应用便携式近红外光谱分析仪对6种苹果进行无损检测， 运用Kernel Isomap结合广义回归神经网络的方法分别建立苹果酸度和抗坏血酸定量分析模型。 结果表明: 采用Kernel Isomap方法能够使模型具有良好的预测能力。 苹果酸度模型校正集相关系数Rc=0.9994， 预测集相关系数Rp=0.979 9， RMSEP=0.055 8。 苹果抗坏血酸模型校正集相关系数Rc=0.989 1， 预测集相关系数Rp=0.927 2， RMSEP=4.043 1。 研究结果表明， 使用该便携式近红外光谱仪可以用于苹果样本的酸度和抗坏血酸含量的测定。

Near infrared (NIR) spectroscopy technology based on a portable NIR analyzer, combined with kernel Isomap algorithm and generalized regression neural network (GRNN) has been applied to establishing quantitative models for prediction of acidity and vitamin C in six kinds of apple samples. The obtained results demonstrated that the fitting and the predictive accuracy of the models with kernel Isomap algorithm were satisfactory. The correlation between actual and predicted values of calibration samples (Rc) obtained by the acidity model was 0.999 4, and for prediction samples (Rp) was 0.979 9. The root mean square error of prediction set (RMSEP) was 0.055 8. For the vitamin C model, Rc was 0.989 1, Rp was 0.927 2, and RMSEP was 4.043 1. Results proved that the portable NIR analyzer can be a feasible tool for the determination of acidity and vitamin C in apples.