Application of novel nanocomposite-modified electrodes for identifying rice wines of different brands.
Application of novel nanocomposite-modified electrodes for identifying rice wines of different brands.
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新型纳米复合材料修饰电极在不同品牌黄酒鉴别中的应用
DOI：
10.1039/c8ra00164b
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发表时间：
2018-04-09
期刊：
影响因子：
3.9
通讯作者：
Wang, Jun
中科院分区：
文献类型：
--
作者：
Wei, Zhenbo;Yang, Yanan;Zhu, Luyi;Zhang, Weilin;Wang, Jun
本文采用自制的聚酸性Chrome蓝K（PACBK）/金纳米粒子（AuNP）/玻碳电极（GCE）、聚对氨基苯磺酸（PABSA）/金纳米粒子（AuNP）/玻碳电极（GCE）和聚谷氨酸（PGA）/铜纳米粒子（CuNP）/玻碳电极（GCE）对不同品牌的黄酒进行了鉴别。利用扫描电子显微镜和循环伏安法对修饰电极进行了表征。利用该修饰电极对黄酒样品进行了多频大幅度脉冲伏安法检测。应用计时电流法记录响应值，并使用“面积法”从原始响应中提取与葡萄酒品牌相关的特征数据。主成分分析，局部保持投影和线性判别分析应用于不同的葡萄酒的分类，所有三种方法都提出了类似的好结果。极端学习机（ELM），支持向量机库（LIB-SVM）和反向传播神经网络（BPNN）被应用于预测葡萄酒品牌，BPNN最适合基于测试数据集的预测（R2 = 0.9737和MSE = 0.2673）。该修饰电极可应用于不同品牌米酒的模式识别，在食品质量检测方面也具有潜在的应用价值。
In this paper, poly(acid chrome blue K) (PACBK)/AuNP/glassy carbon electrode (GCE), polysulfanilic acid (PABSA)/AuNP/GCE and polyglutamic acid (PGA)/CuNP/GCE were self-fabricated for the identification of rice wines of different brands. The physical and chemical characterization of the modified electrodes were obtained using scanning electron microscopy and cyclic voltammetry, respectively. The rice wine samples were detected by the modified electrodes based on multi-frequency large amplitude pulse voltammetry. Chronoamperometry was applied to record the response values, and the feature data correlating with wine brands were extracted from the original responses using the ‘area method’. Principal component analysis, locality preserving projections and linear discriminant analysis were applied for the classification of different wines, and all three methods presented similarly good results. Extreme learning machine (ELM), the library for support vector machines (LIB-SVM) and the backpropagation neural network (BPNN) were applied for predicting wine brands, and BPNN worked best for prediction based on the testing dataset (R2 = 0.9737 and MSE = 0.2673). The fabricated modified electrodes can therefore be applied to identify rice wines of different brands with pattern recognition methods, and the application also showed potential for the detection aspects of food quality analysis.
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影响因子：
2.9
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