Abstract:

This study aimed to explore a quantitative method for detecting the contents of zearalenone and aflatoxin B1 in
moldy corn using electronic nose. Firstly, the integral values of the electronic nose response signals of corn samples with
different mildew levels were extracted and used as feature parameters for establishing a predictive model for predicting
the contents of zearalenone and aflatoxin B1 in moldy corn samples employing principal component regression (PCR),
partial least squares regression (PLSR), back-propagation (BP) neural network, and least squares support vector machine
(LS-SVM), respectively. The results from the different models developed were compared. It was shown that the prediction
accuracy of the PCR model was the worst among four models, the PLSR model had better prediction accuracy, and the
BP neural network and LS-SVM models provided the most accurate predictions. The PCR, PLSR, BP neural network and
LS-SVM models gave good predictions of zearalenone with relative errors less than 5% for 23, 45, 63, and 67 out of 70
samples, respectively, while they provided good predictions of aflatoxin B1 with relative errors less than 5% for 19, 41, 62
and 65 out of the 70 samples. At the same time, different training and test sets were used to examine the robustness of the BP
neural network and LS-SVM models. The results showed that the BP neural network architecture, and the kernel function
and kernel parameter of LS-SVM remained unchanged. The prediction accuracy of the two models was still good, showing
that both models are of high robustness.how

Key words: electronic nose, zearalenone, aflatoxin B1, partial least squares regression, BP neural network, least squares support vector machinem