A sensometric approach to the development of mortadella with healthier fats. (March 2018)

• Record Type: Journal Article
• Authors: Saldaña, Erick; de Oliveira Garcia, Aline; Selani, Miriam Mabel; Haguiwara, Marcia M.H.; de Almeida, Marcio Aurelio; Siche, Raúl; Contreras-Castillo, Carmen J.
• Is Part Of: Meat science. Volume 137(2018)
• Page Start: 176
• Page End: 190
• Publication Date: 2018-03
• Subjects: Meat product -- Descriptive analysis -- Check-all-that-apply questions -- Penalty analysis; Meat -- Periodicals; Meat industry and trade -- Periodicals; Viande -- Périodiques; Viande -- Industrie -- Périodiques; Meat; Meat industry and trade; Periodicals; 641.36
• DOI: 10.1016/j.meatsci.2017.11.027
• Languages: English
• ISSNs: 0309-1740

Abstract

Abstract: The aim of this study was to evaluate the sensory characteristics of mortadellas with different fat contents using Descriptive Analysis (DA) and Check-all-that-apply (CATA) questions and their relationship with overall liking (OL). Five mortadella samples were studied. Sample 1, containing 16% fat. Sample 2 was formulated with 8% fat. Samples 3 and 4 were prepared by replacing 50% fat by a pre-emulsion composed of fish, canola and olive oil. A commercial fat-reduced sample was also studied. The sensory characteristics of the samples were evaluated by DA. Besides, eighty-four consumers evaluated their OL using a 9-point scale and answered the CATA questions. Replacement or reduction of fat caused changes in the sensory characteristics of the samples. Commercial mortadella was the most liked. CATA questions along with penalty analysis and partial least squares regression (PLSR) of dummy variables on the OL helped to identify the attributes to be changed in the mortadella formulations. Highlights: Sensory characteristics of healthy mortadellas using DA and CATA questions were studied. Mortadellas formulated with animal fat substitutions (pre-emulsions) were the least accepted. Mortadella reformulation promoted changes in texture attributes. Penalty analysis and PLSR helped to identify the main drivers of liking.