Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics
| dc.contributor.author | Class, Lisa-Carina | |
| dc.contributor.author | Kuhnen, Gesine | |
| dc.contributor.author | Rohn, Sascha | |
| dc.contributor.author | Kuballa, Jürgen | |
| dc.date.accessioned | 2021-09-14T10:58:26Z | |
| dc.date.available | 2021-09-14T10:58:26Z | |
| dc.date.issued | 2021-08-04 | |
| dc.date.updated | 2021-09-13T12:13:49Z | |
| dc.description.abstract | Deep 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.eissn | 2304-8158 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/13576 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-12363 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 630 Landwirtschaft und verwandte Bereiche | de |
| dc.subject.other | deep learning | en |
| dc.subject.other | machine learning | en |
| dc.subject.other | metabolomics | en |
| dc.subject.other | food authenticity | en |
| dc.subject.other | food fraud | en |
| dc.subject.other | shelf-life | en |
| dc.subject.other | peptide sequencing | en |
| dc.subject.other | mass spectrometry | en |
| dc.title | Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.articlenumber | 1803 | en |
| dcterms.bibliographicCitation.doi | 10.3390/foods10081803 | en |
| dcterms.bibliographicCitation.issue | 8 | en |
| dcterms.bibliographicCitation.journaltitle | Foods | en |
| dcterms.bibliographicCitation.originalpublishername | MDPI | en |
| dcterms.bibliographicCitation.originalpublisherplace | Basel | en |
| dcterms.bibliographicCitation.volume | 10 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 3 Prozesswissenschaften::Inst. Lebensmitteltechnologie und Lebensmittelchemie::FG Lebensmittelchemie und Analytik | de |
| tub.affiliation.faculty | Fak. 3 Prozesswissenschaften | de |
| tub.affiliation.group | FG Lebensmittelchemie und Analytik | de |
| tub.affiliation.institute | Inst. Lebensmitteltechnologie und Lebensmittelchemie | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |