Non-Destructive Brand Identification of Car Wax Using Visible and Near-Infrared Spectroscopy

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.