马铃薯干物质含量高光谱检测中变量选择方法比较

Comparison of Different Variable Selection Methods on Potato Dry Matter Detection by Hyperspectral Imaging Technology

为提高利用高光谱成像技术快速检测马铃薯干物质含量的精度,比较了主成分分析法(PCA)、组合间隔偏最小二乘法(siPLS)、遗传偏最小二乘法(GA-PLS)、无信息变量消除法(UVE)以及竞争性自适应重加权算法(CARS)等变量选择方法。在此基础上提出一种竞争性自适应重加权算法与连续投影算法(SPA)相结合的波长选择方法,最终将原始光谱变量从678个减少到了27个。用27个变量建立多元线性回归模型,模型预测集相关系数Rp为0.86,预测均方根误差为1.06%。实验结果表明:高光谱成像技术能够对马铃薯干物质含量进行检测,同时CARS-SPA是一种有效的变量选择方法。

In order to improve precision determination of dry matter content in potatoes by hyperspectral image technology,several variable selection methods such as PCA,siPLS,GA-PLS,UVE and competitive adaptive reweighed sampling（CARS） were compared.A combinatorial method named CARS-SPA（successive projections algorithm） was proposed to select variables from 678 wavelength variables.The number of wavelength variables was reduced to 27.A multivariate linear regression model（MLR） based on these 27 wavelength variables was developed to predict DM content with R p of 0.86,and RMSEP of 1.06%.It was concluded that hyperspectral imaging technology could be used to detect potato dry matter concentration and CARS-SPA was a feasible and efficient algorithm for the spectral variable selection.