Geographical profiling of wood samples via ATR-FTIR spectroscopy and machine learning algorithms: Application in wood forensics
Illegal activities associated with deforestation for the lumber and furniture industries pose significant threats to plant and animal biodiversity, as well as natural resources. Accurate identification of wood sources is vital, yet traditional laboratory techniques often fall short in precisely dete...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Forensic Science International: Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665910724000264 |
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| author | Suraj Garg Akanksha Sharma Vishal Sharma |
| author_facet | Suraj Garg Akanksha Sharma Vishal Sharma |
| author_sort | Suraj Garg |
| collection | DOAJ |
| description | Illegal activities associated with deforestation for the lumber and furniture industries pose significant threats to plant and animal biodiversity, as well as natural resources. Accurate identification of wood sources is vital, yet traditional laboratory techniques often fall short in precisely determining the chemical composition of samples for classification. This study aims to leverage ATR-FTIR spectroscopy alongside machine learning algorithms to construct a robust model for discerning the geographical origins of wood samples from India. By systematically comparing various machine learning classifiers, we address the limitations of subjective visual interpretation and evaluate their accuracy using wood spectral data. Logistic regression emerges as the most effective classifier for distinguishing Eucalyptus (75 % accuracy), Dalbergia (68 % accuracy), and Populus (81.5 % accuracy) species. Through a methodology encompassing data pre-processing, classifier selection, and performance evaluation, this research offers promising tools for combating challenges posed by illegal wood trafficking and transportation. The outcomes hold significant potential for enhancing wildlife crime prevention efforts by facilitating the tracing illicit timber sources, apprehension of perpetrators, and implementation of preventive measures. |
| format | Article |
| id | doaj-art-3fd963b0db4f456cbdc520126a7b3c91 |
| institution | OA Journals |
| issn | 2665-9107 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Forensic Science International: Reports |
| spelling | doaj-art-3fd963b0db4f456cbdc520126a7b3c912025-08-20T01:58:16ZengElsevierForensic Science International: Reports2665-91072024-12-011010037710.1016/j.fsir.2024.100377Geographical profiling of wood samples via ATR-FTIR spectroscopy and machine learning algorithms: Application in wood forensicsSuraj Garg0Akanksha Sharma1Vishal Sharma2Institute of Forensic Science and Criminology, Panjab University, Chandigarh 160014, IndiaInstitute of Forensic Science and Criminology, Panjab University, Chandigarh 160014, IndiaCorresponding author.; Institute of Forensic Science and Criminology, Panjab University, Chandigarh 160014, IndiaIllegal activities associated with deforestation for the lumber and furniture industries pose significant threats to plant and animal biodiversity, as well as natural resources. Accurate identification of wood sources is vital, yet traditional laboratory techniques often fall short in precisely determining the chemical composition of samples for classification. This study aims to leverage ATR-FTIR spectroscopy alongside machine learning algorithms to construct a robust model for discerning the geographical origins of wood samples from India. By systematically comparing various machine learning classifiers, we address the limitations of subjective visual interpretation and evaluate their accuracy using wood spectral data. Logistic regression emerges as the most effective classifier for distinguishing Eucalyptus (75 % accuracy), Dalbergia (68 % accuracy), and Populus (81.5 % accuracy) species. Through a methodology encompassing data pre-processing, classifier selection, and performance evaluation, this research offers promising tools for combating challenges posed by illegal wood trafficking and transportation. The outcomes hold significant potential for enhancing wildlife crime prevention efforts by facilitating the tracing illicit timber sources, apprehension of perpetrators, and implementation of preventive measures.http://www.sciencedirect.com/science/article/pii/S2665910724000264WoodWood ForensicsGeographic locationSpectroscopyMachine Learning |
| spellingShingle | Suraj Garg Akanksha Sharma Vishal Sharma Geographical profiling of wood samples via ATR-FTIR spectroscopy and machine learning algorithms: Application in wood forensics Forensic Science International: Reports Wood Wood Forensics Geographic location Spectroscopy Machine Learning |
| title | Geographical profiling of wood samples via ATR-FTIR spectroscopy and machine learning algorithms: Application in wood forensics |
| title_full | Geographical profiling of wood samples via ATR-FTIR spectroscopy and machine learning algorithms: Application in wood forensics |
| title_fullStr | Geographical profiling of wood samples via ATR-FTIR spectroscopy and machine learning algorithms: Application in wood forensics |
| title_full_unstemmed | Geographical profiling of wood samples via ATR-FTIR spectroscopy and machine learning algorithms: Application in wood forensics |
| title_short | Geographical profiling of wood samples via ATR-FTIR spectroscopy and machine learning algorithms: Application in wood forensics |
| title_sort | geographical profiling of wood samples via atr ftir spectroscopy and machine learning algorithms application in wood forensics |
| topic | Wood Wood Forensics Geographic location Spectroscopy Machine Learning |
| url | http://www.sciencedirect.com/science/article/pii/S2665910724000264 |
| work_keys_str_mv | AT surajgarg geographicalprofilingofwoodsamplesviaatrftirspectroscopyandmachinelearningalgorithmsapplicationinwoodforensics AT akankshasharma geographicalprofilingofwoodsamplesviaatrftirspectroscopyandmachinelearningalgorithmsapplicationinwoodforensics AT vishalsharma geographicalprofilingofwoodsamplesviaatrftirspectroscopyandmachinelearningalgorithmsapplicationinwoodforensics |