Infrared Spectroscopy Combined with Chemometrics to Test Ink Type of Straight Liquid Ballpoint Pen
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摘要: 检验鉴定书写墨水是法庭科学领域中一项重要的工作。利用傅里叶变换红外光谱仪对市面上常见的20种直液式走珠笔墨水样品进行检验，采取化学计量学实现了对直液式走珠笔墨水的快速检验。对傅里叶变换红外光谱数据做标准化处理，采取自动基线矫正、峰面积归一化、Savitzky-Golay 5点平滑3种方法对谱图做预处理，通过误差平方和指标（sum of the squares errors, SSE）确定分类K的最优取值，对样品进行K-均值聚类分析并解释聚类结果。采取主成分分析方法验证K-均值聚类的结果，利用组均值均等检验考察主成分变量对Fisher判别分析（Fisher discriminant analysis, FDA）模型的贡献程度，构建直液式走珠笔墨水的FDA判别模型。结果表明，K-均值聚类将全部墨水样本聚类为3类，PCA-FDA（principal components analysis-fisher discriminant analysis）模型能够对不同类别直液式走珠笔墨水达到100%的预测分类，经交叉验证后正确率达到100%。红外光谱与PCA-FDA模型结合可用于直液式走珠笔墨水的快速、准确检验鉴定。Abstract: The inspection and identification of writing ink are important in the field of forensic science. A Fourier transform infrared spectrometer was used to test 20 types of direct liquid ball pen ink samples, and chemometrics was used to rapidly test the direct liquid ball pen ink. The Fourier transform infrared spectrum data were standardized, and the spectrum was pre-processed using three methods: automatic baseline correction, peak area normalization, and Savitzky-Golay 5-point smoothing. The optimal value of classification K was determined using the sum of squares error(SSE). The samples were analyzed using K-means clustering, and the clustering results were explained. The principal component analysis method was used to verify the results of K-means clustering. The group mean equality test was used to investigate the contribution of principal component variables to the Fisher discriminant analysis (FDA) model, and the FDA discriminant model of straight liquid ball pen ink was constructed. The results show that all the ink samples were clustered into three categories using K-means clustering. The principal components analysis–Fisher discriminant analysis (PCA–FDA) model achieved 100% prediction and classification of different categories of straight-liquid ball pen inks with an accuracy of 100% after cross-validation. Infrared spectroscopy combined with the PCA–FDA model can be used for rapid and accurate inspection and identification of direct liquid ball pen inks.
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表 1 样品聚类结果
Table 1. Clustering results of samples
Classification Sample number 1
21#、2#、3#、4#、5#、6#、7#、8#、9#、10#
11#、12#、13#3 14#、15#、16#、17#、18#、19#、20# 
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表 2 各组平均值的均等性检验
Table 2. Equality verification of the average value of each group
Variable Wilks Lambda F df1 df2 Sig PC1 0.108 69.946 2 17 0.000 PC2 0.061 131.335 2 17 0.000 PC3 0.974 0.223 2 17 0.802 PC4 0.975 0.217 2 17 0.807 PC5 0.988 0.106 2 17 0.900 PC6 0.998 0.021 2 17 0.980
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表 3 Fisher判别函数摘要
Table 3. Summary of Fisher's discriminant functions
Function Variance contribution% Cumulative contribution% Correlation Function test Wilks’Lambda Sig F1 81.0 81.0 0.990 1 to 2 0.002 0.000 F2 19.0 100.0 0.958 2 0.083 0.000
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表 4 分类结果
Table 4. Classification results
Categories Predicted 1 2 3
Observed1 10(10) 0 0 2 0 3(3) 0 3 0 0 7(7)
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