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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
  • 39 Downloads

Abstract

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.

Keywords

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

List of Symbols

ANN

Artificial neural network

ANOVA

Analysis of variance

ARD

Average relative deviation (%)

c1, c2

Acceleration constants

DD

Decompressed state duration (s)

DF

Degree of freedom

DIC

Instant controlled pressure drop treatment

DSC

Differential scanning calorimetry

DT

Drying time (min)

Hij

Inputs to hidden layer

Hoj

Outputs of the hidden layer

HRY

Head rice yield (%)

LI

Input layer

LH

Hidden layer

Lo

Output layer

MSE

Mean square error

Oj

Final output of the output layer

pg

Position of the best particle

pi

Best fitness value of the ith particle

PSO

Particle swarm optimization

R2

Coefficient of determination

rand ()

Random values between 0 and 1

RSM

Response surface methodology

RVA

Rapid Visco-Analyzer

SPSO

Standard particle swarm optimization

\(T_{\text{h}}\)

Bias values of the hidden layer

To

Bias values of the output layer

TPA

Texture profile analyzer

TP

Treatment pressure (MPa)

TT

Treatment time (s)

vid

dth dimension velocity for the ith particle

w

Inertia weight for balancing the global and local search ability

wb

Wet basis (%)

Wih

Interconnection weights of input and hidden layers

Who

Interconnection weights of hidden and output layers

xid

dth dimension location for the ith particle

x1

TP (MPa)

x2

TT (s)

x3

DD (s)

β0

Constant term

βi

Linear effects

βii

Quadratic effects

βij

Interaction effects

φ1, φ2

Random numbers distribution

Notes

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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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