An evaluation of biochemical, structural and volatile changes of dry‐cured pork using a combined ion mobility spectrometry, hyperspectral and confocal imaging approach
BACKGROUND Food processing induces various modifications that affect the structure, physical and chemical properties of food products and hence the acceptance of the product by the consumer. In this work, the evolution of volatile components, 2‐thiobarbituric acid reactive substances (TBARS), moistu...
Gespeichert in:
Veröffentlicht in: | Journal of the science of food and agriculture 2021-11, Vol.101 (14), p.5972-5983 |
---|---|
Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 5983 |
---|---|
container_issue | 14 |
container_start_page | 5972 |
container_title | Journal of the science of food and agriculture |
container_volume | 101 |
creator | Tian, Xiao‐Yu Aheto, Joshua H Huang, Xingyi Zheng, Kaiyi Dai, Chunxia Wang, Chengquan Bai, Jun‐Wen |
description | BACKGROUND
Food processing induces various modifications that affect the structure, physical and chemical properties of food products and hence the acceptance of the product by the consumer. In this work, the evolution of volatile components, 2‐thiobarbituric acid reactive substances (TBARS), moisture content (MC) and microstructural changes of pork was investigated by hyperspectral (HSI) and confocal imaging (CLSM) techniques in synergy with gas chromatography–ion mobility spectrometry (GC‐IMS). Models based on partial least squares regression (PLSR) were developed using the full HSI spectrum variables as well as optimum variables selected through a competitive adaptive reweighted sampling algorithm.
RESULTS
Prediction results for MC and TBARS using multiplicative scatter correction pre‐processed spectra models demonstrated greater efficiency and predictability with determination coefficient of prediction of 0.928, 0.930 and root mean square error of prediction of 0.114, 1.002, respectively. Major structural changes were also observed during CLSM imaging, which were greatly pronounced in pork samples oven cooked for 15 and 20 h. These structural changes could be related to the denaturation of the major meat components, which could explain the loss of moisture and the formation of TBARS visualized from the HSI chemical distribution maps. GC‐IMS identified 35 volatile components, including hexanal and pentanal, which are also known to have a higher lipid oxidation specificity.
CONCLUSION
The synergistic application of HSI, CLSM and GC‐IMS enhanced data mining and interpretation and provided a convenient way for analyzing the chemical, structural and volatile changes occurring in meat during processing. © 2021 Society of Chemical Industry. |
doi_str_mv | 10.1002/jsfa.11251 |
format | Article |
fullrecord | <record><control><sourceid>proquest_webof</sourceid><recordid>TN_cdi_proquest_miscellaneous_2513240622</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2513240622</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3571-290a34fb9e4185f32f009748d365276b2997998cc0221668656767810d373b9d3</originalsourceid><addsrcrecordid>eNqNkkuO1DAQhi0EYpqBDQdAltiMYHrwI3HiZavF8NBILIB15DhOt5vEDnY8KDuOwDW4Fieh0mlmwQKxslX-6q_Hb4SeUnJFCWGvDrFVV5SynN5DK0pksSaEkvtoBY9sndOMnaFHMR4IIVIK8RCdcV7moiD5Cv3cOGxuVZfUaL3DvsW19XpveqtVd4njGJIeU1AdVq7Bt74DrjNY75XbmTjzTZh-ff-hUzANHnz4glO0bocV1r6vrYPoLNz72nZ2nHAcjB6D780Ypku8nwYTltCphPau9VAb217tjkLDELzS-8foQau6aJ6cznP0-fr1p-3b9c2HN--2m5u15nlB10wSxbO2liajZd5y1sLURVY2XOSsEDWTspCy1Bp2Q4UoBSxCFCUlDS94LRt-ji4WXSj7NZk4Vr2N2nSdcsanWMGaOcuIYAzQ53-hB5-Cg-6AKiQrZM4EUC8WSgcfYzBtNQQYLkwVJdVsYDUbWB0NBPjZSTLVvWnu0D-OAfByAb6Z2rdRW-O0ucPAYpHlpKQZ3MgsV_4_vbXj8RdsfXIjpNJTKhg-_aPn6v3H683S_W8GCMhv</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2579279526</pqid></control><display><type>article</type><title>An evaluation of biochemical, structural and volatile changes of dry‐cured pork using a combined ion mobility spectrometry, hyperspectral and confocal imaging approach</title><source>Wiley Online Library Journals Frontfile Complete</source><creator>Tian, Xiao‐Yu ; Aheto, Joshua H ; Huang, Xingyi ; Zheng, Kaiyi ; Dai, Chunxia ; Wang, Chengquan ; Bai, Jun‐Wen</creator><creatorcontrib>Tian, Xiao‐Yu ; Aheto, Joshua H ; Huang, Xingyi ; Zheng, Kaiyi ; Dai, Chunxia ; Wang, Chengquan ; Bai, Jun‐Wen</creatorcontrib><description>BACKGROUND
Food processing induces various modifications that affect the structure, physical and chemical properties of food products and hence the acceptance of the product by the consumer. In this work, the evolution of volatile components, 2‐thiobarbituric acid reactive substances (TBARS), moisture content (MC) and microstructural changes of pork was investigated by hyperspectral (HSI) and confocal imaging (CLSM) techniques in synergy with gas chromatography–ion mobility spectrometry (GC‐IMS). Models based on partial least squares regression (PLSR) were developed using the full HSI spectrum variables as well as optimum variables selected through a competitive adaptive reweighted sampling algorithm.
RESULTS
Prediction results for MC and TBARS using multiplicative scatter correction pre‐processed spectra models demonstrated greater efficiency and predictability with determination coefficient of prediction of 0.928, 0.930 and root mean square error of prediction of 0.114, 1.002, respectively. Major structural changes were also observed during CLSM imaging, which were greatly pronounced in pork samples oven cooked for 15 and 20 h. These structural changes could be related to the denaturation of the major meat components, which could explain the loss of moisture and the formation of TBARS visualized from the HSI chemical distribution maps. GC‐IMS identified 35 volatile components, including hexanal and pentanal, which are also known to have a higher lipid oxidation specificity.
CONCLUSION
The synergistic application of HSI, CLSM and GC‐IMS enhanced data mining and interpretation and provided a convenient way for analyzing the chemical, structural and volatile changes occurring in meat during processing. © 2021 Society of Chemical Industry.</description><identifier>ISSN: 0022-5142</identifier><identifier>EISSN: 1097-0010</identifier><identifier>DOI: 10.1002/jsfa.11251</identifier><identifier>PMID: 33856705</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Adaptive algorithms ; Adaptive sampling ; Agriculture ; Agriculture, Multidisciplinary ; attribute visualization ; Chemical properties ; Chemistry ; Chemistry, Applied ; Data mining ; Denaturation ; Drying ovens ; Food processing ; Food Science & Technology ; Gas chromatography ; Hexanal ; Imaging ; Ionic mobility ; Ions ; Least squares method ; Life Sciences & Biomedicine ; lipid oxidation ; Lipid peroxidation ; Lipids ; Meat ; microstructure ; Mobility ; Moisture content ; multivariate analysis ; nondestructive detection ; Oxidation ; Physical Sciences ; Pork ; Predictions ; Regression analysis ; Science & Technology ; Scientific imaging ; Spectrometry ; Spectroscopy ; Thiobarbituric acid ; volatile compounds ; Water content</subject><ispartof>Journal of the science of food and agriculture, 2021-11, Vol.101 (14), p.5972-5983</ispartof><rights>2021 Society of Chemical Industry.</rights><rights>Copyright © 2021 Society of Chemical Industry</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>9</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000645081400001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c3571-290a34fb9e4185f32f009748d365276b2997998cc0221668656767810d373b9d3</citedby><cites>FETCH-LOGICAL-c3571-290a34fb9e4185f32f009748d365276b2997998cc0221668656767810d373b9d3</cites><orcidid>0000-0002-7006-7118 ; 0000-0001-7665-4316 ; 0000-0003-2665-8655 ; 0000-0001-6245-0072 ; 0000-0002-7904-4561</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjsfa.11251$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjsfa.11251$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27903,27904,45553,45554</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33856705$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tian, Xiao‐Yu</creatorcontrib><creatorcontrib>Aheto, Joshua H</creatorcontrib><creatorcontrib>Huang, Xingyi</creatorcontrib><creatorcontrib>Zheng, Kaiyi</creatorcontrib><creatorcontrib>Dai, Chunxia</creatorcontrib><creatorcontrib>Wang, Chengquan</creatorcontrib><creatorcontrib>Bai, Jun‐Wen</creatorcontrib><title>An evaluation of biochemical, structural and volatile changes of dry‐cured pork using a combined ion mobility spectrometry, hyperspectral and confocal imaging approach</title><title>Journal of the science of food and agriculture</title><addtitle>J SCI FOOD AGR</addtitle><addtitle>J Sci Food Agric</addtitle><description>BACKGROUND
Food processing induces various modifications that affect the structure, physical and chemical properties of food products and hence the acceptance of the product by the consumer. In this work, the evolution of volatile components, 2‐thiobarbituric acid reactive substances (TBARS), moisture content (MC) and microstructural changes of pork was investigated by hyperspectral (HSI) and confocal imaging (CLSM) techniques in synergy with gas chromatography–ion mobility spectrometry (GC‐IMS). Models based on partial least squares regression (PLSR) were developed using the full HSI spectrum variables as well as optimum variables selected through a competitive adaptive reweighted sampling algorithm.
RESULTS
Prediction results for MC and TBARS using multiplicative scatter correction pre‐processed spectra models demonstrated greater efficiency and predictability with determination coefficient of prediction of 0.928, 0.930 and root mean square error of prediction of 0.114, 1.002, respectively. Major structural changes were also observed during CLSM imaging, which were greatly pronounced in pork samples oven cooked for 15 and 20 h. These structural changes could be related to the denaturation of the major meat components, which could explain the loss of moisture and the formation of TBARS visualized from the HSI chemical distribution maps. GC‐IMS identified 35 volatile components, including hexanal and pentanal, which are also known to have a higher lipid oxidation specificity.
CONCLUSION
The synergistic application of HSI, CLSM and GC‐IMS enhanced data mining and interpretation and provided a convenient way for analyzing the chemical, structural and volatile changes occurring in meat during processing. © 2021 Society of Chemical Industry.</description><subject>Adaptive algorithms</subject><subject>Adaptive sampling</subject><subject>Agriculture</subject><subject>Agriculture, Multidisciplinary</subject><subject>attribute visualization</subject><subject>Chemical properties</subject><subject>Chemistry</subject><subject>Chemistry, Applied</subject><subject>Data mining</subject><subject>Denaturation</subject><subject>Drying ovens</subject><subject>Food processing</subject><subject>Food Science & Technology</subject><subject>Gas chromatography</subject><subject>Hexanal</subject><subject>Imaging</subject><subject>Ionic mobility</subject><subject>Ions</subject><subject>Least squares method</subject><subject>Life Sciences & Biomedicine</subject><subject>lipid oxidation</subject><subject>Lipid peroxidation</subject><subject>Lipids</subject><subject>Meat</subject><subject>microstructure</subject><subject>Mobility</subject><subject>Moisture content</subject><subject>multivariate analysis</subject><subject>nondestructive detection</subject><subject>Oxidation</subject><subject>Physical Sciences</subject><subject>Pork</subject><subject>Predictions</subject><subject>Regression analysis</subject><subject>Science & Technology</subject><subject>Scientific imaging</subject><subject>Spectrometry</subject><subject>Spectroscopy</subject><subject>Thiobarbituric acid</subject><subject>volatile compounds</subject><subject>Water content</subject><issn>0022-5142</issn><issn>1097-0010</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><recordid>eNqNkkuO1DAQhi0EYpqBDQdAltiMYHrwI3HiZavF8NBILIB15DhOt5vEDnY8KDuOwDW4Fieh0mlmwQKxslX-6q_Hb4SeUnJFCWGvDrFVV5SynN5DK0pksSaEkvtoBY9sndOMnaFHMR4IIVIK8RCdcV7moiD5Cv3cOGxuVZfUaL3DvsW19XpveqtVd4njGJIeU1AdVq7Bt74DrjNY75XbmTjzTZh-ff-hUzANHnz4glO0bocV1r6vrYPoLNz72nZ2nHAcjB6D780Ypku8nwYTltCphPau9VAb217tjkLDELzS-8foQau6aJ6cznP0-fr1p-3b9c2HN--2m5u15nlB10wSxbO2liajZd5y1sLURVY2XOSsEDWTspCy1Bp2Q4UoBSxCFCUlDS94LRt-ji4WXSj7NZk4Vr2N2nSdcsanWMGaOcuIYAzQ53-hB5-Cg-6AKiQrZM4EUC8WSgcfYzBtNQQYLkwVJdVsYDUbWB0NBPjZSTLVvWnu0D-OAfByAb6Z2rdRW-O0ucPAYpHlpKQZ3MgsV_4_vbXj8RdsfXIjpNJTKhg-_aPn6v3H683S_W8GCMhv</recordid><startdate>202111</startdate><enddate>202111</enddate><creator>Tian, Xiao‐Yu</creator><creator>Aheto, Joshua H</creator><creator>Huang, Xingyi</creator><creator>Zheng, Kaiyi</creator><creator>Dai, Chunxia</creator><creator>Wang, Chengquan</creator><creator>Bai, Jun‐Wen</creator><general>John Wiley & Sons, Ltd</general><general>Wiley</general><general>John Wiley and Sons, Limited</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QL</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7ST</scope><scope>7T5</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7TM</scope><scope>7U5</scope><scope>7U9</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>H94</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M7N</scope><scope>P64</scope><scope>SOI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-7006-7118</orcidid><orcidid>https://orcid.org/0000-0001-7665-4316</orcidid><orcidid>https://orcid.org/0000-0003-2665-8655</orcidid><orcidid>https://orcid.org/0000-0001-6245-0072</orcidid><orcidid>https://orcid.org/0000-0002-7904-4561</orcidid></search><sort><creationdate>202111</creationdate><title>An evaluation of biochemical, structural and volatile changes of dry‐cured pork using a combined ion mobility spectrometry, hyperspectral and confocal imaging approach</title><author>Tian, Xiao‐Yu ; Aheto, Joshua H ; Huang, Xingyi ; Zheng, Kaiyi ; Dai, Chunxia ; Wang, Chengquan ; Bai, Jun‐Wen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3571-290a34fb9e4185f32f009748d365276b2997998cc0221668656767810d373b9d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adaptive algorithms</topic><topic>Adaptive sampling</topic><topic>Agriculture</topic><topic>Agriculture, Multidisciplinary</topic><topic>attribute visualization</topic><topic>Chemical properties</topic><topic>Chemistry</topic><topic>Chemistry, Applied</topic><topic>Data mining</topic><topic>Denaturation</topic><topic>Drying ovens</topic><topic>Food processing</topic><topic>Food Science & Technology</topic><topic>Gas chromatography</topic><topic>Hexanal</topic><topic>Imaging</topic><topic>Ionic mobility</topic><topic>Ions</topic><topic>Least squares method</topic><topic>Life Sciences & Biomedicine</topic><topic>lipid oxidation</topic><topic>Lipid peroxidation</topic><topic>Lipids</topic><topic>Meat</topic><topic>microstructure</topic><topic>Mobility</topic><topic>Moisture content</topic><topic>multivariate analysis</topic><topic>nondestructive detection</topic><topic>Oxidation</topic><topic>Physical Sciences</topic><topic>Pork</topic><topic>Predictions</topic><topic>Regression analysis</topic><topic>Science & Technology</topic><topic>Scientific imaging</topic><topic>Spectrometry</topic><topic>Spectroscopy</topic><topic>Thiobarbituric acid</topic><topic>volatile compounds</topic><topic>Water content</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tian, Xiao‐Yu</creatorcontrib><creatorcontrib>Aheto, Joshua H</creatorcontrib><creatorcontrib>Huang, Xingyi</creatorcontrib><creatorcontrib>Zheng, Kaiyi</creatorcontrib><creatorcontrib>Dai, Chunxia</creatorcontrib><creatorcontrib>Wang, Chengquan</creatorcontrib><creatorcontrib>Bai, Jun‐Wen</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Environment Abstracts</collection><collection>Immunology Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environment Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of the science of food and agriculture</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tian, Xiao‐Yu</au><au>Aheto, Joshua H</au><au>Huang, Xingyi</au><au>Zheng, Kaiyi</au><au>Dai, Chunxia</au><au>Wang, Chengquan</au><au>Bai, Jun‐Wen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An evaluation of biochemical, structural and volatile changes of dry‐cured pork using a combined ion mobility spectrometry, hyperspectral and confocal imaging approach</atitle><jtitle>Journal of the science of food and agriculture</jtitle><stitle>J SCI FOOD AGR</stitle><addtitle>J Sci Food Agric</addtitle><date>2021-11</date><risdate>2021</risdate><volume>101</volume><issue>14</issue><spage>5972</spage><epage>5983</epage><pages>5972-5983</pages><issn>0022-5142</issn><eissn>1097-0010</eissn><abstract>BACKGROUND
Food processing induces various modifications that affect the structure, physical and chemical properties of food products and hence the acceptance of the product by the consumer. In this work, the evolution of volatile components, 2‐thiobarbituric acid reactive substances (TBARS), moisture content (MC) and microstructural changes of pork was investigated by hyperspectral (HSI) and confocal imaging (CLSM) techniques in synergy with gas chromatography–ion mobility spectrometry (GC‐IMS). Models based on partial least squares regression (PLSR) were developed using the full HSI spectrum variables as well as optimum variables selected through a competitive adaptive reweighted sampling algorithm.
RESULTS
Prediction results for MC and TBARS using multiplicative scatter correction pre‐processed spectra models demonstrated greater efficiency and predictability with determination coefficient of prediction of 0.928, 0.930 and root mean square error of prediction of 0.114, 1.002, respectively. Major structural changes were also observed during CLSM imaging, which were greatly pronounced in pork samples oven cooked for 15 and 20 h. These structural changes could be related to the denaturation of the major meat components, which could explain the loss of moisture and the formation of TBARS visualized from the HSI chemical distribution maps. GC‐IMS identified 35 volatile components, including hexanal and pentanal, which are also known to have a higher lipid oxidation specificity.
CONCLUSION
The synergistic application of HSI, CLSM and GC‐IMS enhanced data mining and interpretation and provided a convenient way for analyzing the chemical, structural and volatile changes occurring in meat during processing. © 2021 Society of Chemical Industry.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><pmid>33856705</pmid><doi>10.1002/jsfa.11251</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-7006-7118</orcidid><orcidid>https://orcid.org/0000-0001-7665-4316</orcidid><orcidid>https://orcid.org/0000-0003-2665-8655</orcidid><orcidid>https://orcid.org/0000-0001-6245-0072</orcidid><orcidid>https://orcid.org/0000-0002-7904-4561</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0022-5142 |
ispartof | Journal of the science of food and agriculture, 2021-11, Vol.101 (14), p.5972-5983 |
issn | 0022-5142 1097-0010 |
language | eng |
recordid | cdi_proquest_miscellaneous_2513240622 |
source | Wiley Online Library Journals Frontfile Complete |
subjects | Adaptive algorithms Adaptive sampling Agriculture Agriculture, Multidisciplinary attribute visualization Chemical properties Chemistry Chemistry, Applied Data mining Denaturation Drying ovens Food processing Food Science & Technology Gas chromatography Hexanal Imaging Ionic mobility Ions Least squares method Life Sciences & Biomedicine lipid oxidation Lipid peroxidation Lipids Meat microstructure Mobility Moisture content multivariate analysis nondestructive detection Oxidation Physical Sciences Pork Predictions Regression analysis Science & Technology Scientific imaging Spectrometry Spectroscopy Thiobarbituric acid volatile compounds Water content |
title | An evaluation of biochemical, structural and volatile changes of dry‐cured pork using a combined ion mobility spectrometry, hyperspectral and confocal imaging approach |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T03%3A47%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_webof&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=An%20evaluation%20of%20biochemical,%20structural%20and%20volatile%20changes%20of%20dry%E2%80%90cured%20pork%20using%20a%20combined%20ion%20mobility%20spectrometry,%20hyperspectral%20and%20confocal%20imaging%20approach&rft.jtitle=Journal%20of%20the%20science%20of%20food%20and%20agriculture&rft.au=Tian,%20Xiao%E2%80%90Yu&rft.date=2021-11&rft.volume=101&rft.issue=14&rft.spage=5972&rft.epage=5983&rft.pages=5972-5983&rft.issn=0022-5142&rft.eissn=1097-0010&rft_id=info:doi/10.1002/jsfa.11251&rft_dat=%3Cproquest_webof%3E2513240622%3C/proquest_webof%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2579279526&rft_id=info:pmid/33856705&rfr_iscdi=true |