Quantitative detection of azodicarbonamide in wheat flour by near-infrared spectroscopy based on two-step feature selection

Abstract

Excessive azodicarbonamide (ADA) in flour would do harm to the health of consumers. How to quickly and accurately detect the content of ADA in flour was of great significance. Based on the rapid, efficient and non-destructive advantages of near-infrared spectroscopy (NIRS) technology in material detection, the NIRS technology was used to quantitatively detect ADA in 101 wheat flour samples in this study. Firstly, after eliminating 1 abnormal sample with isolation forest (IF) method, the remaining 100 sample sets were divided into the training set and the prediction set using sample set partitioning based on joint x-y distances (SPXY) method. Then, the prediction performance of the three models under various spectral preprocessing methods and combined methods were compared. Among them, the performance of random forest (RF) model combined with second derivative (2D) was proved to be the best. Then, RReliefF and maximal information coefficient (MIC) were used for the first-step feature selection, respectively. On this basis, the second-step feature selection was performed based on the elastic net (EN). Among them, the performance of MIC ​+ ​EN was proved to be the best, and 40 effective features were selected. Finally, the 2D ​+ ​MIC ​+ ​EN ​+ ​RF model was established to predict the content of ADA in wheat flour. The coefficient of determination (R2), root mean square error of prediction (RMSEP) and relative percent difference (RPD) of the model on the prediction set reached 0.99814, 2.91345, and 23.54332, respectively. The results showed that NIRS technology could accurately detect the content of ADA in flour, and the two-step feature selection method could be effectively used for feature selection.