【摘要】 遗传算法不受搜索空间限制性假设的约束,利用简单的编码技术和繁殖机制来解决复杂近红外光谱数据的优化问题。文章采用遗传算法的波段选择法(R-SGA)对砂梨近红外光谱进行了波段优化,得到丰水、圆黄、黄金三种梨的R-SGA最佳因子数分别为10,12和16,并分别建立了单一品种GA-PLS模型;丰水梨和黄金梨的GA-PLS模型精度高于全谱PLS模型,其模型的RMSEP分别为0.608/0.632和0.524/0.540;圆黄梨GA-PLS模型精度(RMSEP=0.610)与全谱PLS模型(RMSEP=0.595)相当。经波段优化分析表明,使用552个数据点建立多品种砂梨混合模型,具有较高稳健性和预测性(RMSEC=0.627,RMSEP=0.641)。结果表明:基于遗传算法进行波段优化可以提高砂梨糖度模型精度,提高建模效率,同时说明建立多品种砂梨糖度通用模型是可行的。更多还原

【Abstract】 Genetic algorithm is widely used in NIRS data optimization,which is not limited by searching space.The region selecting by genetic algorithms(R-SGA)was applied in building calibration model of soluble solid content(SSC)of Pyrus pyrifolia,and the number of variables used to build calibration was further reduced from 2 075 to 690 in all of 3 models.Studies were performed to build GA-PLS models by different R-SGA latent variables,and the optimal R-SGA latent variables of Hosui,Wonhwang and Whangkeumbae pear were 10,12 and 16,respectively.The R-SGA procedure was found to perform well(RMSEP=0.608 and 0.524 for Hosui and Whangkeumbae pear respectively),leading to calibration models that significantly outperform those based on full-spectrum analyses(RMSEP=0.632,0.540).The prediction precision of GA-PLS models was similar to FULL-PLS for Wonhwang pear,with RMSEP of 0.610/0.595.In addition,the selected regions from R-SGA methods were used to build mixed model of 3 Pyrus pyrifolia varieties.The results indicated that the prediction precision of GA-PLS model was close to that of the full spectrum model,with RMSEP of 0.641 and 0.645,respectively.This work proved that the R-SGA could find optimal values for several disparate variables associated with the calibration model and that the PLS procedure could be integrated into the objective function driving the optimization,and it was feasible to build a universal model of different Pyrus pyrifolia varieties.更多还原