Part I RisksRisks
4.3 Strategies for optimisation of cleaning and disinfection
Mode of action
In principle chlorhexidine attacks the outer cell layer but not sufficiently to induce lysis or cell death. However, after crossing the cell wall it damages the cytoplasmic membrane (bacteria) or plasma membrane (yeast) (McDonnell and Russell, 1999). Polymeric biguanide appears to have a non-specific mode of attack against cell membranes resulting in quick cell death.
Summary
The different effective concentrations for the biocides are summarised in Table 4.3. It is obvious that, depending on the type of application or type and metabolic state of the microorganism, the proper disinfectant must be chosen.
4.3.2 Application of the right cleaning/disinfecting conditions
The combination of concentration, mechanical action, time and temperature is of major importance for efficient cleaning and disinfection. The applied concentration should not be higher or lower than the advised concentration.
Too high concentrations can lead to insolubility and increased corrosiveness.
Concerning protein fouling, it is known that too high concentrations of alkali (>0.5%) result in polymerisation of the protein and form a rubbery layer (Bird, 1994; Jeurnink and Brinkman, 1994). These kinds of rubbery layers obstruct and prevent the penetration of cleaning and disinfecting solution into the fouling, resulting in a decreased fouling removal rate (Jeurnink et al., 1996). Concerning starch fouling, the concentrations of alkali needed vary between 9 and 20% (w/v) (Bird, 1994), which is quite different from those for dairy processes. Therefore, the applied concentration depends on the type of fouling.
An increase in temperature results in increased efficiency. However, for dairy processes at temperatures above 80 ëC the opposite effect can be achieved, as proteins coagulate, resulting in an increase of fouling instead of a decrease. In addition, for all processes, cleaning at temperatures above 80 ëC results in higher energy consumption use without extra cleaning benefit and can lead to damage to the equipment (corrosion). An optimal working temperature therefore is around 70 ëC. In combination with the 0.5% alkaline solution (for dairy environments) this is sufficient to inactivate any vegetative pathogenic microorganism (Jeurnink et al., 1996). In the case of membrane systems even lower temperatures (40±60 ëC) are advised, owing to the rather vulnerable composition of the membranes and its modules (Shorrock et al., 1998).
Contact time is the third important parameter of disinfection processes. The longer the contact time, the greater the number of microorganisms inactivated.
In most cases there is a direct link between contact time and concentration.
There are various models predicting the inactivation of a disinfectant but not all of them are easy to use (e.g. too many unknown parameters). In general, the simple Chick-Watson (1908) log-linear model is used (Lambert and Johnston, 2000; Kamase et al., 2003; Cho et al., 2003):
log N1 N0
ÿkCnt 4:1
where N1 number of surviving microorganisms, N0 initial number of microorganisms, k disinfection rate constant, C disinfectant concentration, n dilution coefficient and t contact time.
The dilution coefficient (n) differs per type of disinfectant. For example, for QACs n 1, which implies that by halving the concentration the contact time (t) is doubled. For ethanol n 10 which implies an efficiency reduction by a factor of 210(= 1024) when halving the concentration (Krop, 1990).
The effect of mechanical action is obvious; the more mechanical energy is put into the removal of the fouling the more efficiently the fouling will be Pathogen resistance to sanitisers 79
removed, thus a more efficient cleaning process is obtained (Gibson et al., 1999). However, there is a limit, as too much mechanical action (e.g. by using metal scrubbing devices) may cause damage to the cleaned object/surface.
The final effect depends on the right combination of the conditions discussed.
However, it remains possible to choose for different combinations of conditions as long as `the sum' of the conditions will be the same, e.g. a reduction of the concentration can be compensated by an increase in time or mechanical action (Krop, 1990).
4.3.3 Influence of neutralising components
Prior to disinfecting, the equipment or surface to be treated should not contain any components that can inactivate the disinfectant. Organic matter (e.g. food residues, milk stone, blood) are well known for their neutralising effect. In general these organic materials interfere by reacting with the biocide, leaving a reduced concentration of antimicrobial agent for attack on microorganisms. In addition to organic materials, surface-active agents and metal ions can act as an interfering substrate (Russell, 1999a).
4.3.4 Monitoring
As shown, a lot of characteristics concerning the application of disinfectants and the inactivation of microorganisms are known. But knowledge does not guarantee appropriate control of the process. Thorough analysis of available data is necessary to make the right decision with regard of type of disinfectant, process conditions and required effect. Monitoring devices to analyse cleaning and disinfection processes, and databases containing inactivation kinetics of relevant microorganisms in combination with predictive knowledge can be a great help in optimising relevant processes.
With regard to monitoring, OPTICIP, a monitoring device to make and optimise cleaning-in-place (CIP) procedures can be applied (van Asselt and te Giffel, 2002). A typical cleaning procedure of an evaporator before and after optimisation is shown in Fig. 4.1. The system monitors the removal of organic and inorganic fouling off-line in combination with the in-line measurement of parameters such as temperature, flow, conductivity and valve settings. The turbidity of the cleaning solution is a measure for the removal of organic fouling.
The calcium concentration is a measure for the removal of inorganic fouling.
Conductivity measurement is used for separation of the various cleaning phases and gives an indication of the concentration of the cleaning solution used. Sharp slopes between subsequent phases indicate that rinsing and cleaning phases are properly separated (van Asselt et al., 2002). More simplified systems are also available. Johnson-Diversey introduced `Shurlogger', a real-time CIP monitoring system based on flow, temperature and conductance (Dodd, 2003).
However, the fouling removal is not taken into account. Therefore, this system gives less detailed analyses compared to OPTICIP.
80 Handbook of hygiene control in the food industry
Fig. 4.1 OPTICIP, a monitoring device to make and optimise CIP.
A quick monitoring device is the application of ATP as measurement for remaining microorganisms and/or organic substances. The principle is based on the fact that every organic cell contains ATP as energy carrier. The reaction of the enzyme luciferase with ATP results in the emission of light that can be measured by a specific light-measuring device. The more light is emitted the more ATP was present and the more the surface or liquid was contaminated with microorganisms or organic matter. It is even possible to differentiate between microbial and organic ATP. A disadvantage of this method is that the detection limit is relatively high. The minimum concentration of microorganisms is approx. 103±104cfu mlÿ1 (Moore et al., 2001) before this method becomes reliable whereas this amount is already crossing the limit of levels of con-tamination. Thus, measuring ATP is suitable for a quick inventory of the cleanliness of equipment or rinsing water. The method is not applicable to determine the antimicrobial activity of disinfecting agents.
A different way of optimising cleaning and disinfecting processing concerns the combination of databases and predictive modelling. NIZO PremiaTMis an example that combines research knowledge with predictive modelling. It is a software platform that is used for optimisation of product properties or process performance. For example, fouling is mainly caused by denaturation of proteins and precipitation of minerals. The denaturation process of -lactoglobulin (an important whey protein) can be described as a consecutive set of reactions (de Jong, 1996). This knowledge can be used to predict the fouling behaviour in heat exchangers of different dairy-type products. By predicting the amount of fouling produced, the optimum running time for heat exchangers can be determined. In addition the composition of the fouling layer is known which makes it possible to choose the right cleaning procedures (cleaning agents, temperature, etc.).
After optimisation with NIZO Premia it appeared possible to reduce the amount of fouling by 50±80%, resulting in longer running times and higher process efficiency (de Jong et al., 2002). Another possibility is using predictive modelling for the design of new processing lines, making the effects visible concerning fouling and product properties. A typical example is the develop-ment of a new type of evaporator at a Dutch dairy company where the use of NIZO Premia resulted in 70% less energy use compared with standard designed evaporators (Vissers et al., 2002).
Thus, predictive modelling is a powerful tool to analyse and optimise critical processes within the food industry.