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Topic Editors

School of Life Science, Shanghai University, Shanghai 200444, China
Department of Metabolism, Digestion and Reproduction, Imperial College London, Chelsea & Westminster Hospital, London, UK
School of Computer Science and Technology, Tianjin University, Tianjin 300072, China
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Bioinformatics, Machine Learning and Risk Assessment in Food Industry

Abstract submission deadline
31 December 2024
Manuscript submission deadline
31 July 2025
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3356

Topic Information

Dear Colleagues,

Bioinformatics, machine learning, and risk assessment play vital roles in the food industry by combining scientific knowledge with computational techniques to enhance food safety and quality. Bioinformatics, the science of collecting, analyzing, and interpreting biological data, combined with machine learning techniques, has enabled researchers and industry professionals to extract valuable insights from vast amounts of genetic, molecular, and sensory data associated with food.

By integrating bioinformatics and machine learning, the food industry can develop sophisticated risk assessment models that enable real-time monitoring of food safety parameters. This integration allows for proactive risk management strategies, reducing the potential for foodborne outbreaks and enhancing consumer trust. Moreover, it facilitates the rapid response to emerging risks, ensuring the safety and quality of food products from farm to fork.

This interdisciplinary field of bioinformatics machine learning and risk assessment in the food industry encompasses diverse applications. It involves the use of computational methods to analyze and predict the functionality and properties of food ingredients, develop personalized nutrition plans, optimize agricultural practices, detect and prevent foodborne illnesses, and improve food safety regulations. By leveraging machine learning algorithms, researchers can identify patterns, correlations, and predictive models that enhance decision-making processes and drive innovation in the food sector.

Dr. Bing Niu
Dr. Suren Rao Sooranna
Dr. Pufeng Du
Topic Editors

Keywords

  • machine learning
  • risk assessment
  • food industry
  • food safety
  • quality control
  • pathogens
  • allergens
  • contamination
  • proactive measures

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Biomolecules
biomolecules 		5.5 8.3 2011 19.2 Days CHF 2700 Submit
Foods
foods 		5.2 5.8 2012 15.9 Days CHF 2900 Submit
Metabolites
metabolites 		4.1 5.3 2011 12.9 Days CHF 2700 Submit
Microorganisms
microorganisms 		4.5 6.4 2013 14.5 Days CHF 2700 Submit
Pathogens
pathogens 		3.7 5.1 2012 16.4 Days CHF 2700 Submit
Bacteria
bacteria 		- - 2022 15.0 days * CHF 1000 Submit

* Median value for all MDPI journals in the first half of 2023.


Preprints is a platform dedicated to making early versions of research outputs permanently available and citable. MDPI journals allow posting on preprint servers such as Preprints.org prior to publication. For more details about reprints, please visit https://www.preprints.org.

Published Papers (2 papers)

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18 pages, 11079 KiB  
Article
DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion
by
Yuzhe Han
,
Qimin Cheng
,
Wenjin Wu
and
Ziyang Huang
Foods 2023, 12(23), 4293; https://doi.org/10.3390/foods12234293 - 28 Nov 2023
Viewed by 298
Abstract
A reasonable and balanced diet is essential for maintaining good health. With advancements in deep learning, an automated nutrition estimation method based on food images offers a promising solution for monitoring daily nutritional intake and promoting dietary health. While monocular image-based nutrition estimation [...] Read more.
A reasonable and balanced diet is essential for maintaining good health. With advancements in deep learning, an automated nutrition estimation method based on food images offers a promising solution for monitoring daily nutritional intake and promoting dietary health. While monocular image-based nutrition estimation is convenient, efficient and economical, the challenge of limited accuracy remains a significant concern. To tackle this issue, we proposed DPF-Nutrition, an end-to-end nutrition estimation method using monocular images. In DPF-Nutrition, we introduced a depth prediction module to generate depth maps, thereby improving the accuracy of food portion estimation. Additionally, we designed an RGB-D fusion module that combined monocular images with the predicted depth information, resulting in better performance for nutrition estimation. To the best of our knowledge, this was the pioneering effort that integrated depth prediction and RGB-D fusion techniques in food nutrition estimation. Comprehensive experiments performed on Nutrition5k evaluated the effectiveness and efficiency of DPF-Nutrition. Full article
(This article belongs to the Topic Bioinformatics, Machine Learning and Risk Assessment in Food Industry)
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14 pages, 6198 KiB  
Article
BacSeq: A User-Friendly Automated Pipeline for Whole-Genome Sequence Analysis of Bacterial Genomes
by
Arnon Chukamnerd
,
Kongpop Jeenkeawpiam
,
Sarunyou Chusri
,
Rattanaruji Pomwised
,
Kamonnut Singkhamanan
and
Komwit Surachat
Microorganisms 2023, 11(7), 1769; https://doi.org/10.3390/microorganisms11071769 - 06 Jul 2023
Viewed by 2580
Abstract
Whole-genome sequencing (WGS) of bacterial pathogens is widely conducted in microbiological, medical, and clinical research to explore genetic insights that could impact clinical treatment and molecular epidemiology. However, analyzing WGS data of bacteria can pose challenges for microbiologists, clinicians, and researchers, as it [...] Read more.
Whole-genome sequencing (WGS) of bacterial pathogens is widely conducted in microbiological, medical, and clinical research to explore genetic insights that could impact clinical treatment and molecular epidemiology. However, analyzing WGS data of bacteria can pose challenges for microbiologists, clinicians, and researchers, as it requires the application of several bioinformatics pipelines to extract genetic information from raw data. In this paper, we present BacSeq, an automated bioinformatic pipeline for the analysis of next-generation sequencing data of bacterial genomes. BacSeq enables the assembly, annotation, and identification of crucial genes responsible for multidrug resistance, virulence factors, and plasmids. Additionally, the pipeline integrates comparative analysis among isolates, offering phylogenetic tree analysis and identification of single-nucleotide polymorphisms (SNPs). To facilitate easy analysis in a single step and support the processing of multiple isolates, BacSeq provides a graphical user interface (GUI) based on the JAVA platform. It is designed to cater to users without extensive bioinformatics skills. Full article
(This article belongs to the Topic Bioinformatics, Machine Learning and Risk Assessment in Food Industry)
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