中文摘要：

为了提高应用近红外光谱分析技术快速测定梨硬度的精度和稳定性,该研究采用联合区间偏最小二乘和遗传算法（siPLS-GA）在校正模型中用来筛选特征光谱区域和波长,通过交互验证法确定模型的主成分因子数和筛选的波长,并以预测均方根误差（RMSEP）和相关系数（Rp）作为模型的评价标准。基于siPLS-GA的最优模型包含4个光谱区、96个变量和10个主成分因子。该模型结果显示：最佳预测模型相关系数（Rp）和RMSEP分别为0.9083和0.5573。研究结果表明,近红外光谱技术结合siPLS-GA建模用于无损、快速测定梨的硬度是可行的。

英文摘要：

In order to improve the detecting precision and robustness in determination of pear firmness by the FT-NIR spectroscopy,in this research,Synergy interval partial least square coupled with genetic algorithm（siPLS-GA）was used to select the efficient spectral regions and wavelengths in calibrating model.The number of components and the number of variables were implemented by the cross-validation.The performance of the final model was evaluated according to the root mean square error of prediction（RMSEP）and correlation coefficient（R）in prediction and calibration sets.The optimal model based on siPLS-GA was obtained with 10 PLS factors,while 4 spectral regions and 96 variables were selected,respectively.The results of final model show that the optimal model can obtain correlation coefficient of 0.9083, and RMSEP of 0.1573 respectively by a prediction set.The research demonstrated that pear firmness could be determined by NIR spectroscopy technique is feasible,and siPLS-GA the superiority in calibrating model.