Instant Controlled Pressure Drop (DIC) Treatment for Improving Process Performance and Milled Rice Quality

  • Sourav Chakraborty
  • Swapnil Prashant Gautam
  • Pranjal Pratim Das
  • Manuj Kumar HazarikaEmail author
Original Contribution


In this study, instant controlled pressure drop (DIC) treatment was applied for enhancing both process and IR-20 rice quality. This treatment was integrated with soaking and hot air drying (soaking–DIC–hot air drying) in order to improve process performance and quality attributes of the rice. For the optimization, the effect of treatment pressure (TP), treatment time (TT) and decompressed state duration (DD) was investigated on hot air drying time (DT) and head rice yield (HRY). Optimized results were obtained by applying an integrated approach of artificial neural network (ANN) and particle swarm optimization (PSO). With values of TP, TT and DD as the inputs, 3-6-2 was found as the best ANN architecture in order to predict DT and HRY of the DIC-treated parboiled rice. Optimum process conditions as obtained from hybrid ANN–PSO approach were TP of 0.6 MPa, TT of 39 s and DD of 14 s with maximum HRY and minimum DT. The results reveal that IR-20 milled rice produced by DIC-treated parboiling method stands better than conventional method in terms of shorter drying time (DT of 200 min against 1140 min for conventional method), higher degree of gelatinization, higher percent of HRY (70% against 55% of total paddy rice for conventional method), lower broken ratio, ease of cooking and improved pasting and texture properties.


Instant controlled pressure drop (DIC) ANN PSO IR-20 

List of Symbols


Artificial neural network


Analysis of variance


Average relative deviation (%)

c1, c2

Acceleration constants


Decompressed state duration (s)


Degree of freedom


Instant controlled pressure drop treatment


Differential scanning calorimetry


Drying time (min)


Inputs to hidden layer


Outputs of the hidden layer


Head rice yield (%)


Input layer


Hidden layer


Output layer


Mean square error


Final output of the output layer


Position of the best particle


Best fitness value of the ith particle


Particle swarm optimization


Coefficient of determination

rand ()

Random values between 0 and 1


Response surface methodology


Rapid Visco-Analyzer


Standard particle swarm optimization


Bias values of the hidden layer


Bias values of the output layer


Texture profile analyzer


Treatment pressure (MPa)


Treatment time (s)


dth dimension velocity for the ith particle


Inertia weight for balancing the global and local search ability


Wet basis (%)


Interconnection weights of input and hidden layers


Interconnection weights of hidden and output layers


dth dimension location for the ith particle


TP (MPa)


TT (s)


DD (s)


Constant term


Linear effects


Quadratic effects


Interaction effects

φ1, φ2

Random numbers distribution



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Copyright information

© The Institution of Engineers (India) 2019

Authors and Affiliations

  1. 1.Department of Food Engineering and TechnologyTezpur UniversityTezpurIndia
  2. 2.Department of Chemical EngineeringIIT GuwahatiGuwahatiIndia

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