Abstract: The aim of this study was to explore the feasibility of identifying and quantifying adulterated sweet potato starch noodles (adulterated with cassava starch and corn starch) using near-infrared spectroscopy (NIRS) and support vector machine (SVM). Qualitative discrimination and quantitative analysis models based on NIRS and SVM were built, which were optimized by spectra pretreatment and spectral variables selection. The results showed that after using standard normal variate transformation and first derivative pretreatment, the accuracy of SVM model for qualitative discrimination based on whole NIRS spectra for identifying adulterated sweet potato starch noodles achieved 100%, which surpassed the Mahalanobis distance discriminant model. Moreover, by using a forward interval support vector machine (fi-SVM) algorithm to screen out spectral variables, the correlation coefficients (r) of the SVM models for cassava starch content and corn starch content reached 0.92 and 0.96, respectively. Besides, the root mean square error of prediction (RMSEP) of these two models reached 11.2 and 7.49, respectively. The results indicated that models based on NIRS and SVM for qualitative discrimination and quantified analysis for adulterated sweet potato starch noodles had high recognition rates and prediction accuracy. Therefore, it is feasible to detect adulteration of sweet potato starch noodles by using NIRS and SVM.
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