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Classification of bottled mineral waters using solution cathode glow discharge optical emission spectroscopy and chemometrics methods

机译:使用溶液阴极辉光放电光发射光谱和化学计量方法对瓶装矿泉水分类

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摘要

Water is the most important substance for daily life and contains minerals which play an important role in nutrition. In this study, seven kinds of bottled water in Chinese market were analysed and identified by the solution-cathode glow discharge optical emission spectroscopy (SCGD-OES) combined with chemometrics methods. The score matrix was obtained by principal component analysis (PCA), and the back propagation artificial neural network (BP-ANN) model was established to identify the kind of the brands of the bottled water based on their spectral properties. The average classification accuracy based on BP-ANN is 99.74%, support vector machine (SVM) model and linear discrimination analysis (LDA) model were also employed to classify the kind of the brands of the bottled water based on the PCA analysis results, and the correct identification rate based on SVM model and LDA model are 99.50% and 98.00%, respectively. It is confirmed that SCGD-OES combined with chemometrics methods is promising for automatic real time, reliable and robust measurement, and can be integrated into a simplified form for non-specialist users.
机译:水是日常生活中最重要的物质,含有在营养中发挥重要作用的矿物质。在这项研究中,通过溶液 - 阴极辉光放电光发射光谱(SCGD-OES)分析并鉴定了中国市场的七种瓶装水。通过主成分分析(PCA)获得得分矩阵,并建立了后传播人工神经网络(BP-ANN)模型,以根据其光谱性能识别瓶装水的品牌。基于BP-ANN的平均分类精度是99.74%,支持向量机(SVM)模型和线性歧视分析(LDA)模型也被用于根据PCA分析结果,以及基于PCA分析结果对瓶装水的品牌进行分类基于SVM模型和LDA模型的正确识别率分别为99.50%和98.00%。确认SCGD-OES与化学计量方法相结合,很有希望自动实时,可靠且稳健的测量,并且可以集成为非专业用户的简化表单。

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