Determination of geographical origin and icariin content of Herba Epimedii using near infrared spectroscopy and chemometrics
Determination of geographical origin and icariin content of Herba Epimedii using near infrared spectroscopy and chemometrics
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DOI：
10.1016/j.saa.2017.10.019
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发表时间：
2018-02-15
影响因子：
4.4
通讯作者：
Chen, Yong
中科院分区：
文献类型：
--
作者：
Yang, Yue;Wu, Yongjiang;Chen, Yong
在这项工作中，近红外（NIR）光谱与化学计量学相结合被用来区分淫羊藿的地理起源。构建了判别分析（DA）、反向传播神经网络（BPNN）、K近邻（KNN）和支持向量机（SVM）四种不同的分类模型，并比较了它们在识别精度方面的性能。结果表明，SVM模型在淫羊藿产地鉴定方面优于其他模型。最佳SVM模型对校准集和预测集的识别率分别高达100%和94.44%。此外，还研究了近红外光谱法结合CARS-PLSR校准模型预测淫羊藿中淫羊藿苷含量的可行性。预测集的判定系数(R-P(2))和均方根误差(RMSEP)分别为0.9269和0.0480。由此可见，近红外光谱技术结合化学计量学在淫羊藿产地及淫羊藿苷含量测定方面具有巨大潜力。该研究可为食品的快速质量控制提供有价值的参考。 (C) 2017 Elsevier B.V. 保留所有权利。
Near infrared (NIR) spectroscopy coupled with chemometrics was used to discriminate the geographical origin of Herba Epimedii in this work. Four different classification models, namely discriminant analysis (DA), back propagation neural network (BPNN), K-nearest neighbor (KNN), and support vector machine (SVM), were constructed, and their performances in terms of recognition accuracy were compared. The results indicated that the SVM model was superior over the other models in the geographical origin identification of Herba Epimedii. The recognition rates of the optimum SVM model were up to 100% for the calibration set and 94.44% for the prediction set, respectively. In addition, the feasibility of NIR spectroscopy with the CARS-PLSR calibration model in prediction of icariin content of Herba Epimedii was also investigated. The determination coefficient (R-P(2)) and root-mean-square error (RMSEP) for prediction set were 0.9269 and 0.0480, respectively. It can be concluded that the NIR spectroscopy technique in combination with chemometrics has great potential in determination of geographical origin and icariin content of Herba Epimedii. This study can provide a valuable reference for rapid quality control of food products. (C) 2017 Elsevier B.V. All rights reserved.