Designing and manufacturing a solar rotary dryer for drying beef and studying its quality and storage characteristics

An indirect rotary solar dryer for drying beef has been manufactured and designed in the food engineering workshop at the College of Agriculture, Basrah University. The dryer contains a solar collector with a length of 1.70 m, depth of 0.25 m, and width of 0.30 m, a chimney and tubular met at mesh. Also, the solar collector includes a drying tube that has a capacity of 5 kg and is made of galvanized iron. Throughout the study period, there was a significant increase in the solar radiation energy (p < 0.05) with daylight hours passed till 12:00 PM. In addition, the maximum level of solar energy has been 810.55 W/m2, which is after that reduced to 680.10 W/m2 at 4:00 PM. In the rotary solar dryer, the drying efficiency was 65.91 % at 60 oC and decreased with the increase in drying temperature. In the drying tube, the average temperature reached 80 oC at 1:00 PM, while the air temperature was 43 oC. Unsalted and salted beef were dried with the use of a different period of storage (0, 14, 28, 42, 56, and 70) days and various (60, 70, and 80 °C) to study theirqualitative properties. The result indicated that there is a decrease in moisture content with the increase in drying time. Furthermore, the chemical composition related to dried unsalted and salted beef indicated that there is a decrease in moisture percentage. At the same time, there is an increased ash percentage, fat and protein after the process of drying. There is an increase in the moisture percentage throughout the periods of storage different from other percentages of the chemical composition, which have been reduced with the increase in the storage periods. It was shown via the results that there was a decrease in the re-hydration with increasing the drying temperature values for unsalted and salted dried beef. Results have reported that there hasbeen a decrease in microbial content regarding dried unsalted and salted beef. Also, it was shown by the results that the model of Artificial Neural Networks (ANNs) showed good results of predicted content of the moisture.