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...
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Veröffentlicht in: | International journal of food properties 2022-12, Vol.25 (1), p.1804-1816 |
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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 |
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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 & 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 & Francis Group, LLC. Published with license by Taylor & 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 & 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 & Francis</general><general>Taylor & Francis Ltd</general><general>Taylor & 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 & 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 & Francis</pub><doi>10.1080/10942912.2022.2106999</doi><tpages>13</tpages><oa>free_for_read</oa></addata></record> |
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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 |
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