Non-invasive meat quality assessment: Exploring the potential of ocular infrared thermography to predict ultimate pH in Nellore beef cattle

Abstract

This study investigated the use of infrared thermography (IRT) to identify the dark, firm, and dry (DFD) phenomenon in Brazilian beef, which is a significant concern for the industry because of its inferior quality and reduced shelf life. This study examined 113 Nellore bulls and analyzed their minimum and maximum ocular temperatures using IRT. The results highlight the efficacy of thermal images (IRTmax) as a significant predictor, with R2 values ranging from 0.84 to 0.88 for calibration models. The inclusion of parameters such as glucose and lactate further enhanced prediction accuracy. The models also revealed that the combination of features, such as lightness (L*), redness (a*), and yellowness (b*), contributed to the precise prediction of pHu, with an R2 of 0.88. In model validation, RMSEP ranged from 0.104 to 0.158, indicating good generalization capability. The RPD, ranging from 1.7 to 2.6, suggests satisfactory quantitative prediction. The statistical significance of all models, evidenced by P-values <0.001, strengthens the reliability of the results. In conclusion, the models support the use of IRT as a tool for identifying pHu alterations in carcasses. When combined with blood parameters, they may exhibit even greater efficiency in predicting pHu in Nelore cattle carcasses, highlighting the potential applicability of these methods in the beef industry.