IMPACTS OF PROCESSING PARAMETERS ON THE QUALITY ATTRIBUTES AND ENERGY CONSUMPTION OF RICE (Oryza sativa LINNAEUS)

Abstract

Information on food properties and energy requirements for processing is a prerequisite in plant design. Inconsistent quality attributes of rice varieties and energy profile of the unit operations hinder acceptability. However, literature is sparse on impacts of processing parameters on quality attributes and energy consumption in rice processing. This study was designed, therefore, to investigate and model the impacts of processing parameters on the quality attributes of five locally grown rice varieties and the associated energy consumption. Optimum rice processing conditions [soaking temperature (65-75°C), soaking time (10-16 h), steaming time (20-30 min) and paddy moisture content (12-16%)] were obtained using Response Surface Methodology (RSM). Paddies of NERICA 8, FARO 52, FARO 61, FARO 61 and FARO 44 varieties were processed to white and parboiled rice using standard procedures. The milling recovery, head milled rice, chalkiness, brown rice recovery, head brown rice, colour, lightness, cooking time and water uptake ratio of each variety were determined using IRRI standard methods. Energy consumptions 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 quality attributes and energy consumptions were separately modelled using Taguchi, RSM and Artificial Neural Network (ANN) techniques for each rice variety. Accuracy of models was determined using coefficient of determination (R2 ) and Mean Square Error (MSE). Multi-objectives function optimizer was used to optimize desirable quality attributes and energy consumptions. Data were analyzed using ANOVA at α0.05. Milling recovery, head milled rice and chalkiness for white rice were 65.3-68.3%; 12.7-48.1% and 65.2-83.0% respectively. The corresponding results for parboiled rice were 56.5-73.5%; 48.5-72.7% and 0.3-19.2% respectively. Brown rice recovery, head brown rice, colour, lightness, cooking time and water uptake ratio were 75.9-82.7%; 74.6-82.2%; 14.1-32.0; 22.9-46.8, 10.0-51.6 min and 2.2-4.9 for parboiled rice. FARO 52 had the best quality attributes. The highest energy consuming operations in white and parboiled rice processing were polishing (1.2 MJ) and drying (24.1 MJ). Quality attributes of the rice varieties varied significantly with processing parameters. Total energy consumption among the rice varieties varied significantly, ranging from 2.3 to iii 2.3 MJ for white rice, and 45.3 to 76.9 MJ for parboiled rice. The ANN models were more accurate for quality attributes [R2 (0.70–0.99); MSE (0.00-10.87)] than Taguchi [R2 (0.15-0.85); MSE (0.04-15.58)], and RSM [R2 (0.22-0.99); MSE (0.01-20.19)]. Taguchi models were more accurate for energy consumption [R2 (0.95-0.97); MSE (1.24-1.96)], than RSM [R2 (0.90-0.92); MSE (4.31-4.72)], and ANN [R2 (0.93-0.94); MSE (3.21-3.52)]. Optimum conditions required for processing the five rice varieties varied significantly. Soaking temperature of 79ºC, 14 h soaking time, 23 min steaming time and 16% paddy moisture content were the optimum conditions for processing FARO 52. The optimum conditions for achieving acceptable quality and minimal energy consumption in the processing of five local rice varieties were established. Artificial neural network performed best for modelling quality attributes of the rice varieties, while Taguchi was the most precise for modelling energy consumption