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当前位置: 首页 > 文章 > 专感器阵列对生乳及其若干形态的差异性识别 食品科学 2013,34 (12) 208-212
Position: Home > Articles > Sensor Array for Discrimination between Raw Milk and Deteriorated Milk FOOD SCIENCE 2013,34 (12) 208-212

专感器阵列对生乳及其若干形态的差异性识别

作  者：
曾祥盛;邓丹雯;石磊;黄赣辉
单  位：
关键词：
生乳;微分脉冲伏安法;主成分分析;欧式距离;传感器阵列
摘  要：
以惰性金属裸电极为基础,对生乳、巴氏乳、陈旧乳、酸败乳以及陈乳掺杂生乳共5类研究对象进行响应区分,考察传感器阵列对生乳及其若干形态的差异性识别能力.对各类研究样品进行微分脉冲伏安法检测,并通过单因素方差分析和主成分分析处理检测结果数据,以单因素方差分析的组内均方值和P值或雅对单一传感器在该测试体系下的响应性能进行初筛和分组,以主成分分析对各种传感器阵列组合的区分辨别效果进行检验,最终确立最优的传感器阵列组合.结果表明:类问欧式距离均大于2个单位,各类样品在主成分得分图中彼此能够较好地分开;以铂、金、钛电极组成的传感器阵列,采用微分脉冲伏安法,对数据横向叠加处理,可通过欧式距离和主成分分析对6种样本液态乳进行有效区分.
译  名：
Sensor Array for Discrimination between Raw Milk and Deteriorated Milk
关键词：
raw milk: differential pulse voltammetry: principal components analysis%euclidean distance%sensor array
摘  要：
The purpose of this study was to discriminate among raw milk, pasteurized milk, aged milk, rancid milk and adulterated milk based on sensor arrays constructed with inert metal electrodes and to investigate identification ability of the sensor array for different milk samples. The data obtained from the determination of samples by differential pulse voltammetry (DPV) were analyzed by one-way analysis of variance (ANOVA) and principal component analysis (PCA). Six sensors were primarily screened and assigned to different groups by response performance with respect to the ANOVA mean square values as well as P or F values, and the discrimination efficiencies of different combinations of the sensors were tested by PCA. As a result, optimal sensor combination was establsihed. The results showed that Euclidean distance above 2 between every two samples and good separation of all the samples in the PCA plots. Detection by DPV with a sensor array consisting of palladium, platinum and gold electrodes and horizonal summation of acquired data allowed effective disrimination among the six milk samples by Euclidean distance and PCA.

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