Rapid quality determination of cherry fruit (Prunus spp.) using artificial olfactory technique as combined with non-linear data extraction model

In this article, quality rapid determination of cherry fruit (Prunus spp.) using artificial olfactory technique (AOT) combined with non-linear data extraction model was studied. AOT system was developed and used for cherry quality detection. AOT system responses to cherry samples stored at 4°C were...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International journal of food properties 2022-12, Vol.25 (1), p.1804-1816
Hauptverfasser: Zhang, Xiuli, Ge, Chao, Ma, Jingyan, Chen, Lixia
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 1816
container_issue 1
container_start_page 1804
container_title International journal of food properties
container_volume 25
creator Zhang, Xiuli
Ge, Chao
Ma, Jingyan
Chen, Lixia
description In this article, quality rapid determination of cherry fruit (Prunus spp.) using artificial olfactory technique (AOT) combined with non-linear data extraction model was studied. AOT system was developed and used for cherry quality detection. AOT system responses to cherry samples stored at 4°C were recorded. At the same time, physical/chemical indexes, such as human sensory evaluation (HSE), firmness, color, pH, total soluble solids (TSS), and reducing sugar content (RSC), were examined to provide quality references to the cherry samples. AOT data was analyzed by principal component analysis (PCA), and bilayer stochastic resonance (BSR) models. PCA only partially discriminated the cherry samples. The signal-to-noise ratio (SNR) maximum values (SNR-Max) generated by BSR successfully discriminated all the samples. Multiple variable regression (MVR) between cherry physical/chemical indexes and BSR SNR-Max values was conducted. Results indicated that BSR was suitable for cherry quality rapid evaluation. Cherry quality examination model was built based on linear fitting regression on BSR eigen values. Validation tests results indicated that the developed model has good forecasting accuracy. The proposed method had some advantages, such as rapid responses, high accuracy, easy operation, etc.
doi_str_mv 10.1080/10942912.2022.2106999
format Article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_proquest_journals_2742864092</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_5f350599c42a424d8b0bd11b4ede8462</doaj_id><sourcerecordid>2811983304</sourcerecordid><originalsourceid>FETCH-LOGICAL-c432t-d55291641d78c75f00f775b2c6396ecaac10090663d7e0a21a21a350f36608a13</originalsourceid><addsrcrecordid>eNp9kVtrFDEcxQdRsFY_ghDwpT7MmttkJm9K0VooKKLP4b-5dLNkkm2Soe638COb6VYffBBCbvzOSQ6n614TvCF4wu8IlpxKQjcU0zYRLKSUT7ozMjDaUzaJp23fmH6FnncvStljTCZG8Fn36xscvEF3CwRfj8jYavPsI1SfIkoO6Z3N-YhcXnxFF1_zEpeCyuGweYuW4uMtgly989pDQCk40DU1vFq9i_5usQgK0mne-mgNuvd1h2KKfWhHyMhABWR_1txU63NzMja87J45CMW-elzPux-fPn6__NzffLm6vvxw02vOaO3NMLQ0ghMzTnocHMZuHIct1YJJYTWAJhhLLAQzo8VAyTrYgB0TAk9A2Hl3ffI1CfbqkP0M-agSePVwkfKtWqPpYNXgmnCQUnMKnHIzbfHWELLl1tiJC9q8Lk5eh5xa6FLV7Iu2IUC0aSmKToTIiTHMG_rmH3SflhxbUkVHTifBsVwNhxOlcyolW_f3gwSrtXP1p3O1dq4eO2-69yedjy7lGe5TDkZVOIaUXYaofVHs_xa_Afk3s1c</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2742864092</pqid></control><display><type>article</type><title>Rapid quality determination of cherry fruit (Prunus spp.) using artificial olfactory technique as combined with non-linear data extraction model</title><source>Taylor &amp; Francis Open Access</source><source>DOAJ Directory of Open Access Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Zhang, Xiuli ; Ge, Chao ; Ma, Jingyan ; Chen, Lixia</creator><creatorcontrib>Zhang, Xiuli ; Ge, Chao ; Ma, Jingyan ; Chen, Lixia</creatorcontrib><description>In this article, quality rapid determination of cherry fruit (Prunus spp.) using artificial olfactory technique (AOT) combined with non-linear data extraction model was studied. AOT system was developed and used for cherry quality detection. AOT system responses to cherry samples stored at 4°C were recorded. At the same time, physical/chemical indexes, such as human sensory evaluation (HSE), firmness, color, pH, total soluble solids (TSS), and reducing sugar content (RSC), were examined to provide quality references to the cherry samples. AOT data was analyzed by principal component analysis (PCA), and bilayer stochastic resonance (BSR) models. PCA only partially discriminated the cherry samples. The signal-to-noise ratio (SNR) maximum values (SNR-Max) generated by BSR successfully discriminated all the samples. Multiple variable regression (MVR) between cherry physical/chemical indexes and BSR SNR-Max values was conducted. Results indicated that BSR was suitable for cherry quality rapid evaluation. Cherry quality examination model was built based on linear fitting regression on BSR eigen values. Validation tests results indicated that the developed model has good forecasting accuracy. The proposed method had some advantages, such as rapid responses, high accuracy, easy operation, etc.</description><identifier>ISSN: 1094-2912</identifier><identifier>ISSN: 1532-2386</identifier><identifier>EISSN: 1532-2386</identifier><identifier>DOI: 10.1080/10942912.2022.2106999</identifier><language>eng</language><publisher>Abingdon: Taylor &amp; Francis</publisher><subject>Artificial olfactory technique ; Bilayer stochastic resonance ; cherries ; Cherry fruit ; color ; Feature extraction ; firmness ; fruits ; humans ; principal component analysis ; Principal components analysis ; Prunus ; quality ; Sensory evaluation ; signal-to-noise ratio ; sugar content</subject><ispartof>International journal of food properties, 2022-12, Vol.25 (1), p.1804-1816</ispartof><rights>2022 Xiuli Zhang, Chao Ge, Jingyan Ma and Lixia Chen. Published with license by Taylor &amp; Francis Group, LLC. Published with license by Taylor &amp; Francis Group, LLC. © 2022 Xiuli Zhang, Chao Ge, Jingyan Ma and Lixia Chen 2022</rights><rights>2022 Xiuli Zhang, Chao Ge, Jingyan Ma and Lixia Chen. Published with license by Taylor &amp; Francis Group, LLC. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c432t-d55291641d78c75f00f775b2c6396ecaac10090663d7e0a21a21a350f36608a13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/10942912.2022.2106999$$EPDF$$P50$$Ginformaworld$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/10942912.2022.2106999$$EHTML$$P50$$Ginformaworld$$Hfree_for_read</linktohtml><link.rule.ids>314,778,782,862,2098,27485,27907,27908,59124,59125</link.rule.ids></links><search><creatorcontrib>Zhang, Xiuli</creatorcontrib><creatorcontrib>Ge, Chao</creatorcontrib><creatorcontrib>Ma, Jingyan</creatorcontrib><creatorcontrib>Chen, Lixia</creatorcontrib><title>Rapid quality determination of cherry fruit (Prunus spp.) using artificial olfactory technique as combined with non-linear data extraction model</title><title>International journal of food properties</title><description>In this article, quality rapid determination of cherry fruit (Prunus spp.) using artificial olfactory technique (AOT) combined with non-linear data extraction model was studied. AOT system was developed and used for cherry quality detection. AOT system responses to cherry samples stored at 4°C were recorded. At the same time, physical/chemical indexes, such as human sensory evaluation (HSE), firmness, color, pH, total soluble solids (TSS), and reducing sugar content (RSC), were examined to provide quality references to the cherry samples. AOT data was analyzed by principal component analysis (PCA), and bilayer stochastic resonance (BSR) models. PCA only partially discriminated the cherry samples. The signal-to-noise ratio (SNR) maximum values (SNR-Max) generated by BSR successfully discriminated all the samples. Multiple variable regression (MVR) between cherry physical/chemical indexes and BSR SNR-Max values was conducted. Results indicated that BSR was suitable for cherry quality rapid evaluation. Cherry quality examination model was built based on linear fitting regression on BSR eigen values. Validation tests results indicated that the developed model has good forecasting accuracy. The proposed method had some advantages, such as rapid responses, high accuracy, easy operation, etc.</description><subject>Artificial olfactory technique</subject><subject>Bilayer stochastic resonance</subject><subject>cherries</subject><subject>Cherry fruit</subject><subject>color</subject><subject>Feature extraction</subject><subject>firmness</subject><subject>fruits</subject><subject>humans</subject><subject>principal component analysis</subject><subject>Principal components analysis</subject><subject>Prunus</subject><subject>quality</subject><subject>Sensory evaluation</subject><subject>signal-to-noise ratio</subject><subject>sugar content</subject><issn>1094-2912</issn><issn>1532-2386</issn><issn>1532-2386</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>0YH</sourceid><sourceid>DOA</sourceid><recordid>eNp9kVtrFDEcxQdRsFY_ghDwpT7MmttkJm9K0VooKKLP4b-5dLNkkm2Soe638COb6VYffBBCbvzOSQ6n614TvCF4wu8IlpxKQjcU0zYRLKSUT7ozMjDaUzaJp23fmH6FnncvStljTCZG8Fn36xscvEF3CwRfj8jYavPsI1SfIkoO6Z3N-YhcXnxFF1_zEpeCyuGweYuW4uMtgly989pDQCk40DU1vFq9i_5usQgK0mne-mgNuvd1h2KKfWhHyMhABWR_1txU63NzMja87J45CMW-elzPux-fPn6__NzffLm6vvxw02vOaO3NMLQ0ghMzTnocHMZuHIct1YJJYTWAJhhLLAQzo8VAyTrYgB0TAk9A2Hl3ffI1CfbqkP0M-agSePVwkfKtWqPpYNXgmnCQUnMKnHIzbfHWELLl1tiJC9q8Lk5eh5xa6FLV7Iu2IUC0aSmKToTIiTHMG_rmH3SflhxbUkVHTifBsVwNhxOlcyolW_f3gwSrtXP1p3O1dq4eO2-69yedjy7lGe5TDkZVOIaUXYaofVHs_xa_Afk3s1c</recordid><startdate>20221231</startdate><enddate>20221231</enddate><creator>Zhang, Xiuli</creator><creator>Ge, Chao</creator><creator>Ma, Jingyan</creator><creator>Chen, Lixia</creator><general>Taylor &amp; Francis</general><general>Taylor &amp; Francis Ltd</general><general>Taylor &amp; Francis Group</general><scope>0YH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QR</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7S9</scope><scope>L.6</scope><scope>DOA</scope></search><sort><creationdate>20221231</creationdate><title>Rapid quality determination of cherry fruit (Prunus spp.) using artificial olfactory technique as combined with non-linear data extraction model</title><author>Zhang, Xiuli ; Ge, Chao ; Ma, Jingyan ; Chen, Lixia</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c432t-d55291641d78c75f00f775b2c6396ecaac10090663d7e0a21a21a350f36608a13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial olfactory technique</topic><topic>Bilayer stochastic resonance</topic><topic>cherries</topic><topic>Cherry fruit</topic><topic>color</topic><topic>Feature extraction</topic><topic>firmness</topic><topic>fruits</topic><topic>humans</topic><topic>principal component analysis</topic><topic>Principal components analysis</topic><topic>Prunus</topic><topic>quality</topic><topic>Sensory evaluation</topic><topic>signal-to-noise ratio</topic><topic>sugar content</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Xiuli</creatorcontrib><creatorcontrib>Ge, Chao</creatorcontrib><creatorcontrib>Ma, Jingyan</creatorcontrib><creatorcontrib>Chen, Lixia</creatorcontrib><collection>Taylor &amp; Francis Open Access</collection><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>International journal of food properties</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Xiuli</au><au>Ge, Chao</au><au>Ma, Jingyan</au><au>Chen, Lixia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Rapid quality determination of cherry fruit (Prunus spp.) using artificial olfactory technique as combined with non-linear data extraction model</atitle><jtitle>International journal of food properties</jtitle><date>2022-12-31</date><risdate>2022</risdate><volume>25</volume><issue>1</issue><spage>1804</spage><epage>1816</epage><pages>1804-1816</pages><issn>1094-2912</issn><issn>1532-2386</issn><eissn>1532-2386</eissn><abstract>In this article, quality rapid determination of cherry fruit (Prunus spp.) using artificial olfactory technique (AOT) combined with non-linear data extraction model was studied. AOT system was developed and used for cherry quality detection. AOT system responses to cherry samples stored at 4°C were recorded. At the same time, physical/chemical indexes, such as human sensory evaluation (HSE), firmness, color, pH, total soluble solids (TSS), and reducing sugar content (RSC), were examined to provide quality references to the cherry samples. AOT data was analyzed by principal component analysis (PCA), and bilayer stochastic resonance (BSR) models. PCA only partially discriminated the cherry samples. The signal-to-noise ratio (SNR) maximum values (SNR-Max) generated by BSR successfully discriminated all the samples. Multiple variable regression (MVR) between cherry physical/chemical indexes and BSR SNR-Max values was conducted. Results indicated that BSR was suitable for cherry quality rapid evaluation. Cherry quality examination model was built based on linear fitting regression on BSR eigen values. Validation tests results indicated that the developed model has good forecasting accuracy. The proposed method had some advantages, such as rapid responses, high accuracy, easy operation, etc.</abstract><cop>Abingdon</cop><pub>Taylor &amp; Francis</pub><doi>10.1080/10942912.2022.2106999</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1094-2912
ispartof International journal of food properties, 2022-12, Vol.25 (1), p.1804-1816
issn 1094-2912
1532-2386
1532-2386
language eng
recordid cdi_proquest_journals_2742864092
source Taylor & Francis Open Access; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals
subjects Artificial olfactory technique
Bilayer stochastic resonance
cherries
Cherry fruit
color
Feature extraction
firmness
fruits
humans
principal component analysis
Principal components analysis
Prunus
quality
Sensory evaluation
signal-to-noise ratio
sugar content
title Rapid quality determination of cherry fruit (Prunus spp.) using artificial olfactory technique as combined with non-linear data extraction model
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-17T06%3A10%3A12IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Rapid%20quality%20determination%20of%20cherry%20fruit%20(Prunus%20spp.)%20using%20artificial%20olfactory%20technique%20as%20combined%20with%20non-linear%20data%20extraction%20model&rft.jtitle=International%20journal%20of%20food%20properties&rft.au=Zhang,%20Xiuli&rft.date=2022-12-31&rft.volume=25&rft.issue=1&rft.spage=1804&rft.epage=1816&rft.pages=1804-1816&rft.issn=1094-2912&rft.eissn=1532-2386&rft_id=info:doi/10.1080/10942912.2022.2106999&rft_dat=%3Cproquest_doaj_%3E2811983304%3C/proquest_doaj_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2742864092&rft_id=info:pmid/&rft_doaj_id=oai_doaj_org_article_5f350599c42a424d8b0bd11b4ede8462&rfr_iscdi=true