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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 |
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creator | Kothakota, Anjineyulu Pandiselvam, Ravi Siliveru, Kaliramesh Pandey, Jai Prakash Sagarika, Nukasani Srinivas, Chintada H Sai 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 |
format | article |
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, 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.</description><identifier>ISSN: 2304-8158</identifier><identifier>EISSN: 2304-8158</identifier><identifier>DOI: 10.3390/foods10122975</identifier><identifier>PMID: 34945526</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>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</subject><ispartof>Foods, 2021-12, Vol.10 (12), p.2975</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 by the authors. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c481t-89118eb238805dfe5f321948c139b93d8a08c20c57c224e96f15c220411e4cd73</citedby><cites>FETCH-LOGICAL-c481t-89118eb238805dfe5f321948c139b93d8a08c20c57c224e96f15c220411e4cd73</cites><orcidid>0000-0003-0593-7663 ; 0000-0003-0996-8328</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2661903069/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2661903069?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25731,27901,27902,36989,36990,44566,53766,53768,74869</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34945526$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Kothakota, Anjineyulu</creatorcontrib><creatorcontrib>Pandiselvam, Ravi</creatorcontrib><creatorcontrib>Siliveru, Kaliramesh</creatorcontrib><creatorcontrib>Pandey, Jai Prakash</creatorcontrib><creatorcontrib>Sagarika, Nukasani</creatorcontrib><creatorcontrib>Srinivas, Chintada H Sai</creatorcontrib><creatorcontrib>Kumar, Anil</creatorcontrib><creatorcontrib>Singh, Anupama</creatorcontrib><creatorcontrib>Prakash, Shivaprasad D</creatorcontrib><title>Modeling and Optimization of Process Parameters for Nutritional Enhancement in Enzymatic Milled Rice by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN)</title><title>Foods</title><addtitle>Foods</addtitle><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.</description><subject>Amino acids</subject><subject>artificial neural network (ANN)</subject><subject>Artificial neural networks</subject><subject>Cellulase</subject><subject>Cellulose</subject><subject>Cereals</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Electron micrographs</subject><subject>Enzymes</subject><subject>Food products</subject><subject>Food science</subject><subject>Grain</subject><subject>Hardness</subject><subject>Iron</subject><subject>Mathematical models</subject><subject>milled rice</subject><subject>Minerals</subject><subject>Moisture effects</subject><subject>multiple linear regression (MLR)</subject><subject>Neural networks</subject><subject>Nutrients</subject><subject>Optimization</subject><subject>Phenols</subject><subject>Process parameters</subject><subject>Proteins</subject><subject>Regression analysis</subject><subject>Rice</subject><subject>Scanning electron microscopy</subject><subject>Variables</subject><subject>Xylanase</subject><issn>2304-8158</issn><issn>2304-8158</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkk1vEzEQhlcIRKvSI1dkiUt6SPHnrn1BiqpCKyVpFcHZcrzj1GF3HexdUPqb-JE4SakafJmx_fqZD09RvCf4kjGFP7kQ6kQwoVRV4lVxShnmY0mEfP3CPynOU1rjvBRhktG3xQnjigtBy9PizyzU0PhuhUxXo7tN71v_aHofOhQcuo_BQkro3kTTQg8xIRcimg999DuNadB192A6Cy10PfJd3j5u2_zeoplvGqjRwltAyy2aDU3vNw2gqe_ARLSAVczoXaDRbLq42MefxN47b33mzmGIe9P_DvEHGk3m84t3xRtnmgTnT_as-P7l-tvVzXh69_X2ajIdWy5JP5aKEAlLyqTEonYgHKNEcWkJU0vFammwtBRbUVlKOajSEZE9zAkBbuuKnRW3B24dzFpvom9N3OpgvN4fhLjSJmdqG9C58YpI5yQXmOdgCkRpFFS1tLZaUpFZnw-szbBsoba5T7muI-jxTecf9Cr80rLCuCxlBoyeADH8HCD1uvXJQtOYDsKQNC0Jp1wpvMv743_SdRhi_qWdqiQKM1yqrBofVDaGlCK452QI1rux0kdjlfUfXlbwrP43ROwv9tPJ1g</recordid><startdate>20211203</startdate><enddate>20211203</enddate><creator>Kothakota, Anjineyulu</creator><creator>Pandiselvam, Ravi</creator><creator>Siliveru, Kaliramesh</creator><creator>Pandey, Jai Prakash</creator><creator>Sagarika, Nukasani</creator><creator>Srinivas, Chintada H Sai</creator><creator>Kumar, Anil</creator><creator>Singh, Anupama</creator><creator>Prakash, Shivaprasad D</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QR</scope><scope>7T7</scope><scope>7X2</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>HCIFZ</scope><scope>M0K</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-0593-7663</orcidid><orcidid>https://orcid.org/0000-0003-0996-8328</orcidid></search><sort><creationdate>20211203</creationdate><title>Modeling and Optimization of Process Parameters for Nutritional Enhancement in Enzymatic Milled Rice by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN)</title><author>Kothakota, Anjineyulu ; Pandiselvam, Ravi ; Siliveru, Kaliramesh ; Pandey, Jai Prakash ; Sagarika, Nukasani ; Srinivas, Chintada H Sai ; Kumar, Anil ; Singh, Anupama ; Prakash, Shivaprasad D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c481t-89118eb238805dfe5f321948c139b93d8a08c20c57c224e96f15c220411e4cd73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Amino acids</topic><topic>artificial neural network (ANN)</topic><topic>Artificial neural networks</topic><topic>Cellulase</topic><topic>Cellulose</topic><topic>Cereals</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Electron micrographs</topic><topic>Enzymes</topic><topic>Food products</topic><topic>Food science</topic><topic>Grain</topic><topic>Hardness</topic><topic>Iron</topic><topic>Mathematical models</topic><topic>milled rice</topic><topic>Minerals</topic><topic>Moisture effects</topic><topic>multiple linear regression (MLR)</topic><topic>Neural networks</topic><topic>Nutrients</topic><topic>Optimization</topic><topic>Phenols</topic><topic>Process parameters</topic><topic>Proteins</topic><topic>Regression analysis</topic><topic>Rice</topic><topic>Scanning electron microscopy</topic><topic>Variables</topic><topic>Xylanase</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kothakota, Anjineyulu</creatorcontrib><creatorcontrib>Pandiselvam, Ravi</creatorcontrib><creatorcontrib>Siliveru, Kaliramesh</creatorcontrib><creatorcontrib>Pandey, Jai Prakash</creatorcontrib><creatorcontrib>Sagarika, Nukasani</creatorcontrib><creatorcontrib>Srinivas, Chintada H Sai</creatorcontrib><creatorcontrib>Kumar, Anil</creatorcontrib><creatorcontrib>Singh, Anupama</creatorcontrib><creatorcontrib>Prakash, Shivaprasad D</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Chemoreception Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Agricultural Science Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>SciTech Premium Collection</collection><collection>Agriculture Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Foods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kothakota, Anjineyulu</au><au>Pandiselvam, Ravi</au><au>Siliveru, Kaliramesh</au><au>Pandey, Jai Prakash</au><au>Sagarika, Nukasani</au><au>Srinivas, Chintada H Sai</au><au>Kumar, Anil</au><au>Singh, Anupama</au><au>Prakash, Shivaprasad D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling and Optimization of Process Parameters for Nutritional Enhancement in Enzymatic Milled Rice by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN)</atitle><jtitle>Foods</jtitle><addtitle>Foods</addtitle><date>2021-12-03</date><risdate>2021</risdate><volume>10</volume><issue>12</issue><spage>2975</spage><pages>2975-</pages><issn>2304-8158</issn><eissn>2304-8158</eissn><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
, 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.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>34945526</pmid><doi>10.3390/foods10122975</doi><orcidid>https://orcid.org/0000-0003-0593-7663</orcidid><orcidid>https://orcid.org/0000-0003-0996-8328</orcidid><oa>free_for_read</oa></addata></record> |
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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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