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• W2012484293 abstract "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." @default.
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• W2012484293 date "2009-09-01" @default.
• W2012484293 modified "2023-09-25" @default.
• W2012484293 title "Monitoring storage time and quality attribute of egg based on electronic nose" @default.
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• W2012484293 doi "https://doi.org/10.1016/j.aca.2009.07.049" @default.
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