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Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8392494/ https://www.ncbi.nlm.nih.gov/pubmed/34441579 http://dx.doi.org/10.3390/foods10081803 |
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author | Class, Lisa-Carina Kuhnen, Gesine Rohn, Sascha Kuballa, Jürgen |
author_facet | Class, Lisa-Carina Kuhnen, Gesine Rohn, Sascha Kuballa, Jürgen |
author_sort | Class, Lisa-Carina |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8392494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83924942021-08-28 Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics Class, Lisa-Carina Kuhnen, Gesine Rohn, Sascha Kuballa, Jürgen Foods Review 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. MDPI 2021-08-04 /pmc/articles/PMC8392494/ /pubmed/34441579 http://dx.doi.org/10.3390/foods10081803 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Class, Lisa-Carina Kuhnen, Gesine Rohn, Sascha Kuballa, Jürgen Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics |
title | Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics |
title_full | Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics |
title_fullStr | Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics |
title_full_unstemmed | Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics |
title_short | Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics |
title_sort | diving deep into the data: a review of deep learning approaches and potential applications in foodomics |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8392494/ https://www.ncbi.nlm.nih.gov/pubmed/34441579 http://dx.doi.org/10.3390/foods10081803 |
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