Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-12363
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Main Title: Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics
Author(s): Class, Lisa-Carina
Kuhnen, Gesine
Rohn, Sascha
Kuballa, Jürgen
Type: Article
Language Code: en
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.
URI: https://depositonce.tu-berlin.de/handle/11303/13576
http://dx.doi.org/10.14279/depositonce-12363
Issue Date: 4-Aug-2021
Date Available: 14-Sep-2021
DDC Class: 630 Landwirtschaft und verwandte Bereiche
Subject(s): deep learning
machine learning
metabolomics
food authenticity
food fraud
shelf-life
peptide sequencing
mass spectrometry
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Foods
Publisher: MDPI
Publisher Place: Basel
Volume: 10
Issue: 8
Article Number: 1803
Publisher DOI: 10.3390/foods10081803
EISSN: 2304-8158
Appears in Collections:FG Lebensmittelchemie und Analytik » Publications

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