Macro-micro exploration on dynamic interaction between aflatoxigenic Aspergillus flavus and maize kernels using
Yao Lu1, Beibei Jia2, Seung-Chul Yoon3
1College of Mechanical and Electrical Engineering, Shandong Intelligent Engineering Laboratory of Agricultural Equipment, Shandong Agricultural University, Tai'an 271018, China.
International Journal of Food Microbiology
|March 8, 2024
Summary
This study used visible/near infrared hyperspectral imaging and scanning electron microscopy to understand Aspergillus flavus infection dynamics in maize kernels. The findings enable early detection of fungal contamination and aflatoxins, ensuring food safety.
Area of Science:
- Agricultural Science
- Mycology
- Food Science
Background:
- Aspergillus flavus contamination of maize poses significant risks to grain safety and human health due to aflatoxin production.
- The complex interactions between fungal growth, nutrient depletion, and aflatoxin synthesis in maize remain poorly understood.
- Understanding these dynamics is crucial for developing effective detection and control strategies.
Purpose of the Study:
- To elucidate the dynamic mechanisms of Aspergillus flavus infection and aflatoxin production in maize kernels.
- To investigate organismal interactions at both macro (kernel) and micro (cellular) levels.
- To develop predictive models for fungal growth stages and aflatoxin levels.
Main Methods:
- Visible/near infrared (Vis/NIR) hyperspectral imaging (HSI) was employed to analyze spectral changes in infected maize kernels over time.
- Scanning Electron Microscopy (SEM) was used to visualize fungal morphology and interactions with maize tissues at a microscopic level.
- Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLSDA), and Partial Least Squares Regression (PLSR) were applied for data analysis and model development.
Main Results:
- Vis/NIR-HSI successfully captured dynamic changes in the A. flavus-maize kernel complex, with PCA revealing infection progression.
- SEM provided detailed insights into fungal structures (hyphae, conidia) and nutrient loss from maize tissues (embryo, endosperm).
- High accuracy models were developed for growth stage discrimination (CCR > 93%) and AFB1 prediction (R² > 0.93), with good performance indicated by RPD = 3.58.
Conclusions:
- Combined macro- and micro-level analyses revealed dynamic organismal interactions during A. flavus infection of maize.
- The developed models offer a theoretical foundation for early detection of fungal and aflatoxin contamination in grains.
- This research contributes to ensuring food security by providing tools for rapid and accurate assessment of grain quality.
