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- Title
Diving Deep into the Data: A Review of Deep Learning Approaches and Potential Applications in Foodomics.
- Authors
Class, Lisa-Carina; Kuhnen, Gesine; Rohn, Sascha; Kuballa, Jürgen
- 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.
- Subjects
DEEP learning; DEEP diving; AMINO acid sequence; FOOD chemistry; ELECTRONIC data processing; MASS spectrometry
- Publication
Foods, 2021, Vol 10, Issue 8, p1803
- ISSN
2304-8158
- Publication type
Article
- DOI
10.3390/foods10081803