On the textile fibre’s analysis for forensics, utilizing FTIR spectroscopy and machine learning methods

IF 2.6 3区医学 Q2 CHEMISTRY, ANALYTICAL

Forensic Chemistry Pub Date : 2024-04-05 DOI:10.1016/j.forc.2024.100576

Vishal Sharma, Mamta Mahara, Akanksha Sharma

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Abstract

Fibres are prevalent and can be encountered as trace evidence in various situations. In cases of rape and physical assault, analyzing trace fibre components and assessing their transferability can establish connections between individuals and crime scenes or between perpetrators and victims. This study involved Attenuated Total Reflectance – Fourier Transform Infrared (ATR–FTIR) characterization of 104 fibre samples, including natural fibres like cotton and wool (43 samples) and terry wool and synthetic fibres (61 samples). Prominent peaks in different textile fibre spectra were primarily found in the fingerprint region (1800–450 cm⁻¹). To simplify analysis, the spectral data was reduced to principal components, and sample discrimination was performed using Python’s PyCaret package. Multiple machine learning algorithms were explored for differentiating fibre samples, and the most effective one was selected for further validation. This study demonstrates the feasibility of developing an ATR-FTIR database for additional textile fibre samples, aiding in the detection of unknown or suspect fibres in the future.

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利用傅立叶变换红外光谱和机器学习方法对纺织纤维进行取证分析

纤维很普遍，在各种情况下都可能作为痕迹证据出现。在强奸和人身攻击案件中，分析痕量纤维成分并评估其可转移性可确定个人与犯罪现场之间或犯罪者与受害者之间的联系。这项研究涉及 104 种纤维样本的衰减全反射-傅立叶变换红外光谱（ATR-FTIR）特性分析，包括棉花和羊毛等天然纤维（43 种样本）以及毛圈羊毛和合成纤维（61 种样本）。不同纺织纤维光谱中的显著峰值主要出现在指纹区（1800-450 cm-1）。为了简化分析，我们将光谱数据还原为主成分，并使用 Python 的 PyCaret 软件包进行样品鉴别。研究人员探索了多种机器学习算法来区分纤维样品，并选择了最有效的算法进行进一步验证。这项研究证明了为更多纺织纤维样品开发 ATR-FTIR 数据库的可行性，有助于今后检测未知或可疑纤维。

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来源期刊

Forensic Chemistry CHEMISTRY, ANALYTICAL-

CiteScore

5.70

自引率

14.80%

发文量

审稿时长

46 days

期刊介绍： Forensic Chemistry publishes high quality manuscripts focusing on the theory, research and application of any chemical science to forensic analysis. The scope of the journal includes fundamental advancements that result in a better understanding of the evidentiary significance derived from the physical and chemical analysis of materials. The scope of Forensic Chemistry will also include the application and or development of any molecular and atomic spectrochemical technique, electrochemical techniques, sensors, surface characterization techniques, mass spectrometry, nuclear magnetic resonance, chemometrics and statistics, and separation sciences (e.g. chromatography) that provide insight into the forensic analysis of materials. Evidential topics of interest to the journal include, but are not limited to, fingerprint analysis, drug analysis, ignitable liquid residue analysis, explosives detection and analysis, the characterization and comparison of trace evidence (glass, fibers, paints and polymers, tapes, soils and other materials), ink and paper analysis, gunshot residue analysis, synthetic pathways for drugs, toxicology and the analysis and chemistry associated with the components of fingermarks. The journal is particularly interested in receiving manuscripts that report advances in the forensic interpretation of chemical evidence. Technology Readiness Level: When submitting an article to Forensic Chemistry, all authors will be asked to self-assign a Technology Readiness Level (TRL) to their article. The purpose of the TRL system is to help readers understand the level of maturity of an idea or method, to help track the evolution of readiness of a given technique or method, and to help filter published articles by the expected ease of implementation in an operation setting within a crime lab.