No abstract

No abstract

No abstract

Four meat thawing techniques that are most commonly used in daily life were used: refrigerator thawing, microwave thawing, ambient temperature thawing, and water thawing, to evaluate the physico-chemical and histological alterations in thawed beef. After thawing, the structural, chemical, and physical characteristics of beef meat were evaluated. The results showed that meat thawed in the refrigerator at 4°C was characterized by the highest pH value (5.65 ± 0.02) and a significant difference (P<0.05) compared to meat thawed by other thawing methods. Also for the electrical conductivity, it reached the highest value (1.442 ± 1,012) in the microwave oven (P<0.05); meanwhile, water activity decreased significantly after thawing regardless of the thawing method (P<0.05). On the other hand, refrigerator thawing resulted in the least amount of water loss (1.23%) with P<0.05, while high levels of microwave energy caused significant water loss, represented by thawing loss and cooking loss (4.37% and 44.47%), respectively, with P<0.05. Among different thawing methods, microwave thawing had the highest level of TBARS, with a mean of 0.25 ± 0.034 mg·kg-1 (P<0.05). Regarding the color, the lightness (L*) value in the microwave-thawed samples decreased significantly (P<0.05) compared to the fresh control. Histologically, samples that were thawed in a refrigerator preserved the integrity of the fibers' structure after thawing better than other methods; samples thawed in a microwave, however, caused more structural damage. To ensure that it thaws uniformly and to retain the meat's quality as close to its fresh quality as possible, it is typically advised to thaw meat in a slower, more gradual manner, such as in the refrigerator.

Among the various methods available to determine the storage time of frozen meat, including analyses based on physical and chemical properties, sensory analysis, particularly color changes, is an important aspect of meat acceptability for consumers. In this study, an artificial neural network (ANN) was employed to predict the storage time of the meat based on the CIELAB color space, represented by the Lab* (L*), (a*), and (b*) values measured by a computer vision system at two–month intervals over a period of up to one year. The ANN topology was optimized based on changes in correlation coefficients (R2) and mean square errors (MSE), resulting in a network of 60 neurons in a hidden layer (R2 = 0.9762 and MSE = 0.0047). The ANN model's performance was evaluated using criteria such as mean absolute deviation (MAD), MSE, root mean square error (RMSE), R2, and mean absolute error (MAE), which were found to be 0.0344, 0.0047, 0.0687, 0.9762, and 0.0078, respectively. Overall, these results suggest that using a computer vision–based system combined with artificial intelligence could be a reliable and nondestructive technique for evaluating meat quality throughout its storage time.