应用遗传算法和PLS的近红外光谱预测玉米中淀粉含量的研究

Study on Determination of Starch Content in Maize by Near-infrared Reflectance Spectroscopy with Genetic Algorithm and PLS

中文关键词:  近红外光谱,偏最小二乘法,玉米,淀粉含量,遗传算法,特征选择

英文关键词:near-infrared reflectance spectroscopy (NIRS),partial least squares regression (PLS),maize,starch content,genetic algorithm,feature selection

中文摘要:

      以普通玉米籽粒为试验材料，在应用遗传算法结合偏最小二乘回归法对近红外光谱数据进行特征波长选择的基础上，应用偏最小二乘回归法建立了特征波长测定玉米籽粒中淀粉含量的校正模型.试验结果表明，基于11个特征波长所建立的校正模型，其校正误差（RMSEC）、交叉检验误差（RMSECV）和预测误差（RMSEP）分别为0.30％、0.35％和0.27％，校正数据集和独立的检验数据集的预测值与实际测定值之间的相关系数分别达到0.9279和0.9390，与全光谱数据所建立的预测模型相比，在预测精度上均有所改善，表明应用遗传算法和PLS进行光谱特征选择，能获得更简单和更好的模型，为玉米籽粒中淀粉含量的近红外测定和红外光谱数据的处理提供了新的方法与途径.

英文摘要:

      ]Informative wavelengths were selected from the near-infrared reflectance spectroscopy (NIRS) of maize by genetic algorithm and partial least squares regression (PLS). A calibration model for determination of starch content was built by PLS based on the selected wavelengths of NIRS. The result showed that the root mean square error of calibration (RMSEC), root mean square error of cross validation (RMSECV) and root mean square error of prediction (RMSEP) derived from the calibration model based on the 11 selected wavelengths were 0.30%, 0.35% and 0.27%, respectively. And the coefficients of relationship between measurements and predictions for calibration and independent validation datasets were 0.927 9 and 0.939 0, respectively. The accuracy of prediction was better than the model based on the full NIRS data. It was proved that modeling by PLS based on the feature selection with genetic algorithm and PLS was a simpler, effective and more accurate means for determination of starch content in maize.