Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics

dc.contributor.authorClass, Lisa-Carina
dc.contributor.authorKuhnen, Gesine
dc.contributor.authorRohn, Sascha
dc.contributor.authorKuballa, Jürgen
dc.date.accessioned2021-09-14T10:58:26Z
dc.date.available2021-09-14T10:58:26Z
dc.date.issued2021-08-04
dc.date.updated2021-09-13T12:13:49Z
dc.description.abstractDeep learning is a trending field in bioinformatics; so far, mostly known for image processing and speech recognition, but it also shows promising possibilities for data processing in food analysis, especially, foodomics. Thus, more and more deep learning approaches are used. This review presents an introduction into deep learning in the context of metabolomics and proteomics, focusing on the prediction of shelf-life, food authenticity, and food quality. Apart from the direct food-related applications, this review summarizes deep learning for peptide sequencing and its context to food analysis. The review’s focus further lays on MS (mass spectrometry)-based approaches. As a result of the constant development and improvement of analytical devices, as well as more complex holistic research questions, especially with the diverse and complex matrix food, there is a need for more effective methods for data processing. Deep learning might offer meeting this need and gives prospect to deal with the vast amount and complexity of data.en
dc.identifier.eissn2304-8158
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/13576
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-12363
dc.language.isoenen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subject.ddc630 Landwirtschaft und verwandte Bereichede
dc.subject.otherdeep learningen
dc.subject.othermachine learningen
dc.subject.othermetabolomicsen
dc.subject.otherfood authenticityen
dc.subject.otherfood frauden
dc.subject.othershelf-lifeen
dc.subject.otherpeptide sequencingen
dc.subject.othermass spectrometryen
dc.titleDiving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomicsen
dc.typeArticleen
dc.type.versionpublishedVersionen
dcterms.bibliographicCitation.articlenumber1803en
dcterms.bibliographicCitation.doi10.3390/foods10081803en
dcterms.bibliographicCitation.issue8en
dcterms.bibliographicCitation.journaltitleFoodsen
dcterms.bibliographicCitation.originalpublishernameMDPIen
dcterms.bibliographicCitation.originalpublisherplaceBaselen
dcterms.bibliographicCitation.volume10en
tub.accessrights.dnbfreeen
tub.affiliationFak. 3 Prozesswissenschaften>Inst. Lebensmitteltechnologie und Lebensmittelchemie>FG Lebensmittelchemie und Analytikde
tub.affiliation.facultyFak. 3 Prozesswissenschaftende
tub.affiliation.groupFG Lebensmittelchemie und Analytikde
tub.affiliation.instituteInst. Lebensmitteltechnologie und Lebensmittelchemiede
tub.publisher.universityorinstitutionTechnische Universität Berlinen
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