Fattening performance, Carcass characteristics, chemical composition, and meat quality were evaluated in three sheep breeds: Awassi, Harri, and Najdi. Forty-five lambs of similar weight and age were raised for 90 days under similar conditions. The Harri and Najdi breeds had higher dressing-out percentages than Awassi sheep. The Awassi and Harri breeds had thicker backfat than the Najdi breed. No significant difference was found in moisture, protein, and intramuscular fat among the breeds. However, the Harri breed had a higher ash content than the Awassi and Najdi breeds. The Najdi breed had higher ultimate pH and lower cooking loss than the Awassi and Harri breeds. Awassi and Harri sheep had a higher myofibril fragmentation index, longer sarcomere length, and lower hardness and chewiness than Najdi sheep. Subjectively, no significant differences were detected between the breeds, except for flavor intensity while the Awassi sheep were rated in between and not significantly different. In conclusion, breed affected carcass characteristics, meat composition, and the quality of sheep. The dressing yield was higher in Harri and Najdi than Awassi sheep. Awassi sheep showed superior meat quality characteristics followed by Harri sheep. However, Najdi sheep had the best cooking loss percentage and flavor intensity score.

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Moisture sorption isotherms (MSI) are required to optimize handling, drying, processing and storage of food products. MSI are obtained by solving nonlinear equations iteratively, which is normally a difficult process. A generic approach was successfully used for collective prediction of MSI for 12 cereals and five legumes simultaneously, by taking advantage of the superior computational capabilities of artificial neural networks (ANNs). A total of 779 observations were collected from literature and used in training and validation of ANNs. The ANNs model used product type (grains or legumes), sorption state (adsorption or desorption), temperature and equilibrium moisture content as inputs to predict equilibrium relative humidity. A one-and two-hidden-layer ANNs were implemented. The overall prediction results obtained from ANNs were found to compare well with those reported for conventional analytical MSI models. The average prediction results obtained from the two-hidden-layer ANN for mean square error, deviation modulus and R 2 were 0.009, 4.47% and 0.984, respectively. The results for each product compared well with those obtained from analytical MSI models. Four important issues that are commonly raised when implementing ANNs in prediction of MSIs were explained. These included preventing network over-fitting, minimizing the time and effort needed for ANN architecture optimization, validating reproducibility of the results and validating the network capability to predict new observations. Unlike conventional MSI models, ANNs used the same network to predict MSIs for several products simultaneously. The principle can be easily expanded to predict MSI for other larger sets of various products, which can save time and effort. PRACTICAL APPLICATIONSDetermination of moisture sorption isotherms (MSI) is required for optimization of grain drying, processing, handling and storage operations. They can be also used to evaluate theoretical drying energy requirements and optimum storage conditions for a specific food product. In addition, they are used in food engineering calculations related to equipment design, shelf life evaluation and stability during storage operations. Determination of MSI for food products involves the use of conventional nonlinear MSI models, which requires an iterative solution methodology. The results are also specific to the food product investigated. Artificial neural networks were therefore proposed in this study as an alternative method for the collective prediction of MSI for some cereal grains and legumes.

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The meat productivity of camel in the tropics is still under investigation for identification of better meat breed or type. Therefore, four one-humped Saudi Arabian (SA) camel breeds, Majaheem, Maghateer, Hamrah, and Safrah were experimented in order to differentiate them from each other based on meat measurements. The measurements were biometrical meat traits measured on six intact males from each breed. The results showed higher values of the Majaheem breed than that obtained for the other breeds except few cases such dressing percentage and rib-eye area. In differentiation analysis, the most discriminating meat variables were myofibrillar protein index, meat color components (L* and a*, b*), and cooking loss. Consequently, the Safrah and the Majaheem breeds presented the largest dissimilarity as evidenced by their multivariate means. The canonical discriminant analysis allowed an additional understanding of the differentiation between breeds. Furthermore, two large clusters, one formed by Hamrah and Maghateer in one group along with Safrah. These classifications may assign each breed into one cluster considering they are better as meat producers. The Majaheem was clustered alone in another cluster that might be a result of being better as milk producers. Nevertheless, the productivity type of the camel breeds of SA needs further morphology and genetic descriptions.

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