Prediction of meat quality traits in Nelore cattle by near-infrared reflectance spectroscopy

Author(s):Show less -

Braga Magalhaes, Ana Fabricia ^[1] ; de Almeida Teixeira, Gustavo Henrique ^[1] ; Herrera Rios, Ana Cristina ^[1] ; dos Santos Silva, Danielly Beraldo ^[1] ; Macedo Mota, Lucio Flavio ^[1] ; Magalhaes Muniz, Maria Malane ^[1] ; Medeiros de Morais, Camilo de Lelis ^{[2, 3]} ; Gomes de Lima, Kassio Michell ^[3] ; Cunha Junior, Luis Carlos ^[1] ; Baldi, Fernando ^[1] ; Carvalheiro, Roberto ^[1] ; de Oliveira, Henrique Nunes ^[1] ; Loyola Chardulo, Luis Artur ^[4] ; de Albuquerque, Lucia Galvao ^[1]

Affiliation

^[1] Sao Paulo State Univ Unesp, Dept Anim Sci, Sch Agr & Veterinarian Sci, BR-14884900 Jaboticabal, SP - Brazil

^[2] Univ Cent Lancashire, Sch Pharm & Biomed Sci, Preston PR1 2HE, Lancs - England

^[3] Univ Fed Rio Grande do Norte, Inst Chem Biol Chem & Chemometr, BR-59072970 Natal, RN - Brazil

^[4] Sao Paulo State Univ Unesp, Coll Vet & Anim Sci, Dept Anim Nutr & Improvement, BR-18618970 Botucatu, SP - Brazil

• Document type: Journal article
• Source: JOURNAL OF ANIMAL SCIENCE; v. 96, n. 10, p. 4229-4237, OCT 2018.
• Web of Science Citations: 3

Abstract

The main definition for meat quality should include factors that affect consumer appreciation of the product. Physical laboratory analyses are necessary to identify factors that affect meat quality and specific equipment is used for this purpose, which is expensive and destructive, and the analyses are usually time consuming. An alternative method to performing several beef analyses is near-infrared reflectance spectroscopy (NIRS), which permits to reduce costs and to obtain faster, simpler, and nondestructive measurements. The objective of this study was to evaluate the feasibility of NIRS to predict shear force {[}Warner-Bratzler shear force (WBSF)], marbling, and color ({*} a = redness; b{*} = yellowness; and L{*} = lightness) in meat samples of uncastrated male Nelore cattle, that were approximately 2-yr-old. Samples of longissimus thoracis (n = 644) were collected and spectra were obtained prior to meat quality analysis. Multivariate calibration was performed by partial least squares regression. Several preprocessing techniques were evaluated alone and in combination: raw data, reduction of spectral range, multiplicative scatter correction, and 1st derivative. Accuracies of the calibration models were evaluated using the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), coefficient of determination in the calibration ((RC)-C-2), and prediction ((RP)-P-2) groups. Among the different preprocessing techniques, the reduction of spectral range provided the best prediction accuracy for all traits. The NIRS showed a better performance to predict WBSF (RMSEP = 1.42 kg, (RP)-P-2 = 0.40) and b{*} color (RMSEP = 1.21, (RP)-P-2 = 0.44), while its ability to accurately predict L{*} (RMSEP = 1.98, (RP)-P-2 = 0.16) and a{*} (RMSEP = 1.42, (RP)-P-2 = 0.17) was limited. NIRS was unsuitable to predict subjective meat quality traits such as marbling in Nelore cattle. (AU)

• FAPESP's process: 09/16118-5 - Genomic tools to genetic improvement of direct economic important traits in Nelore cattle
• Grantee: Lucia Galvão de Albuquerque
• Support Opportunities: Research Projects - Thematic Grants