Multivariate evaluation of physiological response and heat emission in Quarter horses supplemented with Amazonian Tucumã oil (Astrocaryum vulgare)

Abstract

The aim of this study was to evaluate, through multivariate analysis, the effects of supplementation with Amazonian tucumã oil (Astrocaryum vulgare) on physiological and thermal variables in Quarter Horses. A total of 17 Quarter Horses (9 males and 8 females) were randomly assigned to one of three dietary treatments: 1) CON (no supplementation), 2) TUC (supplemented with 20 mL/day of tucumã oil), and 3) LAC (supplemented with 20 g/day of a probiotic blend based on Lactobacillus spp.). The animals received a 50:50 roughage-to-concentrate diet composed of Cynodon spp. hay and a commercial concentrate (CP = 13%, DE = 3.2 Mcal/kg, DM = 89%, Fat = 5%). Physiological and thermal parameters were measured at 7:00 a.m. post-supplementation, including rectal and skin temperatures (RT, ST), respiratory rate (RR), heart rate (HR), and infrared heat emission from the eyes, ears, muzzle, and face. Data were analyzed using the PROC PRINCOMP procedure (SAS 9.2). Principal component analysis (PCA) was conducted using ten physiological and thermal variables measured on days 0 and 15. The first two principal components explained 55.2% of the total variance, with 32.8% attributed to PC1 and 22.4% to PC2. Distinct grouping patterns were observed, influenced by both treatment and time, with a clear shift in Day 15 observations—particularly in the TUC and LAC groups—along the PC1 and PC2 axes. The variables that contributed most to PC1 were RR, ST, and right eye emission, whereas PC2 was mainly driven by RT and left eye emission. These findings suggest that the combination of physiological and surface thermal parameters effectively distinguishes metabolic responses according to supplementation type and evaluation period. Overall, PCA proved to be a sensitive tool for detecting physiological changes related to supplementation. Infrared thermography, combined with basic physiological measurements, may be an efficient method for monitoring metabolic responses in horses.