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

• Published in: Journal of animal science Vol. 96; no. 10; pp. 4229 - 4237
• Main Authors: Magalhães, Ana Fabrícia Braga, Teixeira, Gustavo Henrique de Almeida, Ríos, Ana Cristina Herrera, Silva, Danielly Beraldo Dos Santos, Mota, Lúcio Flávio Macedo, Muniz, Maria Malane Magalhães, de Morais, Camilo de Lelis Medeiros, de Lima, Kássio Michell Gomes, Cunha Júnior, Luis Carlos, Baldi, Fernando, Carvalheiro, Roberto, Oliveira, Henrique Nunes de, Chardulo, Luis Artur Loyola, Albuquerque, Lucia Galvão de
• Format: Journal Article
• Language: English
• Published: United States 29-09-2018
• Subjects: Animals; Calibration; Cattle - growth & development; Cattle - physiology; Color; Feasibility Studies; Least-Squares Analysis; Male; Phenotype; Red Meat - standards; Spectroscopy, Near-Infrared - veterinary
• ISSN: 1525-3163
• DOI: 10.1093/jas/sky284

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 (R2C), and prediction (R2P) 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, R2P = 0.40) and b* color (RMSEP = 1.21, R2P = 0.44), while its ability to accurately predict L* (RMSEP = 1.98, R2P = 0.16) and a* (RMSEP = 1.42, R2P = 0.17) was limited. NIRS was unsuitable to predict subjective meat quality traits such as marbling in Nelore cattle.