Nanoemulsified D-Limonene Reduces the Heat Resistance of Salmonella Senftenberg over 50 Times

Ros−Chumillas, María; Garre, Alberto; Maté, Javier; Palop, Alfredo; Periago, Paula M.

doi:10.3390/nano7030065

Cited by 19 publications

(12 citation statements)

References 38 publications

(55 reference statements)

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“…For E. coli K12 MG1655 in BHI a value of 0.47 (sd = 0.003) was estimated for c , whereas 1.98 (sd = 0.01) was estimated for E. coli CECT 515 in PW. Previous research works have shown that the microbial response to thermal treatments can vary between strains and/or heating media (Aryani et al, 2015 ; Aspridou and Koutsoumanis, 2015 ; Ros-Chumillas et al, 2017 ). Therefore, it is reasonable to consider that stress acclimation may also be dependent of these factors.…”

Section: Resultsmentioning

confidence: 99%

Relevance of the Induced Stress Resistance When Identifying the Critical Microorganism for Microbial Risk Assessment

et al. 2018

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Decisions regarding microbial risk assessment usually have to be carried out with incomplete information. This is due to the large number of possible scenarios and the lack of specific data for the problem considered. Consequently, risk assessment studies are based on the information obtained with a small number of bacterial cells which are considered the most heat resistant and/or more capable of multiplying during storage. The identification of the most resistant strains is usually based on D and z-values, normally estimated from isothermal experiments. This procedure omits the potential effect that the shape of the dynamic thermal profile applied in industry has on the microbial inactivation. One example of such effects is stress acclimation, which is related to a physiological response of the cells during sub-lethal treatments that increases their resistance. In this article, we use a recently published mathematical model to compare the development of thermal resistance for Escherichia coli K12 MG1655 and E. coli CECT 515 using inactivation data already published for these strains. Based only on the isothermal experiments, E. coli K12 MG1655 would be identified as more resistant to the thermal treatment than the CECT 515 strain in the 50–65°C temperature range. However, we conclude that stress acclimation is strain (and/or media)-dependent; the CECT 515 strain has a higher capacity for developing a stress acclimation than K12 MG1655 (300% increase of the D-value for CECT 515, 50% for K12 MG1655). It, thus, has the potential to be more resistant to the thermal treatment than the K12 MG1655 strain for some conditions allowing acclimation. A methodology is proposed to identify for which conditions this may be the case. After calibrating the model parameters representing acclimation using real experimental data, the applicability of the proposed approach is demonstrated using numerical simulations, showing how the CECT 515 strain can be more resistant for some heating profiles. Consequently, the most resistant bacterial strain to a dynamic heating profile should not be identified based only on isothermal experiments (D- and z-value). The relevance of stress acclimation for the treatment studied should also be evaluated.

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Section: Resultsmentioning

confidence: 99%

Relevance of the Induced Stress Resistance When Identifying the Critical Microorganism for Microbial Risk Assessment

et al. 2018

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“…It provides mathematical models whose applications include the prediction of the microbial response to environmental conditions, such as those encountered during storage or food processing [2–4]. Another use of predictive models is inference, where the response to different bacteria is compared in order to, for instance, identify the most resistant bacterial strain to some treatment [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, the D-value describes the treatment time required to reduce the microbial count a 90% [33]. Model parameters estimated under certain conditions are commonly used to, for example, infer the effectiveness of a treatment [6,34]. Therefore, in many situations, the objective of experiments designed in the context of predictive microbiology is not prediction but the estimation of model parameter with enough precision (standard deviation) that enables accurate inference.…”

Section: Introductionmentioning

confidence: 99%

On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods

et al. 2019

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The mathematical models used in predictive microbiology contain parameters that must be estimated based on experimental data. Due to experimental uncertainty and variability, they cannot be known exactly and must be reported with a measure of uncertainty (usually a standard deviation). In order to increase precision (i.e. reduce the standard deviation), it is usual to add extra sampling points. However, recent studies have shown that precision can also be increased without adding extra sampling points by using Optimal Experiment Design, which applies optimization and information theory to identify the most informative experiment under a set of constraints. Nevertheless, to date, there has been scarce contributions to know a priori whether an experimental design is likely to provide the desired precision in the parameter estimates. In this article, two complementary methodologies to predict the parameter precision for a given experimental design are proposed. Both approaches are based on in silico simulations, so they can be performed before any experimental work. The first one applies Monte Carlo simulations to estimate the standard deviation of the model parameters, whereas the second one applies the properties of the Fisher Information Matrix to estimate the volume of the confidence ellipsoids. The application of these methods to a case study of dynamic microbial inactivation, showing how they can be used to compare experimental designs and assess their precision, is illustrated. The results show that, as expected, the optimal experimental design is more accurate than the uniform design with the same number of data points. Furthermore, it is demonstrated that, for some heating profiles, the uniform design does not ensure that a higher number of sampling points increases precision. Therefore, optimal experimental designs are highly recommended in predictive microbiology.

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“…Among the essential oils, D-limonene has shown remarkable antimicrobial properties when combined with thermal treatments 20,[25][26][27] . D-limonene is an important flavour component in citrus essential oil (lemon, orange, tangerine, etc.…”

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confidence: 99%

“…), and has the generally regarded as safe (GRAS) status 28 . Maté et al 25,26 and Ros-Chumillas et al 27 have studied the synergistic effect of a mild heat treatment and an essential oil nanoemulsion. The synergistic effect led to a 100-fold reduction of the thermal resistance of L. monocytogenes both in culture medium 26 and in apple juice 25 , and a 50-fold reduction of the thermal resistance of Salmonella Senftenberg 27 , which are, by far, the largest reductions in microbial heat resistance ever published when combining heat with natural antimicrobials.…”

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confidence: 99%

Limonene nanoemulsified with soya lecithin reduces the intensity of non-isothermal treatments for inactivation of Listeria monocytogenes

Garre

Espín

Huertas

et al. 2020

Sci Rep

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Consumers' demands for ready-to-eat, fresh-like products are on the rise during the last years. This type of products have minimal processing conditions that can enable the survival and replication of pathogenic microorganisms. Among them, Listeria monocytogenes is of special concern, due to its relatively high mortality rate and its ability to replicate under refrigeration conditions. Previous research works have shown that nanoemulsified essential oils in combination with thermal treatments are effective for inactivating L. monocytogenes. However, previous research works were limited to isothermal conditions, whereas actual processing conditions in industry are dynamic. Under dynamic conditions, microorganism can respond unexpectedly to the thermal stress (e.g. adaptation, acclimation or increased sensitivity). In this work, we assess the combination of nanoemulsified Dlimonene with thermal treatments under isothermal and dynamic conditions. The nanoemulsion was prepared following an innovative methodology using soya lecithin, a natural compound as well as the essential oil. Under isothermal heating conditions, the addition of the antimicrobial enables a reduction of the treatment time by a factor of 25. For time-varying treatments, dynamic effects were relevant. Treatments with a high heating rate (20 °C/min) are more effective than those with a slow heating rate (1 °C/min). This investigation demonstrates that the addition of nanoemulsified D-limonene can greatly reduce the intensity of the thermal treatments currently applied in the food industry. Hence, it can improve the product quality without impacting its safety.

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Nanoemulsified D-Limonene Reduces the Heat Resistance of Salmonella Senftenberg over 50 Times

Cited by 19 publications

References 38 publications

Relevance of the Induced Stress Resistance When Identifying the Critical Microorganism for Microbial Risk Assessment

Relevance of the Induced Stress Resistance When Identifying the Critical Microorganism for Microbial Risk Assessment

On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods

Limonene nanoemulsified with soya lecithin reduces the intensity of non-isothermal treatments for inactivation of Listeria monocytogenes

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