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The pH value, glucose content, glycolytic potential, and expression levels of five glycolytic enzymes in lamb longissimus dorsi muscle at different time after slaughter were compared and analyzed, which treated at different cooling rates and stored at different temperatures. The impact of two steps in the process of ultra-fast chilling on the expression levels of glycolytic enzymes was determined. The regulatory mechanism of ultra-fast chilling on the glycolytic rate was elucidated from the perspective of protein expression. The results showed that ultra-fast chilling treatment significantly delayed the decrease of pH and the increase of glycolytic rate, promoted the expression levels of aldolase (ALDOA), glycogen phosphorylase (PYGM), and triosephosphate isomerase (TPI1), and inhibited the expression levels of phosphofructokinase (PFKM) and phosphoglycerate kinase (PGK). After cooling treatment, refrigeration and controlled freezing-point storage delayed glycolysis by inhibiting the expression level of PGK, without altering the effect of ultra-fast chilling. The expression level of PFKM was positively correlated with the rate of glycolysis at different temperatures. It was found that different glycolytic enzymes had different responses to temperature changes. Ultra-fast chilling affected energy supply and demand by changing the expression of enzymes involved in glycolysis. The high expression level of PFKM was associated with fast glycolysis. PFKM can be regarded as a key enzyme in the ultra-fast chilling process.

The gait phase of dairy cows is an important indicator to reflect the severity of lameness. IThe accuracy of available gait segmentation methods was not enough for lameness detection. In this study, a gait phase recognition method based on Gaussian mixture model (GMM) and hidden Markov model (HMM) was proposed and tested. Firstly, wearable inertial sensors LPMS-B2 were used to collect the acceleration and angular velocity signals of cow hind limbs. In order to remove the noise of the system and restore the real dynamic data, Kalman filter was used for data preprocessing. The first-order difference of the angular velocity of the coronal axis was selected as the eigenvalue. Secondly, to analyze the long-term continuous recorded gait sequences of dairy cows, the processed data was clustered by GMM in the unsupervised way. The clustering results were taken as the input of the HMM, and the gait phase recognition of dairy cows was realized by decoding the observed data. Finally, the cow gait was segmented into 3 phases, including the stationary phase, standing phase and swing phase. At the same time, gait segmentation was achieved according to the standing phase and swing phase. The accuracy, recall rate and F1 of the stationary phase were 89.28%, 90.95% and 90.91%, respectively. The accuracy, recall rate and F1 of the standing phase recognition in continuous gait were 91.55%, 86.71% and 89.06%, respectively. The accuracy, recall rate and F1 of the swing phase recognition in continuous gait were 86.67%, 91.51% and 89.03%, respectively. The accuracy of cow gait segmentation was 91.67%, which was 4.23% and 1.1 % higher than that of the event-based peak detection method and dynamic time warping algorithm, respectively. The experimental results showed that the proposed method could overcome the influence of the cow's walking speed on gait phase recognition results, and recognize the gait phase accurately. This experiment provides a new method for the adaptive recognition of the cow gait phase in unconstrained environments. The degree of lameness of dairy cows can be judged by the gait features.