基于DT-KNN-FDA建模的车漆光谱无损鉴别

为了对车漆进行快速、高效、低成本的无损鉴别，采用一种基于指纹区红外吸收光谱结合决策树、k近邻和Fisher判别分析(DT-KNN-FDA)建模的鉴别方法，进行了理论分析和实验验证。收集并取得了车漆共计60个样本的红外吸收光谱实验数据，通过对特征波数的选择，建立并比较了基于决策树、k近邻分析和Fisher判别分析的多分类模型。通过相关性分析提取到了58组调整数据，并以此为基础构建了分类模型。结果表明，DT分类模型、KNN分类模型和FDA分类模型对各样本的总体区分准确率分别为77.80%，72.31%和85.00%；红外光谱结合DT-KNN-FDA分析可实现对车漆不同品牌产品间的区分，分类效果理想。该方法快捷、低耗、有效，具有一定的普适性和参考意义。

Research on non-destructive identification about vehicle paints by DT-KNN-FDA

An identification method based on fingerprint spectroscopy combined with decision tree, k-nearest neighbor, and Fisher discriminant analysis (DT-KNN-FDA) model was proposed to achieve the rapid and non-destructive identification of the vehicle paints and performed by theoretical analysis and experimental verification. The infrared absorption spectroscopy for a total of 60 samples of car paint were collected and obtained as the experimental data. Through the selection of characteristic wave numbers, a multi-classification model based on the DT, KNN analysis, and FDA was established and compared. 58 sets of adjustment data were extracted through correlation analysis, and a classification model was constructed based on this. The results show that the overall discrimination accuracy of DT classification model, KNN classification model and FDA classification model for each sample is 77.80%, 72.31%, and 85.00%, respectively; infrared spectroscopy combined with DT-KNN-FDA analysis can realize the distinction between products of different brands is ideal for classification. This method is fast, accurate, and effective, and has certain universality and significance.