应用遗传算法结合连续投影算法近红外光谱检测土壤有机质研究  

Measurement of Soil Organic Matter with Near Infrared Spectroscopy Combined with Genetic Algorithm and Successive Projection Algorithm

作　　者：章海亮[1,2] 罗微[2] 刘雪梅[2] 何勇[1] 

机构地区：[1]浙江大学生物系统工程与食品科学学院,浙江杭州310058 [2]华东交通大学电气工程与自动化学院,江西南昌330013

出　　处：《光谱学与光谱分析》2017年第2期584-587,共4页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(61134011);江西省科技支持项目(20161BAB202060;20161BBF60060;20151BAB207009;20142BDH80021)资助

摘　　要：应用遗传算法结合连续投影算法近红外光谱检测土壤有机质研究。采集浙江省文城地区农田土壤样品近红外光谱数据,土壤样品数为394个。为简化模型,采用遗传算法结合连续投影算法挑选出18个特征波长建模,应用偏最小二乘回归建立有机质预测模型,建模集的决定系数为0.81,均方根预测误差为0.22,剩余预测偏差为2.31,预测集的决定系数为0.83,均方根预测误差为0.20,剩余预测偏差为2.45。研究发现,遗传算法结合连续投影算法在简化模型同时,模型的预测评价指标同采用全谱波长建模并没有明显降低。因此,遗传算法结合连续投影算法挑选的特征波长可以应用于近红外光谱检测土壤有机质含量。Visible near infrared spectroscopy combined with genetic algorithm and successive projection algorithm was investigated to detect soil organic matter（OM）.A total of 394 soil samples were collected from Wencheng,Zhejiang province.In order to simplify calibration model,a total of 18 characteristic wavelengths were selected with usinggenetic algorithm and successive projections algorithm.These characteristic wavelengths were subjected to partial least squares regression（PLSR）with leaveone-out cross validation to establish calibration model of soil organic matter（OM）with coefficient of determination（R-2）of0.81,0.83,RMSEP of 0.22,0.20 and residual prediction deviation（RPD）of 2.31,2.45 for the calibration set and prediction set respectively.The results showed that using genetic algorithm and successive projections algorithm can simplify the model greatly while the assessing indexes of model such as R-2,RMSEP and RPD were not reduced greatly compared with indexes of model using full spectra data to develop calibration model.Therefore,genetic algorithm combined with successive projections algorithm can be used to simply the model to predict soil organic matter.

关 键 词：近红外光谱 土壤有机质 遗传算法 连续投影算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]