Beijing Key Laboratory of Optimization Design for Modern Agricultural Equipment, College of Engineering, China Agricultural University, Beijing 100083, China.
Key Laboratory of Food Quality and Safety for State Market Regulation, Institute of Food Safety, Chinese Academy of Inspection and Quarantine, Beijing 100176, China.
Sensors (Basel). 2022 Jun 27;22(13):4864. doi: 10.3390/s22134864.
To study the dynamic changes of nutrient consumption and aflatoxin B (AFB) accumulation in peanut kernels with fungal colonization, macro hyperspectral imaging technology combined with microscopic imaging was investigated. First, regression models to predict AFB 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 = 0.95 and R = 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 quantitatively.
为了研究真菌定殖时花生仁中营养物质消耗和黄曲霉毒素 B（AFB）积累的动态变化，本研究采用宏观高光谱成像技术结合微观成像技术进行研究。首先，建立了从 1000 至 2500nm 范围的高光谱数据预测 AFB 含量的回归模型，并比较了 Box-Cox 变换前后数据归一化的结果。结果表明，采用支持向量回归（SVR）模型和竞争自适应重加权抽样（CARS）的二阶导数表现出最佳性能，R2 值分别为 0.95 和 0.93。其次，通过扫描电子显微镜（SEM）、透射电子显微镜（TEM）和同步辐射傅里叶变换红外（SR-FTIR）微光谱技术，对时变微观图像和光谱数据进行了采集和分析。时变数据揭示了花生仁中营养物质损失和 AFB 积累的时间模式。宏观和微观成像技术的结合被证明是一种有效的方法，可以检测产毒真菌感染花生的相互作用机制，并定量预测 AFB 的积累。
