Staling of white wheat bread crumb and effect of maltogenic α-amylases. Part 3: Spatial evolution of bread staling with time by near infrared hyperspectral imaging
•Surface distribution of the effect of anti-staling enzymes visualized with NIR-HSI.•PCA and MCR models for the visualization of the chemical effects of staling in bread.•PLS for pixel-to-pixel prediction of hardness in the slices.•Action of anti-staling enzymes fully studied and understood.•Handlin...
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Veröffentlicht in: | Food chemistry 2021-08, Vol.353, p.129478-129478, Article 129478 |
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creator | Amigo, José Manuel Olmo, Arantxa del Engelsen, Merete Møller Lundkvist, Henrik Engelsen, Søren Balling |
description | •Surface distribution of the effect of anti-staling enzymes visualized with NIR-HSI.•PCA and MCR models for the visualization of the chemical effects of staling in bread.•PLS for pixel-to-pixel prediction of hardness in the slices.•Action of anti-staling enzymes fully studied and understood.•Handling and analyzing time-series hyperspectral images is shown.
This paper explores how the staling of white bread affects the behavior of the whole crumb surface and how that mechanism is interrupted/changed by the addition of maltogenic α-amylases. This is done using near infrared hyperspectral imaging, machine learning methodologies and the knowledge acquired in the previous two manuscripts. Methods like principal component analysis and multivariate curve resolution demonstrate how the constituents of the bread being stored (for 21 days) evolve differently depending on the presence/absence of maltogenic α-amylases and also which parts of the crumb are primarily exposed to changes. The spatial distribution of the hardness is calculated in the entire surface of the slice area during staling by using partial least square regression. This manuscript comprehends one of the largest studies made on white bread staling and proposes a complete methodology using near infrared hyperspectral imaging and machine learning. |
doi_str_mv | 10.1016/j.foodchem.2021.129478 |
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This paper explores how the staling of white bread affects the behavior of the whole crumb surface and how that mechanism is interrupted/changed by the addition of maltogenic α-amylases. This is done using near infrared hyperspectral imaging, machine learning methodologies and the knowledge acquired in the previous two manuscripts. Methods like principal component analysis and multivariate curve resolution demonstrate how the constituents of the bread being stored (for 21 days) evolve differently depending on the presence/absence of maltogenic α-amylases and also which parts of the crumb are primarily exposed to changes. The spatial distribution of the hardness is calculated in the entire surface of the slice area during staling by using partial least square regression. This manuscript comprehends one of the largest studies made on white bread staling and proposes a complete methodology using near infrared hyperspectral imaging and machine learning.</description><identifier>ISSN: 0308-8146</identifier><identifier>EISSN: 1873-7072</identifier><identifier>DOI: 10.1016/j.foodchem.2021.129478</identifier><identifier>PMID: 33730663</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>area ; behavior ; Bread staling ; Enzymes ; evolution ; food chemistry ; hardness ; Hyperspectral ; knowledge ; least squares ; MCR ; NIR ; PCA ; PLS ; principal component analysis ; spatial distribution ; wheat ; white bread ; α-amylases</subject><ispartof>Food chemistry, 2021-08, Vol.353, p.129478-129478, Article 129478</ispartof><rights>2021 Elsevier Ltd</rights><rights>Copyright © 2021 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c449t-429a2c6a7153ea2d6de64ba029f82af0b3a4311944029cf2600ad1bcb7c4395e3</citedby><cites>FETCH-LOGICAL-c449t-429a2c6a7153ea2d6de64ba029f82af0b3a4311944029cf2600ad1bcb7c4395e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0308814621004842$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33730663$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Amigo, José Manuel</creatorcontrib><creatorcontrib>Olmo, Arantxa del</creatorcontrib><creatorcontrib>Engelsen, Merete Møller</creatorcontrib><creatorcontrib>Lundkvist, Henrik</creatorcontrib><creatorcontrib>Engelsen, Søren Balling</creatorcontrib><title>Staling of white wheat bread crumb and effect of maltogenic α-amylases. Part 3: Spatial evolution of bread staling with time by near infrared hyperspectral imaging</title><title>Food chemistry</title><addtitle>Food Chem</addtitle><description>•Surface distribution of the effect of anti-staling enzymes visualized with NIR-HSI.•PCA and MCR models for the visualization of the chemical effects of staling in bread.•PLS for pixel-to-pixel prediction of hardness in the slices.•Action of anti-staling enzymes fully studied and understood.•Handling and analyzing time-series hyperspectral images is shown.
This paper explores how the staling of white bread affects the behavior of the whole crumb surface and how that mechanism is interrupted/changed by the addition of maltogenic α-amylases. This is done using near infrared hyperspectral imaging, machine learning methodologies and the knowledge acquired in the previous two manuscripts. Methods like principal component analysis and multivariate curve resolution demonstrate how the constituents of the bread being stored (for 21 days) evolve differently depending on the presence/absence of maltogenic α-amylases and also which parts of the crumb are primarily exposed to changes. The spatial distribution of the hardness is calculated in the entire surface of the slice area during staling by using partial least square regression. This manuscript comprehends one of the largest studies made on white bread staling and proposes a complete methodology using near infrared hyperspectral imaging and machine learning.</description><subject>area</subject><subject>behavior</subject><subject>Bread staling</subject><subject>Enzymes</subject><subject>evolution</subject><subject>food chemistry</subject><subject>hardness</subject><subject>Hyperspectral</subject><subject>knowledge</subject><subject>least squares</subject><subject>MCR</subject><subject>NIR</subject><subject>PCA</subject><subject>PLS</subject><subject>principal component analysis</subject><subject>spatial distribution</subject><subject>wheat</subject><subject>white bread</subject><subject>α-amylases</subject><issn>0308-8146</issn><issn>1873-7072</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNqNkU1uFDEQhS0EIkPgCpGXbHrwX7u7WYGi8CNFAimwtqrd5RmP-g_bnWjuwwW4CGfCo56whU1ZKn3vPbkeIVecbTnj-s1h66aps3sctoIJvuWiUVX9hGx4XcmiYpV4SjZMsrqoudIX5EWMB8aYYLx-Ti6krCTTWm7Iz7sEvR93dHL0Ye8T5omQaBsQOmrDMrQUxo6ic2jTiRqgT9MOR2_p718FDMceIsYt_QohUfmW3s2QPPQU76d-SX4aT6LVLp6zHnza0-QHpO2RjgiB-tEFCNjR_XHGEOecFbKHH2CXBS_JMwd9xFfn95J8_3Dz7fpTcfvl4-fr97eFVapJhRINCKuh4qVEEJ3uUKsWmGhcLcCxVoKSnDdK5ZV1QjMGHW9tW1klmxLlJXm9-s5h-rFgTGbw0WLfw4jTEo0ohRJ1KUr-HygTNWe61BnVK2rDFGNAZ-aQPxaOhjNzKtMczGOZ5lSmWcvMwqtzxtIO2P2VPbaXgXcrgPko9x6DidbjaLHzIV_QdJP_V8Yfrba2Hg</recordid><startdate>20210815</startdate><enddate>20210815</enddate><creator>Amigo, José Manuel</creator><creator>Olmo, Arantxa del</creator><creator>Engelsen, Merete Møller</creator><creator>Lundkvist, Henrik</creator><creator>Engelsen, Søren Balling</creator><general>Elsevier Ltd</general><scope>6I.</scope><scope>AAFTH</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>20210815</creationdate><title>Staling of white wheat bread crumb and effect of maltogenic α-amylases. Part 3: Spatial evolution of bread staling with time by near infrared hyperspectral imaging</title><author>Amigo, José Manuel ; Olmo, Arantxa del ; Engelsen, Merete Møller ; Lundkvist, Henrik ; Engelsen, Søren Balling</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c449t-429a2c6a7153ea2d6de64ba029f82af0b3a4311944029cf2600ad1bcb7c4395e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>area</topic><topic>behavior</topic><topic>Bread staling</topic><topic>Enzymes</topic><topic>evolution</topic><topic>food chemistry</topic><topic>hardness</topic><topic>Hyperspectral</topic><topic>knowledge</topic><topic>least squares</topic><topic>MCR</topic><topic>NIR</topic><topic>PCA</topic><topic>PLS</topic><topic>principal component analysis</topic><topic>spatial distribution</topic><topic>wheat</topic><topic>white bread</topic><topic>α-amylases</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Amigo, José Manuel</creatorcontrib><creatorcontrib>Olmo, Arantxa del</creatorcontrib><creatorcontrib>Engelsen, Merete Møller</creatorcontrib><creatorcontrib>Lundkvist, Henrik</creatorcontrib><creatorcontrib>Engelsen, Søren Balling</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Food chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Amigo, José Manuel</au><au>Olmo, Arantxa del</au><au>Engelsen, Merete Møller</au><au>Lundkvist, Henrik</au><au>Engelsen, Søren Balling</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Staling of white wheat bread crumb and effect of maltogenic α-amylases. 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This paper explores how the staling of white bread affects the behavior of the whole crumb surface and how that mechanism is interrupted/changed by the addition of maltogenic α-amylases. This is done using near infrared hyperspectral imaging, machine learning methodologies and the knowledge acquired in the previous two manuscripts. Methods like principal component analysis and multivariate curve resolution demonstrate how the constituents of the bread being stored (for 21 days) evolve differently depending on the presence/absence of maltogenic α-amylases and also which parts of the crumb are primarily exposed to changes. The spatial distribution of the hardness is calculated in the entire surface of the slice area during staling by using partial least square regression. This manuscript comprehends one of the largest studies made on white bread staling and proposes a complete methodology using near infrared hyperspectral imaging and machine learning.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>33730663</pmid><doi>10.1016/j.foodchem.2021.129478</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | area behavior Bread staling Enzymes evolution food chemistry hardness Hyperspectral knowledge least squares MCR NIR PCA PLS principal component analysis spatial distribution wheat white bread α-amylases |
title | Staling of white wheat bread crumb and effect of maltogenic α-amylases. Part 3: Spatial evolution of bread staling with time by near infrared hyperspectral imaging |
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