水性木器漆是一种犯罪现场常见的微量物证, 在法庭科学领域广受关注。为了实现对水性木器漆中复杂化学成分的检测分类, 采用具有较高分辨能力和无损检验特点的喇曼光谱, 结合主成分分析和径向基函数神经网络两种数据挖掘技术, 对3种品牌共38个水性木器漆样本的喇曼光谱进行了数据分析。结果表明, 径向基函数模型下可得到准确率为78.9%的分类识别。采用傅里叶变换喇曼光谱结合径向基函数模型实现对水性木器漆的鉴别与分类, 为实践中木器漆的分类研究提供新思路。

Waterborne wood coating is a kind of trace evidence commonly found in crime scenes, and it is widely concerned in the forensic science field. In order to detect and classify the complex chemical components in waterborne wood paints, Raman spectrum, which has high resolving power and non-destructive testing characteristics, were used in this study. Combined with two data mining techniques of principal component analysis and radial basis function neural network, the Raman spectra of 38 waterborne wood lacquer samples from 3 brands were analyzed. The results show that the classification accuracy of 78.9% is obtained under the radial basis function model. Fourier Raman spectroscopy combined with radial basis function model was used to identify and classify waterborne wood coating, which provided new ideas for the classification of wood lacquers in practice.