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Foods
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Food Modelling*
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Food Modelling*

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 12546

Special Issue Editors

Prof. Dr. Ursula Andrea Gonzales-Barron

E-Mail Website
Collection Editor
CIMO Mountain Research Center, School of Agriculture, Polytechnic Institute of Bragança, Santa Apolónia Campus, 5300-253 Bragança, Portugal
Interests: predictive microbiology; quantitative risk assessment; meta-analysis; statistical quality control; Bayesian applications; experimental designs; shelf-life determination
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vasco Cadavez

E-Mail Website
Collection Editor
CIMO Mountain Research Center, School of Agriculture, Polytechnic Institute of Bragança, Santa Apolónia Campus, 5300-253 Bragança, Portugal
Interests: quality of meat and meat products; dynamic modelling; process optimization; linear and non-linear modelling; predictive microbiology; meta-regression; web applications; databases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern food science is supported by reliable research outcomes depicted or confirmed by data modelling and probability science. Although different types of models can be developed in food science, their objectives mostly revolve around explaining or representing physical, chemical, or biological phenomena in foods or food processes and/or estimating meaningful parameters that are necessary for simulation, prediction, control applications, or optimization/intervention strategies. The ever-growing computational resources available have stimulated the advances in food modelling. The modelling of food products and processes can vary in complexity, attending to factors such as the extent of knowledge on the inherent mechanisms and interactions to be described, the extent of uncertainty about food properties, and the intricacy of the dynamic processes/phenomena to be represented. In food science, models can be classified as inferential or predictive, static or dynamic, empirical or mechanistic, deterministic or stochastic, etc. This Topical Collection entitled “Food Modelling” seeks to gather work on new approaches and applications of modelling in diverse food science fields such as process development and optimization, food formulations, thermal and non-thermal processes, fermentation processes, safety and quality in the food chain, predictive microbiology and risk assessment, food control, heat and mass transfer in food engineering problems, sensory analysis, and chemometrics, among others. The Collection also welcomes submissions on the development of new meta-analysis methods and applications in food science, and the construction of open databases and/or software solutions.

Prof. Dr. Ursula Andrea Gonzales-Barron
Prof. Dr. Vasco Cadavez
Collection 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

  • Process optimization
  • Food formulation
  • Predictive microbiology
  • Risk assessment
  • Process control
  • Meta-analysis
  • Simulation
  • Software and databases
  • Chemometrics
  • Big data and data mining

Published Papers (4 papers)

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Research

20 pages, 2182 KiB  
Article
Dynamic Modelling to Describe the Effect of Plant Extracts and Customised Starter Culture on Staphylococcus aureus Survival in Goat’s Raw Milk Soft Cheese
by Beatriz Nunes Silva, Sara Coelho-Fernandes, José António Teixeira, Vasco Cadavez and Ursula Gonzales-Barron
Foods 2023, 12(14), 2683; https://doi.org/10.3390/foods12142683 - 12 Jul 2023
Viewed by 1423
Abstract
This study characterises the effect of a customised starter culture (CSC) and plant extracts (lemon balm, sage, and spearmint) on Staphylococcus aureus (SA) and lactic acid bacteria (LAB) kinetics in goat’s raw milk soft cheeses. Raw milk cheeses were produced with and without [...] Read more.
This study characterises the effect of a customised starter culture (CSC) and plant extracts (lemon balm, sage, and spearmint) on Staphylococcus aureus (SA) and lactic acid bacteria (LAB) kinetics in goat’s raw milk soft cheeses. Raw milk cheeses were produced with and without the CSC and plant extracts, and analysed for pH, SA, and LAB counts throughout ripening. The pH change over maturation was described by an empirical decay function. To assess the effect of each bio-preservative on SA, dynamic Bigelow-type models were adjusted, while their effect on LAB was evaluated by classical Huang models and dynamic Huang–Cardinal models. The models showed that the bio-preservatives decreased the time necessary for a one-log reduction but generally affected the cheese pH drop and SA decay rates (logDref = 0.621–1.190 days; controls: 0.796–0.996 days). Spearmint and sage extracts affected the LAB specific growth rate (0.503 and 1.749 ln CFU/g day−1; corresponding controls: 1.421 and 0.806 ln CFU/g day−1), while lemon balm showed no impact (p > 0.05). The Huang–Cardinal models uncovered different optimum specific growth rates of indigenous LAB (1.560–1.705 ln CFU/g day−1) and LAB of cheeses with CSC (0.979–1.198 ln CFU/g day−1). The models produced validate the potential of the tested bio-preservatives to reduce SA, while identifying the impact of such strategies on the fermentation process. Full article
(This article belongs to the Special Issue Food Modelling*)
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20 pages, 971 KiB  
Article
Meta-Analysis of In Vitro Antimicrobial Capacity of Extracts and Essential Oils of Syzygium aromaticum, Citrus L. and Origanum L.: Contrasting the Results of Different Antimicrobial Susceptibility Methods
by Beatriz Nunes Silva, Olga María Bonilla-Luque, Arícia Possas, Youssef Ezzaky, Abdelkhaleq Elmoslih, José António Teixeira, Fouad Achemchem, Antonio Valero, Vasco Cadavez and Ursula Gonzales-Barron
Foods 2023, 12(6), 1265; https://doi.org/10.3390/foods12061265 - 16 Mar 2023
Cited by 2 | Viewed by 2683
Abstract
Diffusion methods, including agar disk-diffusion and agar well-diffusion, as well as dilution methods such as broth and agar dilution, are frequently employed to evaluate the antimicrobial capacity of extracts and essential oils (EOs) derived from Origanum L., Syzygium aromaticum, and Citrus L. [...] Read more.
Diffusion methods, including agar disk-diffusion and agar well-diffusion, as well as dilution methods such as broth and agar dilution, are frequently employed to evaluate the antimicrobial capacity of extracts and essential oils (EOs) derived from Origanum L., Syzygium aromaticum, and Citrus L. The results are reported as inhibition diameters (IDs) and minimum inhibitory concentrations (MICs), respectively. In order to investigate potential sources of variability in antimicrobial susceptibility testing results and to assess whether a correlation exists between ID and MIC measurements, meta-analytical regression models were built using in vitro data obtained through a systematic literature search. The pooled ID models revealed varied bacterial susceptibilities to the extracts and in some cases, the plant species and methodology utilised impacted the measurements obtained (p < 0.05). Lemon and orange extracts were found to be most effective against E. coli (24.4 ± 1.21 and 16.5 ± 0.84 mm, respectively), while oregano extracts exhibited the highest level of effectiveness against B. cereus (22.3 ± 1.73 mm). Clove extracts were observed to be most effective against B. cereus and demonstrated the general trend that the well-diffusion method tends to produce higher ID (20.5 ± 1.36 mm) than the disk-diffusion method (16.3 ± 1.40 mm). Although the plant species had an impact on MIC, there is no evidence to suggest that the methodology employed had an effect on MIC (p > 0.05). The ID–MIC model revealed an inverse correlation (R2 = 47.7%) and highlighted the fact that the extract dose highly modulated the relationship (p < 0.0001). The findings of this study encourage the use of extracts and EOs derived from Origanum, Syzygium aromaticum, and Citrus to prevent bacterial growth. Additionally, this study underscores several variables that can impact ID and MIC measurements and expose the correlation between the two types of results. Full article
(This article belongs to the Special Issue Food Modelling*)
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26 pages, 1166 KiB  
Article
BIKE: Dietary Exposure Model for Foodborne Microbiological and Chemical Hazards
by Jukka Ranta, Antti Mikkelä, Johanna Suomi and Pirkko Tuominen
Foods 2021, 10(11), 2520; https://doi.org/10.3390/foods10112520 - 20 Oct 2021
Cited by 3 | Viewed by 2500
Abstract
BIKE is a Bayesian dietary exposure assessment model for microbiological and chemical hazards. A graphical user interface was developed for running the model and inspecting the results. It is based on connected Bayesian hierarchical models, utilizing OpenBUGS and R in tandem. According to [...] Read more.
BIKE is a Bayesian dietary exposure assessment model for microbiological and chemical hazards. A graphical user interface was developed for running the model and inspecting the results. It is based on connected Bayesian hierarchical models, utilizing OpenBUGS and R in tandem. According to occurrence and consumption data given as inputs, a specific BUGS code is automatically written for running the Bayesian model in the background. The user interface is based on shiny app. Chronic and acute exposures are estimated for chemical and microbiological hazards, respectively. Uncertainty and variability in exposures are visualized, and a few optional model structures can be used. Simulated synthetic data are provided with BIKE for an example, resembling real occurrence and consumption data. BIKE is open source and available from github. Full article
(This article belongs to the Special Issue Food Modelling*)
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14 pages, 1077 KiB  
Article
Hyperspectral Imaging Coupled with Multivariate Analysis and Image Processing for Detection and Visualisation of Colour in Cooked Sausages Stuffed in Different Modified Casings
by Chao-Hui Feng, Yoshio Makino and Juan F. García Martín
Foods 2020, 9(8), 1089; https://doi.org/10.3390/foods9081089 - 10 Aug 2020
Cited by 14 | Viewed by 3151
Abstract
A hyperspectral imaging system was for the first time exploited to estimate the core colour of sausages stuffed in natural hog casings or in two hog casings treated with solutions containing surfactants and lactic acid in slush salt. Yellowness of sausages stuffed in [...] Read more.
A hyperspectral imaging system was for the first time exploited to estimate the core colour of sausages stuffed in natural hog casings or in two hog casings treated with solutions containing surfactants and lactic acid in slush salt. Yellowness of sausages stuffed in natural hog casings (control group, 20.26 ± 4.81) was significantly higher than that of sausages stuffed in casings modified by submersion for 90 min in a solution containing 1:30 (w/w) soy lecithin:distilled water, 2.5% wt. soy oil, and 21 mL lactic acid per kg NaCl (17.66 ± 2.89) (p < 0.05). When predicting the lightness and redness of the sausage core, a partial least squares regression model developed from spectra pre-treated with a second derivative showed calibration coefficients of determination (Rc2) of 0.73 and 0.76, respectively. Ten, ten, and seven wavelengths were selected as the important optimal wavelengths for lightness, redness, and yellowness, respectively. Those wavelengths provide meaningful information for developing a simple, cost-effective multispectral system to rapidly differentiate sausages based on their core colour. According to the canonical discriminant analysis, lightness possessed the highest discriminant power with which to differentiate sausages stuffed in different casings. Full article
(This article belongs to the Special Issue Food Modelling*)
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