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Application of Machine Learning to Assess the Quality of Food Products - Case Study: Coffee Bean

cris.virtual.author-orcid0000-0002-2535-8370
cris.virtual.author-orcid0000-0001-6699-2987
cris.virtual.author-orcid0000-0001-8257-2931
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cris.virtual.author-orcid0000-0002-3681-153X
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cris.virtualsource.author-orcid898dc715-0fc1-42af-a4d7-0bc909752fee
cris.virtualsource.author-orcid8989a1ba-cd61-4f60-80d5-c1b418028894
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dc.abstract.enModern machine learning methods were used to automate and improve the determination of an effective quality index for coffee beans. Machine learning algorithms can effectively recognize various anomalies, among others factors, occurring in a food product. The procedure for preparing the machine learning algorithm depends on the correct preparation and preprocessing of the learning set. The set contained coded information (i.e., selected quality coefficients) based on digital photos (input data) and a specific class of coffee bean (output data). Because of training and data tuning, an adequate convolutional neural network (CNN) was obtained, which was characterized by a high recognition rate of these coffee beans at the level of 0.81 for the test set. Statistical analysis was performed on the color data in the RGB color space model, which made it possible to accurately distinguish three distinct categories of coffee beans. However, using the Lab* color model, it became apparent that distinguishing between the quality categories of under-roasted and properly roasted coffee beans was a major challenge. Nevertheless, the Lab* model successfully distinguished the category of over-roasted coffee beans.
dc.affiliationWydział Nauk o Żywności i Żywieniu
dc.affiliation.instituteKatedra Mleczarstwa i Inżynierii Procesowej
dc.affiliation.instituteKatedra Technologii Żywności Pochodzenia Roślinnego
dc.affiliation.instituteKatedra Biochemii i Analizy Żywności
dc.contributor.authorPrzybył, Krzysztof
dc.contributor.authorGawrysiak-Witulska, Marzena Bernadeta
dc.contributor.authorBielska, Paulina
dc.contributor.authorRusinek, Robert
dc.contributor.authorGancarz, Marek
dc.contributor.authorDobrzański, Bohdan
dc.contributor.authorSiger, Aleksander
dc.date.access2025-06-04
dc.date.accessioned2025-09-09T11:49:57Z
dc.date.available2025-09-09T11:49:57Z
dc.date.copyright2023-09-28
dc.date.issued2023
dc.description.abstract<jats:p>Modern machine learning methods were used to automate and improve the determination of an effective quality index for coffee beans. Machine learning algorithms can effectively recognize various anomalies, among others factors, occurring in a food product. The procedure for preparing the machine learning algorithm depends on the correct preparation and preprocessing of the learning set. The set contained coded information (i.e., selected quality coefficients) based on digital photos (input data) and a specific class of coffee bean (output data). Because of training and data tuning, an adequate convolutional neural network (CNN) was obtained, which was characterized by a high recognition rate of these coffee beans at the level of 0.81 for the test set. Statistical analysis was performed on the color data in the RGB color space model, which made it possible to accurately distinguish three distinct categories of coffee beans. However, using the Lab* color model, it became apparent that distinguishing between the quality categories of under-roasted and properly roasted coffee beans was a major challenge. Nevertheless, the Lab* model successfully distinguished the category of over-roasted coffee beans.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if2,5
dc.description.number19
dc.description.points100
dc.description.versionfinal_published
dc.description.volume13
dc.identifier.doi10.3390/app131910786
dc.identifier.issn2076-3417
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/4692
dc.identifier.weblinkhttps://www.mdpi.com/2076-3417/13/19/10786
dc.languageen
dc.relation.ispartofApplied Sciences (Switzerland)
dc.relation.pagesart. 10786
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.enartificial intelligence
dc.subject.endeep learning
dc.subject.enconvolutional neural networks (CNNs)
dc.subject.encoffee bean
dc.titleApplication of Machine Learning to Assess the Quality of Food Products - Case Study: Coffee Bean
dc.title.volumeSpecial Issue Approaches to Machine and Deep Learning, Big Data or Modern Analytical Methods in the Agri-Food Industry
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue19
oaire.citation.volume13