中国中药杂志

2020, v.45(16) 3863-3870

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电子鼻融合BP神经网络鉴别生、醋广西莪术及姜黄素类成分的含量预测
Identification of curcumin content in raw and vinegar-processed rhizomes of Curcuma kwangsiensis based on electronic nose combined with back propagation neural network

蓝振威;季德;王淑美;陆兔林;孟江;
LAN Zhen-wei;JI De;WANG Shu-mei;LU Tu-lin;MENG Jiang;Engineering Technology Research Center for Chinese Materia Medica Quality of Universities in Guangdong Province,Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica, State Administration of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Guangdong Pharmaceutical University;School of Pharmacy, Nanjing University of Chinese Medicine;

摘要(Abstract):

为建立一种快速准确识别生、醋广西莪术并预测其姜黄素类化合物含量的科学评价方法,采用一整套仿生学的识别模式,通过电子鼻获取莪术及其醋制品的数字化气味信号,应用反向传播(back propagation, BP)神经网络算法分析数据,以精确度、敏感性和特异性位指标评估判别模型,以相关系数和均方误差评估回归模型。实验结果表明,通过BP神经网络算法建立的电子鼻信号判别模型在训练集、校正集和预测集中的3个指标均为100%,明显优于传统的决策树、朴素贝叶斯、支持向量机、K最邻近和集成分类,能准确地区分生、醋广西莪术;回归模型预测集相关系数和均方误差分别为0.974 8和0.117 5,能很好地预测广西莪术中姜黄素类化合物含量,展示了模仿生物识别模式在中药分析中的优越性。电子鼻气味指纹图谱结合BP神经网络算法,快速、便捷、准确地实现了的判别和回归,这提示了可以有更多的仿生学信息获取和识别模式在中药领域中联合使用,为中药质量的快速评价和标准化提供了思路和方法。
This study aimed to establish a rapid and accurate method for identification of raw and vinegar-processed rhizomes of Curcuma kwangsiensis, in order to predict the content of curcumin compounds for scientific evaluation. A complete set of bionics recognition mode was adopted. The digital odor signal of raw and vinegar-processed rhizomes of Curcuma kwangsiensis were obtained by e-nose, and analyzed by back propagation(BP) neural network algorithm, with the accuracy, the sensitivity and specificity in discriminant model, correlation coefficient as well as the mean square error in regression model as the evaluation indexes. The experimental results showed that the three indexes of the e-nose signal discrimination model established by the neural network algorithm were 100% in training set, correction set and prediction set, which were obviously better than the traditional decision tree, naive bayes, support vector machine, K nearest neighbor and boost classification, and could accurately differentiate the raw and vinegar products. Correlation coefficient and mean square error of the regression model in prediction set were 0.974 8 and 0.117 5 respectively, and could well predict curcumin compounds content in Curcuma kwangsiensis, and demonstrate the superiority of the simulation biometrics model in the analysis of traditional Chinese medicine. By BP neural network algorithm, e-nose odor fingerprint could quickly, conveniently and accurately realize the discrimination and regression, which suggested that more bionics information acquisition and identification patterns could be combined in the field of traditional Chinese medicine, so as to provide ideas and methods for the rapid evaluation and stan-dardization of the quality of traditional Chinese medicine.

关键词(KeyWords): 广西莪术;醋莪术;电子鼻;BP神经网路;姜黄素;含量预测
Curcuma kwangsiensis;vinegar-processed rhizomes of Curcuma kwangsiensis;electronic nose;BP neural network;curcumin;content prediction

Abstract:

Keywords:

基金项目(Foundation): 2016年度新兴产业重大工程中药标准化项目(ZYBZH-Y-SC-40);; 广东省教育厅广东药科大学创新强校工程项目(2016KTSCX064,2018KZDXM040);; 广州市科技局项目(201707010170)

作者(Author): 蓝振威;季德;王淑美;陆兔林;孟江;
LAN Zhen-wei;JI De;WANG Shu-mei;LU Tu-lin;MENG Jiang;Engineering Technology Research Center for Chinese Materia Medica Quality of Universities in Guangdong Province,Key Laboratory of Digital Quality Evaluation of Chinese Materia Medica, State Administration of Traditional Chinese Medicine, School of Traditional Chinese Medicine, Guangdong Pharmaceutical University;School of Pharmacy, Nanjing University of Chinese Medicine;

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