Simultaneous analysis of mildew degree and aflatoxin B1 of wheat by a multi-task deep learning strategy based on microwave detection technology

Abstract

This study proposes a novel qualitative and quantitative analysis method for wheat mildew degree and aflatoxin B1 (AFB1) by microwave detection technology combined with multi-task learning strategy. The team developed a miniaturized microwave detection device with double heterodyne mixing structure, and used this device to obtain the transmission indexes (amplitude and phase) of moldy wheat samples. A multi-task learning model based on convolutional neural network (CNN) structure was designed to realize the self-learning and model calibration of the transmission indexes, and to complete the classification and regression tasks of wheat mildew degree recognition and AFB1 content detection. The obtained results found that in the qualitative analysis, the accuracy, precision, recall, and F1-score of the fusion CNN model were all 100% during the prediction. In the quantitative detection, the RMSEP, RP2 and RPD of the fusion CNN model respectively were 2.0138 μg kg−1, 0.9807 and 7.2849. The overall results reveal that it is feasible to realize simultaneous analysis of the contamination level of wheat mycotoxins by microwave detection technology combined with deep learning, and the application of multi-task learning strategy can effectively avoid the waste of resources in the process of model calibration.