Study of grass carp (Ctenopharyngodon idellus) quality predictive model based on electronic nose

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• 作者：Hui Guohua ; ^{ghui@zjgsu.edu.cn}  ; [Author Vitae] ; Wang Lvye  ; [Author Vitae] ; Mo Yanhong  ; [Author Vitae] ; Zhang Lingxia  ; [Author Vitae]
• 关键词：Electronic nose ; Grass carp ; Quality prediction ; Stochastic resonance ; Signal-to-noise ratio
• 刊名：Sensors and Actuators B ; Chemical
• 出版年：2012
• 期刊代码：161_09254005
• 类别：et
• 出版时间：20 May, 2012
• 卷：166-167
• 期：Complete
• 页码：301-308
• 文件大小：1057 K

摘要

An electronic nose based quality predictive model of grass carp (Ctenopharyngodon idellus) stored at 277 K temperature was proposed in this paper. The changes of sensor array response to samples were caused by the new-generated gas species released by microbial propagations. Principal component analysis method discriminated fresh grass carp samples from medium samples and aged samples. Stochastic resonance signal-to-noise ratio maximums distinguished fresh, medium, and aged grass carp samples successfully. The quality predicting model was developed based on signal-to-noise ratio maximums non-linear fitting regression. Validating experiments demonstrated that the predicting accuracy of this model was 87.5%. This method presented some advantages including easy operation, quick response, high accuracy, good repeatability, etc. This method is promising in aquatic food products quality evaluating applications.