Forensic Analysis of Textile Synthetic Fibers Using a FT-IR Spectroscopy Approach
Synthetic fibers are one of the most valuable trace lines of evidence that can be found in crime scenes. When textile fibers are analyzed properly, they can help in finding a linkage between suspect, victim, and the scene of the crime. Various analytical techniques are used in the examination of sam...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/27/13/4281 |
| _version_ | 1797408610311995392 |
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| author | Abdulrahman Aljannahi Roudha Abdulla Alblooshi Rashed Humaid Alremeithi Ioannis Karamitsos Noora Abdulkarim Ahli Asma Mohammed Askar Ikhlass Mohammed Albastaki Mohamed Mahmood Ahli Sanjay Modak |
| author_facet | Abdulrahman Aljannahi Roudha Abdulla Alblooshi Rashed Humaid Alremeithi Ioannis Karamitsos Noora Abdulkarim Ahli Asma Mohammed Askar Ikhlass Mohammed Albastaki Mohamed Mahmood Ahli Sanjay Modak |
| author_sort | Abdulrahman Aljannahi |
| collection | DOAJ |
| description | Synthetic fibers are one of the most valuable trace lines of evidence that can be found in crime scenes. When textile fibers are analyzed properly, they can help in finding a linkage between suspect, victim, and the scene of the crime. Various analytical techniques are used in the examination of samples to determine relationships between different fabric fragments. In this exploratory study, multivariate statistical methods were investigated in combination with machine learning classification models as a method for classifying 138 synthetic textile fibers using Fourier transform infrared spectroscopy, FT-IR. The data were first subjected to preprocessing techniques including the Savitzky–Golay first derivative method and Standard Normal Variate (SNV) method to smooth the spectra and minimize the scattering effects. Principal Component Analysis (PCA) was built to observe unique patterns and to cluster the samples. The classification model in this study, Soft Independent Modeling by Class Analogy (SIMCA), showed correct classification and separation distances between the analyzed synthetic fiber types. At a significance level of 5%, 97.1% of test samples were correctly classified. |
| first_indexed | 2024-03-09T04:00:53Z |
| format | Article |
| id | doaj.art-de7ab0f772f5486aa843475b8cc93955 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-09T04:00:53Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-de7ab0f772f5486aa843475b8cc939552023-12-03T14:14:15ZengMDPI AGMolecules1420-30492022-07-012713428110.3390/molecules27134281Forensic Analysis of Textile Synthetic Fibers Using a FT-IR Spectroscopy ApproachAbdulrahman Aljannahi0Roudha Abdulla Alblooshi1Rashed Humaid Alremeithi2Ioannis Karamitsos3Noora Abdulkarim Ahli4Asma Mohammed Askar5Ikhlass Mohammed Albastaki6Mohamed Mahmood Ahli7Sanjay Modak8Dubai Police General Headquarters, Dubai 1492, United Arab EmiratesDubai Police General Headquarters, Dubai 1492, United Arab EmiratesDubai Police General Headquarters, Dubai 1492, United Arab EmiratesResearch and Graduate Department, Rochester Institute of Technology, Dubai 1492, United Arab EmiratesDubai Police General Headquarters, Dubai 1492, United Arab EmiratesDubai Police General Headquarters, Dubai 1492, United Arab EmiratesDubai Police General Headquarters, Dubai 1492, United Arab EmiratesDubai Police General Headquarters, Dubai 1492, United Arab EmiratesResearch and Graduate Department, Rochester Institute of Technology, Dubai 1492, United Arab EmiratesSynthetic fibers are one of the most valuable trace lines of evidence that can be found in crime scenes. When textile fibers are analyzed properly, they can help in finding a linkage between suspect, victim, and the scene of the crime. Various analytical techniques are used in the examination of samples to determine relationships between different fabric fragments. In this exploratory study, multivariate statistical methods were investigated in combination with machine learning classification models as a method for classifying 138 synthetic textile fibers using Fourier transform infrared spectroscopy, FT-IR. The data were first subjected to preprocessing techniques including the Savitzky–Golay first derivative method and Standard Normal Variate (SNV) method to smooth the spectra and minimize the scattering effects. Principal Component Analysis (PCA) was built to observe unique patterns and to cluster the samples. The classification model in this study, Soft Independent Modeling by Class Analogy (SIMCA), showed correct classification and separation distances between the analyzed synthetic fiber types. At a significance level of 5%, 97.1% of test samples were correctly classified.https://www.mdpi.com/1420-3049/27/13/4281textile fibersspectroscopyFT-IRforensicPCASIMCA |
| spellingShingle | Abdulrahman Aljannahi Roudha Abdulla Alblooshi Rashed Humaid Alremeithi Ioannis Karamitsos Noora Abdulkarim Ahli Asma Mohammed Askar Ikhlass Mohammed Albastaki Mohamed Mahmood Ahli Sanjay Modak Forensic Analysis of Textile Synthetic Fibers Using a FT-IR Spectroscopy Approach Molecules textile fibers spectroscopy FT-IR forensic PCA SIMCA |
| title | Forensic Analysis of Textile Synthetic Fibers Using a FT-IR Spectroscopy Approach |
| title_full | Forensic Analysis of Textile Synthetic Fibers Using a FT-IR Spectroscopy Approach |
| title_fullStr | Forensic Analysis of Textile Synthetic Fibers Using a FT-IR Spectroscopy Approach |
| title_full_unstemmed | Forensic Analysis of Textile Synthetic Fibers Using a FT-IR Spectroscopy Approach |
| title_short | Forensic Analysis of Textile Synthetic Fibers Using a FT-IR Spectroscopy Approach |
| title_sort | forensic analysis of textile synthetic fibers using a ft ir spectroscopy approach |
| topic | textile fibers spectroscopy FT-IR forensic PCA SIMCA |
| url | https://www.mdpi.com/1420-3049/27/13/4281 |
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