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1209 KiB

Open AccessArticle

A Theoretical Analysis for Assessing the Variability of Secondary Model Thermal Inactivation Kinetic Parameters

by Maria C. Giannakourou and Nikolaos G. Stoforos

Foods 2017, 6(1), 7; https://doi.org/10.3390/foods6010007 - 12 Jan 2017

Cited by 11 | Viewed by 6428

Traditionally, for the determination of the kinetic parameters of thermal inactivation of a heat labile substance, an appropriate index is selected and its change is measured over time at a series of constant temperatures. The rate of this change is described through an [...] Read more.

Traditionally, for the determination of the kinetic parameters of thermal inactivation of a heat labile substance, an appropriate index is selected and its change is measured over time at a series of constant temperatures. The rate of this change is described through an appropriate primary model and a secondary model is applied to assess the impact of temperature. By this approach, the confidence intervals of the estimates of the rate constants are not taken into account. Consequently, the calculated variability of the secondary model parameters can be significantly lower than the actual variability. The aim of this study was to demonstrate the influence of the variability of the primary model parameters in establishing the confidence intervals of the secondary model parameters. Using a Monte Carlo technique and assuming normally distributed D_T values (parameter associated with a primary inactivation model), the error propagating on the D_Tref and z-values (secondary model parameters) was assessed. When D_T confidence intervals were broad, the secondary model’s parameter variability was appreciably high and could not be adequately estimated through the traditional deterministic approach that does not take into account the variation on the D_T values. In such cases, the proposed methodology was essential for realistic estimations. Full article

(This article belongs to the Special Issue Food Modelling)

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1199 KiB

Open AccessArticle

Rheometric Non-Isothermal Gelatinization Kinetics of Chickpea Flour-Based Gluten-Free Mufﬁn Batters with Added Biopolymers

by María Dolores Alvarez, Francisco Javier Cuesta, Beatriz Herranz and Wenceslao Canet

Foods 2017, 6(1), 3; https://doi.org/10.3390/foods6010003 - 02 Jan 2017

Cited by 8 | Viewed by 6174

Abstract

An attempt was made to analyze the elastic modulus (G0) of chickpea ﬂour (CF)-based mufﬁn batters made with CF alone and with added biopolymers (whey protein (WP), xanthan gum (XG), inulin (INL), and their blends) in order to evaluate their suitability to be [...] Read more.

An attempt was made to analyze the elastic modulus (G0) of chickpea ﬂour (CF)-based mufﬁn batters made with CF alone and with added biopolymers (whey protein (WP), xanthan gum (XG), inulin (INL), and their blends) in order to evaluate their suitability to be a wheat ﬂour (WF) substitute in mufﬁns, and to model the heat-induced gelatinization of batters under non-isothermal heating condition from 25 ◦C to 90 ◦C. A rheological approach is proposed to determine the kinetic parameters (reaction order (n), frequency factor (k0), and activation energy (Ea)) using linearly-increasing temperature. Zero-order reaction kinetics adequately described batter gelatinization process, therefore assuming a constant rate independent of the initial G0 value. The change of the derivative of G0 with respect to time (dG0/dt) versus temperature is described by one exponential function with activation energies ranging from 118 to 180 kJ·mol−1. Control wheat gluten batter, with higher and lower starch and protein contents, respectively, than CF-based batters, exhibited the highest Ea value. Formulation of CF-based gluten-free batters with starch and protein contents closer to the levels of WF-based batter could be a strategy to decrease differences in kinetic parameters of mufﬁn batters and, therefore, in technological characteristics of baked mufﬁns. Full article

(This article belongs to the Special Issue Food Modelling)

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1890 KiB

Open AccessFeature PaperArticle

Application of Hybrid Genetic Algorithm Routine in Optimizing Food and Bioengineering Processes

by Jaya Shankar Tumuluru and Richard McCulloch

Foods 2016, 5(4), 76; https://doi.org/10.3390/foods5040076 - 09 Nov 2016

Cited by 16 | Viewed by 6919

Abstract

Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space; however convergence is not guaranteed. This article [...] Read more.

Optimization is a crucial step in the analysis of experimental results. Deterministic methods only converge on local optimums and require exponentially more time as dimensionality increases. Stochastic algorithms are capable of efficiently searching the domain space; however convergence is not guaranteed. This article demonstrates the novelty of the hybrid genetic algorithm (HGA), which combines both stochastic and deterministic routines for improved optimization results. The new hybrid genetic algorithm developed is applied to the Ackley benchmark function as well as case studies in food, biofuel, and biotechnology processes. For each case study, the hybrid genetic algorithm found a better optimum candidate than reported by the sources. In the case of food processing, the hybrid genetic algorithm improved the anthocyanin yield by 6.44%. Optimization of bio-oil production using HGA resulted in a 5.06% higher yield. In the enzyme production process, HGA predicted a 0.39% higher xylanase yield. Hybridization of the genetic algorithm with a deterministic algorithm resulted in an improved optimum compared to statistical methods. Full article

(This article belongs to the Special Issue Food Modelling)

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895 KiB

Open AccessFeature PaperArticle

Fermentation-Assisted Extraction of Isothiocyanates from Brassica Vegetable Using Box-Behnken Experimental Design

by Amit K. Jaiswal and Nissreen Abu-Ghannam

Foods 2016, 5(4), 75; https://doi.org/10.3390/foods5040075 - 04 Nov 2016

Cited by 11 | Viewed by 6161

Abstract

Recent studies showed that Brassica vegetables are rich in numerous health-promoting compounds such as carotenoids, polyphenols, flavonoids, and glucosinolates (GLS), as well as isothiocyanates (ITCs) and are involved in health promotion upon consumption. ITCs are breakdown products of GLS, and typically used in [...] Read more.

Recent studies showed that Brassica vegetables are rich in numerous health-promoting compounds such as carotenoids, polyphenols, flavonoids, and glucosinolates (GLS), as well as isothiocyanates (ITCs) and are involved in health promotion upon consumption. ITCs are breakdown products of GLS, and typically used in the food industry as a food preservative and colouring agent. They are also used in the pharmaceutical industry due to their several pharmacological properties such as antibacterial, antifungal, antiprotozoal, anti-inflammatory, and chemoprotective effects, etc. Due to their widespread application in food and pharmaceuticals, the present study was designed to extract ITCs from York cabbage. In order to optimise the fermentation-assisted extraction process for maximum yield of ITCs from York cabbage, Box-Behnken design (BBD) combined with response surface methodology (RSM) was applied. Additionally, the GLS content of York cabbage was quantified and the effect of lactic acid bacteria (LAB) on GLS was evaluated. A range of GLS such as glucoraphanin, glucoiberin, glucobrassicin, sinigrin, gluconapin, neoglucobrassicin and 4-methoxyglucobrassicin were identified and quantified in fresh York cabbage. The experimental data obtained were fitted to a second-order polynomial equation using multiple regression analysis, and also examined by appropriate statistical methods. LAB facilitated the degradation of GLS, and the consequent formation of breakdown products such as ITCs. Results showed that the solid-to-liquid (S/L) ratio, fermentation time and agitation rate had a significant effect on the yield of ITCs (2.2 times increment). The optimum fermentation conditions to achieve a higher ITCs extraction yield were: S/L ratio of 0.25 w/v, fermentation time of 36 h, and agitation rate of 200 rpm. The obtained yields of ITCs (45.62 ± 2.13 μM sulforaphane equivalent (SFE)/mL) were comparable to the optimised conditions, indicating the accuracy of the model for the fermentation-assisted extraction of ITCs. This method has good prospects in industrial applications for the extraction of ITCs, and can be helpful in the food, pharmaceutical and agricultural sectors. Full article

(This article belongs to the Special Issue Food Modelling)

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965 KiB

Open AccessArticle

A Computational Study of Amensalistic Control of Listeria monocytogenes by Lactococcus lactis under Nutrient Rich Conditions in a Chemostat Setting

by Hassan Khassehkhan and Hermann J. Eberl

Foods 2016, 5(3), 61; https://doi.org/10.3390/foods5030061 - 09 Sep 2016

Cited by 3 | Viewed by 5212

Abstract

We study a previously introduced mathematical model of amensalistic control of the foodborne pathogen Listeria monocytogenes by the generally regarded as safe lactic acid bacteria Lactococcus lactis in a chemostat setting under nutrient rich growth conditions. The control agent produces lactic acids and [...] Read more.

We study a previously introduced mathematical model of amensalistic control of the foodborne pathogen Listeria monocytogenes by the generally regarded as safe lactic acid bacteria Lactococcus lactis in a chemostat setting under nutrient rich growth conditions. The control agent produces lactic acids and thus affects pH in the environment such that it becomes detrimental to the pathogen while it is much more tolerant to these self-inflicted environmental changes itself. The mathematical model consists of five nonlinear ordinary differential equations for both bacterial species, the concentration of lactic acids, the pH and malate. The model is algebraically too involved to allow a comprehensive, rigorous qualitative analysis. Therefore, we conduct a computational study. Our results imply that depending on the growth characteristics of the medium in which the bacteria are cultured, the pathogen can survive in an intermediate flow regime but will be eradicated for slower flow rates and washed out for higher flow rates. Full article

(This article belongs to the Special Issue Food Modelling)

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3802 KiB

Open AccessArticle

Applying Least Absolute Shrinkage Selection Operator and Akaike Information Criterion Analysis to Find the Best Multiple Linear Regression Models between Climate Indices and Components of Cow’s Milk

by Mohammad Reza Marami Milani, Andreas Hense, Elham Rahmani and Angelika Ploeger

Foods 2016, 5(3), 52; https://doi.org/10.3390/foods5030052 - 23 Jul 2016

Cited by 12 | Viewed by 6870

Abstract

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI _new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, [...] Read more.

This study focuses on multiple linear regression models relating six climate indices (temperature humidity THI, environmental stress ESI, equivalent temperature index ETI, heat load HLI, modified HLI (HLI _new), and respiratory rate predictor RRP) with three main components of cow’s milk (yield, fat, and protein) for cows in Iran. The least absolute shrinkage selection operator (LASSO) and the Akaike information criterion (AIC) techniques are applied to select the best model for milk predictands with the smallest number of climate predictors. Uncertainty estimation is employed by applying bootstrapping through resampling. Cross validation is used to avoid over-fitting. Climatic parameters are calculated from the NASA-MERRA global atmospheric reanalysis. Milk data for the months from April to September, 2002 to 2010 are used. The best linear regression models are found in spring between milk yield as the predictand and THI, ESI, ETI, HLI, and RRP as predictors with p-value < 0.001 and R² (0.50, 0.49) respectively. In summer, milk yield with independent variables of THI, ETI, and ESI show the highest relation (p-value < 0.001) with R² (0.69). For fat and protein the results are only marginal. This method is suggested for the impact studies of climate variability/change on agriculture and food science fields when short-time series or data with large uncertainty are available. Full article

(This article belongs to the Special Issue Food Modelling)

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986 KiB

Open AccessArticle

Wine Traceability: A Data Model and Prototype in Albanian Context

by Kreshnik Vukatana, Kozeta Sevrani and Elira Hoxha

Foods 2016, 5(1), 11; https://doi.org/10.3390/foods5010011 - 17 Feb 2016

Cited by 15 | Viewed by 9108

Abstract

Vine traceability is a critical issue that has gained interest internationally. Quality control programs and schemes are mandatory in many countries including EU members and the USA. Albania has transformed most of the EU regulations on food into laws. Regarding the vine sector, [...] Read more.

Vine traceability is a critical issue that has gained interest internationally. Quality control programs and schemes are mandatory in many countries including EU members and the USA. Albania has transformed most of the EU regulations on food into laws. Regarding the vine sector, the obligation of wine producers to keep traceability data is part of the legislation. The analysis on the interviews conducted with Albanian winemakers show that these data are actually recorded only in hard copy. Another fact that emerges from the interviews is that only two producers have implemented the ISO (International Organization for Standardization) standards on food. The purpose of this paper is to develop an agile and automated traceability system based on these standards. We propose a data model and system prototype that are described in the second and third section of this work. The data model is an adaption along the lines of the GS1 (Global Standards One) specifications for a wine supply chain. The proposed prototype has a key component that is mobile access to the information about wine through barcode technology. By using this mechanism the consumer obtains transparency on his expectations concerning the quality criteria. Another important component of the proposed system in this paper is a real-time notification module that works as an alert system when a risk is identified. This can help producers and authorities to have a rapid identification of a contaminated product. It is important in cases when recalling the product from the market or preventing it from reaching the consumer. Full article

(This article belongs to the Special Issue Food Modelling)

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1042 KiB

Open AccessArticle

Optimising the Encapsulation of an Aqueous Bitter Melon Extract by Spray-Drying

by Sing Pei Tan, Tuyen Chan Kha, Sophie Parks, Costas Stathopoulos and Paul D. Roach

Foods 2015, 4(3), 400-419; https://doi.org/10.3390/foods4030400 - 09 Sep 2015

Cited by 33 | Viewed by 7478

Abstract

Our aim was to optimise the encapsulation of an aqueous bitter melon extract by spray-drying with maltodextrin (MD) and gum Arabic (GA). The response surface methodology models accurately predicted the process yield and retentions of bioactive concentrations and activity (R² > [...] Read more.

Our aim was to optimise the encapsulation of an aqueous bitter melon extract by spray-drying with maltodextrin (MD) and gum Arabic (GA). The response surface methodology models accurately predicted the process yield and retentions of bioactive concentrations and activity (R² > 0.87). The optimal formulation was predicted and validated as 35% (w/w) stock solution (MD:GA, 1:1) and a ratio of 1.5:1 g/g of the extract to the stock solution. The spray-dried powder had a high process yield (66.2% ± 9.4%) and high retention (>79.5% ± 8.4%) and the quality of the powder was high. Therefore, the bitter melon extract was well encapsulated into a powder using MD/GA and spray-drying. Full article

(This article belongs to the Special Issue Food Modelling)

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Review

Jump to: Research

2159 KiB

Open AccessReview

A Focus on the Death Kinetics in Predictive Microbiology: Benefits and Limits of the Most Important Models and Some Tools Dealing with Their Application in Foods

by Antonio Bevilacqua, Barbara Speranza, Milena Sinigaglia and Maria Rosaria Corbo

Foods 2015, 4(4), 565-580; https://doi.org/10.3390/foods4040565 - 12 Oct 2015

Cited by 43 | Viewed by 6660

Abstract

Predictive Microbiology (PM) deals with the mathematical modeling of microorganisms in foods for different applications (challenge test, evaluation of microbiological shelf life, prediction of the microbiological hazards connected with foods, etc.). An interesting and important part of PM focuses on the use [...] Read more.

Predictive Microbiology (PM) deals with the mathematical modeling of microorganisms in foods for different applications (challenge test, evaluation of microbiological shelf life, prediction of the microbiological hazards connected with foods, etc.). An interesting and important part of PM focuses on the use of primary functions to fit data of death kinetics of spoilage, pathogenic, and useful microorganisms following thermal or non-conventional treatments and can also be used to model survivors throughout storage. The main topic of this review is a focus on the most important death models (negative Gompertz, log-linear, shoulder/tail, Weibull, Weibull+tail, re-parameterized Weibull, biphasic approach, etc.) to pinpoint the benefits and the limits of each model; in addition, the last section addresses the most important tools for the use of death kinetics and predictive microbiology in a user-friendly way. Full article

(This article belongs to the Special Issue Food Modelling)

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