雪莲花产地鉴别的近红外光谱分析方法

Discrimination of Snow Lotus from Different Geographical Origins by Near Infrared Spectroscopy

以青海、西藏、云南和新疆4个不同产地的雪莲花为研究对象,利用K-最近邻域(KNN)模式识别方法建立雪莲花产地鉴别模型,模型参数K和主成分因子数(PCs)通过交互验证的方法优化;同时比较了标准正态变量变换(SNV)、多元散射校正(MSC)、一阶导数和二阶导数4种预处理方法对模型结果的影响。试验结果显示,通过SNV光谱预处理后,在K为3和PCs为5时,所得到的模型最佳,模型交互验证识别率和预测识别率均为100%。研究表明,近红外光谱技术结合KNN方法可以成功鉴别雪莲花产地。

Snow lotus samples from four different geographical origins （Qinghai,Tibet,Yunnan and Sinkiang） were studied. K-nearest neighbors （KNN）algorithm was applied to build discriminating model as a pattern recognition method. The parameter K of the KNN model and the number of principal component factors （PCs） were optimized. The spectra were preprocessed by four different spectral preprocessing methods of standard normal variate （SNV）,multiplieative scatter correction（MSC）,first derivative and second derivative,and their effects on results of KNN models were compared. Experimental results showed that the optimal model was obtained with PCs 5 and K=3 after SNV spectral preprocessing,and the discriminating rates were all 100% in cross-validation and prediction. The work demonstrated that NIR spectroscopy technique with KNN method could be successfully applied to discriminate snow lotus from different geographical origins.