Browsing by Author "Lambert, Ronald J. W."
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Item Open Access Comparing the antimicrobial effectiveness of NaCl and KCl with a view to salt/sodium replacement.(Elsevier, 2008-05-10) Bidlas, Eva; Lambert, Ronald J. W.A study using a small range of pathogenic bacterial species (Aeromonas hydrophila, Enterobacter sakazakii, Shigella flexneri, Yersinia enterocolitica and 3 strains of Staphylococcus aureus) has shown that potassium chloride has an equivalent antimicrobial effect on these organisms when calculated on a molar basis. Combined NaCl and KCl experiments were carried out and data was analysed using a modification to the Lambert and Lambert [Lambert, R.J.W., and Lambert, R., 2003. A model for the efficacy of combined inhibitors. Journal of Applied Microbiology 95, 734–743.] model for combined inhibitors and showed that in combination KCl is a direct 1:1 molar replacement for the antimicrobial effect of common salt. If this is a general finding then, where salt is used to help preserve a product, partial or complete replacement by KCl is possible.Item Open Access An explanation for the effect of inoculum size on MIC and the growth/no growth interface.(Elsevier, 2008-08-15) Bidlas, Eva; Du, Tingting; Lambert, Ronald J. W.The inoculum effect (IE) is the phenomenon observed where changes in the inoculum size used in an experiment alters the outcome with respect to, for example, the minimum inhibitory concentration of an antimicrobial or the growth/no growth boundary for a given set of environmental conditions. Various hypotheses exist as to the cause of the IE such as population heterogeneity and quorum sensing, as well as the null hypothesis — that it is artefactual. Time to detection experiments (TTD) were carried out on different initial inoculum sizes of several bacterial species (Aeromonas hydrophila, Enterobacter sakazakii, Salmonella Poona, Escherichia coli and Listeria innocua) when challenged with different pH and with combined pH and sodium acetate. Data were modelled using a modification to a Gamma model (Lambert and Bidlas 2007, Int. J. Food Microbiology 115, 204–213), taking into account the inoculum size dependency on the TTD obtained under ideal conditions. The model suggests that changes in minimum inhibitory concentration (MIC) or in the growth–no growth boundary with respect to inoculum size are due to using a smaller or larger inoculum (i.e. is directly related to microbial number) and is not due to other, suggested, phenomena. The model used further suggests that the effect of a changing inoculum size can be modelled independently of any other factor, which implies that a simple 1 to 2-day experiment measuring the TTD of various initial inocula can be used as an adjunct to currently available models.Item Open Access Growth curve prediction from optical density data(Elsevier Science B.V., Amsterdam., 2012-03-15T00:00:00Z) Mytilinaios, Ioannis; Salih, Magdi; Schofield, Hannah K.; Lambert, Ronald J. W.A fundamental aspect of predictive microbiology is the shape of the microbial growth curve and many models are used to fit microbial count data, the modified Gompertz and Baranyi equation being two of the most widely used. Rapid, automated methods such as turbidimetry have been widely used to obtain growth parameters, but do not directly give the microbial growth curve. Optical density (OD) data can be used to obtain the specific growth rate and if used in conjunction with the known initial inocula, the maximum population data and knowledge of the microbial number at a predefined OD at a known time then all the information required for the reconstruction of a standard growth curve can be obtained.Using multiple initial inocula the times to detection (TTD) at a given standard OD were obtained from which the specific growth rate was calculated. The modified logistic, modified Gompertz, 3-phase linear, Baranyi and the classical logistic model (with or without lag) were fitted to the TTD data. In all cases the modified logistic and modified Gompertz failed to reproduce the observed linear plots of the log initial inocula against TTD using the known parameters (initial inoculum, MPD and growth rate). The 3 phase linear model (3PLM), Baranyi and classical logistic models fitted the observed data and were able to reproduce elements of the OD incubation-time curves. Using a calibration curve relating OD and microbial numbers, the Baranyi equation was able to reproduce OD data obtained for Listeria monocytogenes at 37 and 30°C as well as data on the effect of pH (range 7.05 to 3.46) at 30°C.The Baranyi model was found to be the most capable primary model of those examined (in the absence of lag it defaults to the classic logistic model). The results suggested that the modified logistic and the modified Gompertz models should not be used as Primary models for TTD data as they cannot reproduce the observed datItem Restricted An improved model for the analysis of combined antimicrobials: a replacement for the Chou‐Talalay combination index method(Wiley, 2017-10) Anastasiadi, Maria; Polizzi, Karen; Lambert, Ronald J. W.Aims To rationalise confusion in the literature concerning the analysis of combined antimicrobials, specifically to see if the combination index (CI) method of analysis was as rigorous as claimed. Methods & Results data from previous studies of the inhibition of Staphylococcus aureus by mixed antimicrobials were re-analysed using the CI method and a model which takes account of differences in the concentration exponents of individual antimicrobials. Conclusions The Chou-Talalay combination index method for the analysis of combined antimicrobials was found to be valid only in the specific cases where concentration exponents were equal. In these cases the CI method was found to be a function of the residuals of fitting the additive model to the observed data. Where concentration exponents were not equal the CI method was invalid, whereas the additive model took these differences into account. Significance and Impact of Study The CI method can be replaced wholly by the additive model described. The model allows simple regression to be used to analyse whole data sets and provides simple graphical output.Item Open Access Monte Carlo simulation of parameter confidence intervals for non-linear regression analysis of biological data using Microsoft Excel.(Elsevier Science B.V., Amsterdam., 2012-06-18T00:00:00Z) Lambert, Ronald J. W.; Mytilinaios, Ioannis; Maitland, Luke; Brown, Angus M.This study describes a method to obtain parameter confidence intervals from the fitting of non-linear functions to experimental data, using the SOLVER and Analysis ToolPaK Add-In of the Microsoft Excel spreadsheet. Previously we have shown that Excel can fit complex multiple functions to biological data, obtaining values equivalent to those returned by more specialized statistical or mathematical software. However, a disadvantage of using the Excel method was the inability to return confidence intervals for the computed parameters or the correlations between them. Using a simple Monte-Carlo procedure within the Excel spreadsheet (without recourse to programming), SOLVER can provide parameter estimates (up to 200 at a time) for multiple 'virtual' data sets, from which the required confidence intervals and correlation coefficients can be obtained. The general utility of the method is exemplified by applying it to the analysis of the growth of Listeria monocytogenes, the growth inhibition of Pseudomonas aeruginosa by chlorhexidine and the further analysis of the electrophysiological data from the compound action potential of the rodent optic nerve.Item Open Access A new model for the effect of pH on microbial growth: an extension of the Gamma hypothesis(Blackwell Publishing Ltd, 2011-01-31T00:00:00Z) Lambert, Ronald J. W.Aims: To investigate the appropriateness of the extended Lambert-Pearson model (ELPM) to model the effect of pH (as hydrogen and hydroxyl ions) over the whole biokinetic pH range in comparison with other available models. Methods and Results: Data for the effect of pH on microbial growth were obtained from the literature or in-house. Data were examined using several models for pH. Models were compared using the residual mean of squares. Using the ELPM, pH was modelled as hydrogen ions and hydroxyl ions; hence, the model was monotonic in each. The ELPM was able to model data more successfully than the cardinal pH model (CPM) and other models in the majority of cases. Conclusions: Examining the effect of pH as hydrogen and hydroxyl ions has the advantage that the basic form of the ELPM can be retained as each is treated as a distinct antimicrobial effect. With the ELPM, each inhibitor is described by two parameters; from these parameters, the pH(min), pH(opt) and pH(max) can be obtained. Furthermore, the idea of a dose response, absent from other models, becomes important. Significance and Impact of the Study: The CPM is an excellent model for certain situations - where there is a high degree of symmetry between the suboptimal pH and superoptimal pH response and where there are few data points available. The ELPM is more amenable to highly asymmetric behaviour, especially where plateaus of effect around the pH optimum are observed and where the number of data points is not restrictive.