Bioinformatics, Machine Learning and Risk Assessment in Food Industry

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Biomolecules biomolecules	4.8	9.4	2011	18.4 Days	CHF 2700	Submit
Foods foods	4.7	7.4	2012	14.5 Days	CHF 2900	Submit
Metabolites metabolites	3.5	5.7	2011	16.1 Days	CHF 2700	Submit
Microorganisms microorganisms	4.1	7.4	2013	11.7 Days	CHF 2700	Submit
Pathogens pathogens	3.3	6.4	2012	15.3 Days	CHF 2200	Submit
Bacteria bacteria	-	-	2022	27.4 Days	CHF 1000	Submit

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Published Papers (8 papers)

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All Biomolecules Foods Metabolites Microorganisms Pathogens Bacteria

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18 pages, 17310 KiB  

Open AccessArticle

IGLOO: Machine Vision System for Determination of Solubilization Index in Phosphate-Solubilizing Bacteria

by Pablo José Menjívar, Andrés Felipe Solis Pino, Julio Eduardo Mejía Manzano and Efrén Venancio Ramos Cabrera

Microorganisms 2025, 13(4), 860; https://doi.org/10.3390/microorganisms13040860 - 9 Apr 2025

Viewed by 304

Abstract

Phosphorus is an important macronutrient for plant development, but its bioavailability in soil is often limited. Phosphate-solubilizing microorganisms play a vital role in phosphorus biogeochemistry, offering a sustainable alternative to chemical fertilizers, which pose environmental risks. Manual measurements for quantifying phosphate solubilization capacity [...] Read more.

Phosphorus is an important macronutrient for plant development, but its bioavailability in soil is often limited. Phosphate-solubilizing microorganisms play a vital role in phosphorus biogeochemistry, offering a sustainable alternative to chemical fertilizers, which pose environmental risks. Manual measurements for quantifying phosphate solubilization capacity are laborious, subjective, and time-consuming, so there is a need to develop more efficient and objective approaches. This study aimed to develop and validate a machine vision system called IGLOO to automate and optimize the determination of relative phosphate solubilization efficiency in phosphate-solubilizing bacteria. IGLOO was developed using YOLOv8 in conjunction with creating and labeling a dataset of images of bacterial colonies grown in vitro with the bacterial strains Enterobacter R11 and FCRK4. The model was trained with a different number of epochs. IGLOO’s performance was evaluated by comparing its segmentation accuracy with accepted metrics in the domain and by contrasting its solubilization efficiency estimates with experts’ manual measurements. The model achieved greater than 90% accuracy for colony and halo detection, with a relative error of less than 6% compared to manual measurements, demonstrating its reliability by minimizing observer variability. Finally, IGLOO represents a significant advance in the quantitative evaluation of phosphate solubilization of microorganisms because it reduces analysis time and provides objective and reproducible results for agricultural studies. Full article

(This article belongs to the Topic Bioinformatics, Machine Learning and Risk Assessment in Food Industry)

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15 pages, 2751 KiB  

Open AccessArticle

A Novel Slope-Matrix-Graph Algorithm to Analyze Compositional Microbiome Data

by Meng Zhang, Xiang Li, Adelumola Oladeinde, Michael Rothrock, Jr., Anthony Pokoo-Aikins and Gregory Zock

Microorganisms 2024, 12(9), 1866; https://doi.org/10.3390/microorganisms12091866 - 9 Sep 2024

Viewed by 1744

Abstract

Networks are widely used to represent relationships between objects, including microorganisms within ecosystems, based on high-throughput sequencing data. However, challenges arise with appropriate statistical algorithms, handling of rare taxa, excess zeros in compositional data, and interpretation. This work introduces a novel Slope-Matrix-Graph (SMG) [...] Read more.

Networks are widely used to represent relationships between objects, including microorganisms within ecosystems, based on high-throughput sequencing data. However, challenges arise with appropriate statistical algorithms, handling of rare taxa, excess zeros in compositional data, and interpretation. This work introduces a novel Slope-Matrix-Graph (SMG) algorithm to identify microbiome correlations primarily based on slope-based distance calculations. SMG effectively handles any proportion of zeros in compositional data and involves: (1) searching for correlated relationships (e.g., positive and negative directions of changes) based on a “target of interest” within a setting, and (2) quantifying graph changes via slope-based distances between objects. Evaluations on simulated datasets demonstrated SMG’s ability to accurately cluster microbes into distinct positive/negative correlation groups, outperforming methods like Bray–Curtis and SparCC in both sensitivity and specificity. Moreover, SMG demonstrated superior accuracy in detecting differential abundance (DA) compared to ZicoSeq and ANCOM-BC2, making it a robust tool for microbiome analysis. A key advantage is SMG’s natural capacity to analyze zero-inflated compositional data without transformations. Overall, this simple yet powerful algorithm holds promise for diverse microbiome analysis applications. Full article

(This article belongs to the Topic Bioinformatics, Machine Learning and Risk Assessment in Food Industry)

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11 pages, 3882 KiB  

Open AccessArticle

Modifications of the Structural, Nutritional, and Allergenic Properties of Atlantic Cod Induced by Novel Thermal Glycation Treatments

by Xin Dong and Vijaya Raghavan

Foods 2024, 13(14), 2175; https://doi.org/10.3390/foods13142175 - 10 Jul 2024

Cited by 2 | Viewed by 1007

Abstract

This study aimed to assess the effect of novel thermal glycation, utilizing microwave processing (100−150 °C) combined with sugars (glucose and lactose), on the in vitro protein digestibility, peptides, secondary structures, microstructures, and allergenic properties of Atlantic cod. The research demonstrated that microwave [...] Read more.

This study aimed to assess the effect of novel thermal glycation, utilizing microwave processing (100−150 °C) combined with sugars (glucose and lactose), on the in vitro protein digestibility, peptides, secondary structures, microstructures, and allergenic properties of Atlantic cod. The research demonstrated that microwave heating at 150 °C with glucose significantly reduced cod allergenicity by up to 16.16%, while also enhancing in vitro protein digestibility to 69.05%. Glucose was found to be more effective than lactose in conjunction with microwave heating in reducing the allergenicity of Atlantic cod. Moreover, treatments conducted at 150 °C were more effective in increasing in vitro protein digestibility and peptide content compared to those at 100 °C. This study revealed that the novel processing technique of thermal glycation effectively reduced the allergenicity of Atlantic cod. It also offered fresh insights into the potential benefits of combining microwave heating with sugars. Full article

(This article belongs to the Topic Bioinformatics, Machine Learning and Risk Assessment in Food Industry)

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11 pages, 1045 KiB  

Open AccessArticle

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

Cited by 36 | Viewed by 1696

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

(This article belongs to the Topic Bioinformatics, Machine Learning and Risk Assessment in Food Industry)

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18 pages, 4879 KiB  

Open AccessArticle

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 4 | Viewed by 3810

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

(This article belongs to the Topic Bioinformatics, Machine Learning and Risk Assessment in Food Industry)

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19 pages, 2951 KiB  

Open AccessReview

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

Cited by 10 | Viewed by 3752

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

(This article belongs to the Topic Bioinformatics, Machine Learning and Risk Assessment in Food Industry)

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18 pages, 11079 KiB  

Open AccessArticle

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

Cited by 6 | Viewed by 3115

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  

Open AccessArticle

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 - 6 Jul 2023

Cited by 13 | Viewed by 8985

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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