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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Shear force analysis by core location in Longissimus steaks from Nellore cattle using hyperspectral images - A feasibility study

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Author(s):
Balage, Juliana Monteiro [1] ; Amigo, Jose Manuel [2, 3] ; Antonelo, Daniel Silva [1] ; Mazon, Madeline Rezende [1] ; da Luz e Silva, Saulo [1]
Total Authors: 5
Affiliation:
[1] Univ Sao Paulo, Dept Anim Sci, Coll Anim Sci & Food Engn, Ave Duque de Caxias Norte 225, Caixa Postal 23, BR-13635900 Pirassunungo, SP - Brazil
[2] Univ Copenhagen, Fac Life Sci, Dept Food Sci Qual & Technol, Rolighedsvej 30, DK-1958 Frederiksberg C - Denmark
[3] Univ Fed Pernambuco, Dept Fundamental Chem, Av Prof Moraes Rego, 1235 Cidade Univ, Recife, PE - Brazil
Total Affiliations: 3
Document type: Journal article
Source: MEAT SCIENCE; v. 143, p. 30-38, SEP 2018.
Web of Science Citations: 1
Abstract

Industry requires non-destructive real-time methods for quality control of meat in order to improve production efficiency and meet consumer expectations. Near Infrared Hyperspectral Images were used for tenderness evaluation of Nellore beef and the construction of tenderness distribution maps. To investigate whether the selection of the region of interest (ROI) in the image at the exact location where the shear force core was collected improves tenderness prediction and classification models, 50 samples from Longissimus muscle were imaged (1000-2500 nm) and shear force were measured (Warner-Bratzler). The data were analyzed by chemometric techniques (Partial Least Squares together with discriminant analysis - PLS-DA). Classification models using local ROI presented better performance than the ROI models of the whole sample (external validation sensitivity for the tough class = 33% and 70%, respectively), but none could be considered as successful model. However, the more general model had better performance in the tenderness distribution maps, with 72% of predicted images correctly classified. (AU)

FAPESP's process: 15/00293-3 - Beef quality evaluation using near infrared hyperspectral imaging
Grantee:Juliana Monteiro Balage
Support Opportunities: Scholarships in Brazil - Doctorate