Detection of Aflatoxin B1 in Single Peanut Kernels by Combining Hyperspectral and Microscopic Imaging Technologies

To study the dynamic changes of nutrient consumption and aflatoxin B-1 (AFB(1)) accumulation in peanut kernels with fungal colonization, macro hyperspectral imaging technology combined with microscopic imaging was investigated. First, regression models to predict AFB(1) contents from hyperspectral data ranging from 1000 to 2500 nm were developed and the results were compared before and after data normalization with Box-Cox transformation. The results indicated that the second-order derivative with a support vector regression (SVR) model using competitive adaptive reweighted sampling (CARS) achieved the best performance, with R-C(2) = 0.95 and R-V(2) = 0.93. Second, time-lapse microscopic images and spectroscopic data were captured and analyzed with scanning electron microscopy (SEM), transmission electron microscopy (TEM), and synchrotron radiation-Fourier transform infrared (SR-FTIR) microspectroscopy. The time-lapse data revealed the temporal patterns of nutrient loss and aflatoxin accumulation in peanut kernels. The combination of macro and micro imaging technologies proved to be an effective way to detect the interaction mechanism of toxigenic fungus infecting peanuts and to predict the accumulation of AFB(1) quantitatively.

Automated summary

This study investigated the dynamic changes of nutrient consumption and AFB(1) accumulation in peanut kernels with fungal colonization using macro hyperspectral imaging technology combined with microscopic imaging. Regression models were developed to predict AFB(1) contents based on hyperspectral data. Time-lapse microscopic images and spectroscopic data were analyzed to reveal temporal patterns of nutrient loss and AFB(1) accumulation. The combination of macro and micro imaging technologies proved to be an effective method for detecting the interaction mechanism of toxigenic fungus infecting peanuts and predicting AFB(1) accumulation quantitatively.