Gui Xin-Jing, Li Han, Ma Rui, Tian Liang-Yu, Hou Fu-Guo, Li Hai-Yang, Fan Xue-Hua, Wang Yan-Li, Yao Jing, Shi Jun-Han, Zhang Lu, Li Xue-Lin, Liu Rui-Xin
School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China.
Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China.
Front Chem. 2023 Apr 28;11:1179039. doi: 10.3389/fchem.2023.1179039. eCollection 2023.
This paper focuses on determining the authenticity and identifying the species of using electronic nose, electronic tongue, and electronic eye sensors, near infrared and mid-level data fusion. 80 batches of and its counterfeits (including several batches of Hsiao et K.C. Hsia, Maxim, Franch and Maxim) were initially identified by Chinese medicine specialists and by criteria in the 2020 edition of . After obtaining the information from several sensors we constructed single-source PLS-DA models for authenticity identification and single-source PCA-DA models for species identification. We selected variables of interest by VIP value and Wilk's lambda value, and we subsequently constructed the three-source fusion model of intelligent senses and the four-source fusion model of intelligent senses and near-infrared spectroscopy. We then explained and analyzed the four-source fusion models based on the sensitive substances detected by key sensors. The accuracies of single-source authenticity PLS-DA identification models based on electronic nose, electronic eye, electronic tongue sensors and near-infrared were respectively 96.25%, 91.25%, 97.50% and 97.50%. The accuracies of single-source PCA-DA species identification models were respectively 85%, 71.25%, 97.50% and 97.50%. After three-source data fusion, the accuracy of the authenticity identification of the PLS-DA identification model was 97.50% and the accuracy of the species identification of the PCA-DA model was 95%. After four-source data fusion, the accuracy of the authenticity of the PLS-DA identification model was 98.75% and the accuracy of the species identification of the PCA-DA model was 97.50%. In terms of authenticity identification, four-source data fusion can improve the performance of the model, while for the identification of the species the four-source data fusion failed to optimize the performance of the model. We conclude that electronic nose, electronic tongue, electronic eye data and near-infrared spectroscopy combined with data fusion and chemometrics methods can identify the authenticity and determine the species of . Our model explanation and analysis can help other researchers identify key quality factors for sample identification. This study aims to provide a reference method for the quality evaluation of Chinese herbs.
本文聚焦于利用电子鼻、电子舌和电子眼传感器以及近红外和中级数据融合来确定[具体中药名称]的真伪并识别其品种。80批次的[具体中药名称]及其假冒品（包括几批次的[具体假冒品种名称1]、[具体假冒品种名称2]、[具体假冒品种名称3]和[具体假冒品种名称4]）最初由中药专家根据《中国药典》2020年版的标准进行鉴定。在从多个传感器获取信息后，我们构建了用于真伪鉴定的单源偏最小二乘判别分析（PLS - DA）模型和用于品种鉴定的单源主成分分析 - 判别分析（PCA - DA）模型。我们通过变量重要性投影（VIP）值和威尔克斯 lambda 值选择感兴趣的变量，随后构建了智能感官三源融合模型以及智能感官与近红外光谱四源融合模型。然后基于关键传感器检测到的敏感物质对四源融合模型进行解释和分析。基于电子鼻、电子眼、电子舌传感器和近红外的单源真伪PLS - DA鉴定模型的准确率分别为96.25%、91.25%、97.50%和97.50%。单源PCA - DA品种鉴定模型的准确率分别为85%、71.25%、97.50%和97.50%。经过三源数据融合后，PLS - DA鉴定模型的真伪鉴定准确率为97.50%，PCA - DA模型的品种鉴定准确率为95%。经过四源数据融合后，PLS - DA鉴定模型的真伪准确率为98.75%，PCA - DA模型的品种鉴定准确率为97.50%。在真伪鉴定方面，四源数据融合可提高模型性能，而对于品种鉴定，四源数据融合未能优化模型性能。我们得出结论，电子鼻、电子舌、电子眼数据以及近红外光谱结合数据融合和化学计量学方法能够鉴定[具体中药名称]的真伪并确定其品种。我们的模型解释和分析有助于其他研究人员识别用于样本鉴定的关键质量因素。本研究旨在为中药材质量评价提供一种参考方法。
