Application of Machine Learning Using Color and Texture Analysis to Recognize Microwave Vacuum Puffed Pork Snacks

cris.virtual.author-orcid0000-0001-5738-8737
cris.virtual.author-orcid0000-0001-6128-0315
cris.virtual.author-orcid0000-0002-2535-8370
cris.virtual.author-orcid0000-0002-3849-4435
cris.virtual.author-orcid0000-0002-4709-4601
cris.virtual.author-orcid0000-0003-1174-1915
cris.virtual.author-orcid0000-0003-0810-8086
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid73a8f241-62c7-4a1e-96b3-8e6b4e185e6a
cris.virtualsource.author-orcidab187d78-3916-499a-a077-9e8a0069cf71
cris.virtualsource.author-orcid898dc715-0fc1-42af-a4d7-0bc909752fee
cris.virtualsource.author-orcid7583f283-39ec-4125-9e34-6f6a60d31a2d
cris.virtualsource.author-orcid547fae8f-3c82-43d7-9832-0ba1dc522cf2
cris.virtualsource.author-orcid86cf2372-a835-4dbf-b1cb-6200ecf27dd5
cris.virtualsource.author-orcid90398b3a-5dd4-4557-a041-509a3389a7fb
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
dc.abstract.enThe objective of the study was to create artificial neural networks (ANN) capable of highly efficient recognition of modified and unmodified puffed pork snacks for the purposes of obtaining an optimal final product. The study involved meat snacks produced from unmodified and papain modified raw pork (Psoas major) by means of microwave-vacuum puffing (MVP) under specified conditions. The snacks were then analyzed using various instruments in order to determine their basic chemical composition, color and texture. As a result of the MVP process, the moisture-to-protein ratio (MPR) was reduced to 0.11. A darker color and reduction in hardness of approx. 25% was observed in the enzymatically modified products. Multi-layer perceptron networks (MLPN) were then developed using color and texture descriptor training sets (machine learning), which is undoubtedly an innovative solution in this area.
dc.affiliationWydział Nauk o Żywności i Żywieniu
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Mleczarstwa i Inżynierii Procesowej
dc.affiliation.instituteKatedra Inżynierii Wodnej i Sanitarnej
dc.affiliation.instituteKatedra Zarządzania Jakością i Bezpieczeństwem Żywności
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorPawlak, Tomasz
dc.contributor.authorPilarska, Agnieszka
dc.contributor.authorPrzybył, Krzysztof
dc.contributor.authorStangierski, Jerzy
dc.contributor.authorRyniecki, Antoni
dc.contributor.authorCais-Sokolińska, Dorota
dc.contributor.authorPilarski, Krzysztof
dc.contributor.authorPeplińska, Barbara
dc.date.access2026-01-26
dc.date.accessioned2026-02-06T09:08:13Z
dc.date.available2026-02-06T09:08:13Z
dc.date.copyright2022-05-18
dc.date.issued2022
dc.description.abstract<jats:p>The objective of the study was to create artificial neural networks (ANN) capable of highly efficient recognition of modified and unmodified puffed pork snacks for the purposes of obtaining an optimal final product. The study involved meat snacks produced from unmodified and papain modified raw pork (Psoas major) by means of microwave-vacuum puffing (MVP) under specified conditions. The snacks were then analyzed using various instruments in order to determine their basic chemical composition, color and texture. As a result of the MVP process, the moisture-to-protein ratio (MPR) was reduced to 0.11. A darker color and reduction in hardness of approx. 25% was observed in the enzymatically modified products. Multi-layer perceptron networks (MLPN) were then developed using color and texture descriptor training sets (machine learning), which is undoubtedly an innovative solution in this area.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if2,7
dc.description.number10
dc.description.points100
dc.description.versionfinal_published
dc.description.volume12
dc.identifier.doi10.3390/app12105071
dc.identifier.issn2076-3417
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/7191
dc.identifier.weblinkhttps://www.mdpi.com/2076-3417/12/10/5071
dc.languageen
dc.relation.ispartofApplied Sciences (Switzerland)
dc.relation.pagesart. 5071
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.enmachine learning
dc.subject.enartificial neural networks
dc.subject.enpork muscle
dc.subject.enpapain
dc.subject.enmicrowave-vacuum drying
dc.subject.enpuffing
dc.subject.entexture analysis
dc.subject.encolor analysis
dc.titleApplication of Machine Learning Using Color and Texture Analysis to Recognize Microwave Vacuum Puffed Pork Snacks
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue10
oaire.citation.volume12