Department of Chemistry, University at Albany, State University of New York , 1400 Washington Avenue, Albany, New York 12222, United States.
Anal Chem. 2016 Aug 2;88(15):7453-6. doi: 10.1021/acs.analchem.6b01173. Epub 2016 Jun 27.
Bearing in mind forensic purposes, a nondestructive and rapid method was developed for race differentiation of peripheral blood donors. Blood is an extremely valuable form of evidence in forensic investigations so proper analysis is critical. Because potentially miniscule amounts of blood traces can be found at a crime scene, having a method that is nondestructive, and provides a substantial amount of information about the sample, is ideal. In this study Raman spectroscopy was applied with advanced statistical analysis to discriminate between Caucasian (CA) and African American (AA) donors based on dried peripheral blood traces. Spectra were collected from 20 donors varying in gender and age. Support vector machines-discriminant analysis (SVM-DA) was used for differentiation of the two races. An outer loop subject-wise cross-validation (CV) method evaluated the performance of the SVM classifier for each individual donor from the training data set. The performance of SVM-DA, evaluated by the area under the curve (AUC) metric, showed 83% probability of correct classification for both races, and a specificity and sensitivity of 80%. This preliminary study shows promise for distinguishing between different races of human blood. The method has great potential for real crime scene investigation, providing rapid and reliable results, with no sample preparation, destruction, or consumption.
考虑到法医学的目的，开发了一种非破坏性和快速的方法来区分外周血供体的种族。血液是法医学调查中极其有价值的证据形式，因此正确的分析至关重要。因为在犯罪现场可能会发现极小量的血液痕迹，所以拥有一种非破坏性的方法，并能提供关于样本的大量信息是理想的。在这项研究中，拉曼光谱结合先进的统计分析被应用于根据干燥的外周血痕迹区分白人和非裔美国人供体。从 20 名不同性别和年龄的供体中采集了光谱。支持向量机-判别分析（SVM-DA）用于区分这两个种族。外循环受试者交叉验证（CV）方法用于评估 SVM 分类器对训练数据集中每个个体供体的性能。SVM-DA 的性能，通过曲线下面积（AUC）指标进行评估，显示出对两种种族的正确分类概率为 83%，特异性和敏感性分别为 80%。这项初步研究表明，该方法有望区分不同种族的人类血液。该方法具有很大的潜力用于实际的犯罪现场调查，提供快速可靠的结果，无需样品制备、破坏或消耗。
