Abstract
In this study, different oxidation levels of peanut oils were prepared by heating different brands of oils to different times, the peroxide value (PV) and acid value (AV) were determined as reference values. The fluorescence intensity (F), absorption (µa) and reduced scattering coefficients (µ’s) of oils were obtained by using an independently-developed spectra measurement system, which based on laser induced fluorescence and integrating sphere techniques. Principal component analysis (PCA) were conducted on three kinds of spectra, the principal components (PCs) were extracted and clustering trend were analyzed. Finally the regression models for PV and AV based on different integrations of the first five PCs of three kinds of spectra were calibrated by using different algorithms: multiple linear regression (MLR), partial least squares regression (PLSR), support vector regression (SVR) and artificial neural network (ANN). The results indicated that the optimal prediction results could be achieved by ANN based on the integration of F, µa and µ’s for PV, and SVR based on the integration of F, µa and µ’s for AV, with maximum determination coefficients for validation set (R2v) of 0.873 and 0.854 respectively, and minimum root mean square errors for validation set (RMSEV) of 2.896 meq·kg− 1 and 0.154 mg·g− 1. The proposed novel method which considering the disentangling effect of µa and µ’s on fluorescence can realize robust detection for oxidation degree of peanut oils.