000095884 001__ 95884
000095884 005__ 20220405150420.0
000095884 0247_ $$2doi$$a10.3390/foods9010069
000095884 0248_ $$2sideral$$a116816
000095884 037__ $$aART-2020-116816
000095884 041__ $$aeng
000095884 100__ $$0(orcid)0000-0001-9660-8579$$aCalanche, J.$$uUniversidad de Zaragoza
000095884 245__ $$aDesign of predictive tools to estimate freshness index in farmed sea bream (Sparus aurata) stored in ice
000095884 260__ $$c2020
000095884 5060_ $$aAccess copy available to the general public$$fUnrestricted
000095884 5203_ $$aThis research studied sea bream freshness evolution through storage time in ice by determining different quality parameters and sensory profiles. Predictive models for freshness index, storage time, and microbial counts were designed from these data. Physico-chemical parameters were assessed to evaluate the quality of fish; microbial growth was controlled to ensure food safety, and sensory analyses were carried out to characterize quality deterioration. Predictive models were developed and improved with the aim of being used as tools for quality management in the seafood industry. Validation was conducted in order to establish the accuracy of models. There was a good relationship between the physico-chemical and microbiological parameters. Sensory analysis and microbial counts allowed for the establishment of a shelf-life of 10 days, which corresponded to a poor quality (according to the European Community''s system of grading fish for marketing purposes), with a freshness index lower than 50%. Sensory profiles showed that gill and flesh texture were the most vulnerable attributes during storage in ice related to spoilage. The predictive models for the freshness index (%) and ice storage time (h) exhibited an accuracy close to 90% following practical validation.
000095884 536__ $$9info:eu-repo/grantAgreement/ES/DGA/Grupo Meat Science and Technology
000095884 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000095884 590__ $$a4.35$$b2020
000095884 591__ $$aFOOD SCIENCE & TECHNOLOGY$$b37 / 144 = 0.257$$c2020$$dQ2$$eT1
000095884 592__ $$a0.774$$b2020
000095884 593__ $$aFood Science$$c2020$$dQ1
000095884 593__ $$aHealth (social science)$$c2020$$dQ1
000095884 593__ $$aPlant Science$$c2020$$dQ1
000095884 593__ $$aMicrobiology$$c2020$$dQ1
000095884 593__ $$aHealth Professions (miscellaneous)$$c2020$$dQ1
000095884 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000095884 700__ $$aPedrós, S.
000095884 700__ $$0(orcid)0000-0003-2205-6913$$aRoncalés, P.$$uUniversidad de Zaragoza
000095884 700__ $$0(orcid)0000-0002-3764-0189$$aBeltrán, J. A.$$uUniversidad de Zaragoza
000095884 7102_ $$12008$$2780$$aUniversidad de Zaragoza$$bDpto. Produc.Animal Cienc.Ali.$$cÁrea Tecnología de Alimentos
000095884 773__ $$g9, 1 (2020), 69 [16 pp]$$pFoods$$tFoods$$x2304-8158
000095884 8564_ $$s714666$$uhttps://zaguan.unizar.es/record/95884/files/texto_completo.pdf$$yVersión publicada
000095884 8564_ $$s487965$$uhttps://zaguan.unizar.es/record/95884/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000095884 909CO $$ooai:zaguan.unizar.es:95884$$particulos$$pdriver
000095884 951__ $$a2022-04-05-14:39:37
000095884 980__ $$aARTICLE