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中国精品科技期刊2020
黄赣辉, 石磊, 曾祥盛, 邓丹雯. 综合型传感器阵列对掺杂生乳的识别[J]. 食品工业科技, 2014, (06): 63-66. DOI: 10.13386/j.issn1002-0306.2014.06.071
引用本文: 黄赣辉, 石磊, 曾祥盛, 邓丹雯. 综合型传感器阵列对掺杂生乳的识别[J]. 食品工业科技, 2014, (06): 63-66. DOI: 10.13386/j.issn1002-0306.2014.06.071
HUANG Gan-hui, SHI Lei, ZENG Xiang-sheng, DENG Dan-wen. Comprehensive sensors array recognization for the raw milk adulteration[J]. Science and Technology of Food Industry, 2014, (06): 63-66. DOI: 10.13386/j.issn1002-0306.2014.06.071
Citation: HUANG Gan-hui, SHI Lei, ZENG Xiang-sheng, DENG Dan-wen. Comprehensive sensors array recognization for the raw milk adulteration[J]. Science and Technology of Food Industry, 2014, (06): 63-66. DOI: 10.13386/j.issn1002-0306.2014.06.071

综合型传感器阵列对掺杂生乳的识别

Comprehensive sensors array recognization for the raw milk adulteration

  • 摘要: 以钯、铂、金、钛、钨和银6种自制电极组为工作电极,与Ag/AgCl参比电极和铂对电极组成非选择性传感器组,再结合pH电极、电导率电极和钠度计电极3种选择性电极组成综合型传感器阵列,连接到电化学工作站和安装SPSS统计学软件电脑后,构成生乳掺杂检测系统。对荷斯坦牛生乳及掺杂不同浓度尿素和三聚氰胺,碳酸氢钠的样本乳、陈放乳、巴氏乳、酸败乳采用电化学方法进行检测,获得开路电位、交流阻抗谱、微分脉冲伏安数据,非选择性与选择性传感器组数据分别采用模式识别法进行计算,并获得各被测样本与生乳标准数据库的欧氏距离,再以不同权重得出综合欧氏距离,通过图表得到表达。结果表明,非选择性传感器阵列组对掺杂生乳具有良好的判别能力,在此基础上构建的加装有选择性传感器的综合型传感器阵列对掺杂的甑别效果优于前者。 

     

    Abstract: The comprehensive sensors array was comprised of nonselective detectors and selective detectors.The nonselective detectors array consists of work electrode (such as palladium, platinum, gold, titanium, wolfram and silver electrodes) , Ag/AgCl reference electrode and platinum auxiliary electrode. The selective detectors array was composed of pH electrode, conductivity electrode and sodium ion analyzer electrode. And then established the raw milk adulteration detecting system by connected to electrochemical workstation and computer with the statistics software SPSS. The Holstein cattle raw milk, the milk samples adultered with different concenrtration of ureophil, melamine and sodium bicarbonate, aged milk, pasteurized milk, rancid milk were detected through this system with electrochemical method. The open circuit potential, ac impedance spectrascopy and differential voltammetry data was obtained by nonselective and selective detectors. Then these data were dealt with through pattern recognition methodes, and the euclidean distances between different samples with standard raw milk database. The comprehensive eucilidean distances were calculated in different statistical weight, and displayed through chart. The results showed that the nonselective sensors array had good distinguished ability to the adultered raw milk, however, the comprehensive sensors array was much better in distinguishing adulteration.

     

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