Predictive model of aflatoxin contamination risk associated with granary-stored corn with versicolorin A monitoring and logistic regression

ABSTRACT

Predictions of aflatoxin (AF) in grain at post-harvest can be useful for ensuring the safety of stored grain. Versicolorin (Ver) A, a precursor of AFB₁, can serve as an early indicator of AF contamination, even when AFs themselves are present at undetectable levels. In the current research, we developed a probabilistic model based on logistic regression and Ver A levels to estimate the risk of AF contamination in stored corn. Moisture content, aflatoxigenic fungal load, and initial and maximum values of Ver A in the first three sampling cycles were experimentally determined as the four important factors for the probabilistic model. Both internal and external model validations were shown to be high at 96.4% and 93.3%, respectively. For high-risk samples, a precise model was developed to predict the maximum period of safe storage, which can be useful for decision-making by the stakeholders in feed and food supply chain. Our findings provide a basis tool for establishing an early warning system for AF contamination in granaries, which can improve global food safety.

Graphical Abstract

Assay procedures of the molecular imprinted polymer based chemiluminescence method for determination of chloramphenicol

KEYWORDS:

• Aflatoxin
• aflatoxin contamination
• predictive mycology
• logistic regression
• versicolorin A

Supplementary material

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Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by The National Basic Research Program of China (973 Program) under the Grant 2013CB127804, which focused on early monitoring and predictive modelling of mycotoxins on storage crops, and Guangdong Province Rural Science and Technology Key Project of China under the Grant 2012A020200003, which focused on the safety of food and agricultural products.