Predictive assessment of ochratoxin A accumulation in grape juice based-medium by Aspergillus carbonarius using neural networks

Abstract

Aims: To study the ability of multi-layer perceptron artificial neural networks (MLP-ANN) and radial-basis function networks (RBFNs) to predict ochratoxin A (OTA) concentration over time in grape-based cultures of Aspergillus carbonarius under different conditions of temperature, water activity (a(w)) and sub-inhibitory doses of the fungicide carbendazim. Methods and Results: A strain of A. carbonarius was cultured in a red grape juice-based medium. The input variables to the network were temperature (20-28 degrees C), a(w) (0 center dot 94-0 center dot 98), carbendazim level (0-450 ng ml(-1)) and time (3-15 days after the lag phase). The output of the ANNs was OTA level determined by liquid chromatography. Three algorithms were comparatively tested for MLP. The lowest error was obtained by MLP without validation. Performance decreased when hold-out validation was accomplished but the risk of over-fitting is also lower. The best MLP architecture was determined. RBFNs provided similar performances but a substantially higher number of hidden nodes were needed. Conclusions: ANNs are useful to predict OTA level in grape juice cultures of A. carbonarius over a range of a(w), temperature and carbendazim doses. Significance and Impact of the Study: This is a pioneering study on the application of ANNs to forecast OTA accumulation in food based substrates. These models can be similarly applied to other mycotoxins and fungal species.