|ZHANG, HAICHENG - China Agricultural University|
|JIA, BEIBEI - Chinese Academy Of Inspection And Quarantine|
|LU, YAO - China Agricultural University|
|GUO, XIAOHUAN - China Agricultural University|
|WANG, WEI - China Agricultural University|
Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/24/2022
Publication Date: 6/27/2022
Citation: Zhang, H., Jia, B., Lu, Y., Yoon, S.C., Ni, X., Zhuang, H., Guo, X., Wang, W. 2022. Detection of aflatoxin B1 in single peanut kernels by combining hyperspectral and microscopic imaging technologies. Sensors. 22(13):4864. https://doi.org/10.3390/s22134864.
Interpretive Summary: Aflatoxins are dangerous toxins produced by Aspergillus fungi found on agricultural crops, such as corns and peanuts. Crops infected with Aflatoxins pose a great threat to humans and animals alike. Thus, there is a strong need for research on techniques and methods to detect and analyze contaminated nuts and grains rapidly and non-destructively . In this study, macro hyperspectral imaging technology was used to predict the amount of aflatoxin B1 (AFB1) in peanut kernels and combined with microscopic imaging techniques to analyze the dynamic changes of nutrient content in peanut kernels infected by AFB1. The goodness of fit of a macro hyperspectral image prediction model, measured in R square was 0.93 for validation data. Time-lapse microscopic data obtained by scanning electron microscope (SEM), transmission electron microscopy (TEM), and synchrotron radiation-Fourier transform infrared (SR-FTIR) microspectroscopy revealed the temporal patterns of nutrient loss and aflatoxin accumulation in peanut kernels. The study found that the combination of macro and micro imaging techniques would be an effective way to predict the accumulation of AFB1 and analyze the interaction mechanism between peanut kernels and aflatoxins.
Technical Abstract: To study the dynamic changes of nutrient consumption and aflatoxin B1 (AFB1) accumulation in peanut kernels with fungal colonization, macro hyperspectral imaging technology combined with microscopic imaging was investigated. First, regression models to predict AFB1 contents from hyperspectral data ranging from 1000 to 2500 nm were developed and results were compared before and after data normalization with Box-Cox transformation. The results indicated that the second-order derivative with support vector regression (SVR) model using competitive adaptive reweighted sampling (CARS) achieved the best performance with RC2 = 0.95 and RV2 = 0.93. Second, time-lapse microscopic images and spectroscopic data were captured and analyzed with scanning electron microscope (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 on peanuts and predict the accumulation of AFB1 quantitatively.