Quantitative Analysis of Peanut Oil Adulteration Based on Data Fusion of Multi-source Spectra

Abstract

This study aimed to analyze peanut oil adulteration based on chemometrics combined Raman and near-infrared (NIR) spectrum. The spectral data of 134 adulterated oil samples were collected by laser Raman and NIR spectrometer. The Raman spectra data and NIR were preprocessed respectively by different preprocessing approaches. The backward interval partial least squares (BiPLS) method was applied to extract the featured wavelengths. Based on the full spectrum and the characteristic wavelengths, adulteration quantity prediction models were established by the support vector machine regression (SVR) method. According to the analysis, the SVR model could predict the adulteration content in the peanut oil. Furthermore, the correlation coefficient R was greater than 0.97, and the mean square error (MSE) was smaller than 3.2E-4. The SVR model had advantageous properties such as strong generalization ability and good prediction accuracy. The results showed that Raman and NIR fusion analysis was effective in the quantitative analysis of the adulteration in peanut oil. Multi-spectral analysis for edible oil adulteration is an important field of study.