{"title":"x射线荧光光谱客观比较方法的发展与评价。","authors":"Meghan Prusinowski PhD, Evie Nguyen MSFS, Cedric Neumann PhD, Tatiana Trejos PhD","doi":"10.1111/1556-4029.70105","DOIUrl":null,"url":null,"abstract":"<p>This study provides statistical support for X-ray Fluorescence (XRF) spectral comparisons using quantitative similarity measures. A set of electrical tapes originating from different rolls (94 rolls, 24 brands, 54 product types, four countries of manufacture) and an additional subset originating from the same source (20 samples from the same roll) are characterized via XRF. Noise in spectra is filtered using Fast Fourier Transform, and baselines are corrected using a second derivative–constrained weighted regression. Then, spectral contrast angle ratios (SCAR) are calculated for each pairwise comparison (<i>n</i> = 4561). The SCAR metric can capture information on the variability between the compared samples and the variability within same-source samples. Based on that measure, a threshold minimizing erroneous associations or exclusions is proposed. In addition, SCAR is used to classify samples using cluster analysis. An automated approach to sample comparison utilizing a random forest algorithm assists in identifying the basis for similarities or differences between compared spectra. This study describes a more objective approach to reporting opinions and probabilistic determinations of spectral data that can be used as a model for other fields and materials. The use of the SCAR metric can support the forensic examiner's decision-making process and add transparency in various ways.</p>","PeriodicalId":15743,"journal":{"name":"Journal of forensic sciences","volume":"70 5","pages":"1785-1800"},"PeriodicalIF":1.8000,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and evaluation of methods for objective comparison of x-ray fluorescence spectra\",\"authors\":\"Meghan Prusinowski PhD, Evie Nguyen MSFS, Cedric Neumann PhD, Tatiana Trejos PhD\",\"doi\":\"10.1111/1556-4029.70105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This study provides statistical support for X-ray Fluorescence (XRF) spectral comparisons using quantitative similarity measures. A set of electrical tapes originating from different rolls (94 rolls, 24 brands, 54 product types, four countries of manufacture) and an additional subset originating from the same source (20 samples from the same roll) are characterized via XRF. Noise in spectra is filtered using Fast Fourier Transform, and baselines are corrected using a second derivative–constrained weighted regression. Then, spectral contrast angle ratios (SCAR) are calculated for each pairwise comparison (<i>n</i> = 4561). The SCAR metric can capture information on the variability between the compared samples and the variability within same-source samples. Based on that measure, a threshold minimizing erroneous associations or exclusions is proposed. In addition, SCAR is used to classify samples using cluster analysis. An automated approach to sample comparison utilizing a random forest algorithm assists in identifying the basis for similarities or differences between compared spectra. This study describes a more objective approach to reporting opinions and probabilistic determinations of spectral data that can be used as a model for other fields and materials. The use of the SCAR metric can support the forensic examiner's decision-making process and add transparency in various ways.</p>\",\"PeriodicalId\":15743,\"journal\":{\"name\":\"Journal of forensic sciences\",\"volume\":\"70 5\",\"pages\":\"1785-1800\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2025-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of forensic sciences\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/1556-4029.70105\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICINE, LEGAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of forensic sciences","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/1556-4029.70105","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICINE, LEGAL","Score":null,"Total":0}
Development and evaluation of methods for objective comparison of x-ray fluorescence spectra
This study provides statistical support for X-ray Fluorescence (XRF) spectral comparisons using quantitative similarity measures. A set of electrical tapes originating from different rolls (94 rolls, 24 brands, 54 product types, four countries of manufacture) and an additional subset originating from the same source (20 samples from the same roll) are characterized via XRF. Noise in spectra is filtered using Fast Fourier Transform, and baselines are corrected using a second derivative–constrained weighted regression. Then, spectral contrast angle ratios (SCAR) are calculated for each pairwise comparison (n = 4561). The SCAR metric can capture information on the variability between the compared samples and the variability within same-source samples. Based on that measure, a threshold minimizing erroneous associations or exclusions is proposed. In addition, SCAR is used to classify samples using cluster analysis. An automated approach to sample comparison utilizing a random forest algorithm assists in identifying the basis for similarities or differences between compared spectra. This study describes a more objective approach to reporting opinions and probabilistic determinations of spectral data that can be used as a model for other fields and materials. The use of the SCAR metric can support the forensic examiner's decision-making process and add transparency in various ways.
期刊介绍:
The Journal of Forensic Sciences (JFS) is the official publication of the American Academy of Forensic Sciences (AAFS). It is devoted to the publication of original investigations, observations, scholarly inquiries and reviews in various branches of the forensic sciences. These include anthropology, criminalistics, digital and multimedia sciences, engineering and applied sciences, pathology/biology, psychiatry and behavioral science, jurisprudence, odontology, questioned documents, and toxicology. Similar submissions dealing with forensic aspects of other sciences and the social sciences are also accepted, as are submissions dealing with scientifically sound emerging science disciplines. The content and/or views expressed in the JFS are not necessarily those of the AAFS, the JFS Editorial Board, the organizations with which authors are affiliated, or the publisher of JFS. All manuscript submissions are double-blind peer-reviewed.