Standardisation of near infrared hyperspectral imaging for quantification and classification of DON contaminated wheat samples
Near infrared hyperspectral imaging (HSI-NIR) is considered a promising technique able to replace time-con- suming, costly and destructive classic methods to predict and classify deoxynivalenol (DON) contaminated wheat kernels or samples by its concentration and level of contamination, respectively....
| Autores: | , , , , |
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| Formato: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2019 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10459.1/68087 |
| Acesso em linha: | https://doi.org/10.1016/j.foodcont.2019.107074 http://hdl.handle.net/10459.1/68087 |
| Access Level: | acceso abierto |
| Palavra-chave: | Hyperspectral imaging Deoxynivalenol Near infrared Cereal sorting Contamination prediction |
| Resumo: | Near infrared hyperspectral imaging (HSI-NIR) is considered a promising technique able to replace time-con- suming, costly and destructive classic methods to predict and classify deoxynivalenol (DON) contaminated wheat kernels or samples by its concentration and level of contamination, respectively. The main objective of the present study was to standardise the HSI-NIR image acquisition method in naturally contaminated whole wheat kernels to obtain a high accuracy method to quantify and classify samples according to DON levels. To confirm the results, wheat samples were analysed by high performance liquid chromatography as the reference method to determine their DON levels. Hyperspectral images for single kernels and whole samples were obtained and spectral data were processed by multivariate analysis software. The initial work revealed that HSI-NIR was able to overcome kernel orientation, position and pixel selection. The subsequent developed Partial Least Squares (PLS) prediction achieved a RMSEP (Root Mean Square Error of Prediction) of 405 μg/kg and 1174 μg/kg for a cross-validated model and an independent set validated model, respectively. Moreover, the classification ac- curacy obtained by Linear Discriminant Analysis (LDA) was 62.7% for two categories depending on the EU maximum level (1250 μg/kg). Despite of the results are not accurate enough for DON quantification and sample classification, they can be considered a starting point for further improved protocols for DON management. |
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