Enhancing the application of near-infrared spectroscopy in grain mycotoxin detection: An exploration of a transfer learning approach across contaminants and grains

Abstract

Cereals are a primary source of sustenance for humanity. Monitoring, controlling, and preventing mycotoxins in cereals are vital for ensuring the safety of the cereals and their derived products. This study introduces transfer learning strategies into chemometrics to improve deep learning models applied to spectral data from different grains or toxins. Three transfer learning methods were explored for their potential to quantitatively detect fungal toxins in cereals. The feasibility of transfer learning was demonstrated by predicting wheat zearalenone (ZEN) and peanut aflatoxin B1 (AFB1) sample sets on different instruments. The results indicated that the second transfer method is effective in detecting toxins. For FT-NIR spectrometry, the transfer model achieved an R² of 0.9356, a relative prediction deviation (RPD) of 3.9497 for wheat ZEN prediction, and an R² of 0.9419 with an RPD of 4.1551 for peanut AFB1 detection. With NIR spectrometry, effective peanut AFB1 detection was also achieved, yielding an R² of 0.9386 and an RPD of 4.0434 in the prediction set. These results suggest that the proposed transfer learning approach can successfully update a source domain model into one that is suitable for tasks in the target domain. This study provides a viable solution to the problem of poor adaptability of single-source models, presenting a more universally applicable method for spectral detection of fungal toxins in cereals.