Evaluation of performance of metal oxide electronic nose for detection of aflatoxin in artificially and naturally contaminated maize

Abstract

Aflatoxins are of great concern for food safety and security due to their impact on human health and the agriculture economy in developing countries. This study aimed to evaluate the potential use of a field portable metal oxide sensors based electronic nose to detect aflatoxin contamination in Kenyan maize varieties that were artificially and naturally infected with Aspergillus flavus. Mutual information was used to select features from the electronic nose sensor signals for classification of the samples. The effectiveness of selected features to discriminate between the different classes of samples was evaluated by support vector machines and k-nearest neighbour with leave-one-out cross-validation. External validation was also conducted by analysing samples naturally contaminated with A. flavus using the classification model generated with samples that had been artificially inoculated with the aflatoxigenic A. flavus. Cross-validated classification accuracies ranged from 72% to 88% for maize samples artificially inoculated with A. flavus and 61–86% for samples naturally infected with A. flavus. Classification accuracies achieved with external validation for maize samples naturally contaminated with aflatoxins ranged from 58% to 78% and were relatively consistent with accuracies obtained from internal validation. Results suggest that the electronic nose could be a promising cost-effective screening method to detect aflatoxin contamination in maize.