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

An electronic nose based quality predictive model of grass carp (Ctenopharyngodon idellus) stored at 277K 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 analy...

Full description

Saved in:
Bibliographic Details
Published in:Sensors and actuators. B, Chemical Chemical, 2012-05, Vol.166-167, p.301-308
Main Authors: Guohua, Hui, Lvye, Wang, Yanhong, Mo, Lingxia, Zhang
Format: Article
Language:English
Subjects:
Ctenopharyngodon idella
Electronic nose
foods
Grass carp
prediction
principal component analysis
Quality prediction
Signal-to-noise ratio
Stochastic resonance
temperature
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:An electronic nose based quality predictive model of grass carp (Ctenopharyngodon idellus) stored at 277K 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.
ISSN:0925-4005
1873-3077
DOI:10.1016/j.snb.2012.02.066