ABSTRACT
ABSTRACT The aim of this study was to classify and characterize the compositional quality of milk from river buffaloes (Bubalus bubalis) and cows (Bos spp.) in Colombia based on the fat, protein, and total solid (TS) contents. Using a hierarchical procedure, data on milk from river buffaloes (n = 7,726) and cows (n = 49,330) were filtered and subjected to cluster analysis in order to generate three groups: Normal (N), High quality (HQ) and Poor Quality (PQ). The categorized database was then randomly separated into two sets (training and validation) and a discriminant analysis was applied. In total, 37.3% of river buffalo milk samples were classified as N (6.80% fat, 4.34% protein, and 16.80% TS), 13% as HQ (9.41% fat, 4.93% protein, and 19.50% TS), and 43.7% as PQ (3.95% fat, 3.92% protein, and 13.7% TS). In contrast, 41.8% of cow milk samples were classified as N (3.64% fat, 3.37% protein, 12.42% TS), 41.2% as PQ (2.71% fat, 3.08% protein, and 10.6% TS), and 16.9% as HQ (5.46% fat, 4.01% protein, and 14.82% TS). The discriminant models for both river buffalo and cow milk were able to classify milk in the N and PQ groups with >90% accuracy, and that in the HQ group with >85% accuracy.
RESUMEN El objetivo de este estudio fue caracterizar y clasificar la calidad composicional de la leche de búfala (Bubalus bubalis) y de vaca (Bos spp.) en Colombia con base en los contenidos de grasa, proteína y sólidos totales. Mediante un procedimiento jerárquico los datos de leche de búfalos de agua (n = 7,726) y vacas (n = 49,330) se filtraron y se sometieron a análisis de conglomerados para generar tres grupos: Normal (N), Alta calidad (HQ) y Calidad deficiente (PQ). La base de datos categorizada se separó aleatoriamente en dos conjuntos (entrenamiento y validación) y se aplicó un análisis discriminante. En total, 37,3% de las muestras de leche de búfalo de agua se clasificaron como N (6,80% de grasa, 4,34% de proteína y 16,80% de TS); 13% como HQ (9,41% de grasa, 4,93% de proteína y 19,50% de TS) y 43,7 % como PQ (3,95% de grasa, 3,92% de proteína y 13,7% de TS). En contraste, el 41,8% de las muestras de leche de vaca se clasificaron como N (3,64% grasa, 3,37% proteína, 12,42% TS); 16,9% como HQ (5,46% de grasa, 4,01% de proteína y 14,82% de TS) y 41,2% como PQ (2,71% grasa, 3,08% proteína y 10,6% TS). Los modelos discriminantes para el búfalo de agua y la leche de vaca fueron capaces de clasificar la leche en los grupos N y PQ con una precisión >90% y en el grupo HQ con >85% de precisión.
ABSTRACT
Background: Poor reproductive efficiency of river buffalos hampers the production capabilities of animals. Buffalos are mainly considered poor breeders owing to the constrained expression of estrus behavior. Failure to display heat signs is an indication of improper functionality of signaling peptides to trigger on a series of behavioral changes, which can be detectable by breeders for timely insemination of females. This might cause an animal to be a repeat breeder. Genomic variations underlying synthesis of signaling peptides can be a useful marker to select superior animals with better reproductive efficiency. In this context, the current study was designed to analyze the CYP19A1 gene in Nili-Ravi buffalo. Results: A total of 97 animals were selected and were divided into two groups on the basis of their heat score. PCR amplification and sequencing of the amplicons were performed using the specific sets of primer, and then, sequences were analyzed for novel variants. A total of 11 polymorphic sites were identified illustrating phenotypic variation in the heat score. Most of the loci were found homologous. Single Nucleotide Polymorphisms (SNPs) were analyzed for association with silent estrus. A three-dimensional protein model was also generated to locate the position of exonic SNPs. Conclusion: This study illustrated that polymorphic sites in the CYP19A1 gene provided potential markers for selection of buffalos with better estrus behavior.