The maximal deoxynivalenol （DON） and fumonisins 131 ＋ B2 （FUM） contents in cereals are dictated by the European regulation 1126/2007. The direct measurement of these mycotoxins is a tedious and expensive process....The maximal deoxynivalenol （DON） and fumonisins 131 ＋ B2 （FUM） contents in cereals are dictated by the European regulation 1126/2007. The direct measurement of these mycotoxins is a tedious and expensive process. Our study is based on an alternative tool： near infrared spectroscopy. Different models were developed on 374 maize samples to predict their DON and FUM contents. Several parameters have been determined and used in a multivariate data analysis. Three models were developed： （1） a classification model based on Discriminant Factor Analysis （DFA）, （2） a linear model based on ANalysis of COVAriance （ANCOVA） and （3） a Partial Least Squares Discriminant Analysis model （PLS-DA）. Firstly, the performances of the DFA model for assessing DON and FUM risk were similar： 69 and 72% of the validation samples were respectively well classified. In the second part, the performances of the ANCOVA model for DON were higher than for FUM. The r2 was worth respectively 0.85 and 0.69. In the last part, the performances of the PLS-DA models were better for FUM than for DON. These results show that an evaluation of the mycotoxin risk is possible by analyzing selected kernel parameters measurable by secondary analytical such as near-infrared spectroscopy. Further work is needed to improve the models, adding more samples and using non linear approaches.展开更多
文摘The maximal deoxynivalenol （DON） and fumonisins 131 ＋ B2 （FUM） contents in cereals are dictated by the European regulation 1126/2007. The direct measurement of these mycotoxins is a tedious and expensive process. Our study is based on an alternative tool： near infrared spectroscopy. Different models were developed on 374 maize samples to predict their DON and FUM contents. Several parameters have been determined and used in a multivariate data analysis. Three models were developed： （1） a classification model based on Discriminant Factor Analysis （DFA）, （2） a linear model based on ANalysis of COVAriance （ANCOVA） and （3） a Partial Least Squares Discriminant Analysis model （PLS-DA）. Firstly, the performances of the DFA model for assessing DON and FUM risk were similar： 69 and 72% of the validation samples were respectively well classified. In the second part, the performances of the ANCOVA model for DON were higher than for FUM. The r2 was worth respectively 0.85 and 0.69. In the last part, the performances of the PLS-DA models were better for FUM than for DON. These results show that an evaluation of the mycotoxin risk is possible by analyzing selected kernel parameters measurable by secondary analytical such as near-infrared spectroscopy. Further work is needed to improve the models, adding more samples and using non linear approaches.
