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Yeh YL, Wen CY, Hsieh CL, Chang YH, Wang SM. In vitro metabolic studies and machine learning analysis of mass spectrometry data: A dual strategy for differentiating alpha-pyrrolidinohexiophenone (α-PHP) and alpha-pyrrolidinoisohexanophenone (α-PiHP) in urine analysis. Forensic Sci Int 2024; 361:112134. [PMID: 38996540 DOI: 10.1016/j.forsciint.2024.112134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/22/2024] [Accepted: 07/01/2024] [Indexed: 07/14/2024]
Abstract
Synthetic cathinones are some of the most prevalent new psychoactive substances (NPSs) globally, with alpha-pyrrolidinoisohexanophenone (α-PiHP) being particularly noted for its widespread use in the United States, Europe, and Taiwan. However, the analysis of isomeric NPSs such as α-PiHP and alpha-pyrrolidinohexiophenone (α-PHP) is challenging owing to similarities in their retention times and mass spectra. This study proposes a dual strategy based on in vitro metabolic experiments and machine learning-based classification modelling for differentiating α-PHP and α-PiHP in urine samples: (1) in vitro metabolic experiments using pooled human liver microsomes and liquid chromatography tandem quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) were conducted to identify the key metabolites of α-PHP and α-PiHP from the high-resolution MS/MS spectra. After 5 h incubation, 71.4 % of α-PHP and 64.7 % of α-PiHP remained unmetabolised. Nine phase I metabolites were identified for each compound, including primary β-ketone reduction (M1) metabolites. Comparing the metabolites and retention times confirmed the efficacy of in vitro metabolic experiments for differentiating NPS isomers. Subsequently, analysis of seven real urine samples revealed the presence for various metabolites, including M1, that could be used as suitable detection markers at low concentrations. The aliphatic hydroxylation (M2) metabolite peak counts and metabolite retention times were used to determine α-PiHP use. (2) Classification models for the parent compounds and M1 metabolites were developed using principal component analysis for feature extraction and logistic regression for classification. The training and test sets were devised from the spectra of standard samples or supernatants from in vitro metabolism experiments with different incubation times. Both models had classification accuracies of 100 % and accurately identified α-PiHP and its M1 metabolite in seven real urine samples. The proposed methodology effectively distinguished between such isomers and confirmed their presence at low concentrations. Overall, this study introduces a novel concept that addresses the complexities in analysing isomeric NPSs and suggests a path towards enhancing the accuracy and reliability of NPS detection.
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Affiliation(s)
- Ya-Ling Yeh
- Department of Forensic Science, Central Police University, Taoyuan City, Taiwan (ROC); Forensic Science Center, Taoyuan Police Department, Taoyuan City, Taiwan (ROC).
| | - Che-Yen Wen
- Department of Forensic Science, Central Police University, Taoyuan City, Taiwan (ROC)
| | - Chin-Lin Hsieh
- Forensic Science Center, Criminal Investigation Bureau, National Police Agency, Taipei City, Taiwan (ROC)
| | - Yu-Hsiang Chang
- Forensic Science Center, Criminal Investigation Bureau, National Police Agency, Taipei City, Taiwan (ROC)
| | - Sheng-Meng Wang
- Department of Forensic Science, Central Police University, Taoyuan City, Taiwan (ROC).
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Hwang J, Choi KO, Jeong S, Lee S. Machine learning identification of edible vegetable oils from fatty acid compositions and hyperspectral images. Curr Res Food Sci 2024; 8:100742. [PMID: 38708100 PMCID: PMC11066601 DOI: 10.1016/j.crfs.2024.100742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Hyperspectral imaging analysis combined with machine learning was applied to identify eight edible vegetable oils, and its classification performance was compared with the chemical method based on fatty acid compositions. Furthermore, the degree of adulteration in vegetable oils was quantitatively investigated using machine learning-enabled hyperspectral approaches. The hyperspectral absorbance spectra of palm oil with a high degree of saturation were distinctly different from those of the other liquid oils. The flaxseed and olive oils exhibited the dominant hyperspectral intensities at 1170/1671 and 1212/1415 nm, respectively. Linear discriminant analysis demonstrated that two linear discriminants could explain a significant portion of the total variability, accounting for 96.0% (fatty acid compositions) and 98.9% (hyperspectral images). When the hyperspectral results were used as datasets for three machine learning models (decision tree, random forest, and k-nearest neighbor), several instances to incorrectly classify grapeseed and sunflower oils were detected, while olive, palm, and flaxseed oils were successfully identified. The machine learning models showed a great classification performance that exceeded 98.9% from the hyperspectral images of the vegetable oils, which was comparable to the fatty acid composition-based chemical method in identifying edible vegetable oils. In addition, the random forest model was the most effective in ascertaining adulteration levels in binary oil blends (R2 > 0.992 and RMSE < 2.75).
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Affiliation(s)
- Jeongin Hwang
- Department of Food Science and Biotechnology, Seoul, 05006, South Korea
| | - Kyeong-Ok Choi
- Department of Food Science and Technology, Chungnam National University, Daejeon, 34134, South Korea
| | - Sungmin Jeong
- Carbohydrate Bioproduct Research Center, Sejong University, Seoul, 05006, South Korea
| | - Suyong Lee
- Department of Food Science and Biotechnology, Seoul, 05006, South Korea
- Carbohydrate Bioproduct Research Center, Sejong University, Seoul, 05006, South Korea
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Koehler A, Scroferneker ML, de Souza NMP, de Moraes PC, Pereira BAS, de Souza Cavalcante R, Mendes RP, Corbellini VA. Rapid Classification of Serum from Patients with Paracoccidioidomycosis Using Infrared Spectroscopy, Univariate Statistics, and Linear Discriminant Analysis (LDA). J Fungi (Basel) 2024; 10:147. [PMID: 38392819 PMCID: PMC10890592 DOI: 10.3390/jof10020147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/18/2024] [Accepted: 02/09/2024] [Indexed: 02/24/2024] Open
Abstract
Paracoccidioidomycosis (PCM) is a systemic mycosis that is diagnosed by visualizing the fungus in clinical samples or by other methods, like serological techniques. However, all PCM diagnostic methods have limitations. The aim of this study was to develop a diagnostic tool for PCM based on Fourier transform infrared (FTIR) spectroscopy. A total of 224 serum samples were included: 132 from PCM patients and 92 constituting the control group (50 from healthy blood donors and 42 from patients with other systemic mycoses). Samples were analyzed by attenuated total reflection (ATR) and a t-test was performed to find differences in the spectra of the two groups. The wavenumbers that had p < 0.05 had their diagnostic potential evaluated using receiver operating characteristic (ROC) curves. The spectral region with the lowest p value was used for variable selection through principal component analysis (PCA). The selected variables were used in a linear discriminant analysis (LDA). In univariate analysis, the ROC curves with the best performance were obtained in the region 1551-1095 cm-1. The wavenumber that had the highest AUC value was 1264 cm-1, achieving a sensitivity of 97.73%, specificity of 76.01%, and accuracy of 94.22%. The total separation of groups was obtained in the PCA performed with a spectral range of 1551-1095 cm-1. LDA performed with the eight wavenumbers with the greatest weight from the group discrimination in the PCA obtained 100% accuracy. The methodology proposed here is simple, fast, and highly accurate, proving its potential to be applied in the diagnosis of PCM. The proposed method is more accurate than the currently known diagnostic methods, which is particularly relevant for a neglected tropical mycosis such as paracoccidioidomycosis.
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Affiliation(s)
- Alessandra Koehler
- Postgraduate Program of Medicine: Medical Sciences, Universidade Federal do Rio Grande do Sul-UFRGS, Porto Alegre 90035-003, Brazil
| | - Maria Lúcia Scroferneker
- Postgraduate Program of Medicine: Medical Sciences, Universidade Federal do Rio Grande do Sul-UFRGS, Porto Alegre 90035-003, Brazil
- Department of Microbiology, Immunology and Parasitology, ICBS, Universidade Federal do Rio Grande do Sul-UFRGS, Porto Alegre 90050-170, Brazil
| | | | - Paulo Cezar de Moraes
- Postgraduate Program of Medicine: Medical Sciences, Universidade Federal do Rio Grande do Sul-UFRGS, Porto Alegre 90035-003, Brazil
| | | | - Ricardo de Souza Cavalcante
- Tropical Diseases Area, School of Medicine, Universidade Estadual Paulista-UNESP, Botucatu 18618-687, Brazil
| | - Rinaldo Pôncio Mendes
- Tropical Diseases Area, School of Medicine, Universidade Estadual Paulista-UNESP, Botucatu 18618-687, Brazil
| | - Valeriano Antonio Corbellini
- Department of Sciences, Humanities and Education, Postgraduate Program in Health Promotion, Postgraduate Program in Environmental Technology, Universidade de Santa Cruz do Sul-UNISC, Santa Cruz do Sul 96815-900, Brazil
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Xu Q, Zhou L, Lv M, Chen Z, Hu C, Xiang P, Chen H. Nontargeted screening based on EI-MS spectra using statistical methods: An investigative study of synthetic indole/indazole cannabinoids. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2023; 37:e9524. [PMID: 37062936 DOI: 10.1002/rcm.9524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/11/2023] [Accepted: 04/14/2023] [Indexed: 06/17/2023]
Abstract
RATIONALE Mass spectrometry has evolved into a highly powerful tool for qualitative and quantitative chemical analyses. However, the identification of trace amounts of previously unknown structures in complex chemical matrix environments remains challenging. The rapid emergence of new synthetic cannabinoid substances is a typical example of this. Existing laboratory techniques are mostly based on methods used for lists of known illegal compounds. This situation poses a challenge to traditional data analysis and the risk of missing the compounds. Therefore, we propose to develop and validate a statistical model to classify newly emerging synthetic cannabinoid substances into a structural class or subclass. METHODS We obtained 70 electrospray ionization spectra of indole/indazole synthetic cannabinoids from both the actual standard analysis and the SWGDRUG mass spectral library (version 3.10). Each sample consisted of 330 m/z variables and corresponding relative intensities. We first cleared the variables with a variance below 0.1. Principal component analysis (PCA) was performed on the variance-filtered data, and the two principal components were retained to generate new data for hierarchical clustering. After hierarchical clustering, we used the receiver operating characteristic method in this cluster. RESULTS Seventy synthetic indole/indazole cannabinoids were classified into four clusters. The side chain of cluster 1 is mainly fluorobenzyl, cluster 2 is pentyl, cluster 3 includes compounds from several structures, and cluster 4 is mainly fluoropentyl. The most relevant characteristic ions are m/z 109, m/z 252, and m/z 253 for cluster 1; m/z 144 and m/z 214 for cluster 2; and m/z 232 and m/z 233 for cluster 4. CONCLUSIONS This study provides a more objective and less time-consuming solution for characterizing synthetic cannabinoids. And this work validates the ability of PCA to extract characteristic fragment ions.
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Affiliation(s)
- Qing Xu
- Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai, China
- School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Liying Zhou
- Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai, China
| | - Min Lv
- Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai, China
- School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Zhuonan Chen
- Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai, China
- School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Chi Hu
- School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Ping Xiang
- Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai, China
| | - Hang Chen
- Academy of Forensic Science, Shanghai Key Laboratory of Forensic Medicine, Shanghai, China
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Bonetti JL, Kranenburg RF, Schoonderwoerd E, Samanipour S, van Asten AC. Instrument-independent chemometric models for rapid, calibration-free NPS isomer differentiation from mass spectral GC-MS data. Forensic Sci Int 2023:111650. [PMID: 37028998 DOI: 10.1016/j.forsciint.2023.111650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/27/2023] [Accepted: 03/21/2023] [Indexed: 03/30/2023]
Abstract
Chemometric analysis of mass spectral data for the purpose of differentiating positional isomers of novel psychoactive substances has seen a substantial increase in popularity in recent years. However, the process of generating a large and robust dataset for chemometric isomer identification is time consuming and impractical for forensic laboratories. To begin to address this problem, three sets of ortho/meta/para positional ring isomers (fluoroamphetamine (FA), fluoromethamphetamine (FMA), and methylmethcathinone (MMC)) were analyzed using multiple GC-MS instruments at three distinct laboratories. A diverse assortment of instrument manufacturers, model types, and parameters was utilized in order to incorporate substantial instrumental variation. The dataset was randomly split into 70% training and 30% validation sets, stratified by instrument. Following an approach based on Design of Experiments, the validation set was used to optimize the preprocessing steps performed prior to Linear Discriminant Analysis. Using the optimized model, a minimum m/z fragment threshold was determined to allow analysts to assess whether an unknown spectrum is of sufficient abundance and quality to be compared to the model. To assess the robustness of the models, a test set was developed utilizing two instruments from a fourth laboratory that was not involved in the generation of the primary dataset in addition to spectra from widely used mass spectral libraries. Of the spectra that reached the threshold, the classification accuracy was 100% for all three isomer types. Only two of the test and validation spectra that did not reach the threshold were misclassified. The results indicate that forensic illicit drug experts world-wide can use these models for robust NPS isomer identification on the basis of preprocessed mass spectral data without the need for acquiring reference drug standards and creating instrument specific GC-MS reference datasets. The continued robustness of the models could be ensured through international collaboration to collect data that captures all potential GC-MS instrumental variation encountered in forensic illicit drug analysis laboratories. This would allow every forensic institute to confidently assign isomeric structures without the need for additional chemical analysis.
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Wu X, He F, Wu B, Zeng S, He C. Accurate Classification of Chunmee Tea Grade Using NIR Spectroscopy and Fuzzy Maximum Uncertainty Linear Discriminant Analysis. Foods 2023; 12:foods12030541. [PMID: 36766070 PMCID: PMC9913903 DOI: 10.3390/foods12030541] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 01/12/2023] [Accepted: 01/21/2023] [Indexed: 01/28/2023] Open
Abstract
The grade of tea is closely related to tea quality, so the identification of tea grade is an important task. In order to improve the identification capability of the tea grade system, a fuzzy maximum uncertainty linear discriminant analysis (FMLDA) methodology was proposed based on maximum uncertainty linear discriminant analysis (MLDA). Based on FMLDA, a tea grade recognition system was established for the grade recognition of Chunmee tea. The process of this system is as follows: firstly, the near-infrared (NIR) spectra of Chunmee tea were collected using a Fourier transform NIR spectrometer. Next, the spectra were preprocessed using standard normal variables (SNV). Then, direct linear discriminant analysis (DLDA), maximum uncertainty linear discriminant analysis (MLDA), and FMLDA were used for feature extraction of the spectra, respectively. Finally, the k-nearest neighbor (KNN) classifier was applied to classify the spectra. The k in KNN and the fuzzy coefficient, m, were discussed in the experiment. The experimental results showed that when k = 1 and m = 2.7 or 2.8, the accuracy of the FMLDA could reach 98.15%, which was better than the other two feature extraction methods. Therefore, FMLDA combined with NIR technology is an effective method in the identification of tea grade.
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Affiliation(s)
- Xiaohong Wu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
- High-Tech Key Laboratory of Agricultural Equipment and Intelligence of Jiangsu Province, Jiangsu University, Zhenjiang 212013, China
- Correspondence:
| | - Fei He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Bin Wu
- Department of Information Engineering, Chuzhou Polytechnic, Chuzhou 239000, China
| | - Shupeng Zeng
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
| | - Chengyu He
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
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Sharp J, Do D, Tyler Davidson J. Assessment of the similarity between in-source collision-induced dissociation (IS-CID) fragment ion spectra and tandem mass spectrometry (MS/MS) product ion spectra for seized drug identifications. Forensic Chem 2022. [DOI: 10.1016/j.forc.2022.100441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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8
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Bonetti JL, Samanipour S, van Asten AC. Utilization of Machine Learning for the Differentiation of Positional NPS Isomers with Direct Analysis in Real Time Mass Spectrometry. Anal Chem 2022; 94:5029-5040. [PMID: 35297608 PMCID: PMC8968871 DOI: 10.1021/acs.analchem.1c04985] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
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The differentiation
of positional isomers is a well established
analytical challenge for forensic laboratories. As more novel psychoactive
substances (NPSs) are introduced to the illicit drug market, robust
yet efficient methods of isomer identification are needed. Although
current literature suggests that Direct Analysis in Real Time–Time-of-Flight
mass spectrometry (DART-ToF) with in-source collision induced dissociation
(is-CID) can be used to differentiate positional isomers, it is currently
unclear whether this capability extends to positional isomers whose
only structural difference is the precise location of a single substitution
on an aromatic ring. The aim of this work was to determine whether
chemometric analysis of DART-ToF data could offer forensic laboratories
an alternative rapid and robust method of differentiating NPS positional
ring isomers. To test the feasibility of this technique, three positional
isomer sets (fluoroamphetamine, fluoromethamphetamine, and methylmethcathinone)
were analyzed. Using a linear rail for consistent sample introduction,
the three isomers of each type were analyzed 96 times over an eight-week
timespan. The classification methods investigated included a univariate
approach, the Welch t test at each included ion;
a multivariate approach, linear discriminant analysis; and a machine
learning approach, the Random Forest classifier. For each method,
multiple validation techniques were used including restricting the
classifier to data that was only generated on one day. Of these classification
methods, the Random Forest algorithm was ultimately the most accurate
and robust, consistently achieving out-of-bag error rates below 5%.
At an inconclusive rate of approximately 5%, a success rate of 100%
was obtained for isomer identification when applied to a randomly
selected test set. The model was further tested with data acquired
as a part of a different batch. The highest classification success
rate was 93.9%, and error rates under 5% were consistently achieved.
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Affiliation(s)
- Jennifer L Bonetti
- Van't Hoff Institute for Molecular Sciences, University of Amsterdam, P.O. Box 94157, Amsterdam 1090 GD, The Netherlands.,Virginia Department of Forensic Science, Norfolk, Virginia 23606, United States
| | - Saer Samanipour
- Van't Hoff Institute for Molecular Sciences, University of Amsterdam, P.O. Box 94157, Amsterdam 1090 GD, The Netherlands
| | - Arian C van Asten
- Van't Hoff Institute for Molecular Sciences, University of Amsterdam, P.O. Box 94157, Amsterdam 1090 GD, The Netherlands.,Co van Ledden Hulsebosch Center (CLHC), Amsterdam Center for Forensic Science and Medicine, 1098 XH Amsterdam, The Netherlands
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Liliedahl RE, Davidson JT. The differentiation of synthetic cathinone isomers using GC-EI-MS and multivariate analysis. Forensic Chem 2021. [DOI: 10.1016/j.forc.2021.100349] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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10
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Kranenburg RF, Stuyver LI, de Ridder R, van Beek A, Colmsee E, van Asten AC. Deliberate evasion of narcotic legislation: Trends visualized in commercial mixtures of new psychoactive substances analyzed by GC-solid deposition-FTIR. Forensic Chem 2021. [DOI: 10.1016/j.forc.2021.100346] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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Sauzier G, van Bronswijk W, Lewis SW. Chemometrics in forensic science: approaches and applications. Analyst 2021; 146:2415-2448. [PMID: 33729240 DOI: 10.1039/d1an00082a] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Forensic investigations are often reliant on physical evidence to reconstruct events surrounding a crime. However, there remains a need for more objective approaches to evidential interpretation, along with rigorously validated procedures for handling, storage and analysis. Chemometrics has been recognised as a powerful tool within forensic science for interpretation and optimisation of analytical procedures. However, careful consideration must be given to factors such as sampling, validation and underpinning study design. This tutorial review aims to provide an accessible overview of chemometric methods within the context of forensic science. The review begins with an overview of selected chemometric techniques, followed by a broad review of studies demonstrating the utility of chemometrics across various forensic disciplines. The tutorial review ends with the discussion of the challenges and emerging trends in this rapidly growing field.
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Affiliation(s)
- Georgina Sauzier
- School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth, Western Australia 6845, Australia.
| | - Wilhelm van Bronswijk
- School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth, Western Australia 6845, Australia.
| | - Simon W Lewis
- School of Molecular and Life Sciences, Curtin University, GPO Box U1987, Perth, Western Australia 6845, Australia.
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Feeney W, Moorthy AS, Sisco E. Spectral trends in GC-EI-MS data obtained from the SWGDRUG mass spectral library and literature: A resource for the identification of unknown compounds. Forensic Chem 2020; 31:10.1016/j.forc.2022.100459. [PMID: 36578315 PMCID: PMC9793444 DOI: 10.1016/j.forc.2022.100459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Rapid identification of new or emerging psychoactive substances remains a critical challenge in forensic drug chemistry laboratories. Current analytical protocols are well-designed for confirmation of known substances yet struggle when new compounds are encountered. Many laboratories initially attempt to classify new compounds using gas chromatography-electron ionization-mass spectrometry (GC-EI-MS). Though there is a large body of research focused on the analysis of illicit substances with GC-EI-MS, there is little high-level discussion of mass spectral trends for different classes of drugs. This manuscript compiles literature information and performs simple exploratory analyses on evaluated GC-EI-MS data to investigate mass spectral trends for illicit substance classes. Additionally, this work offers other important aspects: brief discussions of how each class of drugs is used; illustrations of EI mass spectra with proposed structures of commonly observed ions; and summaries of mass spectral trends that can help an analyst classify new illicit compounds.
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Affiliation(s)
- William Feeney
- Corresponding author at: Surface and Trace Chemical Analysis Group, Material Measurement Laboratory, 100 Bureau Drive, Gaithersburg, MD 20899, USA. (W. Feeney)
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Gilbert N, Mewis RE, Sutcliffe OB. Classification of fentanyl analogues through principal component analysis (PCA) and hierarchical clustering of GC–MS data. Forensic Chem 2020. [DOI: 10.1016/j.forc.2020.100287] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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14
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Stuhmer EL, McGuffin VL, Waddell Smith R. Discrimination of seized drug positional isomers based on statistical comparison of electron-ionization mass spectra. Forensic Chem 2020. [DOI: 10.1016/j.forc.2020.100261] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Kranenburg RF, Verduin J, Stuyver LI, de Ridder R, van Beek A, Colmsee E, van Asten AC. Benefits of derivatization in GC–MS-based identification of new psychoactive substances. Forensic Chem 2020. [DOI: 10.1016/j.forc.2020.100273] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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16
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Revealing hidden information in GC–MS spectra from isomeric drugs: Chemometrics based identification from 15 eV and 70 eV EI mass spectra. Forensic Chem 2020. [DOI: 10.1016/j.forc.2020.100225] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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17
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Quinn M, Brettell T, Joshi M, Bonetti J, Quarino L. Identifying PCP and four PCP analogs using the gold chloride microcrystalline test followed by raman microspectroscopy and chemometrics. Forensic Sci Int 2020; 307:110135. [DOI: 10.1016/j.forsciint.2019.110135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 12/16/2019] [Accepted: 12/20/2019] [Indexed: 10/25/2022]
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18
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Jang I, Lee JU, Lee JM, Kim BH, Moon B, Hong J, Oh HB. LC–MS/MS Software for Screening Unknown Erectile Dysfunction Drugs and Analogues: Artificial Neural Network Classification, Peak-Count Scoring, Simple Similarity Search, and Hybrid Similarity Search Algorithms. Anal Chem 2019; 91:9119-9128. [DOI: 10.1021/acs.analchem.9b01643] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Inae Jang
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Jae-ung Lee
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Jung-min Lee
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Beom Hee Kim
- College of Pharmacy, Kyunghee University, Seoul 02447, Republic of Korea
| | - Bongjin Moon
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Jongki Hong
- College of Pharmacy, Kyunghee University, Seoul 02447, Republic of Korea
| | - Han Bin Oh
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
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Davidson JT, Jackson GP. The differentiation of 2,5-dimethoxy-N-(N-methoxybenzyl)phenethylamine (NBOMe) isomers using GC retention indices and multivariate analysis of ion abundances in electron ionization mass spectra. Forensic Chem 2019. [DOI: 10.1016/j.forc.2019.100160] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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