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.

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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.

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The hot air drying characteristics of fermented‐cooked (FC) cassava chips were investigated at a temperature of 50, 60, and 70°C and the fan speed of 0.5, 0.9, and 1.3 m/s. Proximate compositions, functional characteristics, and moisture diffusion parameters of the FC cassava chips were also studied. Furthermore, the applicability of mathematical (MM) and Gaussian process regression (GPR) – based modeling approaches for modeling drying kinetics of the chips was analyzed. Effective diffusivity (Deff) increased with an increase in temperature and fan speed and ranged between 1.1 × 108 and 6 × 108 m2/s. The activation energy (Ea) decreased with fan speed level up to 0.9 m/s and fluctuated between 0.9 and 1.3 m/s. Ea was from 46 to 57 KJ/mol. The drying rate decreased with an increase in temperature and fan speed. Process variables also showed a significant effect on the proximate compositions. FTIR result revealed that drying affected the functional characteristics of the chips. The GPR‐based model showed superiority and can therefore be used for optimization and control monitoring which are necessary for product standardization. Practical Applications Drying of food materials happens to be a major unit operation in the most food process line, prompting the establishment of its drying conditions important. Drying kinetic modeling is very crucial for accurate control of the drying process in the industries. This study shows that GPR‐ based models performed better than mathematical models for modeling the drying curve of FC cassava chips. It also shows how some processing operation affects the functional properties of the chips. GPR‐models are useful in developing robust control systems for industrial drying processes.

Locust beans dehulling machine was design, develop, and evaluated. The machine was evaluated at different speed and boiling time for its performance efficiency. The results showed that with increase in the boiling time from the first to second hr the throughput capacity, qualitative, and quantitative dehulling efficiency increased but from the second to the fourth hr of boiling, it decreased. Whereas as the dehulling speed increase the throughput capacity increased but the quantitative and qualitative dehulling efficiency decreased. Highest throughput capacity and the least labor requirement were recorded at 1 hr boiling, whereas highest quantitative and qualitative dehulling efficiency was recorded at 2 hr of boiling. The boiling time and dehulling speed significantly affects the throughput capacity, quantitative and qualitative dehulling efficiencies of the dehuller, but does not affect labor requirement. The economic analysis reveals that developed dehuller can be useful for small to medium scale locust bean processing industries. Practical applications The developed dehuller is relevant in the dehulling of boiled locust bean seed. This would, therefore, combat the challenges surrounding locust bean processing especially with the rural women dwellers and commercial producers. The dehuller would, therefore, reduce the time consumption and drudgery associated with the processing of locust bean.

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