The rapid detection and quantification of microbial quantity and aflatoxin are crucial for food safety and quality. In order to achieve rapid detection, nutmeg with mildew, but with difficult-to-observe mildew characteristics, was selected as the research object. Its intrinsic component (dehydrodiisoeugenol) and exogenous noxious substances (the total number of colonies and aflatoxin B1) were determined to clarify their changes during the mold process. Subsequently, electronic nose (E-nose) was employed to analyze the odor of nutmeg and was combined with six machine learning algorithms to establish a classification model for samples with different degrees of mold. Finally, three algorithms were chosen as the preferred options to establish the prediction models of indicator content, which can not only identify whether nutmeg is edible but also measure each index. The results demonstrate the enormous potential of E-nose for real-time detection for assessing food safety. In terms of qualitative analysis, the established classification model can achieve a more than 90% true positive rate, suggesting that E-nose could identify early mildew. In quantitative analysis, E-nose combined with Back Propagation Neural Network achieved the highest prediction accuracy, since the correlation coefficient between the predicted value and the measured value of aflatoxin B1 is 0.9776, the TAMC is 0.9443, and the TYMC is 0.9685. This study provides a reference for the rapid and comprehensive quality evaluation of mildew-prone nutmeg, and it confirms that E-nose can be applied as a quick and simple technology.
Keywords: AFB1; electronic nose; microorganism; mildew; nutmeg; prediction model.