Insights in Nutrition and Agri-Food Science Technology by Use of Smart Algorithm, Machine Learning, Multivariable and Omics Data Analysis

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Biochemistry, Biophysics and Computational Biology".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 14489

Special Issue Editors

Department of Food Science and Technology, University of California, Davis (UC Davis), CA 95616, USA
Interests: phytochemical omics; oilomics; machine-learning-driven Raman spectroscopy; climate-smart technology; plant-based food

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Biological and Agricultural Engineering, Univerisity of Arkansas, Fayetteville, AR 72701, USA
Interests: machine vision; artificial intelligence; smart food manufacturing; precision agriculture
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Faculty of Engineering, Kiel University, 24143 Kiel, Germany
Interests: biosensors; bioinspired materials; molecular modeling
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Keit Spectrometers, Oxford OX11 0RL, UK
Interests: FTIR spectroscopy; Raman spectroscopy; process analytical technology; chemometrics; machine learning; Industry 4.0

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BUCHI Corporation, 19 Lukens Dr # 400, New Castle, DE 19720, USA
Interests: chemometrics; NIR spectroscopy; statistics; econometrics; food technology; process improvement

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School of Food Science and Bioengineering, Yantai Institute of Technology, Yantai 264003, China
Interests: microbiological safety; thermal/non-thermal technology; antimicrobial interventions; predictive microbiology

Special Issue Information

Dear Colleagues,

With continuous innovations and advancements in analytical instruments and computer technology, omics studies based on statistical analysis, such as phytochemical omics, oilomics/ lipidomics, proteomics, glycomics, flavoromics and metabolomics are increasingly popular in the areas of food chemistry and nutrition science. Smart algorithms and machine/deep learning, i.e., genetic algorithm (GA) optimization, random forest classification, logistic regression, and convolutional neural networks (CNN) are emerging data science approaches that have been widely developed to rapidly analyze multidimensional omics datasets from online sensors or offline analytical instruments. Although clearly designed roadmaps that permit the next generation development in the context of Agri-Food 4.0 are still being worked out, this challenging scenario in the Nutrition and Agri-Food Science Technology (NAFST) provides an opportunity for wide-ranging discussions. The objective of this special issue is to build a platform that allows researchers from multidisciplinary fields, i.e., nutrition science, analytical chemistry, food & agriculture science, data & computer science as well as sensor technology, to disseminate innovative ideas that may shape a more sustainable future Agri-Food 4.0. Following these considerations, this Special Issue, “Insights in Nutrition and Agri-Food Science Technology by Use of Smart Algorithm, Machine Learning, Multivariable and Omics Data Analysis” is intended for research and review papers that cover topics such as:

  • The application of multivariable analysis on omics data of NAFST;
  • The application of smart algorithms and machine learning for optimization and decision-making in NAFST;
  • Mathematical modeling and optimization in food science and processing;
  • Online/ wireless/ bio- sensors and rapid data analysis;
  • Offline database mining, analysis, and visualization;
  • Agri-Food database, standardization and protocol;
  • Data analysis for developing Agri-Food 4.0, smart-manufacturing and climate-smart technology;
  • Review of the current advancement of the smart algorithm, machine learning, multivariable, and omics data analysis on NAFST;
  • Technical note of improved or integrated coding-basis data analysis and visualization method based on Python, R and MATLAB®, etc. for analyzing omics dataset from NAFST.

Dr. Hefei Zhao
Dr. Dongyi Wang
Prof. Dr. Zeynep Altintas
Dr. Jonathon Speed
Dr. Isaac R. Rukundo
Dr. Shengqian Sun
Guest Editors

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Keywords

  • multidimensional dataset
  • omics
  • agri-food 4.0
  • sensing
  • sustainable technologies
  • climate-smart
  • machine learning
  • smart algorithm
  • multivariable data analysis
  • data visualization
  • mathematical modelling
  • coding

Published Papers (6 papers)

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Research

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16 pages, 4015 KiB  
Article
Evaluating Moisture Content in Immersion Vacuum-Cooled Sausages with Citrus Peel Extracts Using Hyperspectral Imaging
by Chao-Hui Feng, Hirofumi Arai and Francisco J. Rodríguez-Pulido
Life 2024, 14(5), 647; https://doi.org/10.3390/life14050647 - 20 May 2024
Viewed by 649
Abstract
The moisture content of immersion vacuum-cooled sausages with modified casings containing citrus fruit extracts under different storage conditions was studied using hyperspectral imaging (HSI) associated with chemometrics. Different pre-processing combinations were applied to improve the robustness of the model. The partial least squares [...] Read more.
The moisture content of immersion vacuum-cooled sausages with modified casings containing citrus fruit extracts under different storage conditions was studied using hyperspectral imaging (HSI) associated with chemometrics. Different pre-processing combinations were applied to improve the robustness of the model. The partial least squares regression model, employing the full reflectance spectrum with pre-treatment of the standard normal variate, showed calibration coefficients of determination (Rc2) of 0.6160 and a root mean square error of calibration (RMSEC) of 2.8130%. For the first time, prediction maps developed via HSI visualized the distribution of moisture content in the immersion vacuum-cooled sausages with unique modified casings in response to fluctuating storage conditions. The prediction maps showed exact parts with high water content, which will help us to monitor and prevent mold growth. The combination of HSI with multivariate analysis not only quantifies changes in moisture content but also visually represents them in response to various casing treatments under different storage conditions, illustrating the significant potential for real-time inspection and early mold detection in sausages within the processed meat industry. Full article
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12 pages, 2289 KiB  
Article
Mass Spectrometry-Based Investigation of Sugarcane Exposed to Five Different Pesticides
by Thalisson A. de Souza, Gabriela C. S. Rodrigues, Pedro H. N. de Souza, Lucas S. Abreu, Laiane C. O. Pereira, Marcelo S. da Silva, Josean F. Tavares, Luciana Scotti and Marcus Tullius Scotti
Life 2023, 13(4), 1034; https://doi.org/10.3390/life13041034 - 17 Apr 2023
Viewed by 1439
Abstract
The use of agrochemicals has become a standard practice worldwide to ensure the productivity and quality of sugarcane crops. This study aimed to analyze the metabolic changes in sugarcane culms treated with five different nematicides. The experimental design was randomized in blocks, and [...] Read more.
The use of agrochemicals has become a standard practice worldwide to ensure the productivity and quality of sugarcane crops. This study aimed to analyze the metabolic changes in sugarcane culms treated with five different nematicides. The experimental design was randomized in blocks, and agro-industrial and biometric variables were evaluated. The samples were extracted and then analyzed using LC–MS, LC–MS/MS, and LC–HRMS. The data obtained were submitted to statistical methods (PCA and PLS). Fragmentation patterns, retention time, and UV absorptions of the main features were analyzed. The plantations treated with carbosulfan (T4) obtained higher agricultural productivity and total recoverable sugar (TRS), while the use of benfuracarb (T3) was associated with lower growth and lower TRS. Statistical analysis revealed the contribution of the features at m/z 353 and m/z 515, assigned as chlorogenic acids, which discriminated the groups. The MS profile also supported the occurrence of flavonoids (C-glycosides and O-glycosides) in the samples. Full article
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12 pages, 2256 KiB  
Article
Food Toxicity of Mycotoxin Citrinin and Molecular Mechanisms of Its Potential Toxicity Effects through the Implicated Targets Predicted by Computer-Aided Multidimensional Data Analysis
by Seema Zargar and Tanveer A. Wani
Life 2023, 13(4), 880; https://doi.org/10.3390/life13040880 - 26 Mar 2023
Cited by 7 | Viewed by 2883
Abstract
The mycotoxin citrinin, which can contaminate food, is a major global concern. Citrinin is regarded as an inevitable pollutant in foods and feed since fungi are widely present in the environment. To identify contentious toxicity and lessen its severity by understanding the targets [...] Read more.
The mycotoxin citrinin, which can contaminate food, is a major global concern. Citrinin is regarded as an inevitable pollutant in foods and feed since fungi are widely present in the environment. To identify contentious toxicity and lessen its severity by understanding the targets of citrinin in the human body and the impacted biosynthetic pathways, we analyzed the production of citrinin from Aspergillus flavus and Penicillium notatum and used a thorough bioinformatics analysis to characterize the toxicity and predict genes and protein targets for it. The predicted median fatal dosage (LD50) for citrinin was 105 mg/kg weight, and it belonged to toxicity class 3 (toxic if swallowed). Citrinin was found to be well absorbed by human intestinal epithelium and was a Pgp nonsubstrate (permeability glycoprotein), which means that once it is absorbed, it cannot be pumped out, hence leading to bioconcentration or biomagnification in the human body. The main targets of toxicity were casp3, TNF, IL10, IL1B, BAG3, CCNB1, CCNE1, and CDC25A, and the biological pathways implicated were signal transduction involved in DNA damage checkpoints, cellular and chemical responses to oxidative stress, DNA damage response signal transduction by P53, stress-activated protein kinase signaling cascade, netrin–UNC5B signaling, PTEN gene regulation, and immune response. Citrinin was linked to neutrophilia, squamous cell carcinoma, Fanconi anemia, leukemia, hepatoblastoma, and fatty liver diseases. The transcription factors E2F1, HSF1, SIRT1, RELA, NFKB, JUN, and MYC were found to be responsible. When data mining was performed on citrinin targets, the top five functional descriptions were a cell’s response to an organic cyclic compound, the netrin–UNC5B signaling pathway, lipids and atherosclerosis, thyroid cancer, and controlling the transcription of the PTEN gene. Full article
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16 pages, 2869 KiB  
Article
An Insight into Wheat Germ Oil Nutrition, Identification of Its Bioactive Constituents and Computer-Aided Multidimensional Data Analysis of Its Potential Anti-Inflammatory Effect via Molecular Connections
by Seema Zargar, Tanveer A. Wani and Syed Rizwan Ahamad
Life 2023, 13(2), 526; https://doi.org/10.3390/life13020526 - 14 Feb 2023
Cited by 10 | Viewed by 2479
Abstract
Wheat germ oil (WGO) is the richest source of unexplored antioxidants and anti-inflammatory compounds. In this study, we identified the constituents of WGO by gas chromatography–mass spectrometry (GC-MS). The physicochemical and pharmacokinetic behaviors were evaluated for the top 12 constituents with the common [...] Read more.
Wheat germ oil (WGO) is the richest source of unexplored antioxidants and anti-inflammatory compounds. In this study, we identified the constituents of WGO by gas chromatography–mass spectrometry (GC-MS). The physicochemical and pharmacokinetic behaviors were evaluated for the top 12 constituents with the common target FABP4. Three fatty acids with significant anti-inflammatory activity were evaluated for their interaction with FABP4 by molecular docking. The molecular mechanisms involved in anti-inflammatory responses were analyzed by various in-silico analytical tools and multidimensional data analysis. WGO showed anti-inflammatory activities via FABP4 interacting physically with target genes (77.84%) and by co-expressing with 8.01% genes. Primary targets for inflammatory pathways were PPARα, PPARγ, LPL, LEP, and ADIPOQ, as depicted by gene network enrichment analysis. The key pathways implicated were the metabolism of lipids, PPAR signaling, cellular response to alcohol, oxygen and nitrogen pathway, inflammatory response pathway, and regulation of the inflammatory pathway. The common transcription factors implicated were HNF1, AP2α, CEBP, FOX, STATS, MYC, Zic, etc. In this study, we found that WGO possesses anti-inflammatory potential via FABP4 binding to PPARα, PPARγ, LPL, LEP, and ADIPOQ gene expression by regulatory transcription factors HNF, AP2α, and CEPB. Full article
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Review

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16 pages, 1895 KiB  
Review
Food for Thought: Proteomics for Meat Safety
by Svetlana Tarbeeva, Anna Kozlova, Elizaveta Sarygina, Olga Kiseleva, Elena Ponomarenko and Ekaterina Ilgisonis
Life 2023, 13(2), 255; https://doi.org/10.3390/life13020255 - 17 Jan 2023
Cited by 2 | Viewed by 2269
Abstract
Foodborne bacteria interconnect food and human health. Despite significant progress in food safety regulation, bacterial contamination is still a serious public health concern and the reason for significant commercial losses. The screening of the microbiome in meals is one of the main aspects [...] Read more.
Foodborne bacteria interconnect food and human health. Despite significant progress in food safety regulation, bacterial contamination is still a serious public health concern and the reason for significant commercial losses. The screening of the microbiome in meals is one of the main aspects of food production safety influencing the health of the end-consumers. Our research provides an overview of proteomics findings in the field of food safety made over the last decade. It was believed that proteomics offered an accurate snapshot of the complex networks of the major biological machines called proteins. The proteomic methods for the detection of pathogens were armed with bioinformatics algorithms, allowing us to map the data onto the genome and transcriptome. The mechanisms of the interaction between bacteria and their environment were elucidated with unprecedented sensitivity, specificity, and depth. Using our web-based tool ScanBious for automated publication analysis, we analyzed over 48,000 scientific articles on antibiotic and disinfectant resistance and highlighted the benefits of proteomics for the food safety field. The most promising approach to studying safety in food production is the combination of classical genomic and metagenomic approaches and the advantages provided by proteomic methods with the use of panoramic and targeted mass spectrometry. Full article
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Other

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13 pages, 4539 KiB  
Technical Note
A Coding Basis and Three-in-One Integrated Data Visualization Method ‘Ana’ for the Rapid Analysis of Multidimensional Omics Dataset
by Hefei Zhao and Selina C. Wang
Life 2022, 12(11), 1864; https://doi.org/10.3390/life12111864 - 12 Nov 2022
Cited by 2 | Viewed by 2216
Abstract
With innovations and advancements in analytical instruments and computer technology, omics studies based on statistical analysis, such as phytochemical omics, oilomics/lipidomics, proteomics, metabolomics, and glycomics, are increasingly popular in the areas of food chemistry and nutrition science. However, a remaining hurdle is the [...] Read more.
With innovations and advancements in analytical instruments and computer technology, omics studies based on statistical analysis, such as phytochemical omics, oilomics/lipidomics, proteomics, metabolomics, and glycomics, are increasingly popular in the areas of food chemistry and nutrition science. However, a remaining hurdle is the labor-intensive data process because learning coding skills and software operations are usually time-consuming for researchers without coding backgrounds. A MATLAB® coding basis and three-in-one integrated method, ‘Ana’, was created for data visualizations and statistical analysis in this work. The program loaded and analyzed an omics dataset from an Excel® file with 7 samples * 22 compounds as an example, and output six figures for three types of data visualization, including a 3D heatmap, heatmap hierarchical clustering analysis, and principal component analysis (PCA), in 18 s on a personal computer (PC) with a Windows 10 system and in 20 s on a Mac with a MacOS Monterey system. The code is rapid and efficient to print out high-quality figures up to 150 or 300 dpi. The output figures provide enough contrast to differentiate the omics dataset by both color code and bar size adjustments per their higher or lower values, allowing the figures to be qualified for publication and presentation purposes. It provides a rapid analysis method that would liberate researchers from labor-intensive and time-consuming manual or coding basis data analysis. A coding example with proper code annotations and completed user guidance is provided for undergraduate and postgraduate students to learn coding basis statistical data analysis and to help them utilize such techniques for their future research. Full article
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