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Application of Big Data and Artificial Intelligence in Food Science:...
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Application of Big Data and Artificial Intelligence in Food Science: Current Practice and Future Directions

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: closed (8 November 2024) | Viewed by 10785

Special Issue Editors

Dr. Fady Mohareb

E-Mail Website
Guest Editor
The Bioinformatics Group, Centre for Soil, Agrifood and Biosciences (SABS), Cranfield University, Bedford MK43 0AL, UK
Interests: bioinformatics; computing, simulation and modelling; digital agriculture; drug discovery and development; food quality; food safety
Prof. Dr. George-John E. Nychas

E-Mail Website
Guest Editor
Laboratory of Food Microbiology and Biotechnology, Agricultural University of Athens, 11855 Athens, Greece
Interests: food safety and quality; pathogenic and spoilage bacteria
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Christopher Brewster

E-Mail Website
Guest Editor
1. TNO Soesterberg, Kampweg 5, 3769 DE Soesterberg, The Netherlands
2. Institute of Data Science, Maastricht University, Paul-Henri Spaaklaan 1 (PHS1), 6229 EN Maastricht, The Netherlands
Interests: sustainable agriculture and food systems; ICT in agrifood; environmental and social impact of technologies in agrifood; data science and AI in sustainable agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are extremely pleased to invite you to contribute to this Special Issue entitled “Application of Big Data and Artificial Intelligence in Food Science: Current Practice and Future Directions." This issue aims to showcase cutting-edge research exploring various facets of applying machine learning, deep learning, and big data analytics to food science. We welcome research that delves into various AI and data science methods for analyzing food quality, safety, and authentication, as well as the application of AI to enhance the nutritional value of foods. 

Topics of interest include the following:

  1. Machine learning models for predictive food quality control.
  2. Deep learning approaches for food image analysis and classification.
  3. Big data analytics in food supply chain management.
  4. AI-driven sensory analysis for taste and quality assessment.
  5. IoT applications in monitoring food safety and traceability.
  6. AI application in enhancing the bioavailability of bioactive compounds in foods.

Dr. Fady Mohareb
Prof. Dr. George-John E. Nychas
Prof. Dr. Christopher Brewster
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • artificial intelligence
  • AI-driven food formulation
  • machine learning
  • deep learning
  • food quality
  • food safety
  • food authentication
  • imaging techniques
  • nutrition enhancement

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

16 pages, 4174 KiB  
Article
Quantitative Assessment of Brix in Grafted Melon Cultivars: A Machine Learning and Regression-Based Approach
by Uğur Ercan, Ilker Sonmez, Aylin Kabaş, Onder Kabas, Buşra Calık Zyambo, Muharrem Gölükcü and Gigel Paraschiv
Foods 2024, 13(23), 3858; https://doi.org/10.3390/foods13233858 - 29 Nov 2024
Viewed by 416
Abstract
The article demonstrates the Brix content of melon fruits grafted with different varieties of rootstock using Support Vector Regression (SVR) and Multiple Linear Regression (MLR) model approaches. The analysis yielded primary fruit biochemical measurements on the following rootstocks, Sphinx, Albatros, and Dinero: nitrogen, [...] Read more.
The article demonstrates the Brix content of melon fruits grafted with different varieties of rootstock using Support Vector Regression (SVR) and Multiple Linear Regression (MLR) model approaches. The analysis yielded primary fruit biochemical measurements on the following rootstocks, Sphinx, Albatros, and Dinero: nitrogen, phosphorus, potassium, calcium, and magnesium. Established models were evaluated with Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) metrics. In the test section, the results of the MLR model were calculated as MAE: 0.0728, MAPE: 0.0117, MSE: 0.0088, RMSE: 0.0936, and R2: 0.9472, while the results of the SVR model were calculated as MAE: 0.0334, MAPE: 0.0054, MSE: 0.0016, RMSE: 0.0398, and R2: 0.9904. Despite both models performing well, the SVR model showed superior accuracy, outperforming MLR by 54% to 82% in terms of predictions. The relationships between Brix levels and various nutrients, such as sucrose, glucose, and fructose, were found to be strong, while titratable acidity had a minimal effect. SVR was found to be a more reliable, non-destructive method for melon quality assessment. These findings revealed the relationship between Brix and sugar levels on melon quality. The study highlights the potential of these machine learning models in optimizing the rootstock effect and managing melon cultivation to improve fruit quality. Full article
(This article belongs to the Special Issue Application of Big Data and Artificial Intelligence in Food Science: Current Practice and Future Directions)
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22 pages, 3397 KiB  
Article
Application of Spatial Offset Raman Spectroscopy (SORS) and Machine Learning for Sugar Syrup Adulteration Detection in UK Honey
by Mennatullah Shehata, Sophie Dodd, Sara Mosca, Pavel Matousek, Bhavna Parmar, Zoltan Kevei and Maria Anastasiadi
Foods 2024, 13(15), 2425; https://doi.org/10.3390/foods13152425 - 31 Jul 2024
Cited by 4 | Viewed by 7164
Abstract
Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar [...] Read more.
Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain. Full article
(This article belongs to the Special Issue Application of Big Data and Artificial Intelligence in Food Science: Current Practice and Future Directions)
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25 pages, 1128 KiB  
Article
Probabilistic Modelling of the Food Matrix Effects on Curcuminoid’s In Vitro Oral Bioaccessibility
by Kevin de Castro Cogle, Mirian T. K. Kubo, Franck Merlier, Alexandra Josse, Maria Anastasiadi, Fady R. Mohareb and Claire Rossi
Foods 2024, 13(14), 2234; https://doi.org/10.3390/foods13142234 - 16 Jul 2024
Viewed by 960
Abstract
The bioaccessibility of bioactive compounds plays a major role in the nutritional value of foods, but there is a lack of systematic studies assessing the effect of the food matrix on bioaccessibility. Curcuminoids are phytochemicals extracted from Curcuma longa that have captured public [...] Read more.
The bioaccessibility of bioactive compounds plays a major role in the nutritional value of foods, but there is a lack of systematic studies assessing the effect of the food matrix on bioaccessibility. Curcuminoids are phytochemicals extracted from Curcuma longa that have captured public attention due to claimed health benefits. The aim of this study is to develop a mathematical model to predict curcuminoid’s bioaccessibility in biscuits and custard based on different fibre type formulations. Bioaccessibilities for curcumin-enriched custards and biscuits were obtained through in vitro digestion, and physicochemical food properties were characterised. A strong correlation between macronutrient concentration and bioaccessibility was observed (p = 0.89) and chosen as a main explanatory variable in a Bayesian hierarchical linear regression model. Additionally, the patterns of food matrix effects on bioaccessibility were not the same in custards as in biscuits; for example, the hemicellulose content had a moderately strong positive correlation to bioaccessibility in biscuits (p = 0.66) which was non-significant in custards (p = 0.12). Using a Bayesian hierarchical approach to model these interactions resulted in an optimisation performance of r2 = 0.97 and a leave-one-out cross-validation score (LOOCV) of r2 = 0.93. This decision-support system could assist the food industry in optimising the formulation of novel food products and enable consumers to make more informed choices. Full article
(This article belongs to the Special Issue Application of Big Data and Artificial Intelligence in Food Science: Current Practice and Future Directions)
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18 pages, 2513 KiB  
Article
Phenolic Acid–β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR Study
by Kweeni Iduoku, Marvellous Ngongang, Jayani Kulathunga, Amirreza Daghighi, Gerardo Casanola-Martin, Senay Simsek and Bakhtiyor Rasulev
Foods 2024, 13(13), 2147; https://doi.org/10.3390/foods13132147 - 6 Jul 2024
Viewed by 1336
Abstract
The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid [...] Read more.
The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked into the β-cyclodextrin cavity to generate three different binding constants. The data from the docking study were combined with topological, topographical, and quantum-chemical features from the ligands in a machine learning-based structure–activity relationship study. Three different models for each binding constant were computed using a combination of the genetic algorithm (GA) and multiple linear regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984 for the training and test sets, respectively. The models revealed several factors responsible for binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as the presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area. Full article
(This article belongs to the Special Issue Application of Big Data and Artificial Intelligence in Food Science: Current Practice and Future Directions)
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