The Complex Effect of Food Matrix Fat Content on Thermal Inactivation of Listeria monocytogenes: Case Study in Emulsion and Gelled Emulsion Model Systems

Verheyen, Davy; Govaert, Marlies; Seow, Ti Kian; Ruvina, Jonela; Mukherjee, Vivek; Baka, Maria; Skåra, Torstein; Impe, Jan Van

doi:10.3389/fmicb.2019.03149

Cited by 18 publications

(18 citation statements)

References 48 publications

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“…The underlying mechanisms are not fully understood as HPP resistance and recovery of bacteria also depend on the cell physiology of bacteria present on the food matrix before the treatment. As well, the matrix composition may influence bacterial recovery (Tassou et al, 2007), as was shown for fat content influencing L. monocytogenes recovery from thermal inactivation (Verheyen et al, 2020). Furthermore, the capacity of the bacteria to resist HPP treatment should be dissociated from the ability of the bacterial cells to recover and thrive during the storage conditions.…”

Section: Discussionmentioning

confidence: 99%

Combination of high pressure treatment at 500 MPa and biopreservation with aLactococcus lactisstrain for lowering the bacterial growth during storage of diced cooked ham with reduced nitrite salt

Chaillou¹,

Ramaroson²,

Coeuret³

et al. 2020

Preprint

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We investigated the combined effects of biopreservation and high pressure treatment on bacterial communities of diced cooked ham prepared with diminished nitrite salt. First, bacterial communities of four commercial brands of dice cooked ham from local supermarkets, sampled near the use-by-date, were characterised. The four ham microbiota showed a relative low diversity but harboured quite dissimilar communities. Two ham samples were dominated by different Proteobacteria (Pseudomonas, Serratia for one, Psychrobacter and Vibrio for the other one) while the two others were dominated by Firmicutes (Latilactobacillus and Leuconostoc). Second, sterile diced cooked ham, prepared with reduced level of nitrite was inoculated with the two Proteobacteria-rich microbiota collected from the aforementioned commercial samples together with a Lactococcus lactis protective strain. Dices were then treated at 500 MPa for 5 minutes and bacterial dynamics was monitored during storage at 8°C. Applied alone, none of the treatments stabilized durably the growth of hams microbiota. Nevertheless, the combination of biopreservation and high pressure treatment was efficient to reduce the growth of Proteobacteria spoilage species. However, this effect was dependent on the nature of the initial microbiota, showing that use of biopreservation and high pressure treatment as an alternative to nitrite reduction for ensuring cooked ham microbial safety merits attention but still requires improvement.

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Section: Discussionmentioning

confidence: 99%

Combination of high pressure treatment at 500 MPa and biopreservation with aLactococcus lactisstrain for lowering the bacterial growth during storage of diced cooked ham with reduced nitrite salt

Chaillou¹,

Ramaroson²,

Coeuret³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Temperature control was regulated by means of a Peltier temperature control system and upper heated plate. The model systems were analysed by means of their steady-state behaviour over a temperature range from 20 to 70°C, as described in Verheyen et al (2020b). Briefly, model systems were prepared 24 h prior to measurement, and were loaded onto the bottom plate (1.6 mL per sample) after which a delay of 2 min was set.…”

Section: Rheological Characterisationmentioning

confidence: 99%

“…Thermal inactivation was modelled for the longest treatment time at each temperature (i.e., holding times th of 37.5, 13.4, and 8.8 min for treatments at holding temperatures Th of 59, 64, and 69°C, respectively), enabling the estimation of kinetic parameters over the course of the Shaka process, and the comparison of those parameters among the different model systems. Based on the protocol of Verheyen et al (2019Verheyen et al ( , 2020b, The content is identical to the published paper, but without the final typesetting by the publisher.…”

Section: Estimation Of Thermal Inactivation Kineticsmentioning

confidence: 99%

Thermal inactivation of Listeria monocytogenes in the Shaka agitated reciprocal retort: Influence of food matrix rheology and fat content

Verheyen

Altin

Skipnes

et al. 2021

Food and Bioproducts Processing

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The effect of food matrix rheology and fat content on thermal inactivation of Listeria monocytogenes in the Shaka agitated retort (final retort temperatures of 59, 64, and 69°C) was investigated using four fish-based artificial food model systems: lowviscosity liquid (liquid), high-viscosity liquid (xanthan), and emulsions containing 10% and 20% fat (emulsion 10% and emulsion 20%). Model system rheology, quantified by the consistency index K and the flow behaviour index n, influenced the thermal load to which the systems were exposed during treatments. Thermal loads followed the order liquid ≥ emulsion 10% ≥ emulsion 20% ≥ xanthan, a trend which was also valid for the sublethal injury induced to the cells. Log reductions followed the order liquid ≥ emulsion 10% ≥ xanthan ≥ emulsion 20%, indicating a protective effect of an increased fat content, not related to heat transfer differences. Between approximately 59 and 62°C, the temperature range over which the largest portion of the inactivation was achieved, the maximum specific inactivation rate kmax followed the same trend as the log reductions. Overall, the effect of food matrix rheology on inactivation efficacy could be linked to heat transfer dynamics, while the effect of fat content was more complex.

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“…In the early 2000s, a large number of research groups underwent a paradigm shift towards the use of more structured model systems. Artificial model systems of different structures are frequently used to study the effect of certain influencing factors on microbial growth [ 11 , 15 , 103 , 104 , 105 , 106 , 107 , 108 ] or inactivation [ 109 , 110 , 111 ]. Seldomly, predictive models are developed based on experiments in those more structure model systems.…”

Section: Historical Overview On the Inclusion Of Food Microstructure In Predictive Modelsmentioning

confidence: 99%

The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

Verheyen

Impe

2021

Foods

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Predictive microbiology has steadily evolved into one of the most important tools to assess and control the microbiological safety of food products. Predictive models were traditionally developed based on experiments in liquid laboratory media, meaning that food microstructural effects were not represented in these models. Since food microstructure is known to exert a significant effect on microbial growth and inactivation dynamics, the applicability of predictive models is limited if food microstructure is not taken into account. Over the last 10–20 years, researchers, therefore, developed a variety of models that do include certain food microstructural influences. This review provides an overview of the most notable microstructure-including models which were developed over the years, both for microbial growth and inactivation.

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The Complex Effect of Food Matrix Fat Content on Thermal Inactivation of Listeria monocytogenes: Case Study in Emulsion and Gelled Emulsion Model Systems

Cited by 18 publications

References 48 publications

Combination of high pressure treatment at 500 MPa and biopreservation with aLactococcus lactisstrain for lowering the bacterial growth during storage of diced cooked ham with reduced nitrite salt

Combination of high pressure treatment at 500 MPa and biopreservation with aLactococcus lactisstrain for lowering the bacterial growth during storage of diced cooked ham with reduced nitrite salt

Thermal inactivation of Listeria monocytogenes in the Shaka agitated reciprocal retort: Influence of food matrix rheology and fat content

The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art

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