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Sharma S, Gupta S, Yadav PK. Sex and blood group determination from hair using ATR-FTIR spectroscopy and chemometrics. Int J Legal Med 2024; 138:801-814. [PMID: 37980281 DOI: 10.1007/s00414-023-03123-w] [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: 08/30/2023] [Accepted: 11/06/2023] [Indexed: 11/20/2023]
Abstract
Examination of hair with its intact root is commonly used for DNA profiling of the donor. However, its use for gathering other types of information is less explored. Using attenuated total reflectance-Fourier transform infrared spectroscopy, the present study aims to explore other relevant aspects in a non-destructive manner for forensics. Determining the sex and blood group of human hair samples were the major goals of the study. Sex determination was accomplished by analyzing the differential vibrational intensities and stretching of various chemical groups associated with hair and its proteins. Statistical inference of spectral data was performed using chemometric algorithms such as PCA and PLS-DA. The PLS-DA model determined sex with 100% accuracy and blood grouping with an average accuracy of 95%. The present study is the first of its kind to determine sex and blood grouping from human scalp hair shafts, as far as the author knows. By acting as a preliminary screening test, this study could have significant implications for forensic analysis of crime scene samples. Human and synthetic hair were used in validation studies, resulting in 100% accuracy, specificity, and sensitivity, with 0% false positives and false negatives. The technique ATR FTIR spectroscopy could complement the currently used methods of hair analysis such as physical examination and mitochondrial or genomic DNA analysis.
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Affiliation(s)
- Sweety Sharma
- LNJN NICFS, School of Forensic Sciences, National Forensic Science University, An Institute of National Importance, Ministry of Home Affairs, Govt. of India, Delhi Campus, Delhi, 110085, India.
| | - Srishti Gupta
- LNJN NICFS, School of Forensic Sciences, National Forensic Science University, An Institute of National Importance, Ministry of Home Affairs, Govt. of India, Delhi Campus, Delhi, 110085, India
| | - Praveen Kumar Yadav
- Department of Forensic Science, Sandip University, Nashik, Maharastra, India
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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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Assessment of high-quality counterfeit stamp impressions generated by inkjet printers via texture analysis and likelihood ratio. Forensic Sci Int 2023; 344:111573. [PMID: 36731221 DOI: 10.1016/j.forsciint.2023.111573] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 12/01/2022] [Accepted: 01/24/2023] [Indexed: 01/26/2023]
Abstract
High-quality counterfeit stamp impressions made by inkjet printers remain challenging in questioned document examination and forensic analyses. A dataset comprised of various printed stamp impressions, using ten options of conditions and materials, and hand stamped impressions was generated. In this paper, we report printed impressions in pure color and high-quality printing mode are very similar to hand stamped impressions in terms of their microscopic characteristics. These similarities may lead to incorrect conclusions via traditional identification methods. Here, we proposed a method for identifying counterfeit stamp impressions via texture features and image quality parameters extracted from impressions. First, the statistical analysis methods were used to verify a significant difference between the printed and hand stamped impressions. Principal component analysis (PCA) was used to show the variation between the impressions, and the differences between printed and hand stamped impressions were obvious in the three-dimensional plot. After filtering the background of the stamp impressions, image processing analysis was introduced to extract features of gray level co-occurrence matrix (GLCM), segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and image quality metrics (IQM), which were used to characterize the stamp impressions. Finally, specific cases were simulated by random selection, based on the dataset of stamp impressions, and an evaluation system for stamp evidence was established to calculate the likelihood ratios (LRs) under two alternative hypotheses. The likelihood ratio interprets calibrated evaluations on the strength of stamp impressions as evidence. We can also balance these LRs against the rates of misleading evidence with a reasonable performance (equal error rate = 0.048). This paper provides a system to differentiate high-quality printed and hand stamped impressions with reasonable performance.
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Weber A, Hoplight B, Ogilvie R, Muro C, Khandasammy SR, Pérez-Almodóvar L, Sears S, Lednev IK. Innovative Vibrational Spectroscopy Research for Forensic Application. Anal Chem 2023; 95:167-205. [PMID: 36625116 DOI: 10.1021/acs.analchem.2c05094] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Alexis Weber
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States.,SupreMEtric LLC, 7 University Pl. B210, Rensselaer, New York 12144, United States
| | - Bailey Hoplight
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Rhilynn Ogilvie
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Claire Muro
- New York State Police Forensic Investigation Center, Building #30, Campus Access Rd., Albany, New York 12203, United States
| | - Shelby R Khandasammy
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Luis Pérez-Almodóvar
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Samuel Sears
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States
| | - Igor K Lednev
- Department of Chemistry, University at Albany, SUNY, 1400 Washington Avenue, Albany, New York 12222, United States.,SupreMEtric LLC, 7 University Pl. B210, Rensselaer, New York 12144, United States
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