关键词: 高光谱成像；Fisher判别分析；马铃薯；VC含量检测；模型

Abstract: In order to improve the accuracy and reliability of the prediction results of the vitamin C (VC) content in potatoes by hyperspectral imaging, a method for constructing input variables for predictive models based on Fisher discriminant analysis (FDA) separable data fusion was proposed. First, hyperspectral information of 200 potato samples was collected by hyperspectral imaging technology, and by comparing the modeling results obtained with the spectral data before and after preprocessing by 6 spectral preprocessing methods, multiplicative scatter correction (MSC) was determined as the optimal preprocessing method. Second, competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and CARS-SPA algorithm were used to extract the feature wavelengths, and 34 effective feature wavelengths were finally determined through comparative analysis. Third, the effective feature wavelengths were fused by FDA to achieve data separability, and the fused new variables were screened for their capacity to discriminate the differences among samples in order to determine the input variables for predictive models. Finally, predictive models using partial least squares (PLS) and back propagation neural network (BPNN) were established based on the variables selected before and after FDA fusion, and the results from these models were compared and analyzed. It was shown that the correlation coefficient of the BPNN model increased from 0.972 6 to 0.999 0, and the root mean square error (RMSE) reduced from 0.772 3 to 0.172 7 when the 34 effective feature wavelengths extracted by CARS were used for FDA fusion and the first three fused variables were used as the input variables, which not only greatly reduced data dimensionality, but also improved the accuracy of the detection results. Therefore, constructing input variables for the detection model based on FDA separable data fusion could improve the accuracy of the detection of potato VC content.

Key words: hyperspectral imaging; Fisher discriminant analysis; potato; VC content detection; model