Quality tracing of peanuts using an array of metal-oxide based gas sensors combined with chemometrics methods

Quality tracing models were set up for both unshelled peanuts and peanut kernels by applying an array of 18 metal-oxide (MOX) based gas sensors. Acid value, peroxide value and content of crude fat of the peanuts at different storage times were measured by traditional methods as a reference. Classification results for both unshelled peanuts and peanut kernels at different storage times based on Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) were acceptable Storage time, acid value, peroxide value and content of crude fat of peanuts were predicted by Partial Least Squares Regression (PLSR) and SVM on the basis of different normalized datasets. Original datasets, datasets normalized in [0,1] and in [−1,1] were considered. PLSR and SVM provided better prediction results when normalized in [0,1] and [−1,1], respectively. Correlations between adulterated levels (stale peanuts blended in fresh peanuts at levels of 0%, 25%, 50%, 75% and 100%) and sensor signals were researched by PLSR and SVM. It was found that the sensor signals and adulterated levels exhibited good correlation (R2 > 0.801 for training and testing sets by both methods). Meanwhile, The R2 for training and testing sets were 0.941 and 0.896 by applying SVM, respectively, and both of them were correspondingly higher than the R2 for training and testing sets by PLSR (training: R2 = 0.812; testing: R2 = 0.802). The research indicates that the 18 MOX based gas sensors combined with appropriate chemometrics methods can be used as a non-destructive method in detecting peanut quality.