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

Bioinformatics, Machine Learning and Risk Assessment in Food Industry

Abstract submission deadline
31 December 2024
Manuscript submission deadline
31 July 2025
Viewed by
9456

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 16.9 Days CHF 2700 Submit
Foods
foods
5.2 5.8 2012 13.1 Days CHF 2900 Submit
Metabolites
metabolites
4.1 5.3 2011 13.2 Days CHF 2700 Submit
Microorganisms
microorganisms
4.5 6.4 2013 15.1 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 second half of 2023.


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Published Papers (5 papers)

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11 pages, 1045 KiB  
Article
Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information
by Hao Han, Ruyi Sha, Jing Dai, Zhenzhen Wang, Jianwei Mao and Min Cai
Foods 2024, 13(7), 1016; https://doi.org/10.3390/foods13071016 - 26 Mar 2024
Viewed by 374
Abstract
The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this [...] Read more.
The chemical composition and nutritional content of garlic are greatly impacted by its production location, leading to distinct flavor profiles and functional properties among garlic varieties from diverse origins. Consequently, these variations determine the preference and acceptance among diverse consumer groups. In this study, purple-skinned garlic samples were collected from five regions in China: Yunnan, Shandong, Henan, Anhui, and Jiangsu Provinces. Mid-infrared spectroscopy and ultraviolet spectroscopy were utilized to analyze the components of garlic cells. Three preprocessing methods, including Multiple Scattering Correction (MSC), Savitzky–Golay Smoothing (SG Smoothing), and Standard Normalized Variate (SNV), were applied to reduce the background noise of spectroscopy data. Following variable feature extraction by Genetic Algorithm (GA), a variety of machine learning algorithms, including XGboost, Support Vector Classification (SVC), Random Forest (RF), and Artificial Neural Network (ANN), were used according to the fusion of spectral data to obtain the best processing results. The results showed that the best-performing model for ultraviolet spectroscopy data was SNV-GA-ANN, with an accuracy of 99.73%. The best-performing model for mid-infrared spectroscopy data was SNV-GA-RF, with an accuracy of 97.34%. After the fusion of ultraviolet and mid-infrared spectroscopy data, the SNV-GA-SVC, SNV-GA-RF, SNV-GA-ANN, and SNV-GA-XGboost models achieved 100% accuracy in both training and test sets. Although there were some differences in the accuracy of the four models under different preprocessing methods, the fusion of ultraviolet and mid-infrared spectroscopy data yielded the best outcomes, with an accuracy of 100%. Overall, the combination of ultraviolet and mid-infrared spectroscopy data fusion and chemometrics established in this study provides a theoretical foundation for identifying the origin of garlic, as well as that of other agricultural products. Full article
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18 pages, 4879 KiB  
Article
U2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting
by Libo Cao, Liping Zeng, Yaoxuan Wang, Jiayi Cao, Ziyu Han, Yang Chen, Yuxi Wang, Guowei Zhong and Shanlei Qiao
Microorganisms 2024, 12(1), 201; https://doi.org/10.3390/microorganisms12010201 - 18 Jan 2024
Cited by 1 | Viewed by 1144
Abstract
In this paper, an automatic colony counting system based on an improved image preprocessing algorithm and convolutional neural network (CNN)-assisted automatic counting method was developed. Firstly, we assembled an LED backlighting illumination platform as an image capturing system to obtain photographs of laboratory [...] Read more.
In this paper, an automatic colony counting system based on an improved image preprocessing algorithm and convolutional neural network (CNN)-assisted automatic counting method was developed. Firstly, we assembled an LED backlighting illumination platform as an image capturing system to obtain photographs of laboratory cultures. Consequently, a dataset was introduced consisting of 390 photos of agar plate cultures, which included 8 microorganisms. Secondly, we implemented a new algorithm for image preprocessing based on light intensity correction, which facilitated clearer differentiation between colony and media areas. Thirdly, a U2-Net was used to predict the probability distribution of the edge of the Petri dish in images to locate region of interest (ROI), and then threshold segmentation was applied to separate it. This U2-Net achieved an F1 score of 99.5% and a mean absolute error (MAE) of 0.0033 on the validation set. Then, another U2-Net was used to separate the colony region within the ROI. This U2-Net achieved an F1 score of 96.5% and an MAE of 0.005 on the validation set. After that, the colony area was segmented into multiple components containing single or adhesive colonies. Finally, the colony components (CC) were innovatively rotated and the image crops were resized as the input (with 14,921 image crops in the training set and 4281 image crops in the validation set) for the ResNet50 network to automatically count the number of colonies. Our method achieved an overall recovery of 97.82% for colony counting and exhibited excellent performance in adhesion classification. To the best of our knowledge, the proposed “light intensity correction-based image preprocessing→U2-Net segmentation for Petri dish edge→U2-Net segmentation for colony region→ResNet50-based counting” scheme represents a new attempt and demonstrates a high degree of automation and accuracy in recognizing and counting single-colony and multi-colony targets. Full article
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19 pages, 2951 KiB  
Review
Intelligent Biosensors Promise Smarter Solutions in Food Safety 4.0
by Yuehua Chen, Yicheng Wang, Yiran Zhang, Xin Wang, Chen Zhang and Nan Cheng
Foods 2024, 13(2), 235; https://doi.org/10.3390/foods13020235 - 11 Jan 2024
Viewed by 1165
Abstract
Food safety is closely related to human health. However, the regulation and testing processes for food safety are intricate and resource-intensive. Therefore, it is necessary to address food safety risks using a combination of deep learning, the Internet of Things, smartphones, quick response [...] Read more.
Food safety is closely related to human health. However, the regulation and testing processes for food safety are intricate and resource-intensive. Therefore, it is necessary to address food safety risks using a combination of deep learning, the Internet of Things, smartphones, quick response codes, smart packaging, and other smart technologies. Intelligent designs that combine digital systems and advanced functionalities with biosensors hold great promise for revolutionizing current food safety practices. This review introduces the concept of Food Safety 4.0, and discusses the impact of intelligent biosensors, which offer attractive smarter solutions, including real-time monitoring, predictive analytics, enhanced traceability, and consumer empowerment, helping improve risk management and ensure the highest standards of food safety. Full article
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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 1088
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
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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
Cited by 2 | Viewed by 4782
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
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