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Rapid Identification of Kudzu Powder of Different Origins Using Laser-Induced Breakdown Spectroscopy

机译:激光诱导击穿光谱法快速鉴定不同来源的葛根粉

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摘要

The rapid identification of kudzu powder of different origins is of great significance for studying the authenticity identification of Chinese medicine. The feasibility of rapidly identifying kudzu powder origin was investigated based on laser-induced breakdown spectroscopy (LIBS) technology combined with chemometrics methods. The discriminant models based on the full spectrum include extreme learning machine (ELM), soft independent modeling of class analogy (SIMCA), K-nearest neighbor (KNN) and random forest (RF), and the accuracy of models was more than 99.00%. The prediction results of KNN and RF models were best: the accuracy of calibration and prediction sets of kudzu powder from different producing areas both reached 100%. The characteristic wavelengths were selected using principal component analysis (PCA) loadings. The accuracy of calibration set and the prediction set of discrimination models, based on characteristic wavelengths, is all higher than 98.00%. Random forest and KNN have the same excellent identification results, and the accuracy of calibration and prediction sets of kudzu powder from different producing areas reached 100%. Compared with the full spectrum discriminant analysis model, the discriminant analysis model based on the characteristic wavelength had almost the same discriminant effects, and the input variables were reduced by 99.92%. The results of this research show that the characteristic wavelength can be used instead of the LIBS full spectrum to quickly identify kudzu powder from different producing areas, which had the advantages of reducing input, simplifying the model, increasing the speed and improving the model effect. Therefore, LIBS technology is an effective method for rapid identification of kudzu powder from different habitats. This study provides a basis for LIBS to be applied in the genuineness and authenticity identification of Chinese medicine.
机译:快速鉴定不同来源的葛根粉对研究中药的真伪鉴定具有重要意义。基于激光诱导击穿光谱技术(LIBS)结合化学计量学方法研究了快速鉴定葛根粉来源的可行性。基于全谱的判别模型包括极限学习机(ELM),类比的软独立建模(SIMCA),K近邻(KNN)和随机森林(RF),模型的准确性超过99.00% 。 KNN和RF模型的预测结果最好:来自不同产地的葛根粉的校准和预测集的准确性均达到100%。使用主成分分析(PCA)负载选择特征波长。基于特征波长的校准集和判别模型的预测集的准确性均高于98.00%。随机森林和KNN的识别结果相同,来自不同产地的葛根粉的校准和预测集的准确性达到100%。与全光谱判别分析模型相比,基于特征波长的判别分析模型具有几乎相同的判别效果,输入变量减少了99.92%。研究结果表明,可以用特征波长代替LIBS全光谱快速识别不同产地的葛根粉,具有减少投入,简化模型,提高速度和改善模型效果的优点。因此,LIBS技术是一种快速鉴定来自不同生境的葛根粉的有效方法。该研究为LIBS在中药真伪鉴别中的应用提供了依据。

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