Abstract
In this paper, image analysis and electronic nose analysis were used to develop a rapid, reliable and undamaged method of identifying the Semen cuscutae and its adulterants including radish seed and Sinapis alba seeds. The results showed that the highest identification rate was 100% for the training set and 96.5% for the test set based on image analysis and various chemometric techniques including principal component analysis, linear discriminant analysis (LDA), k-nearest neighbor, random forests, artificial neural network and support vector machine analysis. LDA analysis based on electronic nose analysis exhibited better discrimination result, ranging from 95.5–100% for the correct classification rate and 95.4–100% for the cross-validation rate, respectively. LDA model based on electronic nose data from 16 to 30 s was the best, and both the correct classification rate and cross-validation rate reached 100%. These results provided a simple, fast and non-destructive method to identify the true and false of Semen cuscutae, which can serve as a reference to identify the authenticity of the medicinal plants.
Published Version
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