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Prediction of egg freshness during storage using electronic nose.
Yimenu, Samuel M; Kim, J Y; Kim, B S.
Afiliación
  • Yimenu SM; Department of Food Biotechnology, University of Science and Technology (UST), Gajeong-ro, Yuseong-gu, Daejeon, 305-350, Republic of Korea.
  • Kim JY; Department of Food Science and Postharvest Technology, College of Agriculture and Environmental Sciences, Arsi University, P.O. Box 193 Asella, Ethiopia.
  • Kim BS; Smart Food Distribution Research Group, Korea Food Research Institute, 1201-62, Anyangpangyo-ro, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea.
Poult Sci ; 96(10): 3733-3746, 2017 Oct 01.
Article en En | MEDLINE | ID: mdl-28938786
ABSTRACT
The aim of the present study was to investigate the potential of a fast gas chromatography (GC) e-nose for freshness discrimination and for prediction of storage time as well as sensory and internal quality changes during storage of hen eggs. All samples were obtained from the same egg production farm and stored at 20 °C for 20 d. Egg sampling was conducted every 0, 3, 6, 9, 12, 16, and 20 d. During each sampling time, 4 egg cartons (each containing 10 eggs) were randomly selected one carton for Haugh units, one carton for sensory evaluation and 2 cartons for the e-nose experiment. The e-nose study included 2 independent test sets; calibration (35 samples) and validation (28 samples). Every sampling time, 5 replicates were prepared from one egg carton for calibration samples and 4 replicates were prepared from the remaining egg carton for validation samples. Sensors (peaks) were selected prior to multivariate chemometric analysis; qualitative sensors for principal component analysis (PCA) and discriminant factor analysis (DFA) and quantitative sensors for partial least square (PLS) modeling. PCA and DFA confirmed the difference in volatile profiles of egg samples from 7 different storage times accounting for a total variance of 95.7% and 93.71%, respectively. Models for predicting storage time, Haugh units, odor score, and overall acceptability score from e-nose data were developed using calibration samples by PLS regression. The results showed that these quality indices were well predicted from the e- nose signals, with correlation coefficients of R2 = 0.9441, R2 = 0.9511, R2 = 0.9725, and R2 = 0.9530 and with training errors of 0.887, 1.24, 0.626, and 0.629, respectively. As a result of ANOVA, most of the PLS model results were not significantly (P > 0.05) different from the corresponding reference values. These results proved that the fast GC electronic nose has the potential to assess egg freshness and feasibility to predict multiple egg freshness indices during its circulation in the supply chain.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Calidad de los Alimentos / Cromatografía de Gases / Huevos / Nariz Electrónica Tipo de estudio: Prognostic_studies / Qualitative_research / Risk_factors_studies Límite: Animals Idioma: En Revista: Poult Sci Año: 2017 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Calidad de los Alimentos / Cromatografía de Gases / Huevos / Nariz Electrónica Tipo de estudio: Prognostic_studies / Qualitative_research / Risk_factors_studies Límite: Animals Idioma: En Revista: Poult Sci Año: 2017 Tipo del documento: Article