Detection of Aflatoxin B<sub>1</sub> in Single Peanut Kernels by Combining Hyperspectral and Microscopic Imaging Technologies
Haicheng Zhang,
Beibei Jia,
Yao Lu,
Seung-Chul Yoon,
Xinzhi Ni,
Hong Zhuang,
Xiaohuan Guo,
Wenxin Le,
Wei Wang
Affiliations
Haicheng Zhang
Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China
Beibei Jia
Key Laboratory of Food Quality and Safety for State Market Regulation, Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China
Yao Lu
Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China
Seung-Chul Yoon
Quality & Safety Assessment Research Unit, U.S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA
Xinzhi Ni
Crop Genetics and Breeding Research Unit, USDA-ARS, 2747 Davis Road, Tifton, GA 31793, USA
Hong Zhuang
Quality & Safety Assessment Research Unit, U.S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA
Xiaohuan Guo
Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China
Wenxin Le
Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China
Wei Wang
Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China
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 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 RC2 = 0.95 and RV2 = 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 AFB1 quantitatively.
