Scientific Publications - Work Done by Microbiology Reader Bioscreen C

Journal of Applied Microbiology, Volume 87 Issue 5 Page 782  - October 1999

A kinetic study of the effect  of hydrogen peroxide and peracetic acid  against Staphylococcus aureus and Pseudomonas aeruginosa  using the Bioscreen disinfection  method

ABSTRACT

Hydrogen peroxide and peracetic acid at pH 4 were examined against Staphylococcus aureus and Pseudomonas aeruginosa using the published 'Bioscreen' technique of biocide analysis. The data were examined using either classical Chick-Watson (CW) log-linear disinfection kinetics or the empirical, non-linear time Hom model. In some cases, modelling the data with the classical CW method gave good linear correlations, in others, however, deviations from this model were observed. In such cases the Hom model proved an adequate descriptor of the data. The Bioscreen technique therefore gives data which can be analysed using the normal mechanistic and empirical models currently available.

INTRODUCTION

In 1897 Kronig and Paul began the kinetic study of disinfection. Chick (1908) began to address the theory behind disinfection and Watson (1908) produced the rate law of disinfection, the so-called Chick-Watson (CW) log-linear rate law with later refinements from Phelps (1911). Deviations, however, from this log-linear law are commonplace and many explanations for such deviations are found in the literature (Lee & Gilbert 1918; Withell 1942; Roy et al. 1982; Berg et al. 1982; Berg et al. 1988; Wickramanayake & Sproul 1991; Mir et al. 1997; Johnston et al. 1999). Inasmuch as there are two fundamental hypotheses for disinfection, the 'mechanistic' and the 'vitalistic', and to the authors' knowledge no one hypothesis has triumphed over the other, the CW law (a mechanistic mathematical model) remains one of the most useful methods for the analysis of disinfection rate data, exemplified by the elegant work of Mir et al. (1997).

An examination of current methods used to obtain legal approval for a new biocide, such as the CEN microbial suspension test, reveals a 5-min pass or fail test (Anonymous 1996). Many examinations of disinfection kinetics rely on the use of such tests because to do otherwise would be to invite large-scale expense in terms of money and resources. The previous unavailability of a reliable method for large-scale tests was one of the principle reasons for the slow advance of the science. The development of a rapid methodology, the Bioscreen method (Lambert et al. 1998), allows large-scale investigations of disinfectants at a fraction of their traditional cost. Furthermore, the data obtained tend to be more amenable to mathematical modelling than those obtained through normal, traditional plate methods.

Recently, we published work concerning the differences normally observed between the Bioscreen and traditional plate methods (Lambert & van-der Ouderaa 1999). It was shown that the disinfection kinetic parameters obtained using the data from the Bioscreen method are similar to those obtained by the normal, traditional methods. We report herein an extension to this study through the examination and modelling of the disinfection of two common test organisms with two common disinfectants using the Bioscreen methodology.

 MATERIALS, METHODS and MODELS
 

Organisms and methodology

The organisms and methods used have been described elsewhere (Lambert et al. 1998). The biocides were purchased from the Aldrich-Sigma Chemical Company. The pH was adjusted using HCl/NaOH prior to use.

Disinfectant quenching

The quenching agent used in conjunction with the peroxygen compounds was Universal Quenching Agent (Lambert et al. 1998) supplemented with 0·01% catalase. Quenching tests were performed on each of the biocides at levels greater than those used in the study. In all cases, quenching of the disinfectant was achieved within 30 s.

Modelling

Most modelling studies were performed on the Excel spreadsheet or by using the JMP statistic package (SAS Institute Cary, NC, USA).

The basic expression used for constant temperature disinfection kinetics is the CW disinfection law (Watson 1908; Phelps 1911)

where logR is the decimal log reduction in survivors as obtained from the Bioscreen as defined by Lambert et al. (1998), K is the disinfection rate constant, n is the concentration exponent (Hugo & Denyer 1987) and t is the disinfection contact time. The constants K and n are most easily evaluated by using eqn 2 as an alternative form of eqn 1

Where non-linear log reduction/time plots are encountered, one of the most useful modifications to the basic rate law is an empirical model developed by Hom (1972)

where logR is the decimal log reduction in survivors as obtained from the Bioscreen, K is the disinfection rate constant, m is the (Hom model) concentration exponent, h is the time exponent and t is the disinfection contact time. Using multiple linear regression methods, e.g. with the JMP or similar mathematically adept packages, the various constants can be evaluated using eqn 4 as an alternative form of eqn 3

The mean and standard deviation of the residuals (logR _obs-logR _calc) are quoted as a measure of fit of the equation to the observed logR values.

FIGURES
 

Fig. 1  (D)isinfection of Staphylococcus aureus by hydrogenperoxide. , 50 mmol l ¹; , 100 mmol l ¹; , 150 mm...

Figure 2(D)  isinfection of Pseudomonas aeruginosa by hydrogen peroxide. , 50 mmol l ¹; , 100 mmol l ¹; , 1...

Figure 3(D)  isinfection of Pseudomonas aeruginosa by peracetic acid. , 0·01 mmol l ¹; , 0·03 mmol l ¹; , 0·05...

Figure 4(O)  bserved vs calculated for the Hom analysis of the peracetic acid disinfection of Pseudomon...

Table 1   Chick-Watson model; disinfection parameters

Table 2   Hom model; disinfection parameters

RESULTS
 

Hydrogen peroxide disinfection kinetics

The results of a Bioscreen study of the Staphylococcus aureus and Pseudomonas aeruginosa disinfection by hydrogen peroxide at pH 4 are shown in Figs 1 and 2. The data obtained from the Bioscreen method give the logR values at each contact time for each concentration of biocide examined. These data were then examined with the two basic disinfection models, the CW and the Hom, using eqns 2 and 4, respectively. Tables 1 and 2 list the parameters obtained with reference to eqns 1 and 3, respectively. Linear kinetics with a small lag were found for Staph. aureus whereas tailing was observed with Ps. aeruginosa. The presence of a lag or tail can be seen directly from the Hom model. Log-linear kinetics correspond to a time exponent of h = 1, whereas in the presence of lags h > 1 and for tails h < 1. For data which contain both lags and tails, however, the Hom model should not be used.

According to the literature (Hugo & Denyer 1987), the general value for the dilution coefficient for hydrogen peroxide is 0·5, whereas with this work it was found to be 1·25 and 1·07 for Staph. aureus and Ps. aeruginosa, respectively. The values found reflect those expected for a biocide acting as a chemical agent as opposed to a physically damaging one (Hugo & Denyer 1987). However, there is no mention of any pH dependence in the literature and this may be one factor accounting for the discrepancy between this work and previously published studies.

Peracetic acid disinfection kinetics

The disinfection of Staph. aureus with peracetic acid at pH 4 appeared to approximate to log-linear kinetics. The CW analysis, however, resulted in a large standard deviation of the residuals. On examining the data with the Hom model, the standard deviation improved but the calculated data still showed a spread from the observed at higher logR values. A plot of the calculated logR vs observed logR gave a regression fit of logR _calc = 0·9 logR _obs + 0·214, r ² = 0·88. The peracetic acid disinfection of Ps. aeruginosa gave non-linear, tailing, kinetics (Fig. 3). The Hom analysis improved the standard deviation of residuals obtained with the CW analysis from 1·79 to 0·25. A plot of the observed vs the calculated logR values gave a good correlation (Fig. 4), with a best fit regression line of logR _calc = 1·02 logR _obs- 0·046, r ² = 0·98.

In both cases, disinfection with peracetic acid was much faster than with hydrogen peroxide (comparison of the kinetics can be made at a standard time and concentration of 1 min and 1 mol l ¹). A comparison of the rate constant data from Table 2 shows that peracetic acid disinfects Staph. aureus 2  10⁶ times faster than hydrogen peroxide, whereas for Ps. aeruginosa the value was 1·2  10^4. The dilution coefficient of 1 with Ps. aeruginosa suggests that it acts against this organism in a purely chemical manner whereas, with Staph. aureus, the increase in the value of the dilution coefficient may suggest physical and chemical alterations. The increase in the disinfection constant, K, over that of hydrogen peroxide may be a reflection of this.

DISCUSSION

In an earlier publication (Lambert & van-der Ouderaa 1999) it was explained that the Bioscreen method gave log reductions which encompass the level of injury within the population. The work further explained and gave evidence that the longer the applied stress the higher the degree of injury within a population. This suggested that there was a continuum of states between the healthy, injured and non-viable (dead) microbes. As such, it was expected that the disinfection kinetics, as evaluated using the Bioscreen methodology, would give data amenable to the mathematical models and descriptions currently available to the interested scientist. This, of course, also meant that the description of disinfection would suffer from the same problems as every other study: tails and lags and an adequate mechanistic description of what was occurring (see Cerf 1977 for a discussion of this).

The disinfection kinetics of biocides using the data obtained from the Bioscreen methodology can be easily described. The mechanistic log-linear CW and the empirical Hom models have been used to describe laboratory observation. The CW log-linear law is mechanistic in the sense that it follows the principles of mass action (Lee & Gilbert 1918). Non-linearity, with tails or lags present but not both, can be modelled by the Hom empirical model.

The analysis of the rate of disinfection, at constant temperature, requires two principal pieces of information: the rate constant, K, and the dilution coefficient, n. If the disinfection is log-linear then the CW model will provide these two pieces of information. If the disinfection is non-linear then no true mechanistic model is available to provide K and n-values. The next most useful disinfection model is that published by Hom, which can be used to fit the data to give a predictive equation. Since the Hom model is empirical, the parameters obtained should not be considered equivalent to the dilution and disinfection rate constants of the CW interpretation. If we consider the Hom model from the vitalist standpoint then, for cases where h > 1, the Hom model suggests an increasing biocide reactivity with time. If we were to assume that there was a population distribution of resistance to the biocide, as the 'vitalistic theory' states, then h > 1 suggests that the most resistant are killed off first, followed by the least resistant. However, the foregoing statement is flawed if the assumption that the Hom model is other than an empirical model is false.

The results show that the Bioscreen methodology can give useful information on disinfection and can give K and n-values akin to those obtained by other means. Tailing and lags were also observed, which further suggests that both of these phenomena are not artefactual. The basis of the CW interpretation of disinfection kinetics is that the concentration of biocide is in vast excess over the microbes. Indeed, most published work which uses this model quotes this as a given and valid assumption. However, if the levels of biocide were not in vast excess over the microbes and the microbes were able to quench the biocide themselves during the course of the disinfection, then non-linear log survival (or log reduction) time curves must result. We have shown (Johnston et al. 1999) that during the sodium dodecyl sulphate disinfection of Staph. aureus the bulk biocide concentration decreases substantially and that non-linear disinfection kinetics were observed.

It is known that pseudomonads contain high levels of catalase and superoxide dismutase (SOD). These enzymes consume peroxide and, if the peroxide concentration decreased significantly during the course of the disinfection experiment, non-linear disinfection kinetics would result. As an example, the bacterium Micrococcus radiodurans contains large amounts of catalase and SOD (over 10 times greater catalase activity than Ps. aeruginosa). A simple experiment using peroxide strip indicators clearly showed the ability of M. radiodurans to reduce the level of peroxide from 1 mmol l ¹ to undetectable (< 0·015 mmol l ¹) in less than 10 min.

If biocide concentrations can be reduced by the microbes during a disinfection experiment then tailing will be the natural consequence. As such, the basic mechanistic model is flawed for entertaining an invalid assumption that the biocide concentration is constant throughout the disinfection test. The Bioscreen methodology, because it allows more flexibility in disinfection analysis, can be used to probe effectively the hypotheses of disinfection.

REFERENCES

• Anonymous.a a 1996 Chemical Disinfectants and Antiseptics Quantitative Suspension Test for the Evaluation of Bactericidal Activity of Chemical Disinfectants and Antiseptics Used in Food, Industrial, Domestic and Institutional Areas Test Method and Requirements. European Committee for Standardisation, PREN 1276, CEN/TC 216 N 109.

• Berg, J.D., Hoff, J.C., Roberts, P.V. & Matin, A. 1988 Resistance of bacterial subpopulations to disinfection by chlorine dioxide. Journal of the American Water Works Association, 80, 115-119.

• Berg, J.D., Matin, A. & Roberts, P.V. 1982 Effect of antecedent growth conditions on sensitivity of Escherichia coli to chlorine dioxide. Applied and Environmental Microbiology, 44, 814-819.

• Cerf, O. 1977 Tailing of survival curves of bacterial spores. Journal of Applied Bacteriology, 42, 1-19.

• Chick, H. 1908 An investigation into the laws of disinfection. Journal of Hygiene (Cambridge), 8, 92-158.

• Hom, L.W. 1972 Kinetics of chlorine disinfection in an ecosystem. Journal of the Environmental Engineering Division of the American Society of Civil Engineering 98 (SA1), 183-194.

• Hugo, W.B. & Denyer, S.P. 1987 Concentration exponent of disinfectants and preservatives (biocides). In: Preservatives in the Food, Pharmaceutical and Environmental Industries. The Society for Applied Bacteriology Technical Series 22, pp. 281-291. Oxford: Blackwell Scientific.

• Johnston, M.D., Simons, E.-A. & Lambert, R.J.W. 1999 One explanation for the variability of the bacterial suspension test. Journal of Applied Microbiology, in press.

• Kronig, B. & Paul, TH. 1897 Die chemischen Grundlagen der Lehre von der Giftwirkung und Desinfection. Zeitschrift für Hygiene 25, 1-112. Reprinted in English. Milestones in Microbiology (ed. Brock, T). (1965), pp. 163-176. Englewood Cliffs: Prentice-Hall.

• Lambert, R.J.W., Johnston, M.D. & Simons, E.-A. 1998 Disinfectant testing: use of the Bioscreen Microbiological Growth Analyser for laboratory biocides screening. Letters in Applied Microbiology, 26, 288-292.

• Lambert, R.J.W. & van-der Ouderaa, M.-L.H. 1999 An investigation into the differences between the Bioscreen and the traditional plate count disinfectant test methods. Journal of Applied Microbiology, 86, 689-694.

• Lee, R.E. & Gilbert, C.A. 1918 On the application of the Mass Law to the process of disinfection - being a contribution to the 'Mechanistic Theory' as opposed to the 'Vitalistic Theory'. Journal of Physical Chemistry, 22, 348-372.

• Mir, J., Morato, J. & Ribas, F. 1997 Resistance to chlorine of freshwater bacterial strains. Journal of Applied Microbiology, 82, 7-18.

• Phelps, E.B. 1911 The application of certain laws of physical chemistry in the standardisation of disinfectants. Journal of Infectious Diseases, 8, 27-38.

• Roy, D., Chian, E.S.K. & Engelbrecht, R.S. 1982 Mathematical model for enterovirus inactivation by ozone. Water Research, 16, 667-673.

• Watson, H.E. 1908 A note on the variation of the rate of disinfection with change in the concentration of the disinfectant. Journal of Hygiene (Cambridge), 8, 536-542.

• Wickramanayake, G.B. & Sproul, O.J. 1991 Kinetics of the Inactivation of microorganisms. In: Disinfection, Sterilisation and Preservation (ed. Block, S).S, pp. 72-84. Lea and Febiger.

• Withell, E.R. 1942 The significance of the variation in shape of time-survivor curves. Journal of Hygiene, 42, 124-183.

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