Quantitative analysis of aflatoxin B1 in moldy peanuts based on near-infrared spectra with two-dimensional convolutional neural network

Abstract

A two-dimensional convolutional neural network (2D-CNN) model based on Gramian angular summation field (GASF) image coding was proposed to detect aflatoxin B1 (AFB1) in moldy peanuts with high precision. Fourier transform near-infrared spectrometer was employed to acquire near-infrared spectra of moldy peanuts. The GASF was used to encode the NIR spectral features after principal component analysis. A convolutional neural network (CNN) framework was designed and the GASF-2D-CNN model was developed to realize the quantitative detection of AFB1 in peanuts. The results obtained showed that compared with the traditional chemometrics models and one-dimensional CNN (1D-CNN) model, the performance of GASF-2D-CNN model was significantly better than the previous two types of detection models. The root mean square error of prediction (RMSEP), coefficient of determination (RP2), relative percent deviation (RPD), and ratio of performance to interquartile range (RPIQ) of the best GASF-2D-CNN model were 2.0 μg·kg−1, 0.99, 8.3, and 9.3, respectively. The study reveals that GASF image coding technique can effectively mine the potential feature information in NIR spectra, and this technique will have a promising application prospect in the multivariable calibration of spectroscopy.