Capsaicin is the key composition of pepper and can be used as a marker of gutter oil for detection. The feasibility of rapid detection of capsaicin concentration in soybean oil was studied by terahertz spectroscopy. Genetic algorithm (GA) and principal component analysis (PCA) as the pretreatment method were used to obtain the best spectral features. Least square-support vector machine (LS-SVM), back propagation neural network (BPNN), and partial least squares (PLS) were combined with the pretreatment method to obtain the best determination model. The BPNN was combined with GA to obtain the best quantitative prediction results with the correlation coefficient of prediction (R P ), prediction root mean square error (RMSEP), the ratio of prediction to deviation (RPD), and range error ratio (RER) were 0.9309, 0.4030 µg/kg, 17.0421, and 2.4813, respectively. Furthermore, the detection limit of capsaicin could achieve 1.25 µg/kg in soybean oil and the accuracy of discrimination was up to 100% in the prediction set using the LS-SVM combined with GA pre-treatment. The results suggested that terahertz spectroscopy together with chemometric methods would be a promising technique for rapid determination of capsaicin concentration in soybean oil. Meanwhile, it is necessary to perform further experiments with real gutter oil samples before applying the method in practice.
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