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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)

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dc.contributor.author Kothakota, A
dc.contributor.author Pandiselvam, R
dc.contributor.author Siliveru, K
dc.contributor.author Pandey, J P
dc.contributor.author Sagarika, N
dc.contributor.author Srinivas, C H S
dc.contributor.author Kumar, A
dc.contributor.author Singh, A
dc.contributor.author Prakash, S D
dc.date.accessioned 2022-05-12T16:13:50Z
dc.date.available 2022-05-12T16:13:50Z
dc.date.issued 2021-12-03
dc.identifier.citation Foods; 10(12):2975 en_US
dc.identifier.uri https://www.mdpi.com/2304-8158/10/12/2975
dc.identifier.uri http://hdl.handle.net/123456789/3994
dc.description.abstract 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 Aspergillus awamori, MTCC 9166 and Trichoderma reese, 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 (R2) varied between 0.87–0.90, and the sum of square (SSE) was placed within 0.008–8.25. While the ANN R2 (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. en_US
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.subject multiple linear regression (MLR) en_US
dc.subject artificial neural network (ANN) en_US
dc.subject milled rice en_US
dc.subject enzymes en_US
dc.title Modeling and Optimization of Process Parameters for Nutritional Enhancement in Enzymatic Milled Rice by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) en_US
dc.type Article en_US


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  • 2022
    Research articles authored by NIIST researchers published in 2022

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