Near-infrared hyperspectral imaging for deoxynivalenol and ergosterol estimation in wheat samples

The present study aimed to evaluate the use of hyperspectral imaging (HSI)-NIR spectroscopy to assess the presence of DON and ergosterol in wheat samples through prediction and classification models. To achieve these objectives, a first set of bulk samples was scanned by HSI-NIR and divided into two subsamples, one that was analysed for ergosterol and another that was analysed for DON by HPLC. This method was repeated for a second larger set to build prediction and classification models. All the spectra were pretreated and statistically processed by PLS and LDA. The prediction models presented a RMSEP of 1.17 mg/kg and 501 mu g/kg for ergosterol and DON, respectively. Classification achieved an encouraging accuracy of 85.4% for an independent validation set of samples. The results confirm that HSI-NIR may be a suitable technique for ergosterol quantification and DON classification of samples according to the EU legal limit for DON.

Automated summary

The present study successfully evaluated the presence of DON and ergosterol in wheat samples using HSI-NIR spectroscopy and established prediction and classification models. The results indicate that this method can effectively quantify ergosterol and classify DON in samples according to the EU legal limit.