Monitoring storage time and quality attribute of egg based on electronic nose.
The objective of this study was to investigate the potential of an electronic nose (E-nose) technique for monitoring egg storage time and quality attributes. An electronic nose was used to distinguish eggs under cool and room-temperature storage by means of principal component analysis (PCA), linear discriminant analysis (LDA), BP neural network (BPNN) and the combination of a genetic algorithm and BP neural network (GANN). Results showed that the E-nose could distinguish eggs of different storage time under cool and room-temperature storage by LDA, PCA, BPNN and GANN; better prediction values were obtained by GANN than by BPNN. Relationships were established between the E-nose signal and egg quality indices (Haugh unit and yolk factor) by quadratic polynomial step regression (QPSR). The prediction models for Haugh unit and yolk factor indicated a good prediction performance. The Haugh unit model had a standard error of prediction of 3.74 and correlation coefficient 0.91; the yolk factor model had a 0.02 SEP and 0.93 correlation coefficient between predicted and measured values respectively.
Wang
Yongwei
Y
Department of Biosystems Engineering, Zhejiang University, 268 Kaixuan Road, Hangzhou 310029, PR China.
Wang
Jun
J
Zhou
Bo
B
Lu
Qiujun
Q
D000465:Algorithms; D000818:Animals; D016002:Discriminant Analysis; D004531:Eggs / Q000592:standards*; D004581:Electronics / Q000379:methods; D005511:Food Handling / Q000379:methods*; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D013997:Time Factors; D014835:Volatilization