探讨了可见-近红外光谱技术快速无损识别不同品牌车蜡的可行性。 实验一共获得104 样本， 其中40个样本（建模集）用于建立模型， 剩余64个样本（预测集）被用于独立验证建立好的模型。 基于五种不同品牌车蜡的可见-近红外光谱分别建立了线性判别分析（linear Discriminant Analysis， LDA）和最小二乘支持向量机（least square-support vector machine, LS-SVM）模型。 基于两个算法的全波段光谱模型的预测集正确率分别达到了84%和97%。 进一步采用连续投影算法（successive projections algorithm, SPA）算法从751波段中选取了7个特征波段（351, 365, 401, 441, 605, 926和980 nm）。 基于SPA选择的变量建立LS-SVM模型， 准确率依然保持在97%。 说明SPA选择的特征波段包含了对于车蜡品牌鉴别最重要的光谱信息， 而大多数无用信息则被有效剔除。 将SPA与LS-SVM算法的车蜡识别模型在保证正确率的基础上， 还可以大大降低模型计算复杂程度， 说明该模型能快速准确的从车蜡可见-近红外光谱中提取有效信息， 并实现车蜡品牌的无损鉴别。

Visible and near-infrared (Vis-NIR) spectroscopy was applied to identify brands of car wax. A total of 104 samples were obtained for the analysis, in which 40 samples (calibration set) were used for model calibration, and the remaining 64 samples (prediction set) were used to validate the calibrated model independently. Linear discriminant analysis (LDA) and least square-support vector machine (LS-SVM) were respectively used to establish identification models for car wax with five brands based on their Vis-NIR spectra. Correct rates for prediction sample set were 84% and 97% for LDA and LS-SVM models, respectively. Spectral variable selection was further conducted by successive projections algorithm, (SPA), resulting in seven feature variables (351, 365, 401, 441, 605, 926, and 980 nm) selected from full range spectra that had 751 variables. The new LS-SVM model established using the feature variables selected by SPA also had the correct rate of 97%, showing that the selected variables had the most important information for brand identification, while other variables with no useful information were eliminated efficiently. The use of SPA and LS-SVM could not only obtain a high correct identification rate, but also simplify the model calibration and calculation. SPA-LS-SVM model could extract the useful information from the Vis-NIR spectra of car wax rapidly and accurately for the non-destructive brand identification of car wax.