Browsing by Author "Rahman Akinoso"
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Item Evaluation of energy consumption patterns in rice processing using Taguchi and artificial neural network models(AgricEngInt: CIGR Journal Open access at http://www.cigrjournal.org, 2022-06-28) Mayowa Saheed Sanusi; Rahman AkinosoThis study was designed to evaluate and model the impact of processing parameters (steaming time, soaking time, paddy moisture content and soaking temperature) on the energy consumption of five rice varieties (NERICA 8, FARO 52, FARO 61, FARO 60 and FARO 44). Energy consumption in the cleaning, soaking, steaming, drying, dehusking, polishing and grading operations were estimated by fitting data on labour, fuel and electricity consumption, time and machine efficiency into standard equations to determine total energy consumption. The energy consumptions were separately modelled using Taguchi and Artificial Neural Network (ANN) models for each rice variety. The accuracy of models was determined using the coefficient of determination (R2) and Mean Square Error (MSE). Total energy consumption among the rice varieties varied significantly, ranging from 2.31 to 2.33 MJ for white rice, and 45.3 to 76.9 MJ for parboiled rice. Paddy moisture content was observed to be the most important process parameter that influenced energy consumption. Taguchi models were more accurate for total energy consumption prediction [R2 (0.95-0.97); MSE (1.24-1.96)], than ANN [R2 (0.93-0.94); MSE (3.21-3.52)]. The study established appropriate processing conditions that can guarantee minimum energy consumption for NERICA 8, FARO 52, FARO 61, FARO 60 and FARO 44.Item Evaluation of Physical, Milling and Cooking Properties of Four New Rice (Oryza Sativa L.) Varieties in Nigeria)(International Journal of Food Studies IJFS, 2017-10-18) Mayowa S. Sanusi; Rahman Akinoso; Nahemiah DanbabaThis comparative study investigated some physical, milling and cooking properties of four new rice varieties (FARO 44, FARO 52, FARO 60 and FARO 61) in Nigeria. The varieties were processed into white rice and their properties analyzed separately using standard procedures. Results showed that paddy length, paddy-length-to-width-ratio, equivalent diameter, sphericity, grain volume, aspect ratio, thousand paddy grain weight, milled rice length, milled rice width, milled rice length to width ratio, milling recovery, head milled rice, broken milled rice, L*, a*, b*, elongation ratio, cooked-rice-lengthto-breadth-ratio, water uptake ratio and cooking time were significantly different (p<0.05) for all the varieties. Milling recovery was found to vary from 65.3 to 68.33%; with FARO 60 having the highest head milled rice. It was observed that FARO 44 had the longest cooking time, elongation ratio and cooked rice length/breadth ratio while FARO 61 was found to have the highest water uptake ratio. There was significant positive correlation (r = 0.824) between percentage head milled rice and milling recovery while negative correlation existed between cooking time and L* (r = - 0.711). This information could be exploited by rice processors in the post-harvest processing of the varieties.Item Modelling and optimising the impact of process variables on brown rice quality and overall energy consumption(Int. J. Postharvest Technology and Innovation, 2021-03-01) Mayowa Saheed Sanusi; Rahman AkinosoThis study aimed to optimise the impact of process variables on brown rice quality (brown rice recovery and head brown rice) and overall energy consumption. The applicability of predictive polynomial regression analysis (PRA) and artificial neural network (ANN) models were evaluated. Process variables [paddy moisture content (12–16%), soaking time (10–16 h), steaming time (20–30 min) and soaking temperature (65–75°C)] were interacted using response surface methodology and their impact on brown rice quality and overall energy consumption were determined. The influence of process variables differs significantly with brown rice recovery and head brown rice. However, a decrease in soaking temperature and paddy moisture content was observed to increase overall energy consumption. ANN shows better predictive accuracy than the PRA model. Optimum conditions that can guarantee maximum quality and minimum overall energy consumption was established at 80°C soaking temperature, 7 h soaking time, 35 min steaming time and 14% paddy moisture contentItem Optimizing the Impacts of Process Factors on the Quality Indices of Multiple Rice (Oryza sativa L.) Varieties using Taguchi Technique(2022-05-01) Mayowa Saheed Sanusi; Rahman Akinoso; Abdulquadri AlakaInconsistence in rice quality during production can hinder its consumers’ acceptability. This study investigated the impacts of process factors on the quality indices of five rice varieties using Taguchi technique. The processing factors [soaking temperature (65-75°C), soaking time (10-16 h), steaming time (20-30 min) and paddy moisture content (12-16%)] were interacted using Taguchi orthogonal array design of L9 (34). Paddy rice of FARO 44, FARO 52, FARO 60, FARO 61 and NERICA 8 were processed into polished parboiled rice using the conditions from the Taguchi interaction. The signal to noise ratio of Taguchi was used to evaluate the influence of processing conditions on the quality indices (milling recovery, head milled rice, white bellies, colour and lightness) using standard procedures. The optimum processing conditions for each rice variety was determined using composite desirability function (CDF) of numerical optimization. The impact of processing factors on the quality indices differs significantly on the rice variety. The milling recovery of the rice varieties ranges from 62.79 to 73.54%, head milled rice (59.81 to 71.63%), lightness value (22.92 -35.82), colour value (21.55 – 28.65) and white bellies (0.16 – 14.17%), respectively. However, the optimum processing conditions vary from one rice variety to the other with CDF that ranges from 0.82 - 0.96. Taguchi technique was successfully used to understand the impact of processing factors on the quality indices. The optimum processing conditions for achieving acceptable quality indices for the five rice varieties were predicted. This information would be useful in process optimization during rice processing.