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利用激光诱导击穿光谱快速鉴别不同来源的葛粉。

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

机构信息

College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.

School of Information Engineering, Zhejiang A & F University, 666 Wusu Street, Hangzhou 311300, China.

出版信息

Sensors (Basel). 2019 Mar 25;19(6):1453. doi: 10.3390/s19061453.

Abstract

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.

摘要

快速鉴定不同来源的葛根粉对于研究中药材的真伪鉴别具有重要意义。本研究采用激光诱导击穿光谱(LIBS)技术结合化学计量学方法,探讨了快速鉴定葛根粉产地来源的可行性。基于全谱的判别模型包括极限学习机(ELM)、类间软独立建模(SIMCA)、K-最近邻(KNN)和随机森林(RF),模型的准确率均在 99.00%以上。其中 KNN 和 RF 模型的预测结果最好:不同产地葛根粉的校正集和预测集的准确率均达到 100%。采用主成分分析(PCA)载荷图对特征波长进行筛选,基于特征波长建立的判别模型的校正集和预测集准确率均在 98.00%以上。随机森林和 KNN 具有相同的优异识别结果,不同产地葛根粉的校正集和预测集准确率均达到 100%。与全谱判别分析模型相比,基于特征波长的判别分析模型的判别效果几乎相同,输入变量减少了 99.92%。结果表明,特征波长可以代替 LIBS 全谱快速鉴定不同产地的葛根粉,具有减少输入、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不同产地葛根粉的有效方法。本研究为 LIBS 技术在中药材真伪鉴别中的应用提供了依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/68c0/6470848/34493cec7e90/sensors-19-01453-g001.jpg

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