Abstract：In the present paper, a non-destructive, simple and rapid analytical method was proposed based on Raman spectroscopy (Raman) combined with principal component analysis (PCA) and support vector machine (SVM) as pattern recognition methods for adulteration of crude soybean oil (CSO). Based on fingerprint characteristics of Raman, the spectra of 28 CSOs, 46 refined edible oils (REOs) and 110 adulterated oil samples were analyzed and used for discrimination model establishment. The preprocessing methods include choosing spectral band of 780~1 800 cm-1, Y-axis intensity correction, baseline correction and normalization in succession. After those series of spectral pretreatment, PCA was usually employed for extracting characteristic variables of all Raman spectral data and 7 principal components which were the highest contributions of all data were used as variables for SVM model. The SVM discrimination model was established by randomly picking 20 CSOs and 95 adulterated oils as calibration set, and 8 CSOs and 35 adulterated oils as validation set. There were 4 kinds of kernel function algorithm (linear, polynomial, RBF, sigmoid) respectively used for establishing SVM models and grid-search for optimization of parameters of all the SVM models. The classification results of 4 models were compared by their discrimination performances and the optimal SVM model was based on linear kernel classification algorithm with 100% accuracy rate of calibration set recognition, a zero misjudgment rate and the lowest detection limit of 2.5%. The above results showed that Raman combined PCA-SVM could discriminate CSO adulteration with refined edible oils. Since Raman spectroscopy is simple, rapid, non-destructive, environment friendly, and suitable for field testing, it will provide an alternative method for edible oil adulteration analysis.
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