METHOD FOR ENUMERATION OF BACTERIA IN LIQUID SAMPLES, AND SAMPLE HOLDER USEFUL FOR THIS METHOD

Abstract

Disclosed is a method for detection and/or quantification of microorganism in a liquid sample, in particular in a water sample, the method comprising the steps of: (a) distributing the liquid sample into a number of different discrete volume portions in a linear distribution pattern, or diluting the liquid sample into a number of dilution samples by a dilution factor of a linear distribution pattern; (b) allowing the microorganism to grow; and (c) applying the Most Probable Number method to the linearly distributed volume portions or the linearly diluted dilution samples to detect and/or quantify the microorganism. The invention also discloses a sample holder for detection and/or quantification of microorganism in a liquid sample, wherein the sample holder is structured to hold the liquid sample in a number of different compartments, wherein the different compartments respectively define a linear volume distribution.

Claims

1. A method for detection and/or quantification of microorganism in a liquid sample, in particular in a water sample, the method comprising the steps of: (a-1) distributing the liquid sample into a number of different discrete volume portions, wherein the different discrete volume portions define linearly increasing volumes, or (a-2) diluting the liquid sample into a number of dilution samples which are defined by linearly increasing dilutions; (b) allowing the microorganism to grow; and (c) applying the Most Probable Number method to the linearly distributed volume portions or the linearly diluted dilution samples to detect and/or quantify the microorganism.

2. (canceled)

3. The method according to claim 1, wherein the linearly increasing volumes represent linearly increasing discrete volume portions—with different volumes following the linearly increasing distribution of the linear set: 1x-, 2x-, 3x-, 4x- and 5x-fold increasing volumes; 1x-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x- and 8x-fold increasing volumes; 1x-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x-, 8x- and 9x-fold increasing volumes; and subsequent linearly increasing volume distributions correspondingly increased by a further number, up to 1x-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x-, 8x-, 9x-, 10x-, 11x-, 12x-, 13x-, 14x-, 15x-, 16x-, 17x-, 18x-, 19x-and 20x-fold increasing volumes.

4. The method according to claim 1, wherein the linearly increasing volumes—or the linearly decreasing dilutions comprises at least triplicate repetitions per each distinct volume portion in the linear distribution of the different discrete volume portions.

5. The method according to claim 1, wherein the liquid sample subjected to step (a) has a volume of about 100 mL, and/or wherein the liquid sample subjected to step (a) contains less than 100 CFU microorganisms per 100 mL.

6. The method according to claim 1, wherein the liquid sample is selected from the group consisting of: drinking water surface or natural water, bathing water, industrial process water, wastewater, and recycled wastewater.

7. The method according to claim 1, wherein the liquid sample is obtained from wastewater, industrial processing water, natural water, bathing water, industrial process water and/or recycled wastewater, wherein said liquid sample is diluted once before subjecting said water sample to step (a).

8. The method according to claim 1, which method further comprises, prior to step (a), suspending a defined sterile lyophilized medium with microorganism-specific detection reagents in a fixed amount of the liquid sample, then distributing said liquid sample in step (a) on a sample holder.

9. The method according to claim 1, wherein the lower limits of 95% confidence intervals linearly increase from 0.001 CFU/mL at MPN estimate of 1 CFU/100 mL to 0.04 CFU/mL at MPN estimate 10 CFU/100 mL and to 0.2 CFU/mL at MPN estimate 40 CFU/mL, and the upper limits of the 95% confidence intervals linearly increase from 0.07 CFU/mL at MPN estimate of 1 CFU/100 mL to 0.2 CFU/mL at MPN estimate 10 CFU/100 mL and to 0.7 CFU/mL at MPN estimate 40 CFU/mL.

10. The method according to claim 1, wherein the presence of bacteria is detected automatically by a positive signal in a defined volume, out of all repetitions of the defined volume and over all different volumes.

11. A sample holder for detection and/or quantification of microorganism in a liquid sample, wherein the sample holder is structured to hold the liquid sample in a number of different compartments, wherein the different compartments respectively define linearly increasing volumes, wherein a compartment defining each of the respective linearly increasing volume is present in triplicate of a same volume each, thereby optionally forming in total 15 compartments in case of 1x-, 2x-, 3x-, 4x- and 5x-fold linear increasing volumes; forming in total 24 compartments in case of 1x-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x- and 8x-fold linear increasing volumes; up to forming 60 compartments in case of 1x-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x-, 8x-, 9x-, 10x-, 11x-, 12x-, 13x-, 14x-, 15x-, 16x-, 17x-, 18x-, 19x-and 20x-fold linear increasing volumes.

12. The sample holder according to claim 11, wherein the different compartments are structured or sized to respectively define a linear distribution consisting of 1x-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x- and 8x-fold linear increasing volumes.

13. (canceled)

14. The sample holder according to claim 11, wherein the sample holder has an outer shape selected from a cylindrical outer shape, a spherical outer shape, a rectangular outer shape or other shape, and/or wherein the sample holder has varying dimensions in terms of height, width and/or radius.

15. The sample holder according to claim 11, wherein the compartments are organized in a spiral order with a defined distance between the compartments and a defined curvature.

16. The sample holder according to claim 11, wherein when the total volume to hold the sample is given as V=100%, for each of three compartments of the linear volume distribution the 1x unit volume is 0.926% of V, the 2x-fold unit volume is 1.852% of V, the 3x-fold unit volume is 2.778% of V, the 4x-fold unit volume is 3.704% of V, the 5x-fold unit volume is 4.63% of V, the 6x-fold unit volume is 5.556% of V, the 7x-fold unit volume is 6.481% of V, and the 8x-fold unit volume is 7.407% of V, wherein each %-volume indication encompasses a ±10% volume tolerance range, and optionally wherein V=100% is 100 mL.

17. The sample holder according to claim 11, having a circular outer shape whose compartments divide the circular outer shaped sample holder into radial sections to define said number of compartments respectively defining the linear volume distribution; or having a rectangular outer shape whose compartments divide the sample holder into rectangular sections having said number of compartments respectively defining the linear volume distribution.

18. (canceled)

19. The sample holder according to claim 11, wherein the holder-forming material is made of a hydrophobic material, or is provided with a hydrophobic coating of at least at a part of or all of the surface facing the holder inner space.

20. The sample holder according claim 11, which sample holder further comprises a cover, wherein the cover is arranged for preventing fluid evaporation, and/or is arranged preventing fluid exchange between compartments of the sample holder.

21. (canceled)

22. A system comprising: a sample holder as defined in claim 11, and a detector for detecting the presence of bacteria by a positive signal in a defined volume, out of all repetitions of the defined volume and over all different volumes, in an automatized form.

23. The method according to claim 1, wherein the linearly increasing distribution consists of 1x-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x- and 8x-fold increasing volumes.

24. The system according to claim 19, wherein the system is adapted to automatically calculate the Most Probable Number from the positive signal results, and optionally the system is further adapted to automatically generate or calculate the upper and lower limits of the 95% confidence intervals.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In the drawings:

[0020] FIG. 1 shows a sample holder according to one embodiment of the present invention in a perspective view from topside;

[0021] FIGS. 2A and 2B show the sample holder according to the embodiment of FIG. 1 in a plan view (FIG. 2A) and in a sectional view (FIG. 2B); and

[0022] FIG. 3 shows the sample holder according to the embodiment of FIG. 1 in a perspective side view, partly sectioned.

[0023] FIG. 4 shows results of confidence intervals upon using a linear distribution of the volumes according to the MPN method according to the present invention. Horizontal axis represents the MPN estimate upon a certain outcome and the vertical axis represents the actual value of the number of bacteria per mL of the sample. The vertical lines represent confidence intervals.

[0024] FIG. 5 shows results of confidence intervals upon an exponential distribution of the volumes for comparison. Horizontal axis represents the MPN estimate upon a certain outcome and the vertical axis represents the actual value of the number of bacteria per mL of the sample. The vertical lines represent confidence intervals.

[0025] FIG. 6 shows the correlation curve established in Example 2 between the actual number of bacteria and optical density.

[0026] FIG. 7 shows the dilutions and preparation of samples used in Example 3, and

[0027] FIG. 8 shows the dilutions and preparation of samples used in Example 4.

DESCRIPTION OF PREFERRED EMBODIMENTS

[0028] This invention relates to a multiplicity of the embodiments of the “Most Probable Number” (MPN) based method and of the sample holder, which is utilized for the MPN for identification of microorganisms in drinking and wastewater samples.

[0029] The invention relates to a sample holder, constructed of a distinct number of compartments, namely at least 15, preferably 24 and at most 60. The compartments are of different volumes following a linear distribution pattern. Each different volume may be present in a multitude of repetitions

[0030] Appropriately, the linear distribution pattern is at least 5-fold, namely with five different volumes following the increasing linear distribution set of x, 2x, 3x, 4x and 5x, preferably and preferably provides the linear distribution pattern of up to 20-fold, namely with 20 different volumes following the linear distribution of x, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, 10x, 11x, 12x, 13x, 14x, 15x, 16x, 17x, 18x, 19x and 20x. [17] Accordingly, the sample holder may be structured to provide a corresponding number of different compartments, the different compartments being sized to respectively define the linear distribution pattern. Thus, the sample holder and thereby the method can beneficially use anyone of the following linear volume distribution patterns with different volumes following the increasing linear distribution of the linear set: [0031] 1x-, 2x-, 3x-, 4x- and 5x-fold volume distributions; [0032] 1x-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x- and 8x-fold volume distributions; [0033] 1x-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x-, 8x- and 9x-fold volume distributions;
and subsequent linear volume distributions correspondingly increased by a further number, up to 1x-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x-, 8x-, 9x-, 10x-, 11x-, 12x-, 13x-, 14x-, 15x-, 16x-, 17x-, 18x-, 19x- and 20x-fold volume distributions. Preferably the linear distribution pattern consists of lx-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x- and 8x-fold volume distributions.

[0034] During the practical utilization, it is useful and provides more reliable results when the number of volume distributions is adapted to the number of expected microorganisms depending on the type of original sample, for example drinking water, waste water, industrial process water, bathing water and surface or natural water. Accordingly, a relatively low number of linearly distributed different volumes in the linear distribution set in the range of, for example, at least 5-fold and at most 12-fold, expecially in case of 8-fold linearly distributed volume proportions of different volumes, is suitable for relatively pure liquids such as drinking water. On the other hand, if a relatively impure liquid sample is to be analyzed, such as waste water or industrial water, a relatively large number of linearly distributed different volumes in the linear distribution set in the range of, for example, at least 12-fold and at most 20-fold is more preferred.

[0035] Considered alternatively or in combination with such adaptation to the type of liquid to be analyzed, it is preferred that the liquid sample subjected to method step (a) contains less than 100 CFU microorganisms per 100 mL, preferably at most 60 CFU microorganisms per 100 mL. Accordingly, if the liquid sample is drinking water, the drinking water beneficially does not have to be diluted before subjecting the drinking water sample to step (a). If on the other hand the liquid sample is obtained from wastewater, industrial processing water and/or natural water, said liquid sample is preferably diluted once before subjecting said water sample to step (a), preferably is diluted at least 1:10, more preferably is diluted 1:100.

[0036] In a preferred embodiment common to any of modifications and embodiments of the sample holder, the respective compartment defining each volume of the linear distribution pattern is present in triplicate of a same volume each. Accordingly the number of the repetitions of the same volume in the linear distribution set of different volumes is at least three, preferably is three. This leads to substantially enhanced and statistically more reliable results. Accordingly, depending on the aforementioned number of linear distributions, the respectively x-fold volume dilutions are present during the analysis method, and is correspondingly present in the sample holder, in a three-fold total number. This means that, optionally, in total 15 compartments are formed in case of lx-, 2x-, 3x-, 4x- and 5x-fold linear distributions of different volumes; in total 24 compartments are formed in case of lx-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x- and 8x-fold linear distributions of different volumes; and so on with correspondingly increasing numbers of linerar regression, up to forming 60 compartments in case of lx-, 2x-, 3x-, 4x-, 5x-, 6x-, 7x-, 8x-, 9x-, 10x-, 11x-, 12x-, 13x-, 14x-, 15x-, 16x-, 17x-, 18x-, 19x- and 20x-fold linear distributions of different volumes. [21] In particularly exemplified embodiments and related variations, the sample holder may have an outer shape selected from a preferably cylindrical outer shape to other shapes such as a spherical, a rectangular outer shape or other possible shapes and/or other dimensions in terms of height and/or width (or radius). Independent from such outer shape, the shape of each compartment may be appropriately selected, e.g from preferably cylindrical compartments, rectangular compartments, spherical compartments or other shape and/or other dimensions in terms of height and/or width (or radius), however in such a way that the volume of each compartment remains unchanged over all different volumes, following the linear pattern and over each of the repetitions of the same volume. Preferably, the sample holder comprises cylindrical compartments as the compartments, which respectively define, said linear volume distribution pattern, more preferably in combination with the sample holder having itself a cylindrical outer shape comprising such cylindrical compartments.

[0037] In a particular embodiment, the sample holder is arranged to define a specific total volume of not less than, preferably about 100 mL for holding the liquid sample. When such a specific total volume for holding the sample is given as V=100%, and when according the above mentioned preferred embodiment the respective compartment defining each volume of the linear distribution pattern is present in triplicate, then for each of three compartments of the linear distribution pattern the 1x unit volume is 0.926% of V, the 2x-fold unit volume is 1.852% of V, the 3x-fold unit volume is 2.778% of V, the 4x-fold unit volume is 3.704% of V, the 5x-fold unit volume is 4.63% of V, the 6x-fold unit volume is 5.556% of V, the 7x-fold unit volume is 6.481% of V, and the 8x-fold unit volume is 7.407% of V, wherein each %-volume indication encompasses a ±10% volume tolerance range, preferably a ±5% volume tolerance range, more preferably a ±1% volume tolerance range. As mentioned, it is preferred in terms of standardization that the liquid sample subjected to step (a) in the method, or correspondingly the total volume holding capacity for the sample in the holder, has a volume of no less than 100 mL and more preferably is 100 mL and accordingly the aforedefined V=100% is 100 mL. [23] In one embodiment, the sample holder is preferably constructed to be composed of 24 compartments, divided into eight triplets, thus with three repetitions of eight different volumes. The volumes following a linear distribution pattern then comprise; V.sub.1=V.sub.1 (the volume of the smallest triplet), V.sub.2=2V.sub.1, V.sub.3=3V.sub.1, V.sub.4=4V.sub.1, V.sub.5=5V.sub.1, V.sub.6=6V.sub.1, V.sub.7=7V.sub.1 and V.sub.8=8V.sub.1 or x (volume of the smallest triplet), 2x, 3x, 4x, 5x, 6x, 7x and 8x. The volumes of the model are preferably: V1=0.926 mL, V2=1.852 mL, V3=2.778 mL, V4=3.704 mL, V5=4.63 mL, V6=5.556 mL, V7=6.481 mL and V8=7.407 mL. The total inner volume of all compartments is no less then 100 mL, preferably about 100 mL, which complies with ISO standards ISO 7899-1, ISO 9308-3 and ISO 9308-2.

[0038] In one embodiment, cylindrical columns form the compartments in the sample holder. Examples of such an embodiment is shown in FIGS. 1-3. The cylindrical compartments comprise, each in triplicate, V.sub.1 making up the smallest volume, V.sub.2=2V.sub.1, V.sub.3=3V.sub.1, V.sub.4=4V.sub.1, V.sub.5=5V.sub.1, V.sub.6=6V.sub.1, V.sub.7=7V.sub.1 and V.sub.8=8V.sub.1 to thereby represent the lx, 2x, 3x, 4x, 5x, 6x, 7x and 8x fold linear volume distribution. In the prefered embodiment, the cylindrical compartments are organized in a conical spiral order with a defined distance between the compartments and a defined curvature (height distance between the compartments, which preferably may be around 5 mm). In the prefered embodiment, the compartments are organized in a counter clockwise helix moving downward towards the center of the sample holder. The volumes are organized in triplets with the outermost triplet (FIGS. 1, 2A, 2B and 3) starting the lefthand helix the triplet of volume V.sub.8. At the volume triplet, V.sub.4, the helix turns to a clockwise helix moving again downward towards the center of the sample holder. In other embodiments, the curvature of the spiral may differ and the neighbouring compartments may be on different height differences. Also, in other embodiments, the distance between neighbouring compartments may differ. Also, in other embodiments, the flux of the helix may be either counter clockwise or clockwise moving either downwards towards the center or downwards from the center towards the edge of the sample holder. The total height of the model may be, for example, around 116 mm and the total diameter of the model may be, for example, 120 mm (FIGS. 2A and 2B). In the preferred embodiment, the compartments are connected via a central channel, preferably of 5 mm width enabling a harmonized flux of the liquid sample. In other embodiments, the central channel may be of different dimensions (depth and/or width). In the preferred embodiment, an additional compartment is added to the model to compensate for the error in measuring and sampling of the 100 mL of the water sample.

[0039] The outline of the preferred embodiment of the model is related to a harmonized distribution of sample in the compartments in which the content of the compartments do not contact each other. Thus, during filling the sample into the sample holder, it is starting with the first compartment with volume V.sub.8 (FIG. 2); when the compartment is filled; the water continuously flows in the next compartment. When the second compartment is filled, the water proceeds in the third compartment, and so on. In the end there is no possibility of the content of the compartment being in contact between different compartments.

[0040] While the model defines relative volume distributions, the absolute volume per compartment in the sample holder embodiment with cylindrical compartment shape depends on the radius or diameter of each cylindrical compartment; correspondingly the heights of each cylindrical compartment also follow a linear pattern.

[0041] In a non-limiting example, the diameter is equal or about 14 mm, thus the heights also following a linear pattern respectively are: h.sub.1=6.01 mm, h.sub.2=12.03 mm, h.sub.3=18.05 mm, h.sub.4=24.06 mm, h.sub.5=30.08 mm, h.sub.6=36.09 mm, h.sub.7=42.10 mm and h.sub.8=48.12 mm. The difference in height between neighbouring compartments is preferably around 5mm. The total height of the model is around 116 mm and the total diameter of the model is 120 mm.

[0042] In a particular embodiment, the volume of the smallest of the eight volume triplicates (V.sub.1) can be calculated as: 3V.sub.1+3x2V.sub.1+3x3V.sub.1+3x4V.sub.1+3x5V.sub.1+3x6V.sub.1+3x7V.sub.1+3x8V.sub.1=100 mL.

[0043] Then, as a total volume capacity for holding the sample, there are present 108x V1=100 mL, V1=0.926 mL=0.926 cm.sup.3

[0044] In yet another aspect the volumes of the eight triplicates is indicated in Table 1 below:

TABLE-US-00001 TABLE 1 Volume Number of repetitions Designation of volume (mL oz cm.sup.3) of the same volume V1 0.926 3 V2 1.852 3 V3 2.778 3 V4 3.704 3 V5 4.63 3 V6 5.556 3 V7 6.481 3 V8 7.407 3

[0045] Also, in the prefered embodiment of the sample holder outline, the compartments are of cylindrical shape with equal diameter, preferably around 14 mm but with heights corresponding the preferred volume of each triplicate. The heights can be calculated as:

[0046] h=V/πr.sup.2, where h is the heiht of each compartment triplicate and r is the radius of each compartment.

[0047] The heights are then preferably as indicated below in Table 2, weherein the height values are approximated values:

TABLE-US-00002 TABLE 2 Number of repetitions Designation of height height (mm) of the same volume h.sub.1 6.01 3 h.sub.2 12.03 3 h.sub.3 18.05 3 h.sub.4 24.06 3 h.sub.5 30.08 3 h.sub.6 36.09 3 h.sub.7 42.10 3 h.sub.8 48.12 3

[0048] Also, in the preferred embodiment, the sample holder is of hydrophobic material—hydrophobic plastic material or other hydrophobic material, capable of retaining fractions of the liquid sample in accordance with the laws of surface tension and enables that no incidence and flow of water between neighbouring compartments occurs or of plastic material with a hydrophobic coating, coating at least at a part of or all of the surface facing the holder inner space. By this hydrophobic plastic material or other hydrophobic material it is possible to effectively retain fractions of the liquid sample in accordance with the laws of surface tension and to allow no incidence and no flow of water between neighbouring compartments to occur.

[0049] In another aspect of the prefered embodiment, a conical shaped plastic module may be connected to the first compartment of the model to enable a more harmonized water flux and further distributions into following compartments.

[0050] In other embodiments of the substrate holder, the number of compartments may vary maintaining the linear pattern and the 100 mL total internal volume. The numbers of compartments may vary from at least 9 to about 60 or more. With 9 compartments in total, customers willl gain a rough estimate of the most probable at extremely low numbers of bacteria. Another precaution is with 60 compartments is that the volume of the smallest compartment triplicate would be 158 μL (158.7 μL) which indicates a higher probability of technical error in volume upon manufacture. Accordingly, a total of 24 compartments is particularly prefered.

[0051] In modified embodiments, other dimensions of the substrate holder regarding total diameter, total height, and radius and height of each compartment, width of the channel and difference in the height between neighbouring compartments can be varied.

[0052] In other embodiments, the outer shape of the model is not limited to a cylindrical shape, and the shape of the compartments is not limited to be cylindrical, although it is prefered because of the harmonized flux of the water upon filling the compartments.

[0053] In another embodiment of the present invention, the sample holder has a round, petri-dish like structure. The petri dish like structure is composed of two parts. One part is the lid of the sample holder. The other part is a disc-like structure, separated into compartments in a specific manner. The disc is divided into multiple circular sections of angles 10°, 20°, 30°, 40°, 50°, 60°, 70° and 80°. Each circular section is further divided into three compartments by two circular rings. In an alternative embodiment of the sample holder having a round, petri-dish like structure, the compartments-forming disc is divided into multiple circular sections of angles 8°, 16°, 24°, 32°, 40°, 48°, 56°, 64° and 72°. Also in this outer shape configuration of the sample holder, each compartment volume is present in triplicate of a same volume each.

[0054] In another embodiment of the present invention, the sample holder has a rectangular outer shape whose compartments divide the sample holder into rectangular sections having said number of compartments respectively defining the linear volume distribution. Again, preferably each compartment volume is present in triplicate of a same volume each.

[0055] In prefered embodiments of the sample holder in a disc like structure and/or in the rectangular outer shape structure, the sum of the volume of the compartments is no less than 100 mL and more preferably is 100 mL, which is in accordance to the ISO standards.

[0056] In one aspect, the volumes of the compartments, comprising the same circular section are equal.

[0057] In another aspect the respective volumes of each compartments comprising each of the eight circular sections follow the pattern x, 2x, 3x, 4x, 5x, 6x, 7x and 8x.

[0058] In the preferred embodiment, where the sum of the volumes of the compartments is no less than 100 mL, the volumes of the compartments are as follows: 0.925 mL for each of the three compartments of the 10° circular section, 1.852 mL for each of the three compartments of the 20° circular section, 2.778 mL for each of the three compartments of the 30° circular section, 3.704 mL for each of the three compartments of the 40° circular section, 4.63 mL for each of the three compartments of the 50° circular section, 5.556 mL for each of the three compartments of the 60° circular section, 70° so 6.481 mL for each of the three compartments of the 70° circular section and 7.407 mL for each of the three compartments of the 80° circular section.

[0059] The height of the petri dish-like sample holder is not relevant, nor is the thickness of the edges forming each compartment.

[0060] In the prefered embodiment, the sample holder is of a plastic material suitable for holding aliquotes of the liquid sample according to the physics of surface tension.

[0061] In the preferred embodiment, the sample holder is related to the MPN method of detection of microorganisms in drinking water samples as in the ideal case, the mean total number of bacteria present in the water sample is between 30-40 CFU/100 mL of water sample. The regulation requires less than 100 CFU/mL of water sample in heterotrophic plate counts after incubation at 36° C. and no unusual changes in heterotrophic plate counts at 22° C. After calculating the 95% confidence intervals for each statistical assessment of the most probable concentration of bacteria, the method is very accurate for total concentrations of bacteria up to 60 CFU/100 mL of drinking water sample. For the preferred embodiment composed of 24 wells with eight different volumes following a linear correlation pattern with each different volume present in triplicates, at lower estimates of MPN for example 1 cell per 100 mL of water sample, the estimate of the standard deviation of the natural logarithm of the MPN estimate limits to or practically reaches the value 1. With increasing estimates of MPN, the estimate of the standard deviation of the natural logarithm of the MPN estimate decreases to value 0.4 at the estimate of the MPN 10 CFU/100 mL and to value of 0.3 at the estimate of the MPN 30 CFU/100 mL, then stabilizes and is still at around 0.3 at the estimate of MPN 40 CFU/100 mL of sample. With further increasing the estimates of MPN, the standard deviation ranges in the values between 0.3 and 0.35. Calculating the lower and upper limits of 95% confidence intervals; at the estimate of MPN 1 CFU/100 ml of sample, the lower limit of the 95% confidence intervals is 0.001 CFU/mL and the upper limit is 0.07 CFU/mL. This in practice means that the actual number of cells in the respective 100 mL water sample should by calculation vary between 1 and 8 cells per 100 mL of water sample, with the Most Probable Number of cells being 1 per 100 mL of water sample. At the MPN estimate of 10 CFU/100 mL the lower limit of the 95% confidence interval is at approximately 0.04 CFU/mL and the upper limit of the 95% confidence interval is at approximately 0.2 CFU/mL. This in practice means that the actual number of cells in the respective 100 mL water sample should by calculation vary between 4 and 20 cells per 100 mL of water sample, with the Most Probable Number of cells being 10 per 100 mL of water sample. At the MPN estimate 30 CFU/100 mL, the lower limit of the 95% confidence interval is 0.16 CFU/mL and the upper limit of the 95%confidence interval is 0.55 CFU/mL. This in practice means that the actual number of cells in the respective 100 mL water sample should by calculation vary between 16 and 55 cells per 100 mL of water sample with the Most Probable Number of cells being 30 per 100 mL of water sample. At the MPN estimate 40 CFU/100 mL the lower limit of the 95% confidence interval is at 0.2 and the upper limit of the 95% confidence interval is at 0.7. This in practice means that the actual number of cells in the respective 100 mL water sample should by calculation vary between 20 and 70 cells per 100 mL of water sample with the Most Probable Number of cells being 40 per 100 mL of water sample. However, one should always also observe the total number of positives out of 24 compartments in determining the minimum possible number of cells (lower limit) present in the sample as every positive compartment indicates at least a single present cell in the respective positive compartment.

[0062] Accordingly, it is possible to calculate the 95% confidence intervals at increasing estimates of the MPN. The calculations of the 95% confidence intervals are made from the estimate of the standard deviation of the natural logarithm of the MPN estimates. First, estimates of the standard deviation of the natural logarithm of the MPN estimates are made at increasing MPN estimates. From the calculations of the standard deviations of the natural logarithm of the MPN estimates, 95% confidence intervals are calculated by multiplying the value of the standard deviation of the natural logarithm of the MPN estimate at a respective MPN estimate with the respective MPN estimate. The standard deviation of the matural logarithm of the MPN estimates is a function and the MPN estimate is a function. As a result of the multiplication of two functions the 95% intervals are increasing linearly with increasing values of MPN estimates.

[0063] Also in other embodiments an automatized form of the sample holder may be developed enabling an automatized detection of positive compartments of a defined volume out of all repetitions of the defined volume and over all different volumes, an automatized calculation of the estimate of the Most Probable Number from the outcome and a further automatized generation or calcualtion of the upper and lower limits of the 95% confidence intervals.

[0064] In the preferred embodiment, the sample holder is related to the MPN method of detection of microorganisms in drinking water samples as in the ideal case, the mean total number of bacteria present in the water sample is between 30-40 CFU/100 mL of water sample. The regulation requires less than 100 CFU/mL of total bacterial in the water sample in heterotrophic plate counts after incubation at 36° C. and no unusual changes in heterotrophic plate counts at 22° C. After calculating the 95% confidence intervals for each statistical assessment of the most probable concentration of bacteria, the method is very accurate for total concentrations of bacteria up to 60 CFU/100 mL of drinking water sample.

[0065] In another aspect, multiple materials may be used to construct the mentioned sample holder.

[0066] In the preferred embodiment, the present invention relates to a MPN method of detection of bacteria, which involves adding a sterile liophylised powdered media to the liqud sample such as the drinking water. The medium is a selective chromogenic medium for the detection of E. coli and other coliform bacteria, containing two different chromogen reagents, one sensitive to E. coli and the other sensitive to other coliform bacteria. The chromogen selective for E. coli targets the E. coli specific β-glucuronidase and has a chromophore linked to a β-D-glucuronide. The chromogen selective for other coliform bacteria alternatively targets β-galactosidase and has a different chromophore attached to a β-D-galactopyranosid. If E. coli and also coliforms other than E. coli are present in a selected compartment, β-glucuronidase is present in the compartment and also β-galactosidase. The enzyme β-glucuronidase will liberate the chromophore attached to β-D-glucuronide and the β-galactosidase will liberate the different chromophore attached to β-D-galactopyranosid. The liberated chromophore attached to β-D-glucuronide will enable medium change to green upon accumulation and the chromophore attached to β-D-galactopyranosid will enable the medium change to yellow upon accumulation. However, the green pigment in the compartment where both E. coli and coliforms other than E. coli are present will overcome the yellow pigment. If only E. coli is present in a compartment, β-glucuronidase is present in the compartment. The enzyme β-glucuronidase will liberate the chromophore attached to β-D-glucuronide and the colour of the compartment will change to green upon accumulation of the chromophore. If coliforms, other than E. coli are present in the compartment, the present enzyme β-galactosidase will liberate the different chromophore attached to β-D-galactopyranosid. This will enable the colour change of the medium to yellow. If other bacteria are present which are neither E. coli nor belong to the group of coliform bacteria, the medium changes to opaque. The output of the method is then defined by the combination of the number of yellow and green compartments of a specific volume out of all repetitions of the same volume and over all different volumes.

[0067] Other embodiments of the present invention relate to the detection of E. coli and other coliform bacteria, with the MPN method in the drinking water sample optionally also involve other choices of selective chromogenic media. The method of detection of positives involves a color change of the sample based as a consequence of free chromophore accumulation and the change in absorption/emission spectra of the sample.

[0068] Other embodiments of the present invention relate to the detection of E. coli and other coliform bacteria in the drinking water sample with the MPN method may involve adding sterile liophylised fluorogenic (and chromogenic) media to the water sample. This medium containes a monosaccharide connected to a fluorophore and a monosaccharide connected to a chromophore—both target different bacterial groups, either E. coli or other coliform bacteria. In this case, the method of detection of positives involves emmiting a light of a specific wavelength upon excitation with UV light, due to accumulation of a fluorophore.

[0069] Also, other embodiments of the present invention may relate to the detection of enterococci in the drinking water sample via the MPN method.

[0070] Further embodiments of the present invention relate to the MPN method of detection of enterococci in the drinking water sample may involve adding sterile, liophylised selective chromogenic or fluorogenic media for the detection of enterococci, with chromogens (or substrate analogues) sensitive to enterococci.

[0071] Further embodiments of the present invention relate to the detection of other bacterial species, e.g. Legionella sp., Bacillus sp., Pseudomonas sp., and others in drinking water, via the MPN method, which involves a choice of a suitable selective media, either chromogenic or fluorogenic.

[0072] Further embodiments of the invention may involve detection of total heterotrophic bacteria or total bacteria in the drinking water via the MPN method, which again involves a choice of suitable medium containing reagents detecting microbial presence.

[0073] Other embodiments of the present invention may also relate to the MPN method of detection of microorganisms in wastewater samples, industrial process water samples, bathing water, surface or natural water samples, recycled wastewater samples. Herein, suitable previous dilutions of the sample are suggested in order to reach the optimal range of 95% confidence intervals.

[0074] Further embodiments of the present invention may involve detection of E. coli and/or other coliform bacteria, enterococci, total bacteria or other specific bacterial groups in the wastewater sample using either selective fluorogenic, chromogenic or other suitable media in a similar way as with the drinking water samples.

[0075] In a further aspect, the present invention provides an automized form of the inventive method in any of the above embodiments, wherein the presence of bacteria is detected by a positive signal in a defined volume, out of all repetitions of the defined volume and over all different volumes. The automatized detection preferably includes an automatized calculation of the estimate of the Most Probable Number from the positive signal results. The method may optionally additionally include a further automatized generation or calculation of the upper and lower limits of the 95% confidence intervals. In another aspect, the present invention provides an automized system comprising the sample holder described above, and a detector for detecting the presence of bacteria accordingly. Suitable means for detection of the positive signals are known. For example, the method may use and the system may comprise an appropriate number of LED light emitters and a corresponding number of sensors sensing the emission of light or fluoresence. For descrition of further features of the inventive method and sample holder reference is made to the above description.

[0076] All described embodiments of the present invention may be linked to an automatized method of detection of either color change or fluorescence intensity, further calculation of the estimate of the Most Probable Number out of the outcome of the experiment and calculation of the 95% confidence intervals from the estimate of the standard deviation of the natural logarithm of the MPN estimate and from the MPN estimate.

[0077] In all described embodiments, the advantage of the present invention is that very accurate statistical results are provided in the concentration range of microorganisms in drinking water (upto 60 CFU per 100 mL) despite a relatively low number of compartments, because the sizes of volumes follow a linear regression.

EXAMPLES

Example 1—Proving the Concept of Better Model Statistics and Providing more Accurate Results Upon Linear Distribution the Volume of Compartments

[0078] In this example, two outlines of the model were taken into account. The first one is the outline with linear dustribution of the volumes, as shown in the folowing Table 3.

TABLE-US-00003 TABLE 3 Volume Number of repetitions Designation of volume (mL oz cm.sup.3) of the same volume V.sub.1 0.926 3 V.sub.2 1.852 3 V.sub.3 2.778 3 V.sub.4 3.704 3 V.sub.5 4.63 3 V.sub.6 5.556 3 V.sub.7 6.481 3 V.sub.8 7.407 3 Where V.sub.1 = V.sub.1, V.sub.2 = 2V.sub.1, V.sub.3 = 3V.sub.1, V.sub.4 = 4V.sub.1, V.sub.5 = 5V.sub.1, V.sub.6 = 6V.sub.1,V.sub.7 = 7V.sub.1, V.sub.8 = 8V.sub.1

[0079] In a comparison example an exponential distribution of the volumes with constant ratio between adjacent different voumes, as shown in Table 4.

TABLE-US-00004 TABLE 4 Volume Number of repetitions Designation of volume (mL oz cm.sup.3) of the same volume V.sub.1 0.131 3 V.sub.2 0.262 3 V.sub.3 0.524 3 V.sub.4 1.048 3 V.sub.5 2.096 3 V.sub.6 4.192 3 V.sub.7 8.384 3 V.sub.8 16.768 3 Where V.sub.1 = V.sub.1, V.sub.2 = 2V.sub.1, V.sub.3 = 4V.sub.1, V.sub.4 = 8V.sub.1, V.sub.5 = 16V.sub.1, V.sub.6 = 32V.sub.1, V.sub.7 = 64V.sub.1, V.sub.8 = 128V.sub.1

[0080] Confidence intervals were calculated with RStudio and ploted in a linear plot. In principle, upon plotting linear plots of confidence intervals, the intervals should increase upon increasing estimate of the must probable number

[0081] Results can be depicted from FIGS. 4 and 5 for linear and exponential distribution of volumes, respectively.

[0082] In case of linear volume distribution, a relatively narrow confidence intervals can be observed up to 0.6 CFU/100 mL of water sampleor 60 CFU/100 mL of water sample, which increase linearly (FIG. 4). If a linear distribution of the volumes intervals is chosen, it is possible to be relatively more flexible regarding the number of compartments, because as can be seen, upon linear distribution, the smallest volume of the model containing 20 different volumes following a linear distribution in triplicates is 130-150 μL, where in case of the exponential volume distribution, the volume 130 μL is reached when the model contains 8 different volumes in triplicates, with different volumes following an exponential distribution. Thus, with the linear distribution volumes decrease more slowly and a sample holder can be arranged with more compartments than upon exponential volume distribution.

[0083] In the case of exponential distribution of volumes for comparison (cf. FIG. 5), one can observe broader confidence intervals, which are non-consistent and do not increase linearly, compared to the linear distribution of the model. In the exponential distribution of different volumes, the 95% confidence intervals are howeverdense through a wider range upto 100 CFU/100 ml of water sample. In addition, the confidence intervals do not increase completely linearly, rather, belts of slightly broader intervals can be seen.

[0084] From this, it is concluded that linear distribution of volumes, although in a narrower range of bacterial concentration, behaves better.

Example 2—Creating the Correlation Curve Between the Concentration of E. coli and OD

[0085] A correlation curve between the actual number of bacteria and optical density was constructed. E. coli was introduced into sterile distilled water via inoculation loop upoto optical density (OD) 1. Thereafter, serial dilutions of 2 were made. Each serial dilution was counted with the Neubauer counting chamber and simultaneously its OD was measured. The below Table 3 and the graph shown in FIG. 6 represent the results with the trendilnes.

TABLE-US-00005 TABLE 5 SAMPLE 600 nm 650 nm 680 nm 550 nm N1 N2 N3 N4 N5 Average N CFU/mL Sample-9 1.0129 0.8670 0.7924 1.1923 466 496 401 432 376 434.2 1085500000 Sample-10, 0.5141 0.4358 0.3959 0.6162 251 214 208 203 138 202.8 507000000 Sample-11, 0.2554 0.2161 0.1958 0.3076 92 90 94 89 88 90.6 226500000 Sample-12, 0.1240 0.1052 0.0954 0.1501 65 60 55 49 51 56 140000000 Sample-13, 0.0607 0.0517 0.0474 0.0743 26 27 27 29 27 27.2 68000000 Sample-14, 0.0264 0.0225 0.0203 0.0333 17 16 14 19 17 16.6 41500000 Sample-15, 0.0099 0.0081 0.0073 0.0139 7 8 8 12 9 8.8 22000000 Sample-16, −0.0004 −0.0009 −0.0013 0.0014 6 6 4 5 5 5.2 13000000

Example 3—Confirmation of the Linear Curve

[0086] In this Example 3 the correlation curve provided in Example 2 was confirmed. For the proof of concept of the MPN model, an initial test suspension of E. coli was prepared with its concentration calibrated at 10.sup.8 CFU/mL (Example 4). In Example 3, we checked whether OD 0.1 or 0.2 is a better initial OD to prepare the original bacterial suspension calibrated at 10.sup.8 CFU/mL, for later experiments (Example 4), thus confirming the linear correlation curve obtained at Example 2. In this Example 3, E. coli was introduced into 3 mL sterile distilled water via inoculation loop upto two different optical densities (OD) 0.1 and 0.2. Afterwards, series of dilutions of 10 were made as in the provided scheme below, transfering 300 μL of the previous dilution into 2700 μL of sterile distilled water as is represented in FIG. 7. At OD 0.2, 1 mL of each of the dilutions 10.sup.−6, 10.sup.−7, 10.sup.−8, 10.sup.−9, 10.sup.−10 and 10.sup.−11 were transfered into 99 mL of sterile distilled water (dH.sub.2O) and filtered through a membrane filter of pore size 0.45 μm. At OD 0.1 1 mL dilutions 10.sup.−7, 10.sup.−8, 10.sup.−9, 10.sup.−10, 10.sup.−11 in 10.sup.−12 were transfered into 99 mL of sterile distilled dH2O and filtered through a cellulose filter of pore size 0.45 μm. The dilutions are depicted in FIG. 7.

[0087] The results depicted in Table 6 below showed that 0.1 is a better OD of the original suspension in the proof of concept experiments.

TABLE-US-00006 TABLE 6 10.sup.−6 10.sup.−7 10.sup.−8 10.sup.−9 10.sup.−10 10.sup.−11 10.sup.−12 OD 0.2 183 20 3 1 0 0 / OD 0.1 / 22 5 1 0 0 0

[0088] Based on the found optimal OD 0.1 for the calibration of the original test suspension to the concentration 10.sup.8 CFU/mL the E. coli cells will be introduced into 6 mL of sterile distilled water via an inoculation loop to the final OD of 0.1 generating the original bacterial suspension. From the original bacterial suspension, a dilution series will be made as depicted in FIG. 8 (Example 4) to reach the final predicted concentrations of bacteria 1 CFU/mL, 3 CFU/mL, 6 CFU/mL, 12-13 CFU/mL, 25 CFU/mL, 50 CFU/mL and 100 CFU/mL. 1 mL of dilutions with the predicted concentration of bacteria 1 CFU/mL, 3 CFU/mL, 6 CFU/mL, 12-13 CFU/mL, 25 CFU/mL, 50 CFU/mL and 100 CFU/mL will be transfered to 99 mL of sterile distilled water with solubilized selective, differential chromogenic medium for detection selective for E. coli to reach the final concentrations 1 CFU/100 mL, 3 CFU/100 mL, 6 CFU/100 mL, 12-13 CFU/100 mL, 25 CFU/100 mL, 50 CFU/100 mL and 100 CFU/100 mL. The spiked samples in the selective lyophilized chromogenic medium with the concentrations 1 CFU/100 mL, 3 CFU/100 mL, 6 CFU/100 mL, 12-13 CFU/100 mL, 25 CFU/100 mL, 50 CFU/100 mL and 100 CFU/100 mL will then be transfered to separate MPN sample holders of the present invention and incubated for 24 h at 36° C. A negative control of 1 mL of sterile distilled water will also be added to to 99 mL of sterile distilled water with solubilized selective, differential chromogenic medium for detection selective for E. coli. Simultaneously, 1 mL of dilutions with the predicted concentration of bacteria 1 CFU/mL, 3 CFU/mL, 6 CFU/mL, 12-13 CFU/mL, 25 CFU/mL, 50 CFU/mL and 100 CFU/mL will be transfered to 99 mL of sterile distilled water to reach the final concentrations 1 CFU/100 mL, 3 CFU/100 mL, 6 CFU/100 mL, 12-13 CFU/100 mL, 25 CFU/100 mL, 50 CFU/100 mL and 100 CFU/100 mL. The spiked samples of concentrations 1 CFU/100 mL, 3 CFU/100 mL, 6 CFU/100 mL, 12-13 CFU/100 mL, 25 CFU/100 mL, 50 CFU/100 mL and 100 CFU/100 mL in sterile distilled water will be filtered through a membrane filter of pore size 0.45 μm. A negative control of 1 mL of sterile distilled water will aslo be added to to 99 mL of sterile distilled water and further filtered through a membrane filter of pore sizes 0.45 μm. The filters with the filtered samples will then be transfered to a solid differential chromogenic agar medium, rehydrated with 1 mL of sterile distilled water and the samples on the solid differential chromogenic agar medium will be incubated for 24 h at 36° C.

Example 4—Proof of Concept of the MPN Model

[0089] Based on the found optimal OD 0.1 for the calibration of the original test suspension to the concentration 10.sup.8 CFU/mL the E. coli cells was introduced into 6 mL of sterile distilled water via an inoculation loop to the final OD of 0.1 generating the original bacterial suspension. From the original bacterial suspension, a dilution series was made as depicted in FIG. 8 (Example 4) to reach the final predicted concentrations of bacteria 1 CFU/mL, 3 CFU/mL, 6 CFU/mL, 12-13 CFU/mL, 25 CFU/mL, 50 CFU/mL and 100 CFU/mL. 1 mL of dilutions with the predicted concentration of bacteria 1 CFU/mL, 3 CFU/mL, 6 CFU/mL, 12-13 CFU/mL, 25 CFU/mL, 50 CFU/mL and 100 CFU/mL were transfered to 99 mL of sterile distilled water with solubilized selective, differential chromogenic medium for detection selective for E. coli to reach the final concentrations 1 CFU/100 mL, 3 CFU/100 mL, 6 CFU/100 mL, 12-13 CFU/100 mL, 25 CFU/100 mL, 50 CFU/100 mL and 100 CFU/100 mL. The spiked samples in the selective lyophilized chromogenic medium with the concentrations 1 CFU/100 mL, 3 CFU/100 mL, 6 CFU/100 mL, 12-13 CFU/100 mL, 25 CFU/100 mL, 50 CFU/100 mL and 100 CFU/100 mL were then transfered to separate MPN sample holders of the present invention and incubated for 24 h at 36° C. A negative control of 1 mL of sterile distilled water was also added to to 99 mL of sterile distilled water with solubilized selective, differential chromogenic medium for detection selective for E. coli. Simultaneously, 1 mL of dilutions with the predicted concentration of bacteria 1 CFU/mL, 3 CFU/mL, 6 CFU/mL, 12-13 CFU/mL, 25 CFU/mL, 50 CFU/mL and 100 CFU/mL were transfered to 99 mL of sterile distilled water to reach the final concentrations 1 CFU/100 mL, 3 CFU/100 mL, 6 CFU/100 mL, 12-13 CFU/100 mL, 25 CFU/100 mL, 50 CFU/100 mL and 100 CFU/100 mL. The spiked samples of concentrations 1 CFU/100 mL, 3 CFU/100 mL, 6 CFU/100 mL, 12-13 CFU/100 mL, 25 CFU/100 mL, 50 CFU/100 mL and 100 CFU/100 mL in sterile distilled water were filtered through a membrane filter of pore size 0.45 μm. A negative control of 1 mL of sterile distilled water was also added to 99 mL of sterile distilled water and further filtered through a membrane filter of pore sizes 0.45 μm. The filters with the filtered samples were then transfered to a solid differential chromogenic agar medium, rehydrated with 1 mL of sterile distilled water and the samples on the solid differential chromogenic agar medium were incubated for 24 h at 36° C. Results are depicted in the Table 7 below.

TABLE-US-00007 TABLE 7 Lower limit of number of Upper limit of number of cells in 100 mL of water cells in 100 mL of water sample based on number of sample based on number of positive compartments positive compartments Number of cells in out of all three over all three 100 mL of water sample compartments of equal compartments of equal based on the alternative volume and over all volume and over all method of membrane filtration eight different volumes eight different volumes (ISO 9308-1: 2014; Water and based on the and based on the quality - Enumeration of Predicted statistical calculation statistical calculation Escherichia coli concentrational of lower limits of of upper limits of and coliform bacteria - range of cell Value of MPN 95% confidence 95% confidence Part 1: Membrane filtration culture estimate intervals intervals method for waters with low (CFU/100 mL) (CFU/100 mL) (CFU/100 mL) (CFU/100 mL) bacterial background flora) 0 0 0 0 0 1 1 1 8 1 3 3 3 10 2 6 6 5 14 5 12-13 13 10 25 14 25 19 12 35 28 50 54 32 93 56 100 93 47 190 114

[0090] Apparently there is a good coherence between the predicted concentration of E. coli, the MPN estimate at the predicted concentration and the result of the membrane filtration. At the pedicted concentration of 1 CFU/mL, the MPN sample holder predicted the most probable number of bacterial cells in 100 mL of the sample to be 1 CFU. The statistical analysis in combintation with the actual total number of positive wells predicted the actual number of bacteria in the 100 mL of water sample to range between 1 CFU and 8 CFU and the membrane filtration resulted in 1 CFU/100 mL of sample. At the pedicted concentration of 3 CFU/mL, the MPN sample holder predicted the most probable number of bacterial cells in 100 mL of the sample to be 3 CFU. The statistical analysis in combination with the actual total number of positive wells predicted the actual number of bacteria in the 100 mL of water sample to range between 3 CFU and 10 CFU and the membrane filtration resulted in 2 CFU/100 mL of sample. At the pedicted concentration of 6 CFU/mL, the MPN sample holder predicted the most probable number of bacterial cells in 100 mL of the sample to be 6 CFU. The statistical analysis in combination with the actual total number of positive wells predicted the actual number of bacteria in the 100 mL of water sample to range between 5 CFU and 14 CFU and the membrane filtration resulted in 5 CFU/100 mL of sample. At the pedicted concentration of 12-13 CFU/mL, the MPN sample holder predicted the most probable number of bacterial cells in 100 mL of the sample to be 13 CFU. The statistical analysis in combintation with the actual total number of positive wells predicted the actual number of bacteria in the 100 mL of water sample to range between 10 CFU and 25 CFU and the membrane filtration resulted in 14 CFU/100 mL of sample. At the pedicted concentration of 25 CFU/mL, the MPN sample holder predicted the most probable number of bacterial cells in 100 mL of the sample to be 19 CFU. The statistical analysis in combination with the actual total number of positive wells predicted the actual number of bacteria in the 100 mL of water sample to range between 12 CFU and 35 CFU and the membrane filtration resulted in 28 CFU/100 mL of sample. At the pedicted concentration of 50 CFU/mL, the MPN sample holder predicted the most probable number of bacterial cells in 100 mL of the sample to be 54 CFU. The statistical analysis in combination with the actual total number of positive wells predicted the actual number of bacteria in the 100 mL of water sample to range between 32 CFU and 93 CFU and the membrane filtration resulted in 56 CFU/100 mL of sample. At the pedicted concentration of 100 CFU/mL, the MPN sample holder predicted the most probable number of bacterial cells in 100 mL of the sample to be 93 CFU. The statistical analysis in combination with the actual total number of positive wells predicted the actual number of bacteria in the 100 mL of water sample to range between 47 CFU and 190 CFU and the membrane filtration resulted in 114 CFU/100 mL of sample. If a defined number of compartments are positive out of all compartments, this may indicate the minimum number of cells present in the sample as each positive compartment surely contains a minimum of one cell. Each compartment may however in coherence with the Poisson distribution may contain more than one cell which may, depending on the volume of the compartment be more probable (according to the Poisson distribution) than containig only one cell.