Patent Description:
Detection and quantification of microorganisms, such as bacteria, is a crucial painpoint of a range of different industrial fields: the drinking water sector, the wastewater sector, the bathing water sector, chemical industry (production of paints, pigments, colorants, slurry), food industry, composts and the pharmaceutical industry for at least two main reasons forming two opposite counter-forces. On one hand, all of these industrial sectors are facing microbial contaminations, which are compromising the quality of their products and simultaneously affecting the health of the users. On the other hand, they are subjected to strong regulatory pressures. In the Drinking water sector, for example, the Drinking Water Directive (<NUM>/<NUM>/EC) reqiures no E. coli, no enterococci and no coliform bacteria present in the water sample. For bottled water, also no P. aeruginsa should be present in the bottled water sample.

A well-known method for detection and estimation/quantification of microorganisms in a liquid sample is the Most Probable Number method (MPN method). The method is based on a random distribution of a total unknown number of bacterial cells dispersed in an initial defined Euclidian volume of the liquid sample into discrete compartments of smaller Euclidian volumes. Until now, two different series of statistical events (statistical operations) were used as standard in the MPN method. The first one is that a series of either 2x or 10x sample dilutions are made. All dilutions of the sample are then distributed in multiple repetitions in wells (spaces) of the same volume. The second one is that the sample containing a defined number (or density of bacterial cells) is distributed between different volumes with either 10x or 2x volume ratio with multiple repetitions of the same volume. Thus there are multiple possibilities of the composition of the compartments - either all compartments may be of the same volume or the compartments may follow a volume pattern of different volumes with either 2x or 10x volume ratio. Each different volume may be present in a multitude of repetitions. Both cases underlie a stochastic geometry process in which the initial unknown number of bacterial cells is or is not diluted beforehand and further randomly partitioned or distributed between several compartments of defined smaller Euclidian volumes, which may be of particular constitution regarding the number of different volumes and the number of repetitions of the same volume - for example the well of a microwell plate or a model modification thereof - and ultimately resides in a novel random spatial distribution in a novel abstract Euclidian space. This statistical process is composed of two steps: Firstly all possible outcomes of the mentioned partitioning an unknown initial number of cells (or dilutions thereof) partitioned in a novel deifned compartment of a particular Euclidian volume can be explained by the Poisson distribution. The equation for the Poisson probability function of the bacterial sample (representing the probability that j bacterial cells will be in a well of a defined volume) can be described as: <MAT> where j represents the number of bacterial cells in the well, d is the dilution factor, V is the volume of the well, µ is the initial bacterial concentration (the most probable number of bacteria per mL of sample) and the product dVµ defines the number of bacterial cells. Each bacterial cell is a point independent of other points in the defined Euclidian mathematical place. Detecting at least a single bacterium in the novel well (or defined compartment) or the novel Euclidian stochastic space is interpreted as a positive result thus indicating the presence of microorganisms in the compartment in question. The absence of bacteria in a respective compartment is calculated from the Poisson distribution as: <MAT>.

The probability of a presence of at least a single bacterium in a compartment with a respective volume is therefore the opposite of the probability of the absence of bacterial cells in the compartment in question and it is calculated as: <MAT>.

The sample with an initial unknown number of bacterial cells is however distributed between multiplicities of compartments of different defined Euclidian volumes (following generally a specific volume pattern of either 10x or 2x volume ratio) with each different volume present in a multitude of repetitions. The second step in the statistical process is thus to calculate the probability of detecting at least a single bacterium (a positive result) in a multitude - a defined number - of wells of the same volume and/or dilution (repeptitions of the same volume and/or dilution) out of all copartments of the same volume and/or dilution and over all different volumes. The probability of detecting a positive result (the presence of at least a single bacterium) in multiple repetitions (from one to all repetitions) of the same defined dilution and/or volume can be further explained by the Binomial distribution. Calculating the probability of detecting at least a single bacterium a respective well of defined Euclidian volume (from the Poisson distribution) thus gives us the weighted probability of the further Binomial distribution and enables us to select a particular Binomial distribution out of a family of Binomial distributions. Let us for example describe a system with x different volumes and y repetitions of the same volume. The Binomial distribution would describe the probability of r positive compartments out of y compartments with the same volume and/or dilution and ppresence would describe the weighted probability calculated by the above refered Poisson distribution describing the probability of at least a single bacterial cell present in a compartment with a defined volume. Pabsence would describe the weighted probability of absence of bacterial cells in a compartment of a particular volume: <MAT>.

The system however is composed of x different volumes, with y repetitions of the same volume. The outcome or the final result is defined by the number of positives (ri) out of yi repetitinos of the same volume i over all different volumes (from i=<NUM> to i=x). The final outcome of the MPN method is described by the likelihood function (L) and is calculated as the product of the Binomial distribution probabilities of obtaining the number of positive compartments (ri) (compartments with at least a single bacterial cell present) out of yi repetitinos of the same volume i for each of the different volumes (from i=<NUM> to i=x). From the likelihood function, the natural logarithm of the likelihood function may be calculated. For the above described system with x different volumes and y repetitions of the same volume, the first derivative of the natural logarithm of this likelihood function with respect to µ can be described as: <MAT>.

Where r is the defined number of positive wells in repetitions of the same dilution and/or volume. The most probable bacterial concentration based on a defined outcome (a certain number of positives (ri) out of yi repetitinos of the same volume i over all different volumes (from i=<NUM> to i=x)) would be the µ at which the natural logarithm of the likelihood function is maximal, which is when its first derivative with respect to µ is zero. The derived µ from the equation is the estimate of the most probable number of microorganisms per mL of sample. If the same initial bacterial concentration would be analysed an infinite number of times, the estimate of the most probable number of bacterial cells per mL of sample would predictively follow a normal distribution with a population average µp - indicating the exact (actual) number of bacterial cells and a population standard deviation σµp. Taking a sample of measurements from the infinite population of measurements, the sample of measurements predictively follows a t-student distribution with a sample average µs and a sample standard deviation sµs. From each sample average and sample standard deviation, an interval can be calculated indicating the range of µ values (between the upper and the lower limit) inside which there is a <NUM>% probability that the actual number of bacterial cells in the sample resides - the <NUM>% confidence intervals. Also taking an infinite numbers of sample measurements from the population of sample measurements; each sample measurement defining its own <NUM>) sample average µs, <NUM>) sample standard deviation sµs and <NUM>) <NUM>% confidence interval, <NUM>% of the <NUM>% confidence intervals would contain the population average µp, thus the exact or actual number of bacterial cells. After calculating an estimate of the Most Probable Number from equating the first derivative of the natural logarithm likelihood function with respect to µ, the variance of the estimate of the MPN can be calculated as the second derivative of the natural logarithm of the Likelihood function with respect to µ. From the variance, the estimate of the standard deviation of the MPN estimate can be calculated and the estimate of the standard deviation of the natural logarithm of the MPN estimate can be calculated by dividing the estimate of the standard deviation of the MPN estimate with the MPN estimate. From the estimate of the standard deviation of the natural logarithm of the MPN estimate, the <NUM>% confidence intervals are calculated as: <MAT> <MAT>.

Where slnµ is the estimate of the standard deviation of the natural logarithm of the MPN estimate µ Ilower limit is the lower limit of the <NUM>% confidence interval and the Iupper limit is the upper limit of the <NUM>% confidence interval.

Each previously described industrial sector is either interested in detecting total bacteria present or is targeting the presence of a defined bacterial species, such as Escherichia coli, or a defined bacterial group, such as coliform bacteria. Obtaining positive results for a targeted bacterial group can be achieved by selective differential chromogenic or fluorogenic media. The selective diferential chromogenic components are conjugates of substrates targeting an enzyme conserved either over all bacterial kingdom, over a defined bacterial group such as coliform bacteria or over a defined species of bacteria such as Escherichia coli with either a chromophore or a fluorophore. If a targeted group of microorganisms is present, the enzyme is present inducing the chemical reaction of substrate utilization and liberation of either the fluorophore or the chromophore. The fluorophore or the chromophore are thus accumulating during the enzyme reaction. The chromophore induces the color change of the medium upon liberation and accumulation and the fluorophore induces the increase of intensity of fluorescence upon liberation and accumulation. A number of selective differential chromogenic media have been developed for the detection of E. coli and coliform bacteria and for the detection of enterococci such as AquaChrom™ECC and AquaChrom™Enterococcus from ChromAgar, COLILERT® and ENTEROLERT® from Idexx or EC Blue™ from HyServe. These selective chrmomogenic media contain selective chromogens. Selective chromogens are substrate analogues - the substrate connected to a chromophore - which are cleaved by an enzyme present only in the targeted bacterial group. If the bacterial group and thus the enzyme are present in the sample, the chromophore will be cleaved of and it will accumulate. The resulting unbound chromophore will enable the specific colouring of the sample as a consequence of its specific absorption/emission spectra.

A number of different methods for utilising such media for the MPN have been developed. One of them is the Idexx Ouantitray™ system (<CIT>). The system is based on two different trays either a tray of <NUM> wells of equal volume size or the <NUM> well tray with two different volume sizes. Also, a number of different patent literature deal with modifications of MPN-based methods and corresponding sample holding devices for bacterial detection, such as <CIT>; <CIT>; <CIT>; <CIT>; <CIT>; <CIT>; <CIT>; <CIT>; <CIT>; <CIT>; <CIT>; <CIT>, <CIT>; UK Pat Ap. <CIT> discloses a sample holder in the form of a sensor strip.

Compartmentizations of prior art sample holders in the patent literature include for instance: a microwell plate containing from <NUM>,<NUM> - 100µL of sample with the reagent (<CIT>); an MPN strip composed of one <NUM>, five <NUM>, and five <NUM> compartments (UK Pat Ap. <CIT>); a sample holder composed of five wells of <NUM> volume, five of <NUM> volume and five of <NUM>,<NUM> volume along with a system of sample distribution between the wells (<CIT>); a sample holder which distributes the sample into discrete compartments via a capilary flow though radially organized capilary channels (<CIT>); a sample holder (also known as Simplate™) is composed of a round incubation plate divided into a plurality of at least <NUM> recessed wells (<CIT>); sample holders composed of sets of microcompartments with volume sizes ranging from <NUM>,<NUM>µL - 25µL (<CIT>).

All of the above-mentioned inventions have certain limitations. One limitation often is the actual complexity of use. Filter-based approaches require additional rinsing and analytical steps after sample filtration to obtain the desired most probable concentration of bacteria, respectively the most probable number of bacteria per mL of sample. CO<NUM>-based detection methods require plural apparati installed for the actual quantification of microorganisms in the sample; and CO<NUM> alone does not indicate whether the microorganism is a prokaryote or an eukaryote, while in some occasions, however, identification of specific bacterial groups or species are required.

As far as sample holders are concerned, the present inventors have identified the statistical nature underlying the MPN method as a main problem, in particular in view of safeguarding a certain desirable confidence interval (CI), which preferably should be at <NUM>% CI. Specifically, according to a likelihood function of the statistical analysis underlying the MPN method - describing the probability of all outcomes according to a defined concentration value (µ value) - not all outcomes are equally probable for a certain µ value. In the case of the MPN method, basically a higher number of µ value estimates can be obtained with a larger number of possible outcomes (a broader µ estimate range), and likewise a larger number of possible outcomes can basically be obtained with a larger number of total compartments of the sample holder (because a larger theoretical number of outcomes is possible). Ideally, most accurate results would theoretically be obtained if infinite number of dilutions were made or if the sample would be distributed in an infinite number of compartments with infinitely small volumes (then the MPN estimate would equal the actual number of bacterial cells in the sample), which however is practically impossible since it would require volumes of compartments practically in the range of bacteria size and the results would be impossible to interpret. It is therefore the challenge to find a compromise between conflicting goals and thus to provide a balance of simplicity on the one hand and statistical accuracy and predictability on the other hand.

It is therefore an object of the present invention to provide a method for detection and/or quantification of microorganism in a liquid sample, which method allows easy performance and simple detection systems as well as associated simplified sample holders and processing, however without detriment to obtain reliable results of microorganism detection and enumeration.

The object is solved by the method for detection and/or quantification of microorganism in a liquid sample as defined in claim <NUM>, as well as by the provision of a sample holder as defined in claim <NUM>. Preferred embodiments are set forth in respective dependent claims to claims <NUM> and <NUM>, respectively. The present invention also provides a use of a sample holder as defined in claim <NUM>, and also a system as defined in claim <NUM>.

In the method according to the present invention, different volumes - which are present in the compartments of the sample holder - follow a linear distribution pattern in the sense of linearly increasing volumes, also meant to represent a linear regression pattern. Alternatively to differently distributed volumes following linear distribution pattern, but following the same principle of linear regression, the method may undertake the step of diluting the liquid sample into a number of samples of different dilutions by a dilution factor following a linear distribution (or linear regression) pattern in the sense of linearly increasing dilutions, and in principle introducing the different dilutions (following the linear distribution pattern) into a sample holder (in an alternative embodiment) with multiple possible repetitions of the same dilution. In line with this common principle, in reality an optimal model was found which incorporates a balance between statistical relevance and ease of result interpretation - a simplified method that provides statistically representative results in the predicted concentration range of bacteria, which at most can be expected in a typical liquid sample at issue. The method particularly provides statistically representative results in a relevant concentrational range of microorganisms between <NUM> CFU (Colony Forming Unts) per <NUM> sample and less than <NUM> CFU/ <NUM> of sample, preferably at most <NUM> CFU per <NUM> of sample. For instance, this concept takes into account an expected concentrational range between <NUM> and <NUM> CFU per <NUM> when analysing drinking water, and an expected concentrational range being <NUM>-times greater, from <NUM>-<NUM> CFU per <NUM>, when analysing wastewater - which original situation however can be easily addressed by appropriate preparation steps, e.g. subjecting drinking water to the analysis method undiluted, while subjecting waster water or industrially born water in a prior appropriate dilution before being subjected to the analysis method.

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.

The invention relates to a sample holder, constructed of a distinct number of compartments, namely at least <NUM>, preferably <NUM> and at most <NUM>. The compartments are of different volumes following a linear distribution pattern. Each different volume may be present in a multitude of repetitions.

Appropriately, the linear distribution pattern is at least <NUM>-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 <NUM>-fold, namely with <NUM> 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.

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:.

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 <NUM>-fold and at most <NUM>-fold, expecially in case of <NUM>-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 <NUM>-fold and at most <NUM>-fold is more preferred.

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 <NUM> CFU microorganisms per <NUM>, preferably at most <NUM> CFU microorganisms per <NUM>. 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 <NUM>: <NUM>, more preferably is diluted <NUM>:<NUM>.

Common to any of modifications and embodiments of the sample holder as such, 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 <NUM> compartments are formed in case of 1x-, 2x-, 3x-, 4x- and 5x- fold linear distributions of different volumes; in total <NUM> compartments are formed in case of 1x-, 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 <NUM> 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 distributions of different volumes.

The sample holder as such has 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 increasing volumes; or a rectangular outer shape whose compartments divide the sample holder into rectangular sections having said number of compartments respectively defining the linear increasing volumes.

In a particular embodiment, the sample holder is arranged to define a specific total volume of not less than, preferably about <NUM> for holding the liquid sample. When such a specific total volume for holding the sample is given as V=<NUM>%, 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 <NUM>,<NUM>% of V, the 2x-fold unit volume is <NUM>,<NUM>% of V, the 3x-fold unit volume is <NUM>,<NUM>% of V, the 4x-fold unit volume is <NUM>,<NUM>% of V, the 5x-fold unit volume is <NUM>,<NUM>% of V, the 6x-fold unit volume is <NUM>,<NUM>% of V, the 7x-fold unit volume is <NUM>,<NUM>% of V, and the 8x-fold unit volume is <NUM>,<NUM>% of V, wherein each %-volume indication encompasses a ± <NUM>% volume tolerance range, preferably a ± <NUM>% volume tolerance range, more preferably a ± <NUM>% 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 <NUM> and more preferably is <NUM> and accordingly the aforedefined V=<NUM>% is <NUM>.

In one embodiment, the sample holder is preferably constructed to be composed of <NUM> compartments, divided into eight triplets, thus with three repetitions of eight different volumes. The volumes following a linear distribution pattern then comprise; V<NUM>=V<NUM> (the volume of the smallest triplet), V<NUM>=2V<NUM>, V<NUM>=3V<NUM>, V<NUM>=4V<NUM>, V<NUM>=5V<NUM>, V<NUM>=6V<NUM>, V<NUM>=7V<NUM> and V<NUM>=8V<NUM> or x (volume of the smallest triplet), 2x, 3x, 4x, 5x, 6x, 7x and 8x. The volumes of the model are preferably: V1=<NUM>,<NUM>, V2=<NUM>,<NUM>, V3=<NUM>,<NUM>, V4= <NUM>,<NUM>, V5=<NUM>,<NUM>, V6=<NUM>,<NUM>, V7=<NUM>,<NUM> and V8=<NUM>,<NUM>. The total inner volume of all compartments is no less then <NUM>, preferably about <NUM>, which complies with ISO standards ISO <NUM>-<NUM>, ISO <NUM>-<NUM> and ISO <NUM>-<NUM>. In one embodiment, cylindrical columns form the compartments in the sample holder. Examples of such an embodiment is shown in <FIG>. The cylindrical compartments comprise, each in triplicate, V<NUM> making up the smallest volume, V<NUM>=2V<NUM>, V<NUM>=3V<NUM>, V<NUM>=4V<NUM>, V<NUM>=5V<NUM>, V<NUM>=6V<NUM>, V<NUM>=7V<NUM> and V<NUM>=8V<NUM> to thereby represent the 1x, 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 <NUM>). 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 (<FIG>, <FIG>, <FIG> and <FIG>) starting the lefthand helix the triplet of volume V<NUM>. At the volume triplet, V<NUM>, 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 <NUM> and the total diameter of the model may be, for example, <NUM> (<FIG>). In the preferred embodiment, the compartments are connected via a central channel, preferably of <NUM> 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 <NUM> of the water sample.

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<NUM> (<FIG>); 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.

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.

In a non-limiting example, the diameter is equal or about <NUM>, thus the heights also following a linear pattern respectively are: hi=<NUM>,<NUM>, h<NUM>=<NUM>,<NUM>, h<NUM>=<NUM>,<NUM>, h<NUM>=<NUM>,<NUM>, h<NUM>=<NUM>,<NUM>, h<NUM>=<NUM>,<NUM>, h<NUM>=<NUM>,<NUM> and h<NUM>=<NUM>,<NUM>. The difference in height between neighbouring compartments is preferably around <NUM>. The total height of the model is around <NUM> and the total diameter of the model is <NUM>.

In a particular embodiment, the volume of the smallest of the eight volume triplicates (Vi) can be calculated as: 3V<NUM> + 3x2V<NUM>+ 3x3V<NUM> + 3x4V<NUM>+3x5V<NUM>+3x6V<NUM>+3x7V<NUM>+3x8V<NUM>=<NUM>.

Then, as a total volume capacity for holding the sample, there are present <NUM> x V1=<NUM>, V1=<NUM>,<NUM>= <NUM>,<NUM><NUM>.

In yet another aspect the volumes of the eight triplicates is indicated in Table <NUM> below:
<IMG>.

Also, in the prefered embodiment of the sample holder outline, the compartments are of cylindrical shape with equal diameter, preferably around <NUM> but with heights corresponding the preferred volume of each triplicate. The heights can be calculated as:<MAT>, where h is the heiht of each compartment triplicate and r is the radius of each compartment.

The heights are then preferably as indicated below in Table <NUM>, weherein the height values are approximated values:
<IMG>.

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.

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.

In other embodiments of the substrate holder, the number of compartments may vary maintaining the linear pattern and the <NUM> total internal volume. The numbers of compartments may vary from at least <NUM> to about <NUM> or more. With <NUM> compartments in total, customers willl gain a rough estimate of the most probable at extremely low numbers of bacteria. Another precaution is with <NUM> compartments is that the volume of the smallest compartment triplicate would be <NUM>µL (<NUM>,7µL) which indicates a higher probability of technical error in volume upon manufacture. Accordingly, a total of <NUM> compartments is particularly prefered.

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.

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.

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 <NUM>°, <NUM>°, <NUM>°, <NUM>°, <NUM>°, <NUM>°, <NUM>° and <NUM>°. 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 <NUM>°, <NUM>°, <NUM>°, <NUM>°, <NUM>°, <NUM>°, <NUM>°, <NUM>° and <NUM>°.

Also in this outer shape configuration of the sample holder, each compartment volume is present in triplicate of a same volume each.

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.

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 <NUM> and more preferably is <NUM>, which is in accordance to the ISO standards.

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

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.

In the preferred embodiment, where the sum of the volumes of the compartments is no less than <NUM>, the volumes of the compartments are as follows: <NUM>,<NUM> for each of the three compartments of the <NUM>° circular section, <NUM>,<NUM> for each of the three compartments of the <NUM>° circular section, <NUM>,<NUM> for each of the three compartments of the <NUM>° circular section, <NUM>,<NUM> for each of the three compartments of the <NUM>° circular section, <NUM>,<NUM> for each of the three compartments of the <NUM>° circular section, <NUM>,<NUM> for each of the three compartments of the <NUM>° circular section, <NUM>° so <NUM>,<NUM> for each of the three compartments of the <NUM>° circular section and <NUM>,<NUM> for each of the three compartments of the <NUM>° circular section.

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

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.

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 <NUM>-<NUM> CFU/<NUM> of water sample. The regulation requires less than 100CFU/mL of water sample in heterotrophic plate counts after incubation at <NUM> and no unusual changes in heterotrophic plate counts at <NUM>. After calculating the <NUM>% 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 60CFU/<NUM> of drinking water sample. For the preferred embodiment composed of <NUM> wells with eight different volumes following a linear correlation pattern with each different volume present in triplicates, at lower estimates of MPN for example <NUM> cell per <NUM> of water sample, the estimate of the standard deviation of the natural logarithm of the MPN estimate limits to or practically reaches the value <NUM>. With increasing estimates of MPN, the estimate of the standard deviation of the natural logarithm of the MPN estimate decreases to value <NUM>,<NUM> at the estimate of the MPN 10CFU/<NUM> and to value of <NUM>,<NUM> at the estimate of the MPN 30CFU/<NUM>, then stabilizes and is still at around <NUM>,<NUM> at the estimate of MPN 40CFU/<NUM> of sample. With further increasing the estimates of MPN, the standard deviation ranges in the values between <NUM>,<NUM> and <NUM>,<NUM>. Calculating the lower and upper limits of <NUM>% confidence intervals; at the estimate of MPN 1CFU/<NUM> of sample, the lower limit of the <NUM>% confidence intervals is <NUM>,<NUM> CFU/mL and the upper limit is <NUM>,07CFU/mL. This in practice means that the actual number of cells in the respective <NUM> water sample should by calculation vary between <NUM> and <NUM> cells per <NUM> of water sample, with the Most Probable Number of cells being <NUM> per <NUM> of water sample. At the MPN estimate of 10CFU/<NUM> the lower limit of the <NUM>% confidence interval is at approximately <NUM>,04CFU/mL and the upper limit of the <NUM>% confidence interval is at approximately <NUM>,2CFU/mL. This in practice means that the actual number of cells in the respective <NUM> water sample should by calculation vary between <NUM> and <NUM> cells per <NUM> of water sample, with the Most Probable Number of cells being <NUM> per <NUM> of water sample. At the MPN estimate 30CFU/<NUM>, the lower limit of the <NUM>% confidence interval is <NUM>,16CFU/mL and the upper limit of the <NUM>%confidence interval is <NUM>,55CFU/mL. This in practice means that the actual number of cells in the respective <NUM> water sample should by calculation vary between <NUM> and <NUM> cells per <NUM> of water sample with the Most Probable Number of cells being <NUM> per <NUM> of water sample. At the MPN estimate 40CFU/<NUM> the lower limit of the <NUM>% confidence interval is at <NUM>,<NUM> and the upper limit of the <NUM>% confidence interval is at <NUM>,<NUM>. This in practice means that the actual number of cells in the respective <NUM> water sample should by calculation vary between <NUM> and <NUM> cells per <NUM> of water sample with the Most Probable Number of cells being <NUM> per <NUM> of water sample. However, one should always also observe the total number of positives out of <NUM> 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.

Accordingly, it is possible to calculate the <NUM>% confidence intervals at increasing estimates of the MPN. The calculations of the <NUM>% 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, <NUM>% 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 <NUM>% intervals are increasing linearly with increasing values of MPN estimates.

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 <NUM>% confidence intervals.

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 <NUM>-<NUM> CFU/<NUM> of water sample. The regulation requires less than 100CFU/mL of total bacterial in the water sample in heterotrophic plate counts after incubation at <NUM> and no unusual changes in heterotrophic plate counts at <NUM>. After calculating the <NUM>% 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 60CFU/<NUM> of drinking water sample.

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

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. 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. 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.

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.

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.

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

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.

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.

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.

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 <NUM>% confidence intervals.

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.

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 <NUM>% 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.

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 <NUM>% confidence intervals from the estimate of the standard deviation of the natural logarithm of the MPN estimate and from the MPN estimate.

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 <NUM> CFU per <NUM>) despite a relatively low number of compartments, because the sizes of volumes follow a linear regression.

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 <NUM>.

In a comparison example an exponential distribution of the volumes with constant ratio between adjacent different voumes, as shown in Table <NUM>.

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.

Results can be depicted from <FIG> for linear and exponential distribution of volumes, respectively.

In case of linear volume distribution, a relatively narrow confidence intervals can be observed up to <NUM>,6CFU/<NUM> of water sampleor 60CFU/<NUM> of water sample, which increase linearly (<FIG>). 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 <NUM> different volumes following a linear distribution in triplicates is <NUM>-150µL, where in case of the exponential volume distribution, the volume <NUM>µL is reached when the model contains <NUM> 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.

In the case of exponential distribution of volumes for comparison (cf. <FIG>), 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 <NUM>% confidence intervals are howeverdense through a wider range upto <NUM> CFU/<NUM> of water sample. In addition, the confidence intervals do not increase completely linearly, rather, belts of slightly broader intervals can be seen.

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

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

In this Example <NUM> the correlation curve provided in Example <NUM> 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 <NUM><NUM>CFU/mL (Example <NUM>). In Example <NUM>, we checked whether OD <NUM>, <NUM> or <NUM>,<NUM> is a better initial OD to prepare the original bacterial suspension calibrated at <NUM><NUM>CFU/mL, for later experiments (Example <NUM>), thus confirming the linear correlation curve obtained at Example <NUM>. In this Example <NUM>, E. coli was introduced into <NUM> sterile distilled water via inoculation loop upto two different optical densities (OD) <NUM>,<NUM> and <NUM>,<NUM>. Afterwards, series of dilutions of <NUM> were made as in the provided scheme below, transfering 300µL of the previous dilution into 2700µL of sterile distilled water as is represented in <FIG>. At OD <NUM>,<NUM>, <NUM> of each of the dilutions <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM> and <NUM>-<NUM> were transfered into <NUM> of sterile distilled water (dHzO) and filtered through a membrane filter of pore size <NUM>,<NUM>. At OD <NUM>,<NUM><NUM> dilutions <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM> in <NUM>-<NUM> were transfered into <NUM> of sterile distilled dHzO and filtered through a cellulose filter of pore size <NUM>,<NUM>. The dilutions are depicted in <FIG>.

The results depicted in Table <NUM> below showed that <NUM>,<NUM> is a better OD of the original suspension in the proof of concept experiments.

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

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

Claim 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-<NUM>) distributing the liquid sample into a number of different discrete volume portions, wherein the different discrete volume portions define linearly increasing volumes, or
(a-<NUM>) diluting the liquid sample into a number of diluted 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.