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基于灰狼算法的近红外光谱变量选择方法研究
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作者单位
武新燕,卞希慧,杨盛,徐沛,王海涛 1.天津工业大学省部共建分离膜与膜过程国家重点实验室环境科学与工程学院2.天津工业大学化学与化工学院3.绍兴市柯桥区污染物总量控制中心 
基金项目:国家留学基金委公派项目(201808120028);天津市中外联合研究中心项目(19PTZWHZ00030);京津冀三地联合攻关项目(19YFSLQY00060)
中文摘要:基于群体智能的灰狼优化(GWO)算法具有参数少、结构简单、易于实现的优点,但在光谱领域的应用较少。该研究将GWO算法引入近红外光谱的变量筛选中,以玉米数据为例,考察了GWO算法中狼群性能、迭代次数、狼群数量及运算效率,并建立了偏最小二乘(PLS)模型对玉米样品中蛋白质、脂肪、水分以及淀粉含量的测定。结果显示,GWO算法运算效率很高,经过参数调优后建立PLS模型,其蛋白质、脂肪、水分及淀粉的保留变量数分别为19、19、14、34,预测均方根误差(RMSEP)从全波长PLS建模的0.245 8、0.122 4、0.339 8、1.105 8分别下降到0.147 7、0.080 1、0.176 2、0.739 8,分别下降了40%、35%、48%、33%,相关系数也相应地提高。因此,GWO算法不仅优化速度快,选择变量数少,还可以显著提高PLS模型的预测精度,是一种近红外光谱变量选择的有效方法。
中文关键词:近红外光谱  变量选择  灰狼算法(GWO)  偏最小二乘(PLS)
 
A Variable Selection Method for Near Infrared Spectroscopy Based on Gray Wolf Optimizer Algorithm
Abstract:Gray wolf optimizer(GWO) algorithm, which is based on swarm intelligence, is easy to implement due to its few parameters and simple structure. However, to our knowledge, few studies used GWO for the spectral analysis. In this study, the GWO was introduced into the variable selection of NIR spectra.Taking corn dataset as an example,the performance,numbers of iterationsnumbers of wolves and efficiency of GWO algorithm were investigated.Based on this,a partial least squares(PLS) model was established to determine the protein,fat,moisture and starch contents in corn samples.Results showed that GWO algorithm was very efficient.With optimized parameters,the retention variable numbers of GWO algorithm for protein,fat,moisture and starch were 19,19,14 and 34,respectively.Compared with root mean square error of prediction(RMSEP) values of the full wavelength PLS model for the four components,those of the GWO-PLS model decreased from 0.245 8,0.122 4,0.339 8 and 1.105 8 to 0.147 7,0.080 1,0.176 2 and 0.739 8,with their decreasing percentages of 40%,35%,48% and 33%,respectively.Meanwhile,the correlation coefficients were increased accordingly.Therefore,GWO algorithm could improve the prediction accuracy of the PLS model apparently with high efficiency and fewer selected variables.It is a promising method for variable selection of NIR spectroscopy.
Key Words:near infrared spectra  variable selection  gray wolf optimizer(GWO)  partial least squares(PLS)
引用本文:武新燕,卞希慧,杨盛,徐沛,王海涛.基于灰狼算法的近红外光谱变量选择方法研究[J].分析测试学报,2020,39(10):1288-1292.
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