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Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion
•Differences in color, texture and odor of PCR samples from different origins.•The optimized BP-NN based on multi-data fusion improves the discrimination rate.•This study provides a new reference for origin traceability of other food products. This study utilized computer vision to extract color and...
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| Published in: | Food chemistry 2024-06, Vol.442, p.138408-138408, Article 138408 |
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| container_end_page | 138408 |
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| container_start_page | 138408 |
| container_title | Food chemistry |
| container_volume | 442 |
| creator | Chen, Peng Fu, Rao Shi, Yabo Liu, Chang Yang, Chenlu Su, Yong Lu, Tulin Zhou, Peina He, Weitong Guo, Qiaosheng Fei, Chenghao |
| description | •Differences in color, texture and odor of PCR samples from different origins.•The optimized BP-NN based on multi-data fusion improves the discrimination rate.•This study provides a new reference for origin traceability of other food products.
This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P |
| doi_str_mv | 10.1016/j.foodchem.2024.138408 |
| format | article |
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This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P < 0.05) from the 118 trait factors that can distinguish PCR from different origins. These factors include color, texture, and odor traits. By integrating multivariate statistical Analysis with the BP neural network algorithm, a novel artificial intelligence algorithm was developed and optimized for traceability of PCR origin. This algorithm achieved a 100% discrimination rate in differentiating PCR samples from various origins. This study offers a valuable reference and data support for developing intelligent algorithms that utilize data fusion from multiple intelligent sensory technologies to achieve rapid traceability of food origins.</description><identifier>ISSN: 0308-8146</identifier><identifier>EISSN: 1873-7072</identifier><identifier>DOI: 10.1016/j.foodchem.2024.138408</identifier><identifier>PMID: 38241985</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>algorithms ; artificial intelligence ; color ; Computer vision ; electronic nose ; flavor ; food chemistry ; Intelligent algorithm ; multivariate analysis ; Multivariate statistics ; odors ; Pericarpium Citri Reticulate ; texture ; Traceability ; Ultra-fast gas phase electronic nose</subject><ispartof>Food chemistry, 2024-06, Vol.442, p.138408-138408, Article 138408</ispartof><rights>2024 Elsevier Ltd</rights><rights>Copyright © 2024 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c348t-544d9748192f8deb66633d48b762fd83a1ba81ecf0d604f18618887b5f9352af3</cites><orcidid>0000-0002-9575-0204</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38241985$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Peng</creatorcontrib><creatorcontrib>Fu, Rao</creatorcontrib><creatorcontrib>Shi, Yabo</creatorcontrib><creatorcontrib>Liu, Chang</creatorcontrib><creatorcontrib>Yang, Chenlu</creatorcontrib><creatorcontrib>Su, Yong</creatorcontrib><creatorcontrib>Lu, Tulin</creatorcontrib><creatorcontrib>Zhou, Peina</creatorcontrib><creatorcontrib>He, Weitong</creatorcontrib><creatorcontrib>Guo, Qiaosheng</creatorcontrib><creatorcontrib>Fei, Chenghao</creatorcontrib><title>Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion</title><title>Food chemistry</title><addtitle>Food Chem</addtitle><description>•Differences in color, texture and odor of PCR samples from different origins.•The optimized BP-NN based on multi-data fusion improves the discrimination rate.•This study provides a new reference for origin traceability of other food products.
This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P < 0.05) from the 118 trait factors that can distinguish PCR from different origins. These factors include color, texture, and odor traits. By integrating multivariate statistical Analysis with the BP neural network algorithm, a novel artificial intelligence algorithm was developed and optimized for traceability of PCR origin. This algorithm achieved a 100% discrimination rate in differentiating PCR samples from various origins. This study offers a valuable reference and data support for developing intelligent algorithms that utilize data fusion from multiple intelligent sensory technologies to achieve rapid traceability of food origins.</description><subject>algorithms</subject><subject>artificial intelligence</subject><subject>color</subject><subject>Computer vision</subject><subject>electronic nose</subject><subject>flavor</subject><subject>food chemistry</subject><subject>Intelligent algorithm</subject><subject>multivariate analysis</subject><subject>Multivariate statistics</subject><subject>odors</subject><subject>Pericarpium Citri Reticulate</subject><subject>texture</subject><subject>Traceability</subject><subject>Ultra-fast gas phase electronic nose</subject><issn>0308-8146</issn><issn>1873-7072</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNqFkU1r3DAQhkVpaLZp_0LQMT14K1n-kG9tl35BICGkZyFLo93Z2pYryQnp3-ofrJZNcs1pGHjeeWEeQs45W3PGm4_7tfPemh2M65KV1ZoLWTH5iqy4bEXRsrZ8TVZMMFlIXjWn5G2Me8ZYybh8Q06FLCveyXpF_l3NCUf8i9OWfrmmEyxBD3mkex9-Uz1sfcC0G6nzgV5DQKPDjMtIN5gC0htIaJZBJw30YrODacYPNCe2ONEUtAHd44DpgfY6gqV-osaP85Ig0DuMmHc9WboMmS2cjoludSzmXYYpDGBS8BMaOvm821xC3XIIvSMnTg8R3j_OM_Lr29fbzY_i8ur7z83ny8KISqairirbtZXkXemkhb5pGiFsJfu2KZ2VQvNeSw7GMduwynHZcCll29euE3WpnTgjF8e7c_B_FohJjRgNDIOewC9RCV4L3nEu2Ito2ZUNa2Xd8ow2R9QEH2MAp-aAow4PijN1cKv26smtOrhVR7c5eP7YsfQj2OfYk8wMfDoCkJ9yhxBUNAiTAYshP1NZjy91_AeWYrvd</recordid><startdate>20240601</startdate><enddate>20240601</enddate><creator>Chen, Peng</creator><creator>Fu, Rao</creator><creator>Shi, Yabo</creator><creator>Liu, Chang</creator><creator>Yang, Chenlu</creator><creator>Su, Yong</creator><creator>Lu, Tulin</creator><creator>Zhou, Peina</creator><creator>He, Weitong</creator><creator>Guo, Qiaosheng</creator><creator>Fei, Chenghao</creator><general>Elsevier Ltd</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0002-9575-0204</orcidid></search><sort><creationdate>20240601</creationdate><title>Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion</title><author>Chen, Peng ; Fu, Rao ; Shi, Yabo ; Liu, Chang ; Yang, Chenlu ; Su, Yong ; Lu, Tulin ; Zhou, Peina ; He, Weitong ; Guo, Qiaosheng ; Fei, Chenghao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c348t-544d9748192f8deb66633d48b762fd83a1ba81ecf0d604f18618887b5f9352af3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>algorithms</topic><topic>artificial intelligence</topic><topic>color</topic><topic>Computer vision</topic><topic>electronic nose</topic><topic>flavor</topic><topic>food chemistry</topic><topic>Intelligent algorithm</topic><topic>multivariate analysis</topic><topic>Multivariate statistics</topic><topic>odors</topic><topic>Pericarpium Citri Reticulate</topic><topic>texture</topic><topic>Traceability</topic><topic>Ultra-fast gas phase electronic nose</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Peng</creatorcontrib><creatorcontrib>Fu, Rao</creatorcontrib><creatorcontrib>Shi, Yabo</creatorcontrib><creatorcontrib>Liu, Chang</creatorcontrib><creatorcontrib>Yang, Chenlu</creatorcontrib><creatorcontrib>Su, Yong</creatorcontrib><creatorcontrib>Lu, Tulin</creatorcontrib><creatorcontrib>Zhou, Peina</creatorcontrib><creatorcontrib>He, Weitong</creatorcontrib><creatorcontrib>Guo, Qiaosheng</creatorcontrib><creatorcontrib>Fei, Chenghao</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Food chemistry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Peng</au><au>Fu, Rao</au><au>Shi, Yabo</au><au>Liu, Chang</au><au>Yang, Chenlu</au><au>Su, Yong</au><au>Lu, Tulin</au><au>Zhou, Peina</au><au>He, Weitong</au><au>Guo, Qiaosheng</au><au>Fei, Chenghao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion</atitle><jtitle>Food chemistry</jtitle><addtitle>Food Chem</addtitle><date>2024-06-01</date><risdate>2024</risdate><volume>442</volume><spage>138408</spage><epage>138408</epage><pages>138408-138408</pages><artnum>138408</artnum><issn>0308-8146</issn><eissn>1873-7072</eissn><abstract>•Differences in color, texture and odor of PCR samples from different origins.•The optimized BP-NN based on multi-data fusion improves the discrimination rate.•This study provides a new reference for origin traceability of other food products.
This study utilized computer vision to extract color and texture features of Pericarpium Citri Reticulatae (PCR). The ultra-fast gas-phase electronic nose (UF-GC-E-nose) technique successfully identified 98 volatile components, including olefins, alcohols, and esters, which significantly contribute to the flavor profile of PCR. Multivariate statistical Analysis was applied to the appearance traits of PCR, identifying 57 potential marker-trait factors (VIP > 1 and P < 0.05) from the 118 trait factors that can distinguish PCR from different origins. These factors include color, texture, and odor traits. By integrating multivariate statistical Analysis with the BP neural network algorithm, a novel artificial intelligence algorithm was developed and optimized for traceability of PCR origin. This algorithm achieved a 100% discrimination rate in differentiating PCR samples from various origins. This study offers a valuable reference and data support for developing intelligent algorithms that utilize data fusion from multiple intelligent sensory technologies to achieve rapid traceability of food origins.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>38241985</pmid><doi>10.1016/j.foodchem.2024.138408</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-9575-0204</orcidid></addata></record> |
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| source | ScienceDirect Journals |
| subjects | algorithms artificial intelligence color Computer vision electronic nose flavor food chemistry Intelligent algorithm multivariate analysis Multivariate statistics odors Pericarpium Citri Reticulate texture Traceability Ultra-fast gas phase electronic nose |
| title | Optimizing BP neural network algorithm for Pericarpium Citri Reticulatae (Chenpi) origin traceability based on computer vision and ultra-fast gas-phase electronic nose data fusion |
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