Research on Detection of Multi-adulteration of Sesame Oils by Near-infrared Spectroscopy

Abstract

Aiming at the adulteration of sesame oil, our research is focused on using near-infrared spectroscopy combined with chemometrics to achieve rapid detection. The near-infrared spectrum of the sample was collected, and the data analysis and modeling were conducted on MATLAB. The raw spectral data was pretreated using multiplicative scatter correction (MSC) and standard normal variate (SNV). Support vector machine (SNM) model was established by using competitive adaptive reweighed sampling (CARS) and combined synergy interval partial least squares (SiPLS) to select characteristic spectral data. The highest correct recognition rate of the qualitative model is 100%, the mean square error MSE of the quantitative model is 0.0829, and the correlation error R is 99.0772%. The results prove that the support vector machine classification model established by near-infrared spectroscopy combined with chemometrics can qualitatively detect whether sesame oil is adulterated. In the meanwhile, the SNM model can quantitatively predict the content of active components.