为简化猕猴桃硬度的预测模型， 利用标准正态变量变换对猕猴桃1　000～2　500　nm近红外光谱进行预处理， 在优选建模波段和采用净分析物预处理（NAP）降低建模主因子数两个方面简化猕猴桃硬度偏最小二乘（PLS）模型。 结果表明， 优选5　189～5　370　cm-1， 4　549～4　620　cm-1， 6　049～6　230　cm-1， 6　999～7　730　cm-1， 6　249～6　614　cm-1等5个波段进行建模， NAP/PLS模型性能最佳， 主因子数为5， 校正集相关系数R2和均方根误差RMSECV分别为0.819　41和0.701　77， 预测集相关系数R2和均方根误差RMSEP为0.780　67和0.882　71。 与简化前的PLS模型相比， 模型不仅更加简洁， 而且预测能力和精度均有所提高。

To simplify the prediction model of kiwifruit firmness, SNV was used to preprocess the near infrared（NIR）spectra （1 000-2 500 nm）of kiwifruit. PLS model simplification by optimizing spectral intervals and decreasing the number of factors through net analyte preprocessing（NAP）was carried out. Results showed that the performance of NAP/PLS model is the best. It was achieved with 5 factors in five wavenumber ranges（5 189-5 370, 4 549-4 620, 6 049-6 230, 6 999-7 730, and 6 249-6 614 cm-1）. The optimal model was achieved with R2=0.819 41 and RMSECV=0.701 77 in the calibration set and R2=0.780 67 and RMSEP=0.882 71 in the prediction set. This indicates that the model not only may efficiently simplify PLS model, but also may improve precision and predictive ability.