Rancidity Prediction of Soybean Oil by Using Near-Infrared Spectroscopy Techniques

Purpose: This study evaluated the feasibility of a near-infrared spectroscopy technique for the rancidity prediction of soybean oil. Methods: A near-infrared spectroscopy technique was used to evaluate the rancidity of soybean oils which were artificially deteriorated. A soybean oil sample was collected, and the acid values were measured using titrimetric analysis. In addition, the transmission spectra of the samples were obtained for whole test periods. The prediction model for the acid value was constructed by using a partial least-squares regression (PLSR) technique and the appropriate spectrum preprocessing methods. Furthermore, optimal wavelength selection methods such as variable importance in projection (VIP) and bootstrap of beta coefficients were applied to select the most appropriate variables from the preprocessed spectra. Results: There were significantly different increases in the acid values from the sixth days onwards during the 14-day test period. In addition, it was observed that the NIR spectra that exhibited intense absorption at 1,195 nm and 1,410 nm could indicate the degradation of soybean oil. The PLSR model developed using the Savitzky-Golay $2^{nd}$ order derivative method for preprocessing exhibited the highest performance in predicting the acid value of soybean oil samples. onclusions: The study helped establish the feasibility of predicting the rancidity of the soybean oil (using its acid value) by means of a NIR spectroscopy together with optimal variable selection methods successfully. The experimental results suggested that the wavelengths of 1,150 nm and 1,450 nm, which were highly correlated with the largest absorption by the second and first overtone of the C-H, O-H stretch vibrational transition, were caused by the deterioration of soybean oil.