Model stability, performance metrics	Efficient with limited samples	Can yield over-optimistic estimates
Double Cross-Validation	Nested validation with inner and outer loops	Robust performance statistics	Minimizes overfitting	Computationally intensive
Bootstrapping	Random sampling with replacement	Parameter distributions, confidence intervals	Powerful for small datasets	Can capture data peculiarities

The double cross-validation procedure has emerged as a particularly robust approach for minimizing overfitting, a common pitfall in model development [86]. This nested validation technique uses an inner loop for model optimization and parameter selection and an outer loop for performance assessment, providing more realistic estimates of how the model will perform on unknown samples. Additionally, hypothesis-driven validation approaches that apply multiple variable selection methods to identify consensus subsets aligned with underlying chemistry provide stronger confirmation of model validity than purely data-driven approaches alone [84]. For forensic applications, establishing a clear chain of validation that demonstrates model performance across relevant evidence types and conditions is essential for courtroom acceptance [81].

Workflow for Validated Chemometric Analysis

The following diagram illustrates the comprehensive workflow for developing and validating chemometric models in forensic applications, integrating both data-driven and hypothesis-driven validation components:

Essential Research Reagents and Materials for Chemometric Analysis

The successful implementation of chemometric approaches in forensic chemistry requires both specialized analytical instrumentation and appropriate data processing tools. The table below catalogues key resources that form the foundation of robust chemometric analysis workflows.

Table 4: Essential Research Reagents and Computational Tools for Chemometric Forensic Analysis

Category/Resource	Specific Examples	Primary Function in Chemometric Analysis
Analytical Instrumentation	FT-IR, Raman spectrometers; HPLC/UHPLC; GC-MS; HRMS	Generate multivariate chemical data for analysis
Chemical Databases	PubChemLite; CompTox Dashboard; NORMAN Suspect List Exchange	Provide reference data for suspect and target screening
Statistical Software	R; Python; MATLAB; SIMCA	Implement statistical algorithms and modeling techniques
Data Preprocessing Tools	"nippy" Python module; Savitzky-Golay smoothing; Standard Normal Variate	Remove artifacts, correct baselines, enhance data quality
Reference Materials	Certified standards; Quality control samples	Ensure analytical validity and instrument calibration
Validation Samples	Ground-truth samples; Blind proficiency samples	Establish model accuracy and performance metrics

Advanced analytical instrumentation forms the foundation of chemometric analysis, with techniques such as Fourier-transform infrared (FT-IR) spectroscopy, Raman spectroscopy, and various chromatographic methods generating the complex multivariate data required for chemometric modeling [81]. The emergence of chromatography coupled to high-resolution mass spectrometry (HRMS) has been particularly transformative for non-targeted screening approaches, enabling the detection of thousands of analytical features in a single sample [87]. For forensic applications, maintaining comprehensive chemical databases is essential for both suspect screening and the identification of unknown compounds through library matching [87].

The computational tools for chemometric analysis range from specialized open-source packages to commercial software platforms. Recent developments have focused on creating automated or semi-automated preprocessing solutions that systematically compare different preprocessing techniques to optimize data quality before modeling [83]. Tools such as the "nippy" Python module have been developed specifically for the semi-automatic comparison of preprocessing techniques for spectroscopic data, allowing analysts to efficiently evaluate multiple preprocessing combinations and their impact on subsequent modeling results [83]. Additionally, the implementation of double cross-validation procedures within these software environments helps minimize overfitting and provides more realistic estimates of model performance on unknown samples [86].

The integration of chemometrics into forensic science represents a paradigm shift toward more objective, statistically rigorous evidence analysis. By providing quantitative frameworks for evidence interpretation, chemometric approaches directly address the critical need for reduced human bias and enhanced reliability in forensic conclusions [81]. The multivariate nature of chemometric methods enables forensic scientists to extract maximum information from complex evidentiary samples, revealing patterns and relationships that would remain hidden through traditional univariate analysis [82].

Despite their demonstrated utility, the widespread adoption of chemometrics in forensic laboratories faces ongoing challenges. Validation requirements remain particularly demanding, as chemometric models must demonstrate not only statistical performance but also adherence to the stringent scientific standards required for legal admissibility [81] [84]. The establishment of comprehensive ground-truth datasets with known error rates represents another critical need for the field, providing benchmarks for model development and evaluation [81]. Furthermore, the forensic community must continue to develop standardized protocols for the application and interpretation of chemometric models to ensure consistency across laboratories and analysts.

Nevertheless, the accelerating pace of research in forensic chemometrics suggests these techniques are poised to become mainstream in forensic investigations. As the field continues to evolve, the integration of advanced machine learning and deep learning approaches with traditional chemometric methods promises to further enhance the capabilities of forensic evidence analysis [83]. By embracing these objective, data-driven methodologies, forensic science can strengthen its scientific foundation, increase courtroom confidence in forensic conclusions, and ultimately enhance the administration of justice through more robust and reliable evidence evaluation.

Conclusion

The successful validation of non-targeted analysis marks a paradigm shift towards more objective, comprehensive, and efficient forensic chemistry. By integrating foundational principles with robust methodological workflows, proactive troubleshooting, and rigorous validation frameworks, NTA moves from a research tool to a reliable asset in the crime lab. The future of forensic NTA lies in the continued development of standardized protocols, expanded spectral databases, and the deeper integration of artificial intelligence and chemometrics. These advancements will not only help clear casework backlogs but also provide stronger, more statistically defensible evidence, ultimately bolstering the integrity of the justice system. Embracing these validated NTA approaches will be crucial for tackling emerging forensic challenges, from novel psychoactive substances to complex environmental toxins.

References

• 1. An Introduction to the Benchmarking and Publications for Non ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8848292/]
• 2. Advances and challenges in non-targeted analysis [https://www.sciencedirect.com/science/article/abs/pii/S0021967324008331]
• 3. Non-targeted analysis (NTA) and suspect screening ... [https://www.nature.com/articles/s41370-023-00574-6]
• 4. From Sample to Verdict: Experts Discuss Emerging ... [https://www.chromatographyonline.com/view/from-sample-to-verdict-experts-discuss-emerging-methods-in-forensic-chemistry]
• 5. New Approaches in Forensic Analytical Chemistry [https://www.frontiersin.org/research-topics/11118/new-approaches-in-forensic-analytical-chemistry/magazine]
• 6. Development and Validation of a Non-Targeted Screening ... [https://www.mdpi.com/1424-8247/16/1/76]
• 7. Establishing performance metrics for quantitative non ... [https://link.springer.com/article/10.1007/s00216-023-05117-4]
• 8. Evaluating non-targeted analysis methods for chemical ... [https://www.nature.com/articles/s41370-023-00547-9]
• 9. Designing a set of reference standards for non-targeted ... [https://www.nature.com/articles/s41370-025-00788-w]
• 10. What is a Non-Targeted Analysis Approach to Chemical ... [https://jordilabs.com/blog/what-is-a-non-targeted-analysis-approach-to-chemical-characterization/]
• 11. High-resolution accurate mass- mass spectrometry based ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12173630/]
• 12. Comparison of data acquisition modes with Orbitrap high- ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7722469/]
• 13. Linking LC-HRMS Features to Aquatic Toxicity [https://www.chromatographyonline.com/view/linking-lc-hrms-features-to-aquatic-toxicity-a-nontargeted-approach-without-compound-identification]
• 14. Integrating non-target analysis and machine learning [https://www.nature.com/articles/s41545-025-00504-z]
• 15. NORMAN guidance on suspect and non-target screening in ... [https://enveurope.springeropen.com/articles/10.1186/s12302-023-00779-4]
• 16. Development and validation of a forensic workflow for the ... [https://www.sciencedirect.com/science/article/pii/S246817092400064X]
• 17. Forensic crime labs are buckling as new technology ... [https://www.route-fifty.com/digital-government/2025/07/forensic-crime-labs-are-buckling-new-technology-increases-demand/406855/?oref=rf-next-story]
• 18. Non-Targeted Analysis (NTA) of Plasma and Liver from ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12298034/]
• 19. Non-Targeted Analysis Research [https://www.epa.gov/chemical-research/non-targeted-analysis-research]
• 20. Validation of a rapid GC–MS method for forensic seized ... [https://www.sciencedirect.com/science/article/abs/pii/S2468170924000614]
• 21. Quantitative non-targeted analysis: Bridging the gap ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8979303/]
• 22. Aspects of matrix in applications of liquid... effects [https://link.springer.com/article/10.1007/s00216-012-6035-2]
• 23. [PDF] Challenges and strategies of matrix ... | Semantic Scholar effects [https://www.semanticscholar.org/paper/Challenges-and-strategies-of-matrix-effects-using-Raposo-Barcel%C3%B3/4ef92866eee4ce88f2c3b03c0eb1189ab4a4a2e1]
• 24. Understanding the Prevalence of γ-hydroxybutyric Acid in Hair ... [https://researchrepository.wvu.edu/etd/12783/]
• 25. Quantitative non-targeted analysis: Bridging the gap ... [https://www.sciencedirect.com/science/article/pii/S016041202100636X]
• 26. Determination of fentanyl in human plasma by head-space ... [https://www.sciencedirect.com/science/article/abs/pii/S073170850600834X#!]
• 27. Dispersive liquid-liquid microextraction, an effective tool for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8264461/]
• 28. Comparison of various sample preparation methods for ... [https://www.sciencedirect.com/science/article/abs/pii/S0165993624000062]
• 29. Recent trends in microsampling and reduced-volume ... [https://www.sciencedirect.com/science/article/pii/S277258202500035X]
• 30. Green and Emerging Microextraction Strategies for ... [https://www.mdpi.com/1420-3049/30/22/4471]
• 31. Development and Validation of a Non-Targeted Screening ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9865759/]
• 32. Rapid non-target screening of organic pollutants in water ... [https://www.sciencedirect.com/science/article/abs/pii/S0165993608000824]
• 33. Modern Instrumental Methods in Forensic Toxicology - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC2745311/]
• 34. Greenness assessment of UPLC/MS/MS method for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10630503/]
• 35. Ultra-High Performance Liquid Chromatography-High ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8069063/]
• 36. Development and validation of the UPLC-MS method for ... [https://pubs.rsc.org/en/content/articlehtml/2022/ra/d2ra04869h]
• 37. The LCGC Blog: Forensic Drug Analysis: GC–MS versus ... [https://www.chromatographyonline.com/view/lcgc-blog-forensic-drug-analysis-gc-ms-versus-lc-ms]
• 38. Molecular networking: An efficient tool for discovering and ... [https://www.sciencedirect.com/science/article/abs/pii/S0731708525000822]
• 39. Ion identity molecular networking for mass spectrometry ... [https://www.nature.com/articles/s41467-021-23953-9]
• 40. Chemometrics in forensic chemistry — Part I [https://www.sciencedirect.com/science/article/abs/pii/S0379073819302129#!]
• 41. Validation of a non-targeted method devoted to ... [https://www.sciencedirect.com/science/article/abs/pii/S1570023223001496]
• 42. Strategies for structure elucidation of small molecules ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9489805/]
• 43. Advancing non-target analysis of emerging environmental ... [https://www.sciencedirect.com/science/article/pii/S0160412025001552]
• 44. Comparing Two Analytical Workflows for Seized Drug ... [https://www.nist.gov/publications/comparing-two-analytical-workflows-seized-drug-analysis-synthetic-cannabinoids]
• 45. Comparison of measured and recommended acceptance ... [https://www.sciencedirect.com/science/article/abs/pii/S2468170918300341]
• 46. State-of-the-Art Analytical Approaches for Illicit Drug Profiling ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9571531/]
• 47. Fire Debris Analysis [https://www.asteetrace.org/firedebris]
• 48. Comparison of decision tree and naïve Bayes algorithms in ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10785596/]
• 49. HEIDM Program [https://www.dhs.gov/science-and-technology/heidm-program]
• 50. Methods for GC/MS Analysis of the Most Commonly Seized ... [https://www.mdpi.com/2227-9040/13/8/286]
• 51. 7 Strategies to Choose the Best Features for Your Product [https://www.productplan.com/learn/strategies-prioritize-product-features/]
• 52. 5 Prioritization Methods in UX Roadmapping [https://www.nngroup.com/articles/prioritization-methods/]
• 53. Prioritization frameworks [https://www.atlassian.com/agile/product-management/prioritization-framework]
• 54. 9 Prioritization Frameworks & Which to Use in 2025 [https://productschool.com/blog/product-fundamentals/ultimate-guide-product-prioritization]
• 55. Statistical analysis in method comparison studies part one [https://acutecaretesting.org/en/articles/statistical-analysis-in-method-comparison-studies-part-one]
• 56. A Look at Matrix Effects [https://www.chromatographyonline.com/view/look-matrix-effects]
• 57. Publications [https://nontargetedanalysis.org/category/news/publications/]
• 58. Development and validation of a 'dilute and shoot' LC–MS ... [https://www.sciencedirect.com/science/article/pii/S1570023225003046]
• 59. Overcoming Matrix Effects in Non-Target Screening [https://pmc.ncbi.nlm.nih.gov/articles/PMC12224152/]
• 60. Identification of post-blast explosive residues using direct- ... [https://www.sciencedirect.com/science/article/abs/pii/S2468170919300827]
• 61. Advances in Detecting and Identifying Explosives After an Attack [https://nij.ojp.gov/topics/articles/advances-detecting-and-identifying-explosives-after-attack]
• 62. Impact of Rainy Environments on Nitrate Ion Detection in ... [https://www.forensicscijournal.com/journals/jfsr/jfsr-aid1092.php]
• 63. How Mass Spectrometry and Ambient Ionization ... [https://www.chromatographyonline.com/view/how-mass-spectrometry-and-ambient-ionization-techniques-are-improving-drug-detection-in-forensics]
• 64. Mass Spectrometric Methods for Non-Targeted Screening ... [https://www.mdpi.com/2297-8739/9/12/415]
• 65. Red analytical performance index (RAPI) and software [https://pubs.rsc.org/en/content/articlelanding/2025/gc/d4gc05298f]
• 66. Automated rapid screening of new psychoactive ... [https://www.sciencedirect.com/science/article/abs/pii/S0379073825002026]
• 67. Machine Learning Applications for Chemical Fingerprinting and... [https://pubmed.ncbi.nlm.nih.gov/35297611/]
• 68. How to Avoid False Positives and False Negatives in ... [https://www.acdlabs.com/blog/how-to-avoid-false-positives-and-false-negatives-in-analytical-chemistry/]
• 69. Sample preparation techniques for suspect and non-target ... [https://www.sciencedirect.com/science/article/pii/S0045653521027788]
• 70. Advances and Challenges in LSD Detection [https://pubmed.ncbi.nlm.nih.gov/41185936/]
• 71. Rapid GC-MS method for screening seized drugs in ... [https://www.frontiersin.org/journals/chemistry/articles/10.3389/fchem.2025.1559279/full]
• 72. (PDF) Non-targeted analysis (NTA) and suspect screening ... [https://www.academia.edu/108349718/Non_targeted_analysis_NTA_and_suspect_screening_analysis_SSA_a_review_of_examining_the_chemical_exposome]
• 73. Validation of a non-targeted method devoted to ... [https://pubmed.ncbi.nlm.nih.gov/37172558/]
• 74. – BP and Publications for Non-Targeted Analysis 4 NTA Benchmarking [https://nontargetedanalysis.org/]
• 75. Standardizing non-targeted analysis reporting to advance exposure... [https://www.nature.com/articles/s41370-022-00490-1?error=cookies_not_supported]
• 76. Adoption of Analytical Methods for Identifying CEC [https://cec-1.itrcweb.org/adoption-of-analytical-methods-for-identifying-cec/]
• 77. Comparative Evaluation of Four Commercially Available ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10299129/]
• 78. Approaches for assessing performance of high-resolution ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9411239/]
• 79. Demonstrating the Use of Non-targeted Analysis for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10198433/]
• 80. A Proposed ChemSpace Tool | Research Highlight | PNNL [https://www.pnnl.gov/publications/finding-better-way-explore-chemical-space-proposed-chemspace-tool]
• 81. Advancing Forensic Science with Chemometrics [https://www.spectroscopyonline.com/view/advancing-forensic-science-with-chemometrics-new-tools-for-objective-evidence-analysis]
• 82. Chemometrics - an overview [https://www.sciencedirect.com/topics/chemistry/chemometrics]
• 83. Trends in artificial intelligence, machine learning, and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10989568/]
• 84. Validation of chemometric models – A tutorial [https://www.sciencedirect.com/science/article/abs/pii/S0003267015008922]
• 85. Validation of chemometric models - a tutorial [https://pubmed.ncbi.nlm.nih.gov/26398418/]
• 86. APPLICATION OF CHEMOMETRICS FOR THE ROBUST ... [https://research.manchester.ac.uk/en/studentTheses/application-of-chemometrics-for-the-robust-analysis-of-chemical-a/]
• 87. HPLC 2025 Preview: Prioritization Strategies in Non-Target ... [https://www.chromatographyonline.com/view/hplc-2025-preview-prioritization-strategies-in-non-target-screening-of-environmental-samples-by-chromatography-with-high-resolution-mass-spectrometry]