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羊肉纯度电子舌快速检测方法

田晓静 王俊 崔绍庆

农业工程学报Issue(20):255-262,8.
农业工程学报Issue(20):255-262,8.DOI:10.3969/j.issn.1002-6819.2013.20.033

羊肉纯度电子舌快速检测方法

Fast discriminating of purity on minced mutton using electronic tongue

田晓静 1王俊 2崔绍庆1

作者信息

  • 1. 浙江大学生物系统工程与食品科学学院,杭州 310058
  • 2. 西北民族大学生命科学与工程学院,兰州 730024
  • 折叠

摘要

Abstract

Cheaper animal protein, such as Chicken as an example, has been fraudulently used as a substitute for more expensive animal proteins, like mutton and beef. The adulteration of mutton has attracted increasing attention. It requires reliable methods for the authentication of mutton adulteration. An electronic tongue with chemically modified field-effect-transistor sensors was employed to analysis the adulteration of chicken in minced mutton. The effects of sample weight on the sensor responses of electronic tongue were studied at three different extraction solutions. Analysis of variance found that the sample weight affected the responses of the sensor significantly. With the help of Principle component analysis (PCA), the optimum experimental parameters were acquired:15 g sample extracted by 100 mL KCl solution. The adulterated mutton was made by mixing mutton with chicken at levels of 0, 20%, 40%, 60%, 80%, and 100% by weight, respectively. With the optimum experimental parameters, 168 samples of adulterated mutton were detected, and the signals were analyzed by pattern recognition techniques to build models for classification of adulterated mutton with different content of chicken, and prediction of the content of chicken in minced mutton. With PCA, the adulterated mutton samples were grouped according to their content of chicken with good classification results, except that samples containing 80%and 100%chicken partially overlapped with each other. Better classification results were found when canonical discriminant analysis (CDA) was employed, as samples containing 80% and 100% chicken were clearly grouped and separated. Multiple linear regression (MLR) and Partial least square analysis (PLS) were employed to build the predictive model for the content of chicken adulterated into minced mutton. Both models could predict the adulteration with a high determination coefficient as high as 0.9925 and 0.9923, respectively. MLR was more effective for the prediction of chicken content. The E-nose proved to be a useful authentication method for meat adulteration detection for its efficiency and high accuracy.

关键词

/主成分分析/模型/电子舌/判别分析/掺假羊肉

Key words

meats/principle component analysis/models/Electronic tongue/discriminant analysis/adulteration of muttons

分类

轻工纺织

引用本文复制引用

田晓静,王俊,崔绍庆..羊肉纯度电子舌快速检测方法[J].农业工程学报,2013,(20):255-262,8.

基金项目

国家科学部支撑计划(2012BAD29B02-4);国家自然科学基金(31071548);博士点基金20100101110133。 ()

农业工程学报

OA北大核心CSCDCSTPCD

1002-6819

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