000129566 001__ 129566
000129566 005__ 20241125101132.0
000129566 0247_ $$2doi$$a10.3390/foods12244426
000129566 0248_ $$2sideral$$a135831
000129566 037__ $$aART-2023-135831
000129566 041__ $$aeng
000129566 100__ $$0(orcid)0000-0001-8042-8688$$aRipoll, Guillermo
000129566 245__ $$aA Machine Learning Approach Investigating Consumers’ Familiarity with and Involvement in the Just Noticeable Color Difference and Cured Color Characterization Scale
000129566 260__ $$c2023
000129566 5060_ $$aAccess copy available to the general public$$fUnrestricted
000129566 5203_ $$aThe aim of this study was to elucidate the relations between the visual color perception and the instrumental color of dry-cured ham, with a specific focus on determining the Just Noticeable Color Difference (JNCD). Additionally, we studied the influence of consumer involvement and familiarity on color-related associations and JNCD. Slices of ham were examined to determine their instrumental color and photos were taken. Consumers were surveyed about color scoring and matching of the pictures; they were also asked about their involvement in food, familiarity with cured ham, and sociodemographic characteristics. Consumers were clustered according to their level of involvement and the JNCD was calculated for the clusters. An interpretable machine learning algorithm was used to relate the visual appraisal to the instrumental color. A JNCD of ΔEab* = 6.2 was established, although it was lower for younger people. ΔEab* was also influenced by the involvement of consumers. The machine-learning algorithm results were better than those obtained via multiple linear regressions when consumers’ psychographic characteristics were included. The most important color variables of the algorithm were L* and hab. The findings of this research underscore the impact of consumers’ involvement and familiarity with dry-cured ham on their perception of color.
000129566 536__ $$9info:eu-repo/grantAgreement/ES/DGA/A17-20R$$9info:eu-repo/grantAgreement/ES/MINECO/AGL2016-78532-R
000129566 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000129566 590__ $$a4.7$$b2023
000129566 592__ $$a0.87$$b2023
000129566 591__ $$aFOOD SCIENCE & TECHNOLOGY$$b38 / 173 = 0.22$$c2023$$dQ1$$eT1
000129566 593__ $$aFood Science$$c2023$$dQ1
000129566 593__ $$aHealth (social science)$$c2023$$dQ1
000129566 593__ $$aPlant Science$$c2023$$dQ1
000129566 593__ $$aHealth Professions (miscellaneous)$$c2023$$dQ1
000129566 593__ $$aMicrobiology$$c2023$$dQ2
000129566 594__ $$a7.4$$b2023
000129566 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000129566 700__ $$0(orcid)0000-0002-0572-9509$$aPanea, Begoña
000129566 700__ $$0(orcid)0000-0002-3005-2675$$aLatorre, María Ángeles$$uUniversidad de Zaragoza
000129566 7102_ $$12008$$2700$$aUniversidad de Zaragoza$$bDpto. Produc.Animal Cienc.Ali.$$cÁrea Producción Animal
000129566 773__ $$g12, 24 (2023), 4426 [16 pp.]$$pFoods$$tFoods$$x2304-8158
000129566 8564_ $$s2315925$$uhttps://zaguan.unizar.es/record/129566/files/texto_completo.pdf$$yVersión publicada
000129566 8564_ $$s2823064$$uhttps://zaguan.unizar.es/record/129566/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000129566 909CO $$ooai:zaguan.unizar.es:129566$$particulos$$pdriver
000129566 951__ $$a2024-11-22-11:59:17
000129566 980__ $$aARTICLE