Modelling the influence of metabolite diffusion on non-starter lactic acid bacteria growth in ripening Cheddar cheese

Czárán, Tamás; Rattray, Fergal P.; Møller, Cleide Oliveira de Almeida; Christensen, Bjarke Bak

doi:10.1016/j.idairyj.2017.12.010

Cited by 15 publications

(9 citation statements)

References 33 publications

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“…This indicates that in the presence of higher concentrations of starter-lysate an increased growth may be expected, even under the limiting conditions as those found during cheese ripening. This is in agreement with Czarán et al (2018) who hypothesized that the availability and the level of nutrients released from SLAB are the main factors determining the rate of NSLAB growth during Cheddar cheese ripening.…”

Section: Effect Of Starter-lysate Concentration and Naclsupporting

confidence: 92%

“…This concept of cannibalism of SLAB by NSLAB was proposed by Thomas (1987) and it has been shown in other studies investigating the effect of sugars and amino acids in supporting NSLAB growth (Adamberg et al, 2005;Møller et al, 2020; J o u r n a l P r e -p r o o f Journal Pre-proof Ortakci et al, 2015;Saidi et al, 2020;Wallace and Fox, 1997). Czarán et al (2018) suggested that the concentration of nutrients released from SLAB is an important factor influencing the rate of NSLAB growth during cheese ripening.…”

Section: Introductionmentioning

confidence: 82%

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Modelling the biphasic growth of non-starter lactic acid bacteria on starter-lysate as a substrate

Møller

Christensen

Rattray

2021

International Journal of Food Microbiology

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Section: Effect Of Starter-lysate Concentration and Naclsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 82%

Modelling the biphasic growth of non-starter lactic acid bacteria on starter-lysate as a substrate

Møller

Christensen

Rattray

2021

International Journal of Food Microbiology

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“…On another experiment, it was reported that S. thermophilus Sfi39 and its three mutants had no lag phase, and the specific growth rate changed between 1.25 − 2.31 h −1 in M17 broth supplemented with 1% (w/v) lactose (LM17) at 42 °C (Zotta et al, 2009). These findings suggest that medium, temperature, and strain can influence the growth parameters (Czárán et. al., 2018).…”

Section: Growth Characteristics Of the Test Bacteriamentioning

confidence: 95%

Growth and effect of garlic (Allium sativum) on selected beneficial bacteria

Altuntaş

Korukluoğlu

2019

Food Sci. Technol

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The growing interest of consumers to the new probiotic product has led the industry to focus on it. But viability of probiotic strain has a big challenge during shelf life of food. Therefore, to know the effect of food additives on survival of probiotic is important. The role of probiotics, garlic and yoghurt on the health of humans is widely known. On this study, we focus on finding answers to the question of whether selected beneficial bacteria will survive in the presence of ground garlic solution or fresh garlic juice and obtaining the growth parameters of that strains. Disk diffusion assays revealed that while Bifidobacterium longum BB536 is the most susceptible (49.37 ± 1.07 mm), Lactobacillus acidophilus 74-2 is the most resistant (no inhibition zone) bacteria to the fresh garlic juice, significantly. However, the strains were not adversely affected in possible usage rates of garlic in some Turkish appetizers. Enumeration assays also confirmed these unexpected findings. The addition of certain quantities of ground garlic or fresh garlic juice that did not show antimicrobial activity for selected probiotic strains are suitable for food matrix, especially yogurt based products.

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“…Gradients within a single colony may be important in affecting the growth in different locations of a colony (Malakar et al, 2000(Malakar et al, , 2003, although the buffered environment of cheese may prevent the existence of pH microgradients, at least in microcolonies (Jeanson et al, 2013). Inoculum level may affect size and distribution of microcolonies and this, in turn, has been shown to affect cheese composition (Boucher et al, 2015a(Boucher et al, , 2015bJeanson et al, 2010), although, in the long term, it might not affect the distribution of small soluble metabolites implicated in some cross-feeding relationships SLAB and LAB, (Czárán et al, 2018) at least in internally ripened cheeses. (Callon et al, 2014;Eppert et al, 1997;Imran et al, 2010) effect: + stimulation of growth, = no effect, -inhibition/death; cost: 0 no cost, € metabolic cost, ?…”

Section: Confirming Interactions In Microbial Association Networkmentioning

confidence: 99%

Microbial association networks in cheese: a meta-analysis

Parente

Zotta

Ricciardi

2021

Preprint

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Interactions among starter and non-starter microorganisms (starter bacteria, naturally occurring or intentionally added non-starter bacteria, yeasts and filamentous fungi, spoilage and pathogenic microorganisms and, finally bacteriophages and even arthropods) deeply affect the dynamics of cheese microbial communities and, as a consequence, multiple aspects of cheese quality, from metabolites affecting the taste, aroma and flavor, to body, texture and color. Understanding and exploiting microbial interactions is therefore key to managing cheese quality. This is true for the simplest systems (fresh cheeses produced from pasteurized milk using defined starters composed solely of Lactic Acid Bacteria) and the more so for complex, dynamic systems, like surface ripened cheese produced from raw milk, in which a dynamic succession of diverse microorganisms is essential for obtained the desired combination of sensory properties while guaranteeing safety. Positive (commensalism, protocooperation) and negative (competition, amensalism, predation and parasitism) among members of the cheese biota have been reviewed multiple times. Although the complex, multidimensional datasets generated by multi-omic approaches to cheese microbiology and biochemistry are ideally suited for the representation of biotic and metabolic interactions as networks, network science concepts and approaches are rarely applied to cheese microbiology. In this review we first illustrate concepts relevant to the description of microbial interaction networks using network science concepts. Then, we briefly review methods used for the inference and analysis of microbial association networks and their potential use in the interpretation of the cheese interactome. Since these methods can only be used for mining microbial associations, a review of the experimental methods used to confirm the nature of microbial interactions among cheese microbes. Finally, we demonstrate the potential of microbial association network inference by mining metataxonomic data stored in the public database DairyFMBN, a specialized version of FoodMicrobionet which collates data on 74 metataxonomic studies on dairy products. Microbial association networks were inferred from 34 studies on cheese with up to 4 different methods and the results discussed to evaluate several aspects (choice of method, level of taxonomic resolution for the analysis, network, node and edge properties) which provide insight on the usefulness of this approach as explorative tool in the detection of microbial interactions in cheese.

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Modelling the influence of metabolite diffusion on non-starter lactic acid bacteria growth in ripening Cheddar cheese

Cited by 15 publications

References 33 publications

Modelling the biphasic growth of non-starter lactic acid bacteria on starter-lysate as a substrate

Modelling the biphasic growth of non-starter lactic acid bacteria on starter-lysate as a substrate

Growth and effect of garlic (Allium sativum) on selected beneficial bacteria

Microbial association networks in cheese: a meta-analysis

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