Fonseca Aline C S, Pereira José F Q, Honorato Ricardo S, Bro Rasmus, Pimentel Maria Fernanda
Federal University of Pernambuco, Department of Fundamental Chemistry, Av, Jornalista Aníbal Fernandes, 50.740-560, Cidade Universitária, Recife, Brazil.
Federal University of Pernambuco, Department of Fundamental Chemistry, Av, Jornalista Aníbal Fernandes, 50.740-560, Cidade Universitária, Recife, Brazil; State University of Campinas, Institute of Chemistry, Campinas, P.O. Box 6154, 13083-970, Brazil.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Feb 15;267(Pt 1):120533. doi: 10.1016/j.saa.2021.120533. Epub 2021 Oct 28.
One of the most important types of evidence in certain criminal investigations is traces of human blood. For a detailed investigation, blood samples must be identified and collected at the crime scene. The present study aimed to evaluate the potential of the identification of human blood in stains deposited on different types of floor tiles (five types of ceramics and four types of porcelain tiles) using a portable NIR instrument. Hierarchical models were developed by combining multivariate analysis techniques capable of identifying traces of human blood (HB), animal blood (AB) and common false positives (CFP). The spectra of the dried stains were obtained using a portable MicroNIR spectrometer (Viavi). The hierarchical models used two decision rules, the first to separate CFP and the second to discriminate HB from AB. The first decision rule, used to separate the CFP, was based on the Q-Residual criterion considering a PCA model. For the second rule, used to discriminate HB and AB, the Q-Residual criterion were tested as obtained from a PCA model, a One-Class SIMCA model, and a PLS-DA model. The best results of sensitivity and specificity, both equal to 100%, were obtained when a PLS-DA model was employed as the second decision rule. The hierarchical classification models built for these same training sets using a PCA or SIMCA model also obtained excellent sensitivity results for HB classification, with values above 94% and 78% of specificity. No CFP samples were misclassified. Hierarchical models represent a significant advance as a methodology for the identification of human blood stains at crime scenes.
在某些刑事调查中，最重要的证据类型之一是人类血液痕迹。为了进行详细调查，必须在犯罪现场识别并采集血样。本研究旨在评估使用便携式近红外仪器识别沉积在不同类型地砖（五种陶瓷和四种瓷砖）上的血迹中人类血液的潜力。通过结合能够识别人类血液（HB）、动物血液（AB）和常见假阳性（CFP）痕迹的多变量分析技术，开发了层次模型。使用便携式微型近红外光谱仪（Viavi）获取干燥污渍的光谱。层次模型使用了两条决策规则，第一条用于区分CFP，第二条用于区分HB和AB。用于区分CFP的第一条决策规则基于考虑主成分分析（PCA）模型的Q残差准则。对于用于区分HB和AB的第二条规则，测试了从PCA模型、单类软独立建模类比法（One-Class SIMCA）模型和偏最小二乘判别分析（PLS-DA）模型获得的Q残差准则。当采用PLS-DA模型作为第二条决策规则时，获得了灵敏度和特异性均等于100%的最佳结果。使用PCA或SIMCA模型为这些相同训练集构建的层次分类模型在HB分类方面也获得了出色的灵敏度结果，特异性值分别高于94%和78%。没有CFP样本被误分类。层次模型作为一种在犯罪现场识别人类血迹的方法，代表了一项重大进展。
