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Sensomics-Assisted Aroma Decoding of Pea Protein Isolates ( Pisum sativum L.)
The aroma of pea protein ( L.) was decrypted for knowledge-based flavor optimization of new food products containing pea protein. Sensomics helped to determine several volatiles via ultra-high performance liquid chromatography tandem mass spectrometry and 3-nitrophenylhydrazine derivatization. Among...
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| Published in: | Foods 2022-01, Vol.11 (3), p.412 |
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| creator | Utz, Florian Spaccasassi, Andrea Kreissl, Johanna Stark, Timo D Tanger, Caren Kulozik, Ulrich Hofmann, Thomas Dawid, Corinna |
| description | The aroma of pea protein (
L.) was decrypted for knowledge-based flavor optimization of new food products containing pea protein. Sensomics helped to determine several volatiles via ultra-high performance liquid chromatography tandem mass spectrometry and 3-nitrophenylhydrazine derivatization. Among the investigated volatiles, representatives of aldehydes, ketones, and acids were reported in literature as especially important in pea and pea-related matrices. After validation of the method and quantitation of the corresponding analytes, sensory reconstitution as well as omission studies of a selected pea protein were performed and revealed nine odor-active compounds as key food odorants (3-methylbutanal, hexanal, acetaldehyde, (
)-2,4-nonadienal, (
)-2-octenal, benzaldehyde, heptanal, 2-methylbutanal, and nonanoic acid). Interestingly, eight out of nine compounds belonged to the chemical class of aldehydes. Statistical heatmap and cluster analysis of all odor activity values of different pea proteins confirmed the obtained sensory results and generalize these nine key food odorants in other pea proteins. The knowledge of key components gained shows potential for simplifying industrial flavor optimization of pea protein-based food. |
| doi_str_mv | 10.3390/foods11030412 |
| format | article |
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L.) was decrypted for knowledge-based flavor optimization of new food products containing pea protein. Sensomics helped to determine several volatiles via ultra-high performance liquid chromatography tandem mass spectrometry and 3-nitrophenylhydrazine derivatization. Among the investigated volatiles, representatives of aldehydes, ketones, and acids were reported in literature as especially important in pea and pea-related matrices. After validation of the method and quantitation of the corresponding analytes, sensory reconstitution as well as omission studies of a selected pea protein were performed and revealed nine odor-active compounds as key food odorants (3-methylbutanal, hexanal, acetaldehyde, (
)-2,4-nonadienal, (
)-2-octenal, benzaldehyde, heptanal, 2-methylbutanal, and nonanoic acid). Interestingly, eight out of nine compounds belonged to the chemical class of aldehydes. Statistical heatmap and cluster analysis of all odor activity values of different pea proteins confirmed the obtained sensory results and generalize these nine key food odorants in other pea proteins. The knowledge of key components gained shows potential for simplifying industrial flavor optimization of pea protein-based food.</description><identifier>ISSN: 2304-8158</identifier><identifier>EISSN: 2304-8158</identifier><identifier>DOI: 10.3390/foods11030412</identifier><identifier>PMID: 35159561</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Acetaldehyde ; Aldehydes ; Analytical chemistry ; Aroma ; Aroma compounds ; Benzaldehyde ; big data analysis ; Calibration ; Chemical compounds ; Cluster analysis ; Consumers ; Experiments ; Flavor ; Flavors ; Food ; Food science ; Hexanal ; High performance liquid chromatography ; high-throughput UHPLC-MS/MS ; Ketones ; Liquid chromatography ; Mass spectrometry ; Mass spectroscopy ; Odorants ; Optimization ; pea protein aroma ; Peas ; Pisum sativum ; Proteins ; Quantitation ; Sensory evaluation ; Signal to noise ratio ; sustainable and innovative food ; Vegetarianism ; Volatile compounds ; Volatiles</subject><ispartof>Foods, 2022-01, Vol.11 (3), p.412</ispartof><rights>2022 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>2022 by the authors. 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c481t-f475a382d381ed13efc1432af1fddf5d6afa75f44e1a0c07df27cb833e48c12c3</citedby><cites>FETCH-LOGICAL-c481t-f475a382d381ed13efc1432af1fddf5d6afa75f44e1a0c07df27cb833e48c12c3</cites><orcidid>0000-0002-6502-173X ; 0000-0003-4057-7165 ; 0000-0001-9598-9242 ; 0000-0001-5342-2600 ; 0000-0002-5252-0409</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2627531129/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2627531129?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25752,27923,27924,37011,37012,44589,53790,53792,74897</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35159561$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Utz, Florian</creatorcontrib><creatorcontrib>Spaccasassi, Andrea</creatorcontrib><creatorcontrib>Kreissl, Johanna</creatorcontrib><creatorcontrib>Stark, Timo D</creatorcontrib><creatorcontrib>Tanger, Caren</creatorcontrib><creatorcontrib>Kulozik, Ulrich</creatorcontrib><creatorcontrib>Hofmann, Thomas</creatorcontrib><creatorcontrib>Dawid, Corinna</creatorcontrib><title>Sensomics-Assisted Aroma Decoding of Pea Protein Isolates ( Pisum sativum L.)</title><title>Foods</title><addtitle>Foods</addtitle><description>The aroma of pea protein (
L.) was decrypted for knowledge-based flavor optimization of new food products containing pea protein. Sensomics helped to determine several volatiles via ultra-high performance liquid chromatography tandem mass spectrometry and 3-nitrophenylhydrazine derivatization. Among the investigated volatiles, representatives of aldehydes, ketones, and acids were reported in literature as especially important in pea and pea-related matrices. After validation of the method and quantitation of the corresponding analytes, sensory reconstitution as well as omission studies of a selected pea protein were performed and revealed nine odor-active compounds as key food odorants (3-methylbutanal, hexanal, acetaldehyde, (
)-2,4-nonadienal, (
)-2-octenal, benzaldehyde, heptanal, 2-methylbutanal, and nonanoic acid). Interestingly, eight out of nine compounds belonged to the chemical class of aldehydes. Statistical heatmap and cluster analysis of all odor activity values of different pea proteins confirmed the obtained sensory results and generalize these nine key food odorants in other pea proteins. The knowledge of key components gained shows potential for simplifying industrial flavor optimization of pea protein-based food.</description><subject>Acetaldehyde</subject><subject>Aldehydes</subject><subject>Analytical chemistry</subject><subject>Aroma</subject><subject>Aroma compounds</subject><subject>Benzaldehyde</subject><subject>big data analysis</subject><subject>Calibration</subject><subject>Chemical compounds</subject><subject>Cluster analysis</subject><subject>Consumers</subject><subject>Experiments</subject><subject>Flavor</subject><subject>Flavors</subject><subject>Food</subject><subject>Food science</subject><subject>Hexanal</subject><subject>High performance liquid chromatography</subject><subject>high-throughput UHPLC-MS/MS</subject><subject>Ketones</subject><subject>Liquid chromatography</subject><subject>Mass spectrometry</subject><subject>Mass spectroscopy</subject><subject>Odorants</subject><subject>Optimization</subject><subject>pea protein aroma</subject><subject>Peas</subject><subject>Pisum sativum</subject><subject>Proteins</subject><subject>Quantitation</subject><subject>Sensory evaluation</subject><subject>Signal to noise ratio</subject><subject>sustainable and innovative food</subject><subject>Vegetarianism</subject><subject>Volatile compounds</subject><subject>Volatiles</subject><issn>2304-8158</issn><issn>2304-8158</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkU1v1DAQhiMEolXpkSuKxKUcUjweO3EuSKvytdIiVgLOlmuPF6-SuNhJJf49LluqLr7MyH70aDxvVb0EdonYs7c-RpcBGDIB_El1ykvTKJDq6aP-pDrPec_K6QEV8ufVCUqQvWzhtPryjaYcx2Bzs8o55JlcvUpxNPV7stGFaVdHX2_J1NsUZwpTvc5xMDPl-qLehryMdTZzuC11c_nmRfXMmyHT-X09q358_PD96nOz-fppfbXaNFYomBsvOmlQcYcKyAGStyCQGw_eOS9da7zppBeCwDDLOud5Z68VIgllgVs8q9YHr4tmr29SGE36raMJ-u9FTDtt0hzsQBpZh6boi42JHqlvuVHeMlLSO2GguN4dXDfL9UjO0jQnMxxJj1-m8FPv4q1WCsvWeRFc3AtS_LVQnvUYsqVhMBPFJWve8p61XMiuoK__Q_dxSVNZ1R3VSQTgfaGaA2VTzDmRfxgGmL7LXR_lXvhXj3_wQP9LGf8AUNCoKw</recordid><startdate>20220130</startdate><enddate>20220130</enddate><creator>Utz, Florian</creator><creator>Spaccasassi, Andrea</creator><creator>Kreissl, Johanna</creator><creator>Stark, Timo D</creator><creator>Tanger, Caren</creator><creator>Kulozik, Ulrich</creator><creator>Hofmann, Thomas</creator><creator>Dawid, Corinna</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/0000-0002-6502-173X</orcidid><orcidid>https://orcid.org/0000-0003-4057-7165</orcidid><orcidid>https://orcid.org/0000-0001-9598-9242</orcidid><orcidid>https://orcid.org/0000-0001-5342-2600</orcidid><orcidid>https://orcid.org/0000-0002-5252-0409</orcidid></search><sort><creationdate>20220130</creationdate><title>Sensomics-Assisted Aroma Decoding of Pea Protein Isolates ( Pisum sativum L.)</title><author>Utz, Florian ; Spaccasassi, Andrea ; Kreissl, Johanna ; Stark, Timo D ; Tanger, Caren ; Kulozik, Ulrich ; Hofmann, Thomas ; Dawid, Corinna</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c481t-f475a382d381ed13efc1432af1fddf5d6afa75f44e1a0c07df27cb833e48c12c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Acetaldehyde</topic><topic>Aldehydes</topic><topic>Analytical chemistry</topic><topic>Aroma</topic><topic>Aroma compounds</topic><topic>Benzaldehyde</topic><topic>big data analysis</topic><topic>Calibration</topic><topic>Chemical compounds</topic><topic>Cluster analysis</topic><topic>Consumers</topic><topic>Experiments</topic><topic>Flavor</topic><topic>Flavors</topic><topic>Food</topic><topic>Food science</topic><topic>Hexanal</topic><topic>High performance liquid chromatography</topic><topic>high-throughput UHPLC-MS/MS</topic><topic>Ketones</topic><topic>Liquid chromatography</topic><topic>Mass spectrometry</topic><topic>Mass spectroscopy</topic><topic>Odorants</topic><topic>Optimization</topic><topic>pea protein aroma</topic><topic>Peas</topic><topic>Pisum sativum</topic><topic>Proteins</topic><topic>Quantitation</topic><topic>Sensory evaluation</topic><topic>Signal to noise ratio</topic><topic>sustainable and innovative food</topic><topic>Vegetarianism</topic><topic>Volatile compounds</topic><topic>Volatiles</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Utz, Florian</creatorcontrib><creatorcontrib>Spaccasassi, Andrea</creatorcontrib><creatorcontrib>Kreissl, Johanna</creatorcontrib><creatorcontrib>Stark, Timo D</creatorcontrib><creatorcontrib>Tanger, Caren</creatorcontrib><creatorcontrib>Kulozik, Ulrich</creatorcontrib><creatorcontrib>Hofmann, Thomas</creatorcontrib><creatorcontrib>Dawid, Corinna</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</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>Directory of Open Access Journals</collection><jtitle>Foods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Utz, Florian</au><au>Spaccasassi, Andrea</au><au>Kreissl, Johanna</au><au>Stark, Timo D</au><au>Tanger, Caren</au><au>Kulozik, Ulrich</au><au>Hofmann, Thomas</au><au>Dawid, Corinna</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sensomics-Assisted Aroma Decoding of Pea Protein Isolates ( Pisum sativum L.)</atitle><jtitle>Foods</jtitle><addtitle>Foods</addtitle><date>2022-01-30</date><risdate>2022</risdate><volume>11</volume><issue>3</issue><spage>412</spage><pages>412-</pages><issn>2304-8158</issn><eissn>2304-8158</eissn><abstract>The aroma of pea protein (
L.) was decrypted for knowledge-based flavor optimization of new food products containing pea protein. Sensomics helped to determine several volatiles via ultra-high performance liquid chromatography tandem mass spectrometry and 3-nitrophenylhydrazine derivatization. Among the investigated volatiles, representatives of aldehydes, ketones, and acids were reported in literature as especially important in pea and pea-related matrices. After validation of the method and quantitation of the corresponding analytes, sensory reconstitution as well as omission studies of a selected pea protein were performed and revealed nine odor-active compounds as key food odorants (3-methylbutanal, hexanal, acetaldehyde, (
)-2,4-nonadienal, (
)-2-octenal, benzaldehyde, heptanal, 2-methylbutanal, and nonanoic acid). Interestingly, eight out of nine compounds belonged to the chemical class of aldehydes. Statistical heatmap and cluster analysis of all odor activity values of different pea proteins confirmed the obtained sensory results and generalize these nine key food odorants in other pea proteins. The knowledge of key components gained shows potential for simplifying industrial flavor optimization of pea protein-based food.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>35159561</pmid><doi>10.3390/foods11030412</doi><orcidid>https://orcid.org/0000-0002-6502-173X</orcidid><orcidid>https://orcid.org/0000-0003-4057-7165</orcidid><orcidid>https://orcid.org/0000-0001-9598-9242</orcidid><orcidid>https://orcid.org/0000-0001-5342-2600</orcidid><orcidid>https://orcid.org/0000-0002-5252-0409</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Foods, 2022-01, Vol.11 (3), p.412 |
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| subjects | Acetaldehyde Aldehydes Analytical chemistry Aroma Aroma compounds Benzaldehyde big data analysis Calibration Chemical compounds Cluster analysis Consumers Experiments Flavor Flavors Food Food science Hexanal High performance liquid chromatography high-throughput UHPLC-MS/MS Ketones Liquid chromatography Mass spectrometry Mass spectroscopy Odorants Optimization pea protein aroma Peas Pisum sativum Proteins Quantitation Sensory evaluation Signal to noise ratio sustainable and innovative food Vegetarianism Volatile compounds Volatiles |
| title | Sensomics-Assisted Aroma Decoding of Pea Protein Isolates ( Pisum sativum L.) |
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