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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...

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Detalles Bibliográficos
Autores principales: Class, Lisa-Carina, Kuhnen, Gesine, Rohn, Sascha, Kuballa, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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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