Infrared Spectroscopy and Machine Learning for Classification of Red Stamp Inks on Questioned Documents

IF 2.1 4区 化学 Q1 SOCIAL WORK
Yong Ju Lee, Chang Woo Jeong, Mi Jung Choi, Tai-Ju Lee, Hyoung Jin Kim
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Abstract

This study demonstrates that integrating infrared spectroscopy with machine learning enables highly accurate, nondestructive classification of red-stamp ink manufacturers. We evaluated five classifiers—partial least squares discriminant analysis (PLS-DA), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and a feed-forward neural network (FNN)—across multiple spectral regions. The FNN trained on second-derivative spectra in the 1700–900 cm−1 region achieved perfect test metrics (F1 = 1.000; AUC = 1.000), while PLS-DA and RF also performed robustly (F1 ≥ 0.933). Variable importance in projection (VIP) analysis identified the 1650–1100 cm−1 subrange as most informative, streamlining feature selection and model training. Applied to three unknown samples, the optimized FNN produced high-confidence manufacturer predictions consistent with expected origins. These results confirm that targeted spectral selection combined with derivative preprocessing markedly enhances nondestructive ink classification for forensic applications.

Abstract Image

Abstract Image

红外光谱和机器学习对可疑文件上红色印章墨水的分类
这项研究表明,将红外光谱与机器学习相结合,可以对红章油墨制造商进行高度准确、无损的分类。我们评估了五种分类器-偏最小二乘判别分析(PLS-DA), k-近邻(k-NN),支持向量机(SVM),随机森林(RF)和前馈神经网络(FNN) -跨越多个光谱区域。在1700-900 cm−1区域的二阶导数光谱上训练的FNN获得了完美的测试指标(F1 = 1.000;AUC = 1.000), PLS-DA和RF也表现良好(F1≥0.933)。可变重要性投影(VIP)分析确定了1650-1100 cm−1的子范围是信息量最大的,简化了特征选择和模型训练。应用于三个未知样本,优化后的FNN产生与预期原点一致的高置信度制造商预测。这些结果证实,结合导数预处理的目标光谱选择显着增强了无损油墨分类在法医应用中的应用。
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来源期刊
Journal of Chemometrics
Journal of Chemometrics 化学-分析化学
CiteScore
5.20
自引率
8.30%
发文量
78
审稿时长
2 months
期刊介绍: The Journal of Chemometrics is devoted to the rapid publication of original scientific papers, reviews and short communications on fundamental and applied aspects of chemometrics. It also provides a forum for the exchange of information on meetings and other news relevant to the growing community of scientists who are interested in chemometrics and its applications. Short, critical review papers are a particularly important feature of the journal, in view of the multidisciplinary readership at which it is aimed.
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