Sichuan Higher Education Institution

Chengdu, China

Sichuan Higher Education Institution

Chengdu, China

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Jia H.,Sichuan University | Jia H.,Sichuan Higher Education Institution | Deng H.,Sichuan University | Liang A.,Sichuan University | Liang A.,Sichuan Higher Education Institution
Journal of the Chinese Cereals and Oils Association | Year: 2013

In this study, the recognition of sesame oil essence adulteration in sesame oil was performed using an electronic nose system. The principal component analysis (PCA), discriminant factor analysis (DFA) and partial least-squares analysis (PLS) and statistical quality control analysis (SQC) were conducted on the obtained data. The results indicated that different samples had different characteristic response signals in electronic nose sensor, the adulteration of sesame oil sample with different proportions could be recognized effectively and DFA was more competent than PCA in distinguishing effect. Sesame oils adulterated with sesame oil essence at levels of adulteration exceeding 50% were distinguished easily by SQC. There was high correlation between electronic nose sensors' response signals and the adulteration ratio of sesame oil essence (correlation coefficient=0.9921) in processing data by the PLS model. PLS could effectively identify the experimental samples of sesame oils adulterated with 0%~100% sesame oil essence. It proved in the test that electronic nose could be applied in adulteration recognition of sesame oil.


Jia H.,Sichuan Higher Institute of Cuisine | Jia H.,Sichuan Higher Education Institution | Lu Y.,Sichuan Higher Institute of Cuisine | Lu Y.,Sichuan Higher Education Institution | And 9 more authors.
Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering | Year: 2011

In order to discuss the feasibility of meat adulteration recognition based on electronic nose, an electronic nose was used to analyze yak meat, beef and pork. The response signals were analyzed by principal component analysis (PCA), discriminant factor analysis (DFA) and partial least-squares analysis (PLS). The results indicated that yak meat, beef and pork samples had different characteristic response signals. Electronic nose could recognize yak meat, beef and pork, and also could recognize yak meat and beef samples at different growing locations, but this recognition was not suitable for pork at different growing locations. And it could also identify minced beef added with different ratio of minced pork. Coefficient of determination between sensors response signals and the ratio of minced pork of PLS model was 0.9762. The prediction error of PLS model was within 7.00%. It was proved that electronic nose could be applied in meat recognition.

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