Application of the principal component analysis, cluster analysis, and partial least square regression on crossbreed Ang
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Application of the principal component analysis, cluster analysis, and partial least square regression on crossbreed Angus-Nellore bulls feedlot finished Lucas S. F. Lopes 1 & Mateus S. Ferreira 1 & Welder A. Baldassini 2 & Rogério A. Curi 2 & Guilherme L. Pereira 2 & Otávio R. Machado Neto 1,2 & Henrique N. Oliveira 1 & J. Augusto II V. Silva 1,2 & Danísio P. Munari 1 & Luis Artur L. Chardulo 1,2 Received: 30 January 2020 / Accepted: 11 September 2020 # Springer Nature B.V. 2020
Abstract Principal component analysis (PCA) and the non-hierarchical clustering analysis (K-means) were used to characterize the most important variables from carcass and meat quality traits of crossbred cattle. Additionally, partial least square (PLS) regression analysis was applied between the carcass measurements and meat quality traits on the classes defined by the cluster analysis. Ninety-seven non-castrated F1 Angus-Nellore bulls feedlot finished were used. After slaughter, hot carcass weight, carcass yield, cold carcass weight, carcass weight losses, pH, and backfat thickness (BFT) were measured. Subsequently, samples of the longissimus thoracis were collected to analyze shear force (SF), cooking loss (CL), meat color (L*, chroma, and hue), intramuscular fat, protein, collagen, moisture, and ashes. Principal component 1 (PC1) was correlated with colorimetric variables, while PC2 was correlated with carcass weights. Afterwards, three clusters (k = 3) were formed and projected in the gradient defined by PC1 and PC2 and allowed distinguishing groups with divergent values for collagen, protein, moisture, CL, SF, and BFT. Animals from high chroma group presented meat with more attractive colors and tenderness (SF = 1.97 to 4.84 kg). Subsequently, the PLS regression on the three chroma groups revealed a good fitness and the coefficients are used to predict the chroma variable from the explanatory variables, which may have practical importance in attempts to predict meat color from carcass and meat quality traits. Thus, PCA, K-means, and PLS regression confirmed the relationship between meat color and tenderness. Keywords Beef cattle . Carcass . Meat color . Multivariate statistics . Tenderness
Introduction Brazil is one of the world’s major beef producer and exporter. Roughly 80% of the Brazilian beef cattle herd is comprised of Zebu cattle (Bos indicus), in which the Nellore breed is the Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11250-020-02402-7) contains supplementary material, which is available to authorized users. * Welder A. Baldassini [email protected] 1
College of Agriculture and Veterinary Science (FCAV), São Paulo State University (UNESP), Jaboticabal, São Paulo, Brazil
2
College of Veterinary Medicine and Animal Science (FMVZ), São Paulo State University (UNESP), Botucatu, São Paulo, Brazil
most used (Ferraz and Felício 2010). Despite their adaptation to tropical climate, Zebu animals take longer to reach targeted sales weight compared to taurine
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