基于Fisher判别分析可分性信息融合的马铃薯VC含量高光谱检测方法  

Hyperspectral Imaging for the Detection of Vitamin C Content in Potatoes Based on Fisher Discriminant Analysis Separable Information Fusion

作　　者：郭林鸽 殷勇[1] 于慧春[1] 袁云霞[1] GUO Linge;YIN Yong;YU Huichun;YUAN Yunxia(College of Food and Bioengineering,Henan University of Science and Technology,Luoyang 471023,China)

机构地区：[1]河南科技大学食品与生物工程学院,河南洛阳471023

出　　处：《食品科学》2024年第7期164-171,共8页Food Science

基　　金：“十三五”国家重点研发计划重点专项(2017YFC1600802)。

摘　　要：为提高马铃薯VC含量检测结果的准确性和可靠性,提出一种基于Fisher判别分析(Fisher discriminant analysis,FDA)可分性数据融合的检测模型输入变量构建方法。首先,利用高光谱成像技术采集200个马铃薯的高光谱信息,通过对比6种预处理方法和原始数据的建模结果,确定多元散射校正为光谱数据的预处理方法;其次,采用竞争性自适应重加权采样(competitive adaptive reweighted sampling,CARS)、连续投影算法(successive projections algorithm,SPA)及CARS-SPA组合算法3种方法提取相应特征波长,通过对比分析最终确定34个有效特征波长;然后,将有效特征波长进行FDA可分性数据融合,根据融合的新变量对样本间差异性判别能力的大小进行筛选,确定构建检测模型的输入变量;最后,分别对FDA融合前后筛选的变量建立偏最小二乘模型和反向传播神经网络(back propagation neural network,BPNN)模型,并对检测结果进行对比分析。结果表明,将CARS算法提取的34个特征波长进行FDA融合,采用前3个融合变量作为构建检测模型的输入变量时,其所建BPNN模型的相关系数由0.9726提高至0.9990,均方根误差由0.7723降低至0.1727,不仅能够极大地降低数据分析维度,而且能够提高检测结果的准确性。因此,基于FDA可分性数据融合构建检测模型输入变量可以提高马铃薯VC含量检测结果的准确性。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 0.9726 to 0.9990,and the root mean square error(RMSE)reduced from 0.7723 to 0.1727 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.

关 键 词：高光谱成像 FISHER判别分析 马铃薯 VC含量检测 模型 

分 类 号：TS215[轻工技术与工程—粮食、油脂及植物蛋白工程]