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Modeling and Optimization of Process Parameters for Nutritional Enhancement in Enzymatic Milled Rice by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN)

This study involves information about the concentrations of nutrients (proteins, phenolic compounds, free amino acids, minerals (Ca, P, and Iron), hardness) in milled rice processed with enzymes; xylanase and cellulase produced by , MTCC 9166 and , MTCC164. Brown rice was processed with 60-100% enzy...

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Published in:Foods 2021-12, Vol.10 (12), p.2975
Main Authors: Kothakota, Anjineyulu, Pandiselvam, Ravi, Siliveru, Kaliramesh, Pandey, Jai Prakash, Sagarika, Nukasani, Srinivas, Chintada H Sai, Kumar, Anil, Singh, Anupama, Prakash, Shivaprasad D
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creator Kothakota, Anjineyulu
Pandiselvam, Ravi
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Kumar, Anil
Singh, Anupama
Prakash, Shivaprasad D
description This study involves information about the concentrations of nutrients (proteins, phenolic compounds, free amino acids, minerals (Ca, P, and Iron), hardness) in milled rice processed with enzymes; xylanase and cellulase produced by , MTCC 9166 and , MTCC164. Brown rice was processed with 60-100% enzyme (40 mL buffer -undiluted) for 30 to 150 min at 30 °C to 50 °C followed by polishing for 20-100 s at a safe moisture level. Multiple linear regression (MLR) and artificial neural network (ANN) models were used for process optimization of enzymes. The MLR correlation coefficient (R ) varied between 0.87-0.90, and the sum of square (SSE) was placed within 0.008-8.25. While the ANN R (correlation coefficient) varied between 0.97 and 0.9999(1), MSE changed from 0.005 to 6.13 representing that the ANN method has better execution across MLR. The optimized cellulase process parameters (87.2% concentration, 80.1 min process time, 33.95 °C temperature and 21.8 s milling time) and xylanase process parameters (85.7% enzyme crude, 77.1 min process time, 35 °C temperature and 20 s) facilitated the increase of Ca (70%), P (64%), Iron (17%), free amino acids (34%), phenolic compounds (78%) and protein (84%) and decreased hardness (20%) in milled rice. Scanning electron micrographs showed an increased rupture attributing to enzymes action on milled rice.
doi_str_mv 10.3390/foods10122975
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xylanase and cellulase produced by , MTCC 9166 and , MTCC164. Brown rice was processed with 60-100% enzyme (40 mL buffer -undiluted) for 30 to 150 min at 30 °C to 50 °C followed by polishing for 20-100 s at a safe moisture level. Multiple linear regression (MLR) and artificial neural network (ANN) models were used for process optimization of enzymes. The MLR correlation coefficient (R ) varied between 0.87-0.90, and the sum of square (SSE) was placed within 0.008-8.25. While the ANN R (correlation coefficient) varied between 0.97 and 0.9999(1), MSE changed from 0.005 to 6.13 representing that the ANN method has better execution across MLR. The optimized cellulase process parameters (87.2% concentration, 80.1 min process time, 33.95 °C temperature and 21.8 s milling time) and xylanase process parameters (85.7% enzyme crude, 77.1 min process time, 35 °C temperature and 20 s) facilitated the increase of Ca (70%), P (64%), Iron (17%), free amino acids (34%), phenolic compounds (78%) and protein (84%) and decreased hardness (20%) in milled rice. 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subjects Amino acids
artificial neural network (ANN)
Artificial neural networks
Cellulase
Cellulose
Cereals
Correlation coefficient
Correlation coefficients
Electron micrographs
Enzymes
Food products
Food science
Grain
Hardness
Iron
Mathematical models
milled rice
Minerals
Moisture effects
multiple linear regression (MLR)
Neural networks
Nutrients
Optimization
Phenols
Process parameters
Proteins
Regression analysis
Rice
Scanning electron microscopy
Variables
Xylanase
title Modeling and Optimization of Process Parameters for Nutritional Enhancement in Enzymatic Milled Rice by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN)
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