LS-SVM的梨可溶性固形物近红外光谱检测的特征波长筛选  

Characteristic Wavelengths Selection of Soluble Solids Content of Pear Based on NIR Spectral and LS-SVM

作　　者：樊书祥 黄文倩[2] 李江波[2] 赵春江[1,2] 张保华[2] 

机构地区：[1]西北农林科技大学机械与电子工程学院,陕西杨凌712100 [2]北京市农林科学院,北京农业智能装备技术研究中心,北京100097

出　　处：《光谱学与光谱分析》2014年第8期2089-2093,共5页Spectroscopy and Spectral Analysis

基　　金：国家科技支撑计划资助项目(2013BAD19B02);2012年北京市农林科学院博士后基金资助

摘　　要：为提高梨可溶性固形物含量(soluble solids content,SSC)的近红外光谱模型的精度和稳定性,以160个梨样品为实验对象,分别对原始光谱、多元散射校正(MSC)和标准正态变量变换(SNV)处理后的光谱,经无信息变量消除算法(UVE)挑选后,再结合遗传算法(GA)和连续投影算法(SPA),筛选梨可溶性固形物的近红外光谱特征波长。将筛选后的波长作为输入变量建立梨可溶性固形物的最小二乘支持向量机(LS-SVM)模型。结果表明经过SNV-UVE-GA-SPA从全波段3112个波长中筛选出的30个特征波长建立的梨可溶性固形物LS-SVM模型效果最好,该模型的预测集相关系数(Rp)和预测均方根误差(RMSEP)分别为0.956和0.271。该模型简单可靠,预测效果好,能满足梨的可溶性固形物含量的快速检测,为在线检测和便携式设备开发提供了理论基础。To improve the precision and robustness of the NIR model of the soluble solid content （SSC）on pear. The total num-ber of 160 pears was for the calibration （n= 120）and prediction （n= 40）. Different spectral pretreatment methods,including standard normal variate （SNV）and multiplicative scatter correction （MSC）were used before further analysis. A combination of genetic algorithm （GA）and successive proj ections algorithm （SPA）was proposed to select most effective wavelengths after un-informative variable elimination （UVE）from original spectra,SNV pretreated spectra and MSC pretreated spectra respectively. The selected variables were used as the inputs of least squares-support vector machine （LS-SVM）model to build models for de-termining the SSC of pear. The results indicated that LS-SVM model built using SNVE-UVE-GA-SPA on 30 characteristic wavelengths selected from full-spectrum which had 3 1 1 2 wavelengths achieved the optimal performance. The correlation coeffi-cient （Rp）and root mean square error of prediction （RMSEP）for prediction sets were 0. 956,0. 271 for SSC. The model is reli-able and the predicted result is effective. The method can meet the requirement of quick measuring SSC of pear and might be im-portant for the development of portable instruments and online monitoring.

关 键 词：近红外光谱 特征波长 最小二乘支持向量机 可溶性固形物 梨 

分 类 号：O657.3[理学—分析化学]