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
The existing seismic reflection pattern classification methods need to convert multidimensional prestack seismic data into one-dimensional vectors for processing,which loses the characteristics of amplitude variation ...The existing seismic reflection pattern classification methods need to convert multidimensional prestack seismic data into one-dimensional vectors for processing,which loses the characteristics of amplitude variation with offset/azimuth in the prestack seismic data.In this study,a tensor discriminant dictionary learning method for classifying prestack seismic reflection patterns is proposed.The method is initially based on the tensor Tucker decomposition algorithm and uses a tensor form to characterize the prestack seismic data with multidimensional features.The tensor discriminant dictionary is then used to reduce the influence of noise on the sample features.Finally,the method uses the Pearson correlation coefficient to measure the correlation degree of the sparse representation coefficients of different types of tensors.The advantages of the new method are as follows.(1)It can retain the rich structural features in different dimensions in the prestack data.(2)It adjusts the threshold of the Pearson correlation coefficient to optimize the classification effect.(3)It fully uses drilling information and expert knowledge and performs calibration training of the sample labels.The numerical-model tests confirm that the new method is more accurate and robust than the traditional support vector machine and K-nearest neighbor classification algorithms.The application of actual data further confirms that the classification results of the new method agree with the geological patterns and are more suitable for the analysis and interpretation of sedimentary facies.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.
基金supported by the National Natural Science Foundation of China(Nos.42130812,42174151,and 41874155).
文摘The existing seismic reflection pattern classification methods need to convert multidimensional prestack seismic data into one-dimensional vectors for processing,which loses the characteristics of amplitude variation with offset/azimuth in the prestack seismic data.In this study,a tensor discriminant dictionary learning method for classifying prestack seismic reflection patterns is proposed.The method is initially based on the tensor Tucker decomposition algorithm and uses a tensor form to characterize the prestack seismic data with multidimensional features.The tensor discriminant dictionary is then used to reduce the influence of noise on the sample features.Finally,the method uses the Pearson correlation coefficient to measure the correlation degree of the sparse representation coefficients of different types of tensors.The advantages of the new method are as follows.(1)It can retain the rich structural features in different dimensions in the prestack data.(2)It adjusts the threshold of the Pearson correlation coefficient to optimize the classification effect.(3)It fully uses drilling information and expert knowledge and performs calibration training of the sample labels.The numerical-model tests confirm that the new method is more accurate and robust than the traditional support vector machine and K-nearest neighbor classification algorithms.The application of actual data further confirms that the classification results of the new method agree with the geological patterns and are more suitable for the analysis and interpretation of sedimentary facies.
