This study applied Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the moisture ratio (MR) during the drying process of yam slices (Dioscorea rotundata) in a hot air convective dryer. Also the effective diffusivity, activation energy, and rehydration ratio were calculated. The experiments were carried out at three (3) drying air temperatures (50, 60, and 70 C), air velocities (0.5, 1, and 1.5 m/s), and slice thickness (3, 6, and 9 mm), and the obtained experimental data were used to check the usefulness of ANFIS in the yam drying process. The result showed efficient applicability of ANFIS in predicting the MR at any time of the drying process with a correlation value (R 2 ) of 0.98226 and root mean square error value (RMSE) of 0.01702 for the testing stage. The effective diffusivity increased with an increase in air velocity, air temperature, and thickness and the values (6.382E -09 to 1.641E -07 m 2 /s). The activation energy increased with an increase in air velocity, but fluctuate within the air temperatures and thickness used (10.59-54.93 KJ/mol). Rehydration ratio was highest at air velocityÂair tem-peratureÂthickness (1.5 m/sÂ70 C  3 mm), and lowest at air velocity  air temperatureÂthickness (0.5 m/ sÂ70 C  3 mm). The result showed that the drying kinetics of Dioscorea rotundata existed in the falling rate period. The drying time decreased with increased temperature, air velocity, and decreased slice thickness. These established results are applicable in process and equipment design, analysis and prediction of hot air convective drying of yam (Dioscorea rotundata) slices.

No abstract

No abstract

No abstract

The primary objective of this study is to determine the hot air drying characteristics and nutritional quality of orange-fleshed sweet potato (OFSP) in a convective dryer.Three temperatures (323.15, 333.15, and 343.15 K) and fan speed levels (0.5, 0.9, and 1.3 m/s) were used. A rehydration study of dried OFSP was also carried out. Modeling and prediction of experimental moisture data were done using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) models. The result showed that the drying rate and rehydration ratio were significantly (p < .05) affected by drying temperature and fan speed levels. The effective diffusivity (D eff ) of the samples ranged from 2.5 × 10 -9 to 4.25 × 10 -9 m 2 /s, and it was found to increase with temperature and fan speed. Protein and fat content appeared to be strongly influenced by drying processing variables, whereas other properties appeared to be insignificant. ANFIS showed better modeling ability than ANNs in predicting the experimental moisture data of OFSP with R 2 and RMSE values of .99786 and 0.0225 respectively. In conclusion, the findings from this research will be useful in product optimization and process monitoring of hot air drying of OFSP, in establishing its drying temperature and fan speed. Practical applicationsDried orange-fleshed sweet potato (OFSP) is utilized as a precursor to many industrial goods and feedstock in the food industry. Establishing the process conditions for drying of OFSP is very important for product adaptability by industries. Modeling the drying kinetic data is useful for developing controls for industrial dryers. Mathematical models have been used in time passes, although they lack the robustness to combine several process variables at time. Therefore, this study applied robust artificial intelligence tool; artificial neural networks, and adaptive neuro-fuzzy inference system (ANFIS) in the prediction of the drying curve of OFSP. Also, the study shows how the process variables affect the quality of the chips. ANFIS showed better prediction ability, and thus can be used in developing robust control systems for industrial drying of OFSP.