{"title":"烟纸作为证据:使用 ATR-FTIR 光谱和机器学习算法进行法医特征分析。","authors":"","doi":"10.1016/j.forsciint.2024.112182","DOIUrl":null,"url":null,"abstract":"<div><p>This research highlights the underestimated significance of cigarette paper as evidence at crime scenes. The primary objective is to distinguish cigarette paper from similar-looking alternatives, addressing the first research objective. The second objective involves identifying cigarette paper brands using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and machine learning (ML) algorithms. Accurate differentiation of cigarette paper from normal paper is emphasized. ATR-FTIR spectroscopy, coupled with principal component analysis (PCA) for dimensionality reduction, is employed for brand identification. Among fifteen ML algorithms compared, the CatBoost classifier excels for both objectives. This research presents a non-destructive, effective method for studying cigarette paper, contributing valuable insights to crime scene investigations.</p></div>","PeriodicalId":12341,"journal":{"name":"Forensic science international","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cigarette paper as evidence: Forensic profiling using ATR-FTIR spectroscopy and machine learning algorithms\",\"authors\":\"\",\"doi\":\"10.1016/j.forsciint.2024.112182\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This research highlights the underestimated significance of cigarette paper as evidence at crime scenes. The primary objective is to distinguish cigarette paper from similar-looking alternatives, addressing the first research objective. The second objective involves identifying cigarette paper brands using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and machine learning (ML) algorithms. Accurate differentiation of cigarette paper from normal paper is emphasized. ATR-FTIR spectroscopy, coupled with principal component analysis (PCA) for dimensionality reduction, is employed for brand identification. Among fifteen ML algorithms compared, the CatBoost classifier excels for both objectives. This research presents a non-destructive, effective method for studying cigarette paper, contributing valuable insights to crime scene investigations.</p></div>\",\"PeriodicalId\":12341,\"journal\":{\"name\":\"Forensic science international\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Forensic science international\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0379073824002639\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, LEGAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Forensic science international","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0379073824002639","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, LEGAL","Score":null,"Total":0}
引用次数: 0
摘要
这项研究强调了香烟纸作为犯罪现场证据的重要性,但这一重要性被低估了。主要目的是区分卷烟纸和外观相似的替代品,从而实现第一个研究目标。第二个目标是利用衰减全反射傅立叶变换红外光谱(ATR-FTIR)和机器学习(ML)算法识别卷烟纸品牌。重点是准确区分卷烟纸和普通纸。ATR-FTIR 光谱与用于降维的主成分分析(PCA)相结合,用于品牌识别。在所比较的 15 种 ML 算法中,CatBoost 分类器在实现这两个目标方面都很出色。这项研究提出了一种研究卷烟纸的非破坏性有效方法,为犯罪现场调查提供了有价值的见解。
Cigarette paper as evidence: Forensic profiling using ATR-FTIR spectroscopy and machine learning algorithms
This research highlights the underestimated significance of cigarette paper as evidence at crime scenes. The primary objective is to distinguish cigarette paper from similar-looking alternatives, addressing the first research objective. The second objective involves identifying cigarette paper brands using attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy and machine learning (ML) algorithms. Accurate differentiation of cigarette paper from normal paper is emphasized. ATR-FTIR spectroscopy, coupled with principal component analysis (PCA) for dimensionality reduction, is employed for brand identification. Among fifteen ML algorithms compared, the CatBoost classifier excels for both objectives. This research presents a non-destructive, effective method for studying cigarette paper, contributing valuable insights to crime scene investigations.
期刊介绍:
Forensic Science International is the flagship journal in the prestigious Forensic Science International family, publishing the most innovative, cutting-edge, and influential contributions across the forensic sciences. Fields include: forensic pathology and histochemistry, chemistry, biochemistry and toxicology, biology, serology, odontology, psychiatry, anthropology, digital forensics, the physical sciences, firearms, and document examination, as well as investigations of value to public health in its broadest sense, and the important marginal area where science and medicine interact with the law.
The journal publishes:
Case Reports
Commentaries
Letters to the Editor
Original Research Papers (Regular Papers)
Rapid Communications
Review Articles
Technical Notes.