Near infrared spectroscopy (NIRS), coupled with principal component analysis and wavelength selection techniques, has been used to develop a robust and reliable reduced-spectrum classifi- cation model for determining the geographical origins of Nanfeng mandarins. The application of the changeable size moving window principal component analysis (CSMWPCA) provided a notably improved classification model, with correct classification rates of 92.00%, 100.00%, 90.00%, 100.00%, 100.00%, 100.00% and 100.00% for Fujian, Guangxi, Hunan, Baishe, Baofeng, Qiawan, Sanxi samples, respectively, as well as, a total classification rate of 97.52% in the wavelength range from 1007 to 1296 nm. To test and apply the proposed method, the procedure was applied to the analysis of 59 samples in an independent test set. Good identification results (correct rate of 96.61%) were also received. The improvement achieved by the application of CSMWPCA method was particularly remarkable when taking the low complexities of the final model (290 variables) into account. The results of the study showed the great potential of NIRS as a fast, nondestructive and environmentally acceptable method for the rapid and reliable determination for geographical classification of Nanfeng mandarins.

采用近红外光谱结合主成分分析（PCA）建立不同产地南丰蜜桔鉴别模型, 实现不同产地南丰蜜桔的快速鉴别。 分别研究一个果园内不同位置的蜜桔, 洽湾、 市山和白舍等南丰县三个不同乡镇的南丰蜜桔, 福建邵武、 广西柳城和江西南丰等三个不同省份的南丰蜜桔之间的差异, 蜜桔保存时间对主成分分析模型的影响。 结果表明同一个果园内不同位置的蜜桔不存在明显差别, 不同产地的蜜桔有很好的分类效果, 蜜桔的短时间保存对近红外光谱的主成分分析模型不会产生明显影响。 不同的光谱预处理方法对主成分分析模型产生较大影响, 多元散射校正（MSC）结合二阶导预处理得到的主成分分析投影具有最佳的分类效果。 该研究可为南丰蜜桔的产地鉴别提供一种参考方法。