Henan University of Chinese Medicine, Zhengzhou, 450008, China.
Department of Pharmacy, The First Affiliated Hospital of Henan University of CM, Zhengzhou, 450000, China.
Sci Rep. 2019 Nov 18;9(1):17006. doi: 10.1038/s41598-019-53210-5.
'Quality evaluation based on color grading' is one of the features used in Chinese medicine discrimination. In order to assess the feasibility of electronic eye (E-eye) in implementing 'quality evaluation based on color grading', the present study applied an IRIS VA400 E-eye to test 58 batches of Corni Fructus samples. Their optical data were acquired and combined with their corresponding classes. A total of four quality discrimination models were produced according to discrimination analysis (DA), least squares support vector machine (LS-SVM), partial least squares-discrimination analysis (PLS-DA), and principal component analysis-discrimination analysis (PCA-DA). The accuracy rate of the aforementioned 4 cross evaluation models were 86.21%, 89.66%, 81.03% and 91.38%, respectively. Therefore, the PCA-DA method was used to build the final discrimination model for classifying Corni Fructus or discriminating its quality.
基于颜色分级的质量评价是中药鉴别中使用的特征之一。为了评估电子眼(E-eye)在实施“基于颜色分级的质量评价”中的可行性,本研究应用 IRIS VA400 E-eye 测试了 58 批山茱萸样本。采集它们的光学数据,并将其与相应的类别相结合。根据判别分析(DA)、最小二乘支持向量机(LS-SVM)、偏最小二乘判别分析(PLS-DA)和主成分分析判别分析(PCA-DA),共生成了四个质量判别模型。上述四个交叉评价模型的准确率分别为 86.21%、89.66%、81.03%和 91.38%。因此,采用 PCA-DA 方法建立了最终的判别模型,用于分类山茱萸或鉴别其质量。