Macro-micro exploration on dynamic interaction between aflatoxigenic Aspergillus flavus and maize kernels using Vis/NIR hyperspectral imaging and SEM technology.

Aspergillus flavus and its toxic metabolites-aflatoxins infect and contaminate maize kernels, posing a threat to grain safety and human health. Due to the complexity of microbial growth and metabolic processes, dynamic mechanisms among fungal growth, nutrient depletion of maize kernels and aflatoxin production is still unclear. In this study, visible/near infrared (Vis/NIR) hyperspectral imaging (HSI) combined with the scanning electron microscope (SEM) was used to elucidate the critical organismal interaction at kernel (macro-) and microscopic levels. As kernel damage is the main entrance for fungal invasion, maize kernels with gradually aggravated damages from intact to pierced to halved kernels with A. flavus were cultured for 0-120 h. The spectral fingerprints of the A. flavus-maize kernel complex over time were analyzed with principal components analysis (PCA) of hyperspectral images, where the pseudo-color score maps and the loading plots of the first three PCs were used to investigate the dynamic process of fungal infection and to capture the subtle changes in the complex with different hardness of the maize matrix. The dynamic growth process of A. flavus and the interactions of fungus-maize complexes were explained on a microscopic level using SEM. Specifically, fungus morphology, e.g., hyphae, conidia, and conidiophore (stipe) was accurately captured on the microscopic level, and the interaction process between A. flavus and nutrient loss from the maize kernel tissues (i.e., embryo, and endosperm) was described. Furthermore, the growth stage discrimination models based on PLSDA with the results of CCR = 100 %, CCR = 97 %, CCR = 93 %, and the prediction models of AFB based on PLSR with satisfactory performance (R = 0.96, R = 0.95, R = 0.93 and RPD = 3.58) were both achieved. In conclusion, the results from both macro-level (Vis/NIR-HSI) and micro-level (SEM) assessments revealed the dynamic organismal interactions in A. flavus-maize kernel complex, and the detailed data could be used for modeling, and quantitative prediction of aflatoxin, which would establish a theoretical foundation for the early detection of fungal or toxin contaminated grains to ensure food security.