Department of Computer Science, Norwegian University of Science and Technology (NTNU), 2802, Gjøvik, Norway.
Sci Rep. 2021 Mar 22;11(1):6512. doi: 10.1038/s41598-021-85737-x.
Documentation and analysis of crime scene evidences are of great importance in any forensic investigation. In this paper, we present the potential of hyperspectral imaging (HSI) to detect and analyze the beverage stains on a paper towel. To detect the presence and predict the age of the commonly used drinks in a crime scene, we leveraged the additional information present in the HSI data. We used 12 different beverages and four types of paper hand towel to create the sample stains in the current study. A support vector machine (SVM) is used to achieve the classification, and a convolutional auto-encoder is used to achieve HSI data dimensionality reduction, which helps in easy perception, process, and visualization of the data. The SVM classification model was re-established for a lighter and quicker classification model on the basis of the reduced dimension. We employed volume-gradient-based band selection for the identification of relevant spectral bands in the HSI data. Spectral data recorded at different time intervals up to 72 h is analyzed to trace the spectral changes. The results show the efficacy of the HSI techniques for rapid, non-contact, and non-invasive analysis of beverage stains.
犯罪现场证据的记录和分析在任何法医学调查中都非常重要。在本文中，我们展示了高光谱成像（HSI）在检测和分析纸巾上饮料污渍方面的潜力。为了检测犯罪现场常见饮料的存在并预测其年龄，我们利用了 HSI 数据中存在的附加信息。我们使用了 12 种不同的饮料和 4 种类型的纸巾来制作当前研究中的样本污渍。支持向量机（SVM）用于实现分类，而卷积自动编码器用于实现 HSI 数据降维，这有助于数据的易于感知、处理和可视化。在此基础上，我们使用基于体积梯度的波段选择来识别 HSI 数据中的相关光谱波段。分析了在 72 小时内记录的不同时间间隔的光谱数据，以追踪光谱变化。结果表明，HSI 技术可用于快速、非接触和非侵入性地分析饮料污渍。
