Antisepsis, Disinfection, and Sterilization. Gerald E. McDonnell
biocides, neutralization can be by physical removal (the most obvious method being filtration), dilution, or chemical sequestration or inactivation. Filtration is achieved by passing the sample through a 0.1- to 0.4-μm-pore-size filter, trapping the organisms (usually bacteria and fungi), and allowing the biocide to pass through into the filtrate. Chemical neutralizers include sodium thiosulfate (for some oxidizing agents and halogens), Tween and lecithin (for quaternary ammonium compounds [QACs] and chlorhexidine), and sodium sulfite or glycine (for glutaraldehyde). It is important that, other than the neutralization of the biocide, no inhibitory substances (including the neutralizer itself) be present or formed that could inhibit the growth of the test organism on incubation; for example, chlorhexidine has affinity for certain filter materials, which can subsequently inhibit the growth of the test organism on incubation of the filter on growth media. It is therefore important that positive, negative, and neutralization growth controls be included to ensure the correct interpretation of results.
FIGURE 1.18 Typical time kill, or D-value, determination. A known concentration of the test culture is exposed to the biocide, samples are withdrawn at various times and neutralized, and the population of survivors is determined by incubation on growth medium. The actual exposure can be conducted at various temperatures, in the presence or absence of test soils, or under other test conditions.
Following exposure and neutralization of the biocide, the survivor population can be determined qualitatively or quantitatively. Quantitative methods determine the number of survivors by direct enumeration, e.g., by plating onto growth agar for bacteria and fungi (as shown in Fig. 1.18). The data from this analysis can be plotted as a time-log reduction relationship (Fig. 1.19).
From this plot, the decimal reduction time, or D value, can be calculated; it is defined as the time (e.g., in minutes or seconds) at a given temperature required to kill 1 log unit (or 90%) of a given microbial population under stated test conditions (Fig. 1.19). It is usual for the average D value to be determined as the negative reciprocal of the slope (m) of the plotted relationship (−1/slope):
where Δlog N is the change in log10 microbial population, ΔT is the change in time, and m is the slope of the survivor curve.
FIGURE 1.19 Determination of the D value on microbial exposure to a biocide.
FIGURE 1.20 Typical survivor curves on biocide exposure. Curve 1 is concave downward, curve 2 is sigmoidal, and curve 3 is concave upward.
Graphical presentation of the microbial response to a biocide over time can also be useful for analysis of the biocide–microbial-population interaction. Although the data presented in Fig. 1.19 demonstrate a linear relationship, typical biocide survivor curves are often found to be nonlinear (examples are shown in Fig. 1.20). Practically, these data can have many interpretations. For example, curve 1 suggests an initial lag phase in biocidal activity that could be due to the presence of interfering soils or microbial clumping, which limits biocide access to the test microorganism. Curve 3 may be indicative of insufficient biocide concentration or the presence of a microbial sub-population (or mixed population) with greater resistance to the biocide.
Qualitative methods simply determine the presence or absence of growth in a sample, although the data from the analysis can be used to estimate the actual population present by serial dilution if it is accepted that the microbial population is randomly distributed (i.e., not clumped) (Fig. 1.21).
FIGURE 1.21 Qualitative and semiquantitative population determinations.
By serial dilution of the initial sample (in this case, using 10-fold dilutions) in growth media and monitoring of growth, the highest dilution demonstrating growth must have contained ≤0 viable cells and can be used to estimate the most probable number in the initial sample (Fig. 1.22). This analysis is also referred to as a “fraction-negative” determination. In certain situations, these methods can be used to estimate D values (a representation of a test is shown in Fig. 1.22).
In this case, the fraction of observed growth or no growth can be used to estimate the number of survivors in the sample at a specific exposure time. Different mathematical equations are used, for example, the Halvorson-Ziegler equation (to estimate the surviving population):
where Nt is the population at time t, n is the number tested, and r is the number sterile.
Therefore, if we take the example from Fig. 1.22, we can generate the following table:
| Time | n | r | n/r | Nt |
| 1 | 4 | 1 | 4 | 1.4 |
| 2 | 4 | 2 | 2 | 0.7 |
| 3 | 4 | 3 | 1.3 | 0.3 |
From this analysis, the D value can be estimated by plotting (as described above) or using mathematical equations, like the Stumbo-Murphy-Cochran equation:
FIGURE 1.22 D-value estimation using most probable number estimations.
where t is the exposure time, N0 is the initial population, and Nt is the population at time t.
Clearly fraction-negative methods are relatively restrictive, but they are often used in combination with direct-enumeration methods to estimate