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...

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Veröffentlicht in:Journal of the science of food and agriculture 2021-11, Vol.101 (14), p.5972-5983
Hauptverfasser: Tian, Xiao‐Yu, Aheto, Joshua H, Huang, Xingyi, Zheng, Kaiyi, Dai, Chunxia, Wang, Chengquan, Bai, Jun‐Wen
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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
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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 &amp; 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 &amp; Technology ; Gas chromatography ; Hexanal ; Imaging ; Ionic mobility ; Ions ; Least squares method ; Life Sciences &amp; Biomedicine ; lipid oxidation ; Lipid peroxidation ; Lipids ; Meat ; microstructure ; Mobility ; Moisture content ; multivariate analysis ; nondestructive detection ; Oxidation ; Physical Sciences ; Pork ; Predictions ; Regression analysis ; Science &amp; 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 &amp; 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 &amp; 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 &amp; 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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 &amp; 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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 &amp; 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>
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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
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