Unit 10: Bacterial Growth Patterns- Direct Count, The Standard Plate Count, and Indirect Turbidimetric Methods - Biology

Unit 10: Bacterial Growth Patterns- Direct Count, The Standard Plate Count, and Indirect Turbidimetric Methods - Biology

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Unit 10: Bacterial Growth Patterns- Direct Count, The Standard Plate Count, and Indirect Turbidimetric Methods

Unit 10: Bacterial Growth Patterns- Direct Count, The Standard Plate Count, and Indirect Turbidimetric Methods - Biology

Methods for Measurement of Cell Numbers

Measuring techniques involve direct counts, visually or instrumentally, and indirect viable cell counts.

1. Direct microscopic counts are possible using special slides known as counting chambers. Dead cells cannot be distinguished from living ones. Only dense suspensions can be counted (>10 7 cells per ml), but samples can be concentrated by centrifugation or filtration to increase sensitivity.

A variation of the direct microscopic count has been used to observe and measure growth of bacteria in natural environments. In order to detect and prove that thermophilic bacteria were growing in boiling hot springs, T.D. Brock immersed microscope slides in the springs and withdrew them periodically for microscopic observation. The bacteria in the boiling water attached to the glass slides naturally and grew as microcolonies on the surface.

2. Electronic counting chambers count numbers and measure size distribution of cells. For cells the size of bacteria the suspending medium must be very clean. Such electronic devices are more often used to count eucaryotic cells such as blood cells.

3. Indirect viable cell counts, also called plate counts, involve plating out (spreading) a sample of a culture on a nutrient agar surface. The sample or cell suspension can be diluted in a nontoxic diluent (e.g. water or saline) before plating. If plated on a suitable medium, each viable unit grows and forms a colony. Each colony that can be counted is called a colony forming unit (cfu) and the number of cfu's is related to the viable number of bacteria in the sample.

Advantages of the technique are its sensitivity (theoretically, a single cell can be detected), and it allows for inspection and positive identification of the organism counted. Disadvantages are (1) only living cells develop colonies that are counted (2) clumps or chains of cells develop into a single colony (3) colonies develop only from those organisms for which the cultural conditions are suitable for growth. The latter makes the technique virtually useless to characterize or count the total number of bacteria in complex microbial ecosystems such as soil or the animal rumen or gastrointestinal tract. Genetic probes can be used to demonstrate the diversity and relative abundance of procaryotes in such an environment, but many species identified by genetic techniques have so far proven unculturable.

Table 1. Some Methods used to measure bacterial growth

Method Application Comments
Direct microscopic count Enumeration of bacteria in milk or cellular vaccines Cannot distinguish living from nonliving cells
Viable cell count (colony counts) Enumeration of bacteria in milk, foods, soil, water, laboratory cultures, etc. Very sensitive if plating conditions are optimal
Turbidity measurement Estimations of large numbers of bacteria in clear liquid media and broths Fast and nondestructive, but cannot detect cell densities less than 10 7 cells per ml
Measurement of total N or protein Measurement of total cell yield from very dense cultures only practical application is in the research laboratory
Measurement of Biochemical activity e.g. O2 uptake CO2 production, ATP production, etc. Microbiological assays Requires a fixed standard to relate chemical activity to cell mass and/or cell numbers
Measurement of dry weight or wet weight of cells or volume of cells after centrifugation Measurement of total cell yield in cultures probably more sensitive than total N or total protein measurements

Figure 2. Bacterial colonies growing on a plate of nutrient agar. Hans Knoll Institute. Jena, Germany.


In the direct microscopic count, a counting chamber consisting of a ruled slide and a coverslip is employed. It is constructed in such a manner that the coverslip, slide, and ruled lines delimit a known volume. The number of bacteria in a small known volume is directly counted microscopically and the number of bacteria in the larger original sample is determined by extrapolation.

The Petroff-Hausser counting chamber (commonly used in dairy industry) for example, (Figure 16. 1) has small etched squares 1/20 of a millimeter (mm) by 1/20 of a mm and is 1/50 of a mm deep. The volume of one small square therefore is 1/20,000 of a cubic mm or 1/20,000,000 of a cubic centimeter (cc). There are 16 small squares in the large double-lined squares that are actually counted, making the volume of a large double-lined square 1/1,250,000 cc. The normal procedure is to count the number of bacteria in five large double-lined squares and divide by five to get the average number of bacteria per large square. This number is then multiplied by 1,250,000 since the square holds a volume of 1/1,250,000 cc, to find the total number of organisms per cc in the original sample. If the bacteria are diluted, such as by mixing the bacteria with dye before being placed in the counting chamber, then this dilution must also be considered in the final calculations.

The formula used for the direct microscopic count is

The number of bacteria per cc = The average number of bacteria per large double-lined square X The dilution factor of the large square (1,250,000) X The dilution factor of any dilutions made prior to placing the sample in the counting chamber, e.g., mixing

16.2.2 Electronic enumeration of cells

A Coulter counter (Fig.16.2) is an apparatus for counting and sizing particles and cells. It is used, for example, for bacteria and air quality particle size distributions. The counter detects change in electrical conductance of a small aperture as fluid containing cells is drawn through. Cells, being non-conducting particles, alter the effective cross-section of the conductive channel.

It was an American inventor named Wallace H. Coulter who was responsible for the theory and design of the Coulter Counter. He first devised the theory behind its operation in 1947 while experimenting with electronics. Coulter determined that electrical charge could be used to determine the size and number of microscopic particles in a solution. This phenomenon is now known as the Coulter Principle. A typical Coulter counter has one or more microchannels that separate two chambers containing electrolyte solutions. When a particle flows through one of the microchannels, it results in the electrical resistance change of the liquid filled microchannel. This resistance change can be recorded as electric current or voltage pulses, which can be correlated to size, mobility, surface charge and concentration of the particles.

Normally, the bacterial sample is diluted by factors of 10 and plated on agar either by pour plate or spread plate technique. After incubation, the number of colonies on a dilution plate showing between 30 and 300 colonies (Fig. 16.4 and Fig. 16.5) is determined. A plate having 30-300 colonies is chosen because this range is considered statistically significant. If there are less than 30 colonies on the plate, small errors in dilution technique or the presence of a few contaminants will have a drastic effect on the final count. Likewise, if there are more than 300 colonies on the plate, there will be poor isolation and colonies will have grown together.

Fig 16.4 Pour plate and spread plate techniques

Fig.16.5 Pour plate and spread plate methods

  • Only living cells develop colonies that are counted
  • Clumps or chains of cells develop into a single colony
  • Colonies develop only from those organisms for which the cultural conditions are suitable for growth.

Table 16.1 Comparison of various methods of measurement of bacterial growth

16.2.4 Membrane filter count method

Fig. 16.6 Membrane filtration method

16.2.5 Turbidity measurement methods

When bacteria growing in a liquid medium are mixed, the culture appears turbid. This is because a bacterial culture acts as a colloidal suspension that blocks and reflects light passing through the culture. Within limits, the light absorbed by the bacterial suspension will be directly proportional to the concentration of cells in the culture. By measuring the amount of light absorbed by a bacterial suspension, one can estimate and compare the number of bacteria present. The instrument used to measure turbidity is a spectrophotometer (Fig. 16.8). It consists of a light source, a filter which allows only a single wavelength of light to pass through, the sample tube containing the bacterial suspension, and a photocell that compares the amount of light coming through the tube with the total light entering the tube. The ability of the culture to block the light can be expressed as either percent of light transmitted through the tube or the amount of light absorbed in the tube. The percent of light transmitted is inversely proportional to the bacterial concentration. The absorbance (or optical density) is directly proportional to the cell concentration.

Fig. 16.8 Turbidity measurement using spectrophotometer

  • Several dilutions can be made of a bacterial stock.
  • A Petroff-Hausser counter can then be used to perform a direct microscopic count on each dilution.
  • Then a spectrophotometer can be used to measure the absorbance of each dilution tube.
  • A standard curve comparing absorbance to the number of bacteria can be made by plotting absorbance versus the number of bacteria per cc.
  • Once the standard curve is completed, any dilution tube of that organism can be placed in a spectrophotometer and its absorbance read. Once the absorbance is determined, the standard curve can be used to determine the corresponding number of bacteria per cc.

16.2.6 Determination of nitrogen content

16.2.7 Determination of dry weight

It is also possible to follow the change in the amount of a cellular component instead of the entire mass of the cell. This method may be chosen because determining dry weights is difficult or when the total weight of the cell is not giving an accurate picture of the number of individuals in a population. In this case, only one component of the cell is followed such as total protein or total DNA. This has some of the same advantages and disadvantages listed above for dry weight. Additionally, the measurement of a cellular component is more labor-intensive than previously mentioned methods since the component of interest has to be partially purified and then subjected to an analysis designed to measure the desired molecule. The assumption in choosing a single component such as DNA is that that component will be relatively constant per cell. This assumption has a problem when growth rates are different because cells growing at high rates actually have more DNA per cell because of multiple initiations of replication.

The bacterial growth can be indirectly estimated by detecting specific changes caused in growth medium as a result of activity and multiplication of bacterial cells. It includes detecting activity cell products such as acid and gas production. The dye reduction tests such as methylene blue and resazurin reduction tests is based on the fact that the color imparted to milk by the addition of a dye such as methylene blue will disappear more or less quickly. The removal of the oxygen from milk and the formation of reducing substances during bacterial metabolism cause the color to disappear. The agencies responsible for the oxygen consumption are the bacteria. Though certain species of bacteria have considerably more influence than others, it is generally assumed that the greater the number of bacteria in milk, the quicker will the oxygen be consumed, and in turn the sooner will the color disappear. Thus, the time of reduction is taken as a measure of the number of organisms in milk although actually it is likely that it is more truly a measure of the total metabolic reactions proceeding at the cell surface of the bacteria. Gas production by bacteria is another major activity which can be taken up as an index of bacterial growth. Detection of gas production using Durham tube and change in color of the growth medium due to reduction of pH sensitive ingredients present in medium are commonly used for detection of acid and gas producing coliforms and yeasts. An apparatus for measuring CO2 production is depicted in (Fig.16.9)

Types of Techniques

Pour plate Technique

A pour plate is a method of melted agar inoculation followed by petri dish incubation. The known volume (usually 0.1-1.0 ml) of culture is pipetted into a sterile Petri plate melted agar medium is then added and mixed well by gently swirling the plate on the tabletop. Because the sample is mixed with the molten agar medium, a larger volume can be used than with the spread plate. However, with the pour plate method, the organism to be counted must be able to briefly withstand the temperature of melted agar, 45°C. After solidification of the gel, the plate is inverted and incubated at 37°C for 24-48 hours.

Colonies form within the agar matrix rather than on top as they do when streaking a plate. Pour plates are useful for quantifying microorganisms that grow in a solid medium. Because the “pour plate” embeds colonies in agar, it can supply a sufficiently oxygen-deficient environment that can allow the growth and quantification of microaerophiles.

Spread Plate Technique

Spread plate technique is one of the methods of quantifying microorganisms on a solid medium. With the spread plate method, a volume of an appropriately diluted culture usually no greater than 0.1 ml is spread over the surface of an agar plate using a sterile glass spreader. The plate is then incubated until the colonies appear, and the number of colonies counted. Instead of embedding microorganisms into agar, as is done with the pour plate method, liquid cultures are spread on the agar surface.

An advantage of spreading a plate over the pour plate method is that cultures are never exposed to 45°C (i.e. melted agar temperatures).
Note: Surface of the plate must be dry so that the liquid that is spread soaks in. Volume greater than 0.1ml is rarely used because the excess liquid does not soak in and may cause the colonies to coalesce as they form, making them difficult to count.

Streak Plate Technique

For organisms that grow well on agar plates, a streak plate is the method of choice for obtaining pure culture.

The key principle of this method is that, by streaking, a dilution gradient (number of cells decrease as they move across the agar and away from the point of inoculation) is established across the face of the plate as bacterial cells are deposited on the agar surface. Because of this dilution gradient, confluent growth occurs on part of the plate where the bacterial cells are not sufficiently separated in other regions of the plate where few bacteria are deposited separate macroscopic colonies develop that can easily be seen with naked eye. Each well isolated colony is assumed to arise from a single bacterium and therefore to represent a clone of a pure culture.

Purpose of Streak Plate Technique: The purpose of the streak plate is to obtain isolated colonies from an inoculum. Isolated colonies represent a clone of cells, being derived from a single precursor cell. Many different streaking patterns can be used to separate individual bacterial cells on the agar surface.

Analytical approaches for quantification of biomass

Depending on the constraint of a selected method for quantification, it will be possible to perform an analysis of a target compound by using offline, at-line, and/or online sampling approaches. Offline biomass quantification approaches are well applied and already approved in biotechnological processes, although the workload to get to the desired result is enhanced compared to at-line or online quantification approaches. Further, offline strategies entail the risks of contaminating the cultivation vessel or the sample itself. At-line measurements have one major advantage over traditional offline techniques the sampling is performed automatically in prescribed intervals. Respectively, at-line biomass quantification is close to real-time analysis. If the installations of online quantification devices are not feasible due to technical issues, available space or financial reasons at-line measurement applications could be applied. Besides that, online biomass quantification approaches are preferred over offline and at-line strategies. At-line biomass quantification approaches reduce the amount of work involved in sampling, even though there are some essential points which have to be considered as they are (1) the transfer of the sample to the measurement device, (2) conditions during the transfer, (3) the homogeneity of the sample, (4) the representativeness of the sample for the whole cultivation, and (5) the recycling of the sample (bypass loop) or the discharge of the sample after measuring.

Online biomass quantification approaches have plenty of advantages over offline and at-line strategies. Sampling and the transfer to the measurement device is being circumvented since the measurement is performed directly in the cultivation vessel or bioreactor. Therefore, no time delay between sampling and measurement in addition of the analysis time itself has to be taken into account for data analysis (Vojinović et al. 2006). The direct measurement in the cultivation vessel can potentially reduce also the risk of contaminating the bioreactor and the possible intoxication of the operator by toxics compounds or pathogenic microorganism (Höpfner et al. 2010). But for that, CIP (clean in place) and SIP (sterilization in place) strategies has to be validated with the compatibility of the used measuring device. Besides that, the bioreactor volume needs to be sufficient for a given measuring device and the recalibration of the equipment has to be considered. In continuous processes, analytic probes/equipment can also be placed in a bypass loop in order to facilitate recalibration and exchange in case of failure, but the representativity of the sample needs to be assessed even though data interpretation and data validation can be challenging. Real-time monitoring gives direct insight into a bioprocess and further information about specific productivities and total yield (Sandnes et al. 2006). Online sensors stand out with their flexible and detailed data processing, while the analyte remains unaffected. Depending on the required information, whether gaining additional data about the concentration of medium components beside the biomass concentration or the viability of a culture, different sensors can be individually introduced into a bioreactor system.

Offline biomass quantification approaches

Biomass quantification can be performed by applying several different methods, all possessing some advantages or disadvantages. Especially when working with offline biomass quantification approaches, washing and purification steps have sometimes to be encountered to be able to quantify the amount of produced biomass.

In this review, we will categorize offline techniques into five different subsections:

Direct cell counting

Direct cell counting sums up all methods based on the enumeration of detectable cells within a liquid medium and consists of:

Fluorescence Activated Cell Sorting (FACS)

Microscopic enumeration

Microscopic enumeration is another term for cell counting. Counting of single cells can be performed by using different approaches. One of the most common approaches is microscopic enumeration that can either rely on using a membrane filter sampling technique (Brock 1983), followed by a cell or nucleus staining procedure (Koch 2007), or by using a counting chamber. Counting chambers are a well applied microbiological tool to directly count cells. Depending on the microorganism, different counting chambers and microscope settings can be applied. For counting bacteria, commonly counting chambers with counting chambers depth of 0.02 mm are used, whereas for counting larger microbes like yeast or algae, a counting chamber depth of 0.1 mm should be preferably applied (Bast 2001b). The two main disadvantages of direct cell counting are the reproducible filling of the counting chamber and the adherence of cells on the glassware surfaces and pipette tip. The market offers a great variety of counting chambers which usually differs in the applicable volume, design of the counting grids and compatibility with different objectives. Besides that, every counting chamber is calibrated for specific objective types. For instance, Neubauer counting cambers are suited for high-dry objectives (Talking et al. 2014), whereas Hawksley counting chambers can be used under oil-immersion objectives (Koch 2007) which are, e.g. more suited for counting small-sized cells. However, without using a cell staining method, distinction between viable, dormant, and dead cells is not possible (Talking et al. 2014). The use of a counting chamber is eased when applying autofluorescent strains. This approach enhances the visibility of cells by excitation of cellular compounds at a specific wavelength, e.g. the UV-inducible blue-green autofluorescence of microorganisms. Many H2-utilizing methanogens can be counted by exposing them to an UV light, subsequently strain originated autofluorescence is induced by special cofactors. Coenzyme F420 absorbs light at a wavelength of 420 nm and emits blue-green light, which can be detected by a fluorescence microscope (Solera et al. 2001 Kumar et al. 2011). Deazaflavin F420 functions as an essential coenzyme within the methanogenesis pathway. The reduced form of F420 (F420H2) functions as an electron donor for methylenetetrahydromethanopterin dehydrogenase (Mtd), cysteine-containing F420-reducing hydrogenase (Frc), and for selenocysteine-containing F420-reducing hydrogenase (Fru) (Hendrickson and Leigh 2008). However, due to their low coenzyme F420 content, counting of acetoclasic methanogens is rather difficult (Kamagata and Mikami 1991 Solera et al. 2001). Another aspect that needs to be considered when applying these enumeration methods is the aggregation state of biomass. For example, Methanosarcina spp. may form aggregates under certain environmental conditions, which complicates counting of single cells by autofluorescence cell enumeration (Solera et al. 2001). Counting autofluorescent methanogens during cultivation in bioreactors is frequently used (Ahn et al. 2000 Solera et al. 2001). The autofluorescence of methanogens could also be used to distinguish methanogens in co-cultures from other microbes which do not express the coenzyme F420.

Electronic enumeration

Electronic enumeration of cells is another approach for determining the cell number. The Coulter counter is routinely used in clinical hematology and for the enumeration of non-filamentous yeast and protozoa. However, this technique is hard to apply to bacteria and other microbes with similar morphological characteristics, like small cell size and elongated shape (Kubitschek 1969).

FACS allows the measurement of scattered light and fluorescence emissions produced by illuminated single cells that are passing through a capillary that is intersected by a laser beam. Once a cell passes through a beam of light a signal is produced. The scattered light and fluorescence emissions of each cell are collected by detectors and are further processed in silico. The in silico process allows the distribution a population with respect to different parameters measured by a given equipment. Forward scattered light, collected in the same direction as the incident light, is related to cell size. Collected side scattered light (angle of 90°) provides information of cell surface properties and internal structure of the cell. Information concerning the cell is obtained by staining the sample with different fluorochromes (Álvarez-Barrientos et al. 2000 Lehtinen 2007). Most FACS have been limited to aerobic microbial systems due to the oxygenated atmosphere of the sort stream and the cell deposition. To test the viability and sort cells, a BD (BD Bioscience) Influx cell sorter was modified for anaerobic working conditions by purging O2 from the sort stream and cell deposition areas (Thompson et al. 2015). This group showed the utility of this device for separating anaerobic target populations from co-cultures, however the method can easily be expanded to the isolation, genotyping, and cultivation of anaerobic microorganisms sorted from complex natural communities.

Colony counting

The amount of viable microorganisms can be elucidated by colony counting (Hungate 1969). This technique can be performed by

Spreading the diluted sample over a solid agar (spread plate method)

Pipetting the culture into a sterile Petri plate and mixing it with molten agar medium (pour plate method) (Postgate 1969)

Pipetting a sample into a small amount of molten but cool agar medium (bearable temperature for the microbe), followed by pouring the mixture onto a sterile agar plate, allowing it to harden (thin layer plates)

Using the thin layer technique, but adding another agar layer on top of it (layered plates)

Filtering the diluted sample with a pre-sterilized filter and placing it onto the sterile agar medium plate (membrane filter method).

In anaerobic microbiology, all these techniques are utilized, but compared to aerobic conditions they require some additional precautions. For solid media, the execution of the colony counting methods (b.1–b.5) has to be carried out under anaerobic conditions. This can be realized by making the media anoxic, counting in a glove box or by using an anoxic chamber for inoculation. In most cases, the samples have to be diluted before plating to obtain an adequate quantity of colony-forming units (CFUs). This number generally lies between 30 and 300 colonies per plate (Sutton 2011). Dilution of samples is a sensitive step since it needs to be compatible with the physiological requirements of the microbe in respect to pH and osmolality (Koch 2007). After preparing and incubating the agar plates, CFUs may be determined by using an appropriate period. However, CFUs mostly consists out of more than one initial starting cell, which must be considered as well (Li et al. 1996 Lehtinen 2007 Madigan et al. 2012). The techniques in this section can only detect viable and culturable microorganisms. Dormant, non-culturable microbes, and microorganisms with very low μ are not detected with the previously described methods (Barer and Harwood 1999 Oliver 2005).

The concentration of viable cells in culture can be estimated by applying the MPN method. The amount of proliferating microbes is determined with MPN by the amount of dilutions, where growth is observable (Kott 1966). This method is based on statistics. MPN has already been applied for anaerobes, especially for estimating the methanogenic population in an anaerobic thermophilic digester and a mesophilic soil sample (Wagner et al. 2012).

Biomass measurement methods

Sometimes it might be preferred to assess the cell mass instead of the real number of cells. Biomass can be measured by determining wet weight or dry weight of a culture sample (Tisa et al. 1982 Guerrero et al. 1985). Cell dry weight is determined by drying pelleted biomass for a defined period of time with approximately 105 °C in glass eprouvettes (Koch 2007), subsequently followed by cooling in a desiccator and weighing. As dry mass corresponds to 10–20% (m/v) of the wet mass (Madigan et al. 2012), also the wet mass can be determined. Wet mass can simply be obtained after centrifugation of the sample and removing of the supernatant (Tisa et al. 1982 Troller 1989). After this process, a packed cell pellet remains, which should be weighed to determine the wet mass (Tisa et al. 1982). The quantification of biomass dry or wet weight can be correlated to other biomass quantification approaches such as spectrophotometry. Furthermore, for improving bioprocess quantification, the elementary composition of biomass can be determined (Mauerhofer et al. 2018) to balance growth stoichiometry on an elemental molar basis.

Light scattering

Light scattering methods are mostly used to monitor the growth of pure cultures (Günther and Bergter 1971). However, methods based on light scattering give mainly information corresponding molecular content/ dry weight and not about the number of cells (Koch 1970). The cell biomass can be estimated through the turbidity of a culture, which is measured with a photometer (fix wavelength) or spectrophotometer (whole wavelength spectrum). The principle of this measurement is based on the absorption of light by cells in the suspension at a certain wavelength but only unscattered light is detected. The amount of cells in the light path decreases the intensity of the incident light beam and gives an indirect correlation of the amount of biomass in the sample. The method of turbidity measurements is better known as determination of the optical density (OD) (Koch 1970 Koch 2007). The more cells are in the suspension the more light is scattered or absorbed and less light can be detected (Madigan et al. 2012). This correlation is described by the Beer–Lambert law, see Eq. 2 (Bast 2001b). The Beer–Lambert law is empirically valid only for OD values < 0.5 (Locher et al. 1992) because of light scattering effects increase with higher cell density. The incoming light beam gets initially scattered by the cells (primary scattered light). If the amount of cells is too high, the possibility for scattering already scattered light (secondary scattered light) is increased, which results in measuring lower OD values than the real extinction value. However, with the preparation of standard curves and appropriate dilution series measuring up to higher OD values is possible (Bast 2001b). A relation between the cell dry weight and the absorbance was found to be directly proportional and shows a linear correlation (Koch 1961).

In Equation 2 Φex (W m −2 ) is the intensity of the incident light, Φin (W m −2 ) is the intensity of outgoing light, s(m 2 mol −1 ) is described as the scattering coefficient, c(mol L −1 ) is the concentration of the cell suspension, and d(m) is the layer thickness. Offline turbidity measurements are being executed by an external photometer. Therefore, a small amount of biomass (up to 1 mL) has to be harvested, further transferred into a dedicated cuvette, and measured at a proper wavelength. Microplate systems in contrary to cuvette spectrophotometers allow measurements even with 100 μL of harvested suspension (Stieber et al. 1994 Turcotte et al. 2004). Investigations on different spectrophotometers showed a high dependency in the OD measurements in respect to geometry and the optical design resulting in different OD values for the same cell suspension. This has to be taken into account when performing measurements with different systems. OD measurements can only be compared when measuring with one specific spectrophotometer. Then OD-based biomass quantification can be correlated to other offline biomass quantification methods. However, the correlation of biomass concentration to light scattering must be individually determined for each organism and growth media. Moreover, the correlation is only valid in a specific range as discussed above.

When performing OD measurements, medium characteristics have to be taken in account, since quantification of microbes within the medium could be affected. Some medium components could impede the quantification of microbes via light scattering, especially when working with dark samples from a digester or manure plant. To overcome darkness, samples including blank could be diluted, which have to be considered later when elucidating the amount of cells. If a dilution is not realizable, due to immense microbial biomass loss, other biomass determination techniques have to be investigated.

At-line biomass measurement

At-line measurements represent an improvement over traditional offline methods and are close to real-time analysis of course the ideal approach is monitoring online, preferably in situ. However, the installation of online measuring devices is not feasible at each bioprocess condition.

Commonly anaerobic digestion plants are regulated based on at-line or offline analytical results (Madsen et al. 2011). By applying an at-line attenuated total reflectance-mid-infrared (ATR-MIR) spectroscopy, ammonium, glucose, methyl oleate, and biomass were investigated in a complex antibiotic fermentation process using Streptomyces clavuligerus (Roychoudhury et al. 2006). At-line information gathered from flow cytometry can also be used to change the biofuel production control strategy to enhance the process yield (da Silva et al. 2012). In principle, almost every measuring deceive can be installed at-line.

Online biomass measurement

The most common in situ measurement devices (Vojinović et al. 2006 Kiviharju et al. 2008 Höpfner et al. 2010) are as follows:

Fluorescence optical sensors

Other spectroscopic sensors

Optical sensors

Measurements of biomass with optical sensors are either based on transmission or backscattering. Probes based on the backscattering principle do not show any limitation in case of increasing biomass concentration compared to transmission probes. Visible optical sensors can produce erroneous responses caused by cell morphology, or interfering gas bubbles (Ulber et al. 2003 Vojinović et al. 2006). Other suspended effects, and the necessity for cleaning of optical sensors are common problems of these probes (Locher et al. 1992). Individual calibration for optical sensors is recommended since the signals depend strongly on the cell morphology. Measurements of cell dry weight and optical online methods showed different correlations according to the investigated strains (Ude et al. 2014).

Fluorescence optical sensors

Fluorescence optical sensors can be employed to measure lifetime fluorescence emitted by microbes in a culture. When applying this method, only viable cells in the population can be detected. In active and living cells NAD(P)H plays an important role for the electron transfer from electron donor to electron acceptor. The signal and amount of NAD(P)H in a biological system was found to correlate with the biomass concentration (Coppella and Rao 1990 Farabegoli et al. 2003). This technique is limited respectively to inferences from medium compounds that emit or absorb between 360 and 450 nm. Therefore, only well-defined medium compositions can be used when applying optical sensors (Marose et al. 1999). Possible interferences by several fluorophores (e.g. FAD, NAD, NADH) can be circumvented with 2D absorption/emission fluorescent spectra measurements or multi-wavelength fluorometry (Morel et al. 2004 Vojinović et al. 2006 Kiviharju et al. 2008). The robustness and the capability of measuring intracellular effects as well as their rapidity in measuring of fluorescent samples are the main advantages of these systems (Locher et al. 1992).

Other spectroscopic sensors

Infrared spectroscopy

Spectroscopic sensors are commonly used to detect infrared light within a range of 0.74–1.00 nm (Landgrebe et al. 2010). Infrared spectroscopy is an analytical technique which is used to analyze a wide variety of organic compounds, substrates, products, metabolites, and biomass. This method is based on molecular vibrations of organic compounds, which have spectral signatures that belong to the infrared domain (Landgrebe et al. 2010). The infrared light is subdivided into three regions: far infrared (FIR), mid-infrared (MIR) and near-infrared (NIR) region. To monitor bioprocesses, two spectroscopic sensor types are available, MIR and NIR probes (Olsson and Nielsen 1997 Landgrebe et al. 2010). Microbial growth can be either measured via light absorption (turbidity) or light scattering (nephelometry) in the visible and NIR ranges (Marose et al. 1999). NIR shows the best correlation between wavelength and biomass at 2300 nm. The majority of media do not absorb light in this NIR region (2300 nm) (Olsson and Nielsen 1997 Marose et al. 1999).

Electrochemical impedance spectroscopy

Low frequency electrochemical impedance spectroscopy (EIS) can be used as an online process tool to monitor viable cell concentrations during cultivations. Via EIS, the relative permittivity between two electrodes affected by cells with an integer cell membrane is detected. This signal is in turn correlated to cell dry weight measurement of the organism of interest. Thus, estimation of viable cell concentration can be conducted. The proposed technique has a high dynamic range from low to high cell densities beyond 40 g/L -1 cell dry weight with low background interferences (Slouka et al. 2016).

Modeling of growth kinetics

Modeling is a powerful tool to get insight into a biological bioprocess. Modeling concepts are mentioned below:

Estimation of volumetric mass bio-density

State estimation

Real-time monitoring of physiological characteristics such as biomass, product, substrate, and precursor concentrations during cultivation is of great importance during biotechnological processes. Particle filter algorithm could be applied for estimating these difficult-to-measure process states. The particle filter represents a new algorithmic framework, combining several already existing methods and techniques (online and offline) for state estimation (Kager et al. 2018).

Estimation of volumetric mass bio-density

The biological biomass density (biomass/bio-volume) referred as bio-density is a physiological variable that can be estimated by using dielectric spectroscopy and a soft sensor based on first principle elemental balances. The combination of both signals allows a real-time estimation of the bio-density during cultivation. Dielectric spectroscopy measures the permittivity of the fermentation broth in dual frequency mode, a high frequency accounting for non-cellular background and a low frequency accounting for the permittivity attributed to living cells. Dielectric spectroscopy estimates the biomass via correlating the permittivity signal, which reflects the encapsulated volume fraction of cells. Soft sensors are software algorithms that calculate non-measured process parameters from readily available process signals. Accurate estimation of the biomass concentration via elemental balancing can be performed. The application of this sensor allows a real-time calculation of specific rates and yield coefficients, which provides insight to physiological changes. The combination of both signals, dielectric spectroscopy and soft sensor, provides a possibility to estimate the volumetric mass (Ehgartner et al. 2014, 2017).

ADM1 model

The anaerobic digestion model No. 1 (ADM1) reflects the major processes steps during digestion and product formation, conversion of complex organic substrates into CH4 and CO2 and inert by-products (Batstone et al. 2002 Jimenez et al. 2015). The kinetic equations consider microbial growth and biomass decay. Therefore, the model incorporates seven microbial trophic groups. Growth of these groups is related to degradation rates of organic matter and is described by Monod-like dependencies. Also, inhibitive effects of pH, H2, ammonium, and fatty acids are considered by equations. The model includes the degradation of complex solids into carbohydrates, proteins, and fats, which get further hydrolyzed to sugars, amino acids, and VFAs. Carbohydrates and proteins are fermented to VFA (acidogenesis) and H2. Fatty acids are converted into acetate and H2. CH4 is produced by acetoclastic and autotrophic, hydrogenotrophic methanogenesis. The physicochemical equations describe ion association and dissociation, and gas–liquid transfer during the digestion process. This differential and algebraic equation set enables the determination of 26 dynamic state concentration variables, and 8 implicit algebraic variables per bioreactor vessel or element. For monitoring of the process, there are further 32 dynamic concentration state variables provided, based on differential equations (Batstone et al. 2002 Jimenez et al. 2015). The ADM1d model is an extension of the ADM1 model and describes biomass distribution within a one-compartment model (Mu et al. 2008).

Chapter 26 - Microbiological Control-Cooling System

SUEZ's microbiological control agents can help treat and protect cooling systems from a variety of micro-organisms and microbiological growth.

Cooling water systems, particularly open recirculating systems, provide a favorable environment for the growth of microorganisms. Microbial growth on wetted surfaces leads to the formation of biofilms. If uncontrolled, such films cause fouling, which can adversely affect equipment performance, promote metal corrosion, and accelerate wood deterioration. These problems can be controlled through proper biomonitoring and application of appropriate cooling water antimicrobials.

Microbiological fouling in cooling systems is the result of abundant growth of algae, fungi, and bacteria on surfaces. Once-through and open or closed recirculating water systems may support microbial growth, but fouling problems usually develop more quickly and are more extensive in open recirculating systems.

Once-through cooling water streams generally contain relatively low levels of the nutrients essential for microbial growth, so growth is relatively slow. Open recirculating systems scrub microbes from the air and, through evaporation, concentrate nutrients present in makeup water. As a result, microbe growth is more rapid. Process leaks may contribute further to the nutrient load of the cooling water. Reuse of wastewater for cooling adds nutrients and also contributes large amounts of microbes to the cooling system.

In addition to the availability of organic and inorganic nutrients, factors such as temperature, normal pH control range, and continuous aeration of the cooling water contribute to an environment that is ideal for microbial growth. Sunlight necessary for growth of algae may also be present. As a result, large, varied microbial populations may develop.

The outcome of uncontrolled microbial growth on surfaces is "slime" formation. Slimes typically are aggregates of biological and nonbiological materials. The biological component, known as the biofilm, consists of microbial cells and their by-products. The predominant by-product, extracellular polymeric substance (EPS), is a mixture of hydrated polymers. These polymers form a gel-like network around the cells and appear to aid attachment to surfaces. The nonbiological components can be organic or inorganic debris from many sources which have become adsorbed to or embedded in the biofilm polymer.

Slimes can form throughout once-through and recirculating systems and may be seen or felt where accessible. In nonexposed areas, slimes can be manifested by decreased heat transfer efficiency or reduced water flow. Wood-destroying organisms may penetrate the timbers of the cooling tower, digesting the wood and causing collapse of the structure. Microbial activity under deposits or within slimes can accelerate corrosion rates and even perforate heat exchanger surfaces.


The microorganisms that form slime deposits in cooling water systems are common soil, aquatic, and airborne microbes (see Figure 26-1). These microbes may enter the system with makeup water, either in low numbers from fresh water sources or in high numbers when the makeup is wastewater. Significant amounts may also be scrubbed from the air as it is drawn through the cooling tower. Process leaks may contribute microorganisms as well.

Bacteria. A wide variety of bacteria can colonize cooling systems. Spherical, rod-shaped, spiral, and filamentous forms are common. Some produce spores to survive adverse environmental conditions such as dry periods or high temperatures. Both aerobic bacteria (which thrive in oxygenated waters) and anaerobic bacteria (which are inhibited or killed by oxygen) can be found in cooling systems.

Fungi. Two forms of fungi commonly encountered are molds (filamentous forms) and yeasts (unicellular forms). Molds can be quite troublesome, causing white rot or brown rot of the cooling tower wood, depending on whether they are cellulolytic (attack cellulose) or lignin degrading. Yeasts are also cellulolytic. They can produce slime in abundant amounts and preferentially colonize wood surfaces.

Algae. Algae are photosynthetic organisms. Green and blue-green algae are very common in cooling systems (blue-green algae are now classified with the bacteria and are called cyanobacteria). Various types of algae can be responsible for green growths which block screens and distribution decks. Severe algae fouling can ultimately lead to unbalanced water flow and reduced cooling tower efficiency. Diatoms (algae enclosed by a silicaceous cell wall) may also be present but generally do not play a significant role in cooling system problems.

Differences and Similarities

Although algae, fungi, and bacteria differ in many respects, they also share many characteristics. These similarities and differences are important in understanding biofouling and its control.

  • Cell size differs according to the complexity of the cell structure. The simpler bacteria and cyanobacteria are much smaller than molds, yeasts, and other algae. Because of their faster metabolisms and rates of growth, these smaller cells are able to reproduce much more rapidly.
  • All microorganisms require water for growth. Although they vary in terms of absolute water requirements and ability to survive dry periods, an active, viable microbial population cannot exist without water.
  • Most microorganisms growing in cooling systems are bound by a rigid cell wall. The cell wall gives the organism its characteristic shape and provides mechanical strength. Immediately inside the cell wall is a cell membrane which functions as a permeability barrier for the cell. The barrier allows the cell to concentrate desirable chemicals, such as nutrients, and exclude or excrete toxic or unwanted chemicals, such as waste materials. Concentration gradients of several orders of magnitude can be established across the membrane. All cells must expend considerable amounts of metabolic energy to maintain an optimal interior condition. One essential characteristic of all microbes is the ability to preserve the necessary organization and integrity of the cell in a hostile and changing environment.
  • All cells must obtain energy and chemical "building blocks" from their environment in order to survive and grow. The ability of each type of cell to fulfill this function in different environments is discussed in the following section.

Microbiological Growth

Among the essential building blocks used by microbial cells, and those needed in largest quantity, are carbon, nitrogen, and phosphorus. Microbes differ in the method they use to obtain carbon. Green algae, cyanobacteria, and certain bacteria can utilize carbon dioxide as a sole carbon source and convert ("fix") it to cellular carbon compounds. Most bacteria, yeast, and molds require preformed carbon compounds and use organic molecules that range from very simple to very complex. In order to meet nitrogen requirements, microbes "fix" atmospheric nitrogen or utilize amines, nitrites, and nitrates present in the environment. Naturally occurring and synthetic inorganic and organic phosphates can be used to meet microbial phosphate requirements.

Microbes have developed many ways to extract energy from their surroundings. Algae and other photosynthetic organisms trap light energy from the sun. Inorganic chemicals, such as ammonia, sulfur, and hydrogen, can be oxidized by certain bacteria to release energy. More commonly, bacteria, yeasts, and molds liberate chemical energy stored in organic compounds, such as sugars, proteins, fats, oils, organic acids, and alcohols.

Aerobic organisms use oxygen to drive the oxidations that release chemical energy. Anaerobes do not use oxygen but may substitute molecules such as sulfate or nitrate in place of oxygen. In the anaerobic energy-yielding process, these oxidizing molecules are reduced, forming sulfides or nitrogen gas. When no acceptable oxidizer is available, some anaerobes can still generate energy, although less efficiently, by coupling oxidation of one half of a substrate molecule to reduction of the other half. Typically, the by-products of this "fermentative" reaction are various organic acids. All microbes extract and collect energy in small, usable packets. Once the energy is made available, there are only minor differences in how it is used.

In the presence of sufficient nutrients, growth and reproduction can occur. Bacteria and cyanobacteria multiply by binary fission, a process in which a cell divides to form two identical daughter cells. Yeasts divide by budding, with a mother cell repeatedly forming single, identical but much smaller daughter cells. Filamentous molds grow by forming new cells at the growing tip of the filament. Green algae can have several patterns of growth, depending on the species, ranging from tip extension to production of several cells from a single cell during one division cycle. As with other cell features, the complexity of growth processes also increases with increasing cell size. Under optimal conditions, some bacteria can double their numbers every 20 to 30 minutes, while molds can take many hours to double in mass.

Microorganisms are also extremely adaptable to changes in their environment. This characteristic is related to cell size and complexity. The simple forms with minimal growth needs and fast growth rates can form many cell generations within a few days. Slight random changes in cellular characteristics during those generations can produce a new cell that is more capable of surviving in a shifted environment. This new cell can soon dominate the environment. Many microbes carry information in unexpressed form for functions to be performed when needed for survival. Changes in the environment can activate this information causing all members of a microbial population to achieve new capabilities as a group, within a single generation.

Usually, cooling waters are not nutrient-rich, so microbes must expend a great deal of energy transporting and concentrating nutrients inside the cell. This process may spend energy resources already in short supply, but it is necessary to allow the biochemical machinery to run at top speed. Because there is strong competition for the available nutrients, those species most efficient at concentrating their essential nutrients will have the opportunity to grow most rapidly. The rate of growth will ultimately be limited by the nutrient which first falls below an optimal concentration, but this will not necessarily be the nutrient in the lowest concentration.

Chemicals applied to cooling systems may, at times, provide added sources of the limiting nutrient and thus contribute to microbial growth in the systems. Alterations of pH may shift a stable population balance to an unbalanced, troublesome state. Although bacteria may be under control at neutral pH, a shift to an acid pH may result in domination by molds or yeast. Because many algae grow most abundantly at an alkaline pH, an attempt to reduce corrosion by raising the pH can lead to an algal bloom.

Seasonal changes also affect growth patterns in cooling water systems. Natural algal communities in a fresh water supply are quite dynamic, and the dominant species can change rapidly with changing temperatures, nutrients, and amounts of sunlight. Cyanobacteria can often be primary colonizers in a cooling system. Seasonal changes which increase their numbers in the makeup water can lead to an algal bloom in the system. In autumn, as falling leaves increase the nutrient level and depress the pH, the bacterial population can increase at the expense of the algal population.

Microbiologists recognize two different populations of microorganisms. Free-floating (planktonic) populations are found in the bulk water. Attached (sessile) populations colonize surfaces. The same kinds of microorganisms can be found in either population, but the sessile population is responsible for biofouling.

Much is known about the formation of biofilms on wetted surfaces such as heat exchanger tubes. Microorganisms on submerged surfaces secrete polymers (predominantly polysaccharides but also proteins), which adhere firmly even to clean surfaces and prevent cells from being swept away by the normal flow of cooling water. These extracellular polymeric substances are hydrated in the natural state, forming a gel-like network around sessile microorganisms. This polymer network contributes to the integrity of the biofilm and acts as a physical barrier hindering toxic materials and predatory organisms from reaching the living cell (see Figure 26-2). Biofilm polymers can also consume oxidizers before they reach and destroy microorganisms. As a result, control of sessile microorganisms requires dosages many times greater than required to control planktonic organisms.

Biofilms develop slowly at first, because only a few organisms can attach, survive, grow, and multiply. As populations increase exponentially, the depth of the biofilm increases rapidly. Biofilm polymers are sticky and aid in the attachment of new cells to the colonized surface as well as the accumulation of nonliving debris from the bulk water. Such debris may consist of various inorganic chemical precipitates, organic flocs, and dead cell masses. Fouling results from these accumulative processes, along with the growth and replication of cells already on the surface and the generation of additional polymeric material by these cells.

When fouling occurs, even mechanical cleaning does not remove all traces of the biofilm. Previously fouled and cleaned surfaces are more rapidly colonized than new surfaces. Residual biofilm materials promote colonization and reduce the lag time before significant fouling reappears.

Biofilms on heat exchange surfaces act as insulating barriers. Heat exchanger performance begins to deteriorate as soon as biofilm thickness exceeds that of the laminar flow region. Microbes and hydrated biopolymers contain large amounts of water, and biofilms can be over 90% water by weight. As a result, biofilms have thermal conductivities very close to that of water and, in terms of heat transfer efficiency, a biofilm is the equivalent of a layer of stagnant water along the heat exchange surface.

In shell-and-tube heat exchangers, the resistance to heat transfer is least in the turbulent flow of the bulk phase, slightly greater across the metal tube walls, and greatest across laminar flow regions. As biofilm thickness increases, so does the apparent thickness of the laminar flow region. Like water, biofilms are 25 to 600 times more resistant to conductive heat transfer than many metals. A small increase in the apparent thickness of the laminar region due to biofilm growth has a significant impact on heat transfer. A thin biofilm reduces heat transfer by an amount equal to a large increase in exchanger tube wall thickness. For example, the resistance to heat transfer of a 1 mm thick accumulation of biofilm on a low carbon steel exchanger wall is equivalent to an 80 mm increase in tube wall thickness.

Biofilms can promote corrosion of fouled metal surfaces in a variety of ways (see Figure 26-3). This is referred to as microbially influenced corrosion (MIC) and is discussed further in Chapter 25. Microbes act as biological catalysts promoting conventional corrosion mechanisms:

  • the simple, passive presence of the biological deposit prevents corrosion inhibitors from reaching and passivating the fouled surface
  • microbial reactions can accelerate ongoing corrosion reactions
  • microbial by-products can be directly aggressive to the metal

The physical presence of a biofilm and biochemical activity within the film change the environment at the fouled surface. Differences between colonized and uncolonized sites may promote a galvanic-like attack. Microbes consume oxygen more rapidly than it can be transferred from the bulk solution, and areas beneath the biofilm become anaerobic and anodic. Repassivation of colonized surfaces is also hindered. Some microbes deprived of oxygen switch to fermentative metabolisms and produce large amounts of organic acids. This can result in local areas of low pH. Growth of anaerobes, such as sulfate-reducing bacteria, is favored in low-oxygen environments. These bacteria can oxidize hydrogen forming at the cathode and depolarize the corrosion cell. Their sulfide by-products may be directly corrosive or may contribute further to the electrochemical differential between fouled and unfouled sites.

In summary, microbes originating in the natural environment colonize cooling systems by capitalizing on favorable environmental conditions. Cooling systems are favorable environments for microorganisms because they contain water, operate in acceptable temperature and pH ranges, and provide nutrients for growth. Microbial attachment to surfaces in untreated systems produces deposits which reduce equipment efficiency and can be highly destructive to cooling equipment.

Because of the speed with which microbes can grow in cooling water systems, frequent monitoring of these systems is essential for the identification of developing problems. Vigilant monitoring of operating data can identify trends, and periodic system inspections show whether or not fouling is occurring. Test coupons and test heat exchangers may be used in operating systems to facilitate monitoring without interrupting system operation.

Deposits collected from the cooling system can be analyzed in the laboratory to determine their chemical composition and biological content. If a deposit has a significant microbiological content, its causative agents should be identified for treatment. The laboratory can identify the agents as predominantly algal, bacterial, or fungal, either microscopically (see Figure 26-4) or by routine cultural isolation and identification.

Microbial counts can also be performed to determine whether populations within the system are stable, increasing, or decreasing. Usually, planktonic populations are monitored by means of the Standard Methods plate count technique. However, not all organisms in the fouling process can be detected by this method. Anaerobic bacteria, such as sulfate-reducers which can cause under-deposit corrosion, are not revealed by aerobic cultural procedures. Special techniques must be used to ensure detection of these organisms (see Figures 26-5 and 26-6). Figure 26-5. Figure 26-6.

Sole reliance on bulk water counts will not provide sufficient information on the extent of surface fouling. Results must be interpreted in light of operating conditions at the time of sample collection. For example, in an untreated system a healthy, stable biofilm population may be present while bulk water counts are low, because few sessile organisms are being released from the fouled surface. If an antimicrobial is applied, bulk water counts may actually increase dramatically. This is due to disruption of the biofilm and sloughing of sessile organisms into the bulk water.

For a better diagnosis, it is necessary to use microbial monitoring techniques that allow more direct assessment of surface conditions. It is possible to clean a known surface area and suspend removed organisms in a known volume of sterile water. After this water is plated, back-calculation provides an approximation of the number of organisms on the original surface.

Another technique involves monitoring biochemical activity on a surface of a known area. A biofouled specimen is incubated with a suitable substrate. The concentration of reaction product found after a specific contact time relates to the numbers and health of organisms on the surface and consequently can be used as a measure of biofouling.

Regardless of which target population or monitoring technique is used, a single, isolated data point has little meaning. Various data must be compiled to generate a profile of microbiological trends in the system. This record should also include observations on equipment performance and operating conditions at the time of sample collection, thereby providing a meaningful context for interpretation of new data.

After it is determined that treatment is necessary to solve a fouling problem, an effective product must be chosen. Preliminary choices may be made only if the causative microbial agent is known, because the spectrum of activity of all antimicrobials is not the same. Some effectively control algae but not bacteria. For others, the reverse is true. For some, the activity spectrum, determined by inhibition of radiolabeled nutrient uptake, is quite broad, covering all common cooling water microbes (see Table 26-1 and Figure 26-7).

Table 26-1. Antimicrobial efficacy.

I50 (ppm)*
*I50 values are the concentrations that inhibit growth of the organism by 50% and are determined by tuse of a 14C-labeled nutrient.

Knowledge of how different antimicrobials affect microorganisms is also useful in choosing the appropriate treatment. Some kill the organisms they contact. Others inhibit growth of organisms but do not necessarily kill them. These biostats can be effective if a suitable concentration is maintained in a system for a sufficient time (a continuous concentration is ideal).

A laboratory evaluation of the relative effectiveness of antimicrobials should be performed. This helps to identify those likely to work against the fouling organisms in the system and to eliminate those with little chance of success. Because the goal of antimicrobial treatment is control or elimination of biofilm organisms, it is helpful to conduct the evaluation with sessile organisms found in deposits, as well as planktonic organisms in the flowing water.

The objective of any treatment program should be to expose the attached microbial population to an antimicrobial dosage sufficient to penetrate and disrupt the biofilm. Generally, cleanup of a fouled system requires higher concentrations of intermittently fed treatment, while maintenance of a clean system can be achieved with low-level continuous or semicontinuous feed. Given a certain level of fouling, the shorter the exposure time allowed by system operating conditions, the higher the required antimicrobial concentration. Conversely, if exposure times are long, control of the same level of fouling may be achieved with lower antimicrobial dosages.

Antimicrobial test results are most relevant when based on contact times derived from the system which is to be treated. Because once-through contact times are typically short, it can be very difficult to simulate these systems in lab testing. The longer contact times associated with recirculating cooling systems are easily duplicated in the lab.

In once-through systems, antimicrobials should be fed continuously to achieve the necessary contact time. Often, only low levels of antimicrobial are affordable on a continuous-feed basis. Semicontinuous treatment may be more economical or may be required because of effluent restrictions. Such an intermittent program for once-through systems must still be designed to achieve an effective antimicrobial concentration throughout the system, using treatment periods which range from minutes to hours per day.

Recirculating systems can also be treated continuously or intermittently, although intermittent treatment programs are more common. The purpose of intermittent treatment in these systems is to generate a high concentration of antimicrobial which will penetrate and disrupt the biofilm and eventually dissipate. When the treatment level drops below the toxic threshold, microbial growth begins again. After a period of multiplication, new growth is removed with another shock dose. As stated earlier, previously fouled surfaces can be recolonized at an accelerated rate. Therefore, the period for growth and removal may vary within a system, and certainly will vary among systems, even those using the same water source.

The time at which an antimicrobial concentra-tion drops below a threshold concentration in a recirculating system can be determined mathematically. Such information can be very useful in planning a schedule for effective and economical slime control. The threshold concentration desired should be estimated from a toxicant evaluation. The theoretical depletion of the antimicrobial from a system can be determined from the following formula:

BD x T Log Cf = log Ci - 2.303V where: Cf = final concentration, ppm Ci = initial concentration, ppm

BD = blowdown and windage loss, gpm V = system capacity, gal T = time, min

It is standard practice to repeat the shock treatment when Cf is 25% of Ci. On this basis, the time interval for antimicrobial addition can be calculated as follows:

Solving this equation for T will indicate how frequently a slug should be added to the system, but this determination is only valid for 75% depletion or two half-lives.

The equations provided are not valid for the following compounds:

  • compounds which are volatile and may be lost during passage over the tower
  • compounds which react with substances in the water (i.e., a demand)
  • compounds which degrade in water

In the planning of a slime control program, any chemical demand of process waters for the antimicrobial being used must also be considered. Failure to allow for the chemical demand may prevent attainment of the necessary threshold concentration and may lead to the failure of the treatment program. The compatibility of the antimicrobial with other treatments added to the water should also be considered.

Many system variables influence the behavior of microbes in the system, and the effects of antimicrobials can also be influenced by these variables. Therefore, careful consideration must be given to the determination of whether, when, and where to treat a cooling water system.

Cost is a primary criterion for selecting a slime control program. It cannot be determined without knowledge or estimation of the individual costs of chemicals, feed equipment, and labor required to apply and monitor the program, along with effluent treatment requirements. In addition, possible adverse effects of implementing the program must be weighed against those which would result if no treatment is made. Knowledge of component costs can help guide implementation of the program. For example, if labor costs are high, it may be more economical to feed an antimicrobial more frequently and reduce the amount of monitoring required. Each system must be considered individually, and seasonal adjustments may also be required.

Antimicrobials used for microbiological control can be broadly divided into two groups: oxidizing and nonoxidizing. Oxidizers, such as chlorine and bromine, are addressed in Chapter 27 nonoxidizers are discussed in the balance of this chapter.

Only a relative distinction can be made between oxidizing and nonoxidizing antimicrobials, because certain nonoxidizers have weak to mild oxidizing properties. The more significant difference between the two groups relates to mode of action. Nonoxidizing antimicrobials exert their effects on microorganisms by reaction with specific cell components or reaction pathways in the cell. Oxidizing antimicrobials are believed to kill by a more indiscriminate oxidation of chemical species on the surface or within the cell.

An understanding of the chemistries and modes of action of antimicrobials is needed to ensure their proper use and an appreciation of their limitations.

Two characteristic mechanisms typify many of the nonoxidizing chemicals applied to cooling systems for biofouling control. In one, microbes are inhibited or killed as a result of damage to the cell membrane. In the other, microbial death results from damage to the biochemical machinery involved in energy production or energy utilization.

Quaternary ammonium compounds (quats) are cationic surface-active molecules. They damage the cell membranes of bacteria, fungi, and algae. As a result, compounds that are normally prevented from entering the cell are able to penetrate this permeability barrier. Conversely, nutrients and essential intracellular components concentrated within the cell leak out. Growth is hindered, and the cell dies. At low concentrations, quats are biostatic because many organisms can survive in a damaged state for some time. However, at medium to high concentrations, quats can control the organisms.

Many antimicrobials interfere with energy metabolism. Because all microbial activity ultimately depends on the orderly transfer of energy, it can be anticipated that interference with the many energy-yielding or energy-trapping reactions will have serious consequences for the cell. Antimicrobials known to inhibit energy metabolism include the following:

  • organotins
  • bis(trichloromethyl) sulfone
  • methylenebis(thiocyanate) (MBT)
  • Beta-bromo-Beta-nitrostyrene (BNS)
  • dodecylguanidine salts
  • bromonitropropanediol (BNPD)

All of these compounds are effective when applied in sufficient concentrations. Dodecylguanidine salts also have surfactant properties, which probably contribute to their effectiveness.

The exact sites or reactions which are affected by such metabolic inhibitors are frequently unknown, although laboratory experiments may provide clues or indirect evidence for specific mechanisms. Tin and other heavy metals in sufficient concentrations cause proteins to lose their characteristic three-dimensional structures, which are required for normal function. Some antimicrobials, such as methylenebis(thiocyanate) (MBT) are believed to bind irreversibly to biomolecules, preventing the sequential reduction and oxidation these molecules must undergo in order to function.

Bromonitropropanediol (BNPD), a newer cooling water control agent, can be shown to catalyze the formation of disulfide bonds (R-S-S-R) between sulfhydryl groups (R-SH). Proteins contain sulfhydryls, and because enzymes are largely protein, it is possible to speculate that the formation of disulfide bonds between adjacent -SH groups may block enzyme activity. Many different enzymes contain sulfhydryl groups, so this antimicrobial may affect a wide range of microbial activities in addition to energy generation.

The mode of action of one common nonoxidizer cannot be categorized as either a surface-active or metabolic inhibitor. The active, dibromonitrilopropionamide (DBNPA), seems to behave somewhat like an oxidizing antimicrobial, reacting quite rapidly with bacterial cells. Studies of the interaction of radioactively labeled [14C]DBNPA with bacteria have shown that the 14C label never penetrates the cell, as would be necessary for it to become involved with energy metabolism. Instead, it binds strongly and rapidly to the cell walls of the bacteria. However, analogous studies with [14C]-Beta-bromo-Beta-nitrostyrene (BNS) have shown that the nitrostyrene penetrates the bacterial cell and accumulates within at concentrations far above the external concentration. Thus, although the mode of action of DBNPA is unknown, it most probably is unlike the other mechanisms known for nonoxiding antimicrobials.

Some antimicrobials used in cooling systems are compounds that spontaneously break down in water, thereby alleviating some potential environmental hazards. This chemical breakdown is often accompanied by a reduction in the toxicity of the compound. The compound can be added to the cooling water system, accomplish its task of killing the microbes in the system, and then break down into less noxious chemicals. Among the antimicrobials which have this attribute are BNS, MBT, DBNPA, and BNPD.

The dynamics of microbial populations in cooling water systems are complex. In situations where one microbial group or species dominates, fouling problems can occur. In other instances, a balanced population mix can exist while no fouling is evident. One explanation for such observations is that when balanced populations coexist, they compete with each other for the available nutrients and control each other's growth. When one group successfully displaces the others, its growth can proceed without competition.

Because of such considerations, some proprietary antimicrobials are formulated to contain more than one active. Proper blending of actives can compensate for limitations in the spectrum of kill shown by one or more of the actives. For example, if antimicrobial A is effective for bacteria but poor for fungi, large amounts of A might have to be used to control potential fungal problems. However, if antimicrobial B is fair for bacteria and good for fungi, a combination of A and B would broaden the spectrum of control and thus be preferable to high concentrations of A alone.

With no increase in the amount of antimicrobial used, the power of a blend can exceed that expected from a simple additive effect. This greatly enhanced performance or synergism is obtained from only certain combinations of actives. Synergism allows microbial control at much lower combined concentrations of A and B than could be achieved with either A or B alone. Use of products composed of synergistic active blends can result in reduced treatment concentrations in the blowdown water, as well as cost savings.

The spectrum of control can also be broadened by sequential feeding of antimicrobials to a system: alternate feeding of two actives can have the same outcome as blending of the actives for simultaneous feeding.

Another variation to be considered is the possible proliferation of resistant microbes in the system. The resistant forms may arise spontaneously by mutation within the cooling system but are much more likely to originate outside of the system. The antimicrobial simply functions to reduce competition by nonresistant forms and permits the unchecked growth of the newly introduced resistant organisms. This is more likely to occur during treatment with a single antimicrobial active ingredient, because the probability of a microbe being resistant to more than one active is extremely low when the actives are dissimilar. Sequentially added and synergistically blended antimicrobials are probably equally effective in eradicating antimicrobial-resistant microbes from a cooling system.

Development of an effective antimicrobial program is aided by an understanding of the mode of action of the products, the system to be treated, and environmental effects. All of these factors play a role in the selection of an effective and economical microbiological control program that is safe for the environment.

Plate Count

The viable plate count, or simply plate count, is a count of viable or live cells. It is based on the principle that viable cells replicate and give rise to visible colonies when incubated under suitable conditions for the specimen. The results are usually expressed as colony-forming units per milliliter (CFU/mL) rather than cells per milliliter because more than one cell may have landed on the same spot to give rise to a single colony. Furthermore, samples of bacteria that grow in clusters or chains are difficult to disperse and a single colony may represent several cells. Some cells are described as viable but nonculturable and will not form colonies on solid media. For all these reasons, the viable plate count is considered a low estimate of the actual number of live cells. These limitations do not detract from the usefulness of the method, which provides estimates of live bacterial numbers.

Microbiologists typically count plates with 30–300 colonies. Samples with too few colonies (<30) do not give statistically reliable numbers, and overcrowded plates (>300 colonies) make it difficult to accurately count individual colonies. Also, counts in this range minimize occurrences of more than one bacterial cell forming a single colony. Thus, the calculated CFU is closer to the true number of live bacteria in the population.

There are two common approaches to inoculating plates for viable counts: the pour plate and the spread plate methods. Although the final inoculation procedure differs between these two methods, they both start with a serial dilution of the culture.

Serial Dilution

The serial dilution of a culture is an important first step before proceeding to either the pour plate or spread plate method. The goal of the serial dilution process is to obtain plates with CFUs in the range of 30–300, and the process usually involves several dilutions in multiples of 10 to simplify calculation. The number of serial dilutions is chosen according to a preliminary estimate of the culture density. Figure 10 illustrates the serial dilution method.

Figure 10. Serial dilution involves diluting a fixed volume of cells mixed with dilution solution using the previous dilution as an inoculum. The result is dilution of the original culture by an exponentially growing factor. (credit: modification of work by “Leberechtc”/Wikimedia Commons)

A fixed volume of the original culture, 1.0 mL, is added to and thoroughly mixed with the first dilution tube solution, which contains 9.0 mL of sterile broth. This step represents a dilution factor of 10, or 1:10, compared with the original culture. From this first dilution, the same volume, 1.0 mL, is withdrawn and mixed with a fresh tube of 9.0 mL of dilution solution. The dilution factor is now 1:100 compared with the original culture. This process continues until a series of dilutions is produced that will bracket the desired cell concentration for accurate counting. From each tube, a sample is plated on solid medium using either the pour plate method (Figure 11) or the spread plate method (Figure 12). The plates are incubated until colonies appear. Two to three plates are usually prepared from each dilution and the numbers of colonies counted on each plate are averaged. In all cases, thorough mixing of samples with the dilution medium (to ensure the cell distribution in the tube is random) is paramount to obtaining reliable results.

Figure 11. In the pour plate method of cell counting, the sample is mixed in liquid warm agar (45–50 °C) poured into a sterile Petri dish and further mixed by swirling. This process is repeated for each serial dilution prepared. The resulting colonies are counted and provide an estimate of the number of cells in the original volume sampled.

Figure 12. In the spread plate method of cell counting, the sample is poured onto solid agar and then spread using a sterile spreader. This process is repeated for each serial dilution prepared. The resulting colonies are counted and provide an estimate of the number of cells in the original volume samples.

The dilution factor is used to calculate the number of cells in the original cell culture. In our example, an average of 50 colonies was counted on the plates obtained from the 1:10,000 dilution. Because only 0.1 mL of suspension was pipetted on the plate, the multiplier required to reconstitute the original concentration is 10 × 10,000. The number of CFU per mL is equal to 50 × 100 × 10,000 = 5,000,000. The number of bacteria in the culture is estimated as 5 million cells/mL. The colony count obtained from the 1:1000 dilution was 389, well below the expected 500 for a 10-fold difference in dilutions. This highlights the issue of inaccuracy when colony counts are greater than 300 and more than one bacterial cell grows into a single colony.

A very dilute sample—drinking water, for example—may not contain enough organisms to use either of the plate count methods described. In such cases, the original sample must be concentrated rather than diluted before plating. This can be accomplished using a modification of the plate count technique called the membrane filtration technique. Known volumes are vacuum-filtered aseptically through a membrane with a pore size small enough to trap microorganisms. The membrane is transferred to a Petri plate containing an appropriate growth medium. Colonies are counted after incubation. Calculation of the cell density is made by dividing the cell count by the volume of filtered liquid.

Watch this video for demonstrations of serial dilutions and spread plate techniques.

The Most Probable Number

The number of microorganisms in dilute samples is usually too low to be detected by the plate count methods described thus far. For these specimens, microbiologists routinely use the most probable number (MPN) method, a statistical procedure for estimating of the number of viable microorganisms in a sample. Often used for water and food samples, the MPN method evaluates detectable growth by observing changes in turbidity or color due to metabolic activity.

A typical application of MPN method is the estimation of the number of coliforms in a sample of pond water. Coliforms are gram-negative rod bacteria that ferment lactose. The presence of coliforms in water is considered a sign of contamination by fecal matter. For the method illustrated in Figure 13, a series of three dilutions of the water sample is tested by inoculating five lactose broth tubes with 10 mL of sample, five lactose broth tubes with 1 mL of sample, and five lactose broth tubes with 0.1 mL of sample. The lactose broth tubes contain a pH indicator that changes color from red to yellow when the lactose is fermented. After inoculation and incubation, the tubes are examined for an indication of coliform growth by a color change in media from red to yellow. The first set of tubes (10-mL sample) showed growth in all the tubes the second set of tubes (1 mL) showed growth in two tubes out of five in the third set of tubes, no growth is observed in any of the tubes (0.1-mL dilution). The numbers 5, 2, and 0 are compared with Figure 1 in Mathematical Basics, which has been constructed using a probability model of the sampling procedure. From our reading of the table, we conclude that 49 is the most probable number of bacteria per 100 mL of pond water.

Figure 13. In the most probable number method, sets of five lactose broth tubes are inoculated with three different volumes of pond water: 10 mL, 1 mL, and 0.1mL. Bacterial growth is assessed through a change in the color of the broth from red to yellow as lactose is fermented.

Think about It

  • What is a colony-forming unit?
  • What two methods are frequently used to estimate bacterial numbers in water samples?

Streak Plate

Separation of a mixed culture into individual colonies that can be subcultured to make pure cultures depends on how well the streak plate is prepared. The goal of streak plate method is to dilute the cells by spreading them out over the surface of the agar. This is accomplished in stages, as will be demonstrated in lab before you try it yourself.

Use the simulated agar surface below to practice the streak pattern using a pen or pencil.

Obtain two TSA plates, and write your name on the bottom half (the half containing the media) around the edge and following the curve (so the writing won’t hide your view of the bacterial colonies once they grow). Also write M. luteus on one plate (the name of the bacteria you will subculture to this plate). On the other, write “mixed” to indicate that you’re subculturing from the mixed culture broth to this plate.

As demonstrated, use a sterilized inoculating loop to pick up one M. luteus colony (or a piece of a colony) and transfer it to the surface of the agar plate. Spread the bacteria over approximately a quarter of the plate, edge to edge. Consider this step 1.

Flame the loop and cool it in the agar. Overlap the step 1 streak 3-4 times to pull out a reduced number of bacteria, and spread them out down the side of the plate. Consider this step 2.

Flame the loop and cool it in the agar. Overlap the step 2 streak 3-4 times and spread over the surface. Continue this process, flaming the loop in between each step, until the entire surface of the agar plate is covered.

After performing this with the M. luteus culture for practice, repeat the process with a drop of the mixed culture broth that you transfer to the plate with a sterile inoculating loop.

Place the streak plate subcultures in an incubator at the temperature and time specified by your instructor.

Spread Plate Technique: Principle, Procedure, Results

Spread plate technique is a viable counting method employed to plate a liquid sample for the purpose of isolating or counting the bacteria present in that sample. A perfect spread plate technique will result in visible and isolated colonies of bacteria that are evenly distributed in the plate and are countable. The technique is most commonly applied for microbial testing of foods or any other samples or to isolate and identify a variety of microbial flora present in the environmental samples e.g. soil.

  • To make accurate dilutions using pipettes (master serial dilution technique).
  • To apply a balanced spread technique using a glass spreader to spread the inoculum evenly on the agar surface.
  • To respect the necessary “short” time interval between agar inoculation and spreading.

In this method, the substance to be tested if not in liquid form is ground and dissolved in a suitable liquid medium. The sample is then diluted in 10 fold serial dilutions and plated in an appropriate medium.

Following incubation, the number of colonies present in the plate is counted. Assuming that each viable organism grows and divides to yield one colony, the number of total bacteria present in a sample is calculated.


  1. Glasswares: screw-capped test tubes, sterile pipettes, glass rod spreader (bent in the shape of a hockey stick), or commercially available sterile spreaders
  2. Medium: Plate count agar or nutrient agar. The surface of the plate must not be too moist because the added liquid must soak in so the cells remain stationary.

Procedure for Spread Plate Technique

  1. Prepare a series of at least 6 test tubes containing 9 ml of sterile distilled water.
  2. Using a sterile pipette, add 1ml of sample in the first tube of the set. Label it as 10-1
  3. Mix the contents well by swirling the tube upside down a few times.
  4. From the first tube, take 1ml of the sample and transfer it to the second tube. Label it as 10 -2.
  5. Repeat the procedure with all the remaining tubes labeling them until 10 -6 .
  1. Pipette out 0.1 ml* from the appropriate desired dilution series onto the center of the surface of an agar plate.
  2. Dip the L-shaped glass spreader (hockey stick) into alcohol.
  3. Flame the glass spreader over a bunsen burner.
  4. Spread the sample evenly over the surface of agar using a cool alcohol-flamed glass rod spreader, carefully rotating the Petri dish underneath at an angle of 45 o at the same time.
  5. Incubate the plate at 37°C for 24-48 hours.

*Note: The volume of the liquid should be 0.1 ml or less. Volumes >0.1 ml are avoided because the excess liquid does not soak in and may cause the colonies to coalesce as they form, making them difficult to count.

Calculation of result:

If your spread plate is successful, after incubation you will get isolated countable colonies evenly spread across the surface of the agar. Count the number of colonies using a magnifying device. Once you count the colonies, multiply by the appropriate dilution factor to determine the colony-forming units (CFU) present per ml in the original sample.

CFU/ml = (no. of colonies x dilution factor) / volume of culture plate

For example, suppose the plate of the 10^6 dilution yielded a count of 130 colonies. Then, the number of bacteria in 1 ml of the original sample can be calculated as follows:

Bacteria/ml = (130) x (10^6) x 10 = 1.3 × 10^9.
(we have multiplied with 10, because we have used 0.1mL while plating the agar plate)


Pour plate is one of the recommended assays (others are membrane filtration and spread plate) for a heterotrophic plate count. It produces a quantifiable result for a colony-forming unit per volume tested.

Confirmatory Test

Some microorganisms other than coliforms also produce acid and gas from lactose fermentation. In order to confirm the presence of coliform, a confirmatory test is done.

  1. 3 mL lactose-broth or brilliant green lactose fermentation tube,
  2. to an agar slant and
  3. 3 mL tryptone water.

Incubate the inoculated lactose-broth fermentation tubes at 37°C and inspect gas formation after 24 ± 2 hours. If no gas production is seen, further incubate up to a maximum of 48 ±3 hours to check gas production.

The agar slants should be incubated at 37°C for 24± 2 hours and Gram-stained preparations made from the slants should be examined microscopically.

The formation of gas in lactose broth and the demonstration of Gram-negative, non-spore-forming bacilli in the corresponding agar indicates the presence of a member of the coliform group in the sample examined.

The absence of gas formation in lactose broth or the failure to demonstrate Gram-negative, non-spore-forming bacilli in the corresponding agar slant constitutes a negative test (absence of coliforms in the tested sample).

  1. Incubate the tryptone water at (44.5 ±0.2°C) for 18-24 hours
  2. Following incubation, add approximately 0.1mL of Kovacs reagent and mix gently.
  3. The presence of indoleis indicated by a red color in the Kovacs reagent, forming a film over the aqueous phase of the medium.

a. Confirmatory tests positive for indole, growth, and gas production show the presence of thermotolerant E. coli.
b. Growth and gas production in the absence of indole confirm thermotolerant coliforms.

Impact of different platelet-rich fibrin (PRF) procurement methods on the platelet count, antimicrobial efficacy, and fibrin network pattern in different age groups: an in vitro study

Platelet-rich fibrin (PRF) procuring protocols have been suggested, differing in speed and time duration. Since different derivation protocols may alter PRF characteristics, the present study was conducted to evaluate the variations in the fibrin network pattern, platelet count, and antimicrobial efficacy of PRF procured using variable centrifugation speeds and time durations in different age groups.

Materials and methods

Sixty healthy subjects participated in the study and were equally divided into three age groups (20–34 years, 35–49 years, 50–65 years). From each age group, total of 6 PRF membranes were fabricated from 10 ml tubes. Three PRF membranes were obtained at 1400, 2800, and 3500 rpm for 8 min while other 3 membranes were obtained after 15 min of centrifugation respectively. The relative centrifugal force (RCF) values were within the spectrum of 228–1425 g. PRF membranes were then subjected to platelet count estimation, antimicrobial activity against oral bacteria, and changes in fibrin network pattern with respect to different age groups and different centrifugation protocols.


Highest platelet concentration, antimicrobial activity, and dense fibrin network were obtained in 20–34 years age group. Intragroup analysis within each group revealed highest platelet count and antimicrobial activity in PRF membranes, obtained at 1400 rpm for 8 min. Denser fibrin network pattern was demonstrated by PRF membranes procured at 3500 rpm for 15 min.


PRF properties, i.e., platelet count, antimicrobial efficacy, and fibrin network, are influenced by technical aspects of PRF preparation (RCF value, centrifugation speed, and time) and patient age.

Clinical relevance

Based on the finding of present study, it can be implied that lower centrifugation speed and time can increase the platelet concentration and antimicrobial activity of the PRF membrane. Contrarily, lowering the speed and time leads to lesser density fibrin network pattern. Centrifugation protocols thus need to be adapted accordingly.

Watch the video: Zellteilung bei Bakterien (May 2022).