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
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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
