GB2412166A

GB2412166A - Rapid particle analyser

Info

Publication number: GB2412166A
Application number: GB0406055A
Authority: GB
Inventors: Marcos Rodrigues; Georgios Chliveros
Original assignee: Sheffield Hallam University
Current assignee: Sheffield Hallam University
Priority date: 2004-03-17
Filing date: 2004-03-17
Publication date: 2005-09-21
Anticipated expiration: 2024-03-17
Also published as: GB2412166B; GB0406055D0

Abstract

A method and apparatus for rapid particle analysis is especially useful for estimating the bacteriological quality of water. The apparatus comprises a laser 301, a sample chamber 302, a first array 303 of detectors 305-307 and a second array 304 of detectors 308-310. The sample chamber contains a fluid sample comprising a plurality of particles, e.g. micro-organisms. The second detector array 304 rotates around the sample chamber 302 by angle w . A processor analyses the data collected at different times and angles n , w and determines a parameter of the particles such as size, shape, internal morphology, refractive index or type of particle.

Description

METHOD AND APPARATUS FOR PARTICLE ANALYSIS

Field of the Invention

The present invention relates to the field of particle analysis, and in particular to the field of particle identification.

Backaround to the Invention

There is a generally recognised need for methods that permit rapid estimation of the bacteriological quality of water. Applications of rapid methods o may range from analysis of wastewater to potable waters' quality assessment.

For example, during emergencies such as line breaks in a water network, there is an urgent need for rapid assessment of the sanitary quality of water. However the reliability of such tests may not be as sensitive as that of standard tests. For example, a rapid test may be compromised because a bacterial limit sought may be higher than that of a minimum risk concentration of bacteria. Moreover, a rapid test may not be specific enough for a specific application, may require reagents not generally available or special handling.

The term 'water' is used generically for it may depend on the context of microbiological examination. Different uses of water, such as water supplies for domestic or industrial use, may require different examinations. It is known to use laser light scattering to measure properties of particles in a fluid, for example particle size distribution. However, a problem with using such techniques for analysing bacteria is that bacteria present a relative refractive index m close to : that of water (commonly known as near-index particles). The degree of light refraction is therefore limited compared to, for example, inorganic particles such as silica, and meaningful results can be difficult to obtain.

A principal indicator of water suitability is the coliform group (genera o Enterobacter, Citrobacter, Klebsiella and Escherichia) as their appearance renders unsuitability and potentially unsafe use. Faecal streptococci and especially their subgroup enterococci are indicators of faecal pollution. Other organisms such as Staphylococcus sp. and Pseudomonas aeruginosa are P1111 spec associated with the respiratory tract or the skin. It is therefore desirable to develop a test that can rapidly detect such organisms.

The European Union has set standards for monitoring the quality of water for over 50 different substances (Council Directive 79/869/EEC and Official Journal of the European Communities No.L, 271/44-53). Regarding coliforms, standards require that at least 95% of samples taken from customers' taps should contain no coliforms, and no samples should contain any faecal coliforms.

Yorkshire Water (a water company) performs around 90,000 tests for coliforms Jo each year to ensure standards are met. In the UK, 1.2 million tests are performed each year. The worldwide market (Western Europe and the United States) is estimated to be greater than 20 million tests per year for coliforms only.

Any technology that brings further improvements either concerning detecting the efficiency of disinfection or detecting harmful bacteria such as coliforms as required by regulations is extremely desirable. Possible uses include: Checking the efficiency of coliform disinfection: After a coliform infection No has been cleaned up, it is desirable to check if any coliforms remain. Large numbers of samples need to be screened. There is also an obligation in the UK to test the water supply randomly, around once each week, and the water supply can be interrupted for a long time when a positive result (i.e. presence of coliforms) is found. If a system is available that can rapidly show that the samples are negative, re- connection to the water supply can be made immediately with minimum disruption to water supply. This is highly desirable as currently "boiling warnings" are issued to customers while waiting confirmation of disinfection, and this has a strong negative effect on the customer's perception of the quality of the water. The longer the period where the water cannot be sold to customers the higher the cost for the water companies. Tests performed in minutes would significantly reduce this problem.

P1111.spec Sample analysis: at the moment, standard plate count tests are performed at the laboratory to detect the presence of conforms. This is a very time consuming process as tap samples are collected and placed in a refrigerated vehicle and transported to a laboratory for testing. Results may not be available for several days. It is therefore desirable to find apparatus and a method that can perform either mobile tests online or at the laboratory.

Check the efficiency of Cryptosporidium disinfection: it would be highly desirable to use new technologies an alternative method to existing methods or o as a step in the verification of the efficiency of disinfection. Yorkshire Water in the UK are required to filtrate and test 1000 litres of water per day for detecting Cryptosporidium. This is a costly procedure and there is a strong desire by the Water Companies to automate this process.

: Several rapid tests for rapid identification of harmful particles in water supplies have been proposed. US 5,582,985 discloses a chemical method for detecting mycobacteria. US 5,770,368 and US 5,693,472 disclose variants of DNA testing for Cryptosporidium in water supplies. US5,229, 849 discloses laser Doppler spectrometry for statistical study of the behaviour of microscopic o organisms moving in a fluid. US 4,548,500 discloses apparatus and a method for identifying particles by the way they move through a light beam and mapping the scattered light against known light scattering maps. US 5,473,428 discloses an interferometric temperature system for detecting microscopic organisms. EP 1367382 discloses methods for identifying particles by illuminating the particles in z a chamber, detecting scattered light by way of optical sensors, and comparing the signals from the optical sensors with predetermined frequency-of-occurrence probability histograms.

Problems associated with the above methods include the time and so complexity required to obtain results, and the methods provided do not directly provide information on important micro-biological issues such as the gram reaction, dormancy or viability counts of the micro-organisms detected.

P11 1 1.spec Light Scattering Techniques It has been suggested that many techniques based on light scattering are sensitive and fast enough to permit rapid detection and can be brought to comply with international standards. A known technique is that of multi-angle light detection and the use of the Rayleigh-Gans-Debye (RDG) approximation. In terms of the instrumentation and data processing, several issues have to be resolved, including multiple scattering, volume corrections due to the detectors' cone of reception and background noise such as buffer solution impurities.

Jo Moreover the RDG applicability conditions restrict its use to cell lengths that may not provide accurate estimates on larger structures, for example aggregates of particles.

Optical data can be obtained from laser light scattering obtained from a circular array of photo detectors (placed at the horizontal plane) and by means of the RDG approximation (e.g. Wyatt 1973. Differential Light Scattering Techniques for Microbiology. In: J.R. Norris and D.W. Ribbons (eds.) Methods in Microbiology, Volume 8. Academic Press: 183-263, and Wyatt 1993. Light scattering and the absolute characterization of macromolecules, Analytica go Chimica Acta, 272, 140). The optical data are interpreted to infer information about the size distribution and external morphology (e.g. sphere, disc, rod) of the cell. Alternatively with the use of a goniometric module and Mie theory 2-layer models, certain fundamental questions have been addressed (Ulanowski Z. and Ludlow l.K. 1993. The influence of cortex on protoplast dehydration in bacterial spores studied with light scattering, Current Microbiology, 26, 31-35).

However, there are issues with these methods both in terms of instrumentation and data processing techniques. In the case of the goniometer, a goniometric module is scanning angles at a horizontal plane relative to the o sample depending on calibration of the alignment of the module and the efficiency of the angular scanning speed of the motor. Hence due to the moving- mechanical parts, the exact design of compensation circuits can introduce errors.

P11 1 1.spec ln terms of the photo detector array, a problem on a dynamic range of the optical sensor is that biological particles, including bacteria, contain weakly scattering material, as most of their bodies contain a high percentage (70% to 86%) of water (Schlegel 1997 7th ed. General Microbiology. London: Cambridge s University Press). As a result, the light scattered by the bacteria is difficult to detect above the background noise, making detection difficult. The number of photons reaching the sensor has to be high enough to exceed the dark current (background or dark radiation), which is not feasible using semiconducting photodiodes. A solution may be the use of Avalanche Photo Diodes (APD) but the low light scattering levels may result in loss of optical information.

In terms of data processing algorithms to characterize the particles, the existing models currently used do not adequately model the complex makeup of prokaryotic cells such as bacteria). In general the cell has a structure that can be represented by a cell wall, the plasma or cytoplasmic membrane, the cytoplasm and the nucleoid. Other morphological characteristics may also appear such as a slime layer (capsule) outside the cell wall or inclusions within the cell's cytoplasm (e.g. spores, granules). The isolation of contributions arising from the optical data and the internal parts of the cell will lead to better understanding of the workings 2 o of the cells.

Furthermore, in a liquid medium, motional effects of the particles are evident. This is due to both flagella of motile bacteria species, and also to translational diffusion, Brownian forces arising from the liquid, rotational motion, number fluctuations and chemical reactions. Brownian particles present a monotonically decreasing (second order) correlation function G(2)( ) defined as G<2'(r) (Is2)e (or (1) where G(2)( ) is a correlation function; (Is2) is the average scattered light intensity o r is the period over which the function is applied.

P1111 spec For translational diffusion, with diffusion coefficient DT, it can be shown (Johnson, 1994) that or [(DTK + (0 _ lo) (2) where con corresponds to to; a' corresponds to 2nf; f is the frequency within the bandwidth investigated fo is the central frequency of a laser K is a constant o This leads to a Lorentzian curve centred at the laser frequency O and a half width at half height of (DTK2). Referring to figure 1 herein, there is illustrated schematically the prior art apparatus of the homodyne experiment.

The apparatus comprises a light source 101 such as a laser, and a series of s photodetectors 102, 103, 104, 105. Light 106 from the light source 101 passes through a sample region 107 and is scattered by particles within the sample region. Scattered light is detected by each photodetector 102-105 and a photocurrent generated by each photodetector 102-105.

So Each photedector 102-105 has a cone of reception. Lines 108 and 109 denote the cone of reception of photodetector 103, and line 110 denotes the optical path axis. The cone of reception comprises the actual volume SO that is illuminated. Compensation must be made for this effect for each detector.

s The photocurrent obtained from each photodetector is used to generate an approximation to G(2)( ), resulting in the following expression for the optical spectrum (Johnson and Gabriel, Laser light scattering, 1994, Dover Publications) It2()=(Is) Dad+ ( ) i(2D K2)2+a,2] (3) where Corresponds to the difference between the central laser frequency o and the resulting frequencies.

P1111 spec Motile bacteria persist on their motion for distances greater than those of Brownian particles. This causes the intensity optical spectrum to widen, thus broadening the spectrum further away from the Lorentzian peak (Nossal, Spectral analysis of laser light scattered from motile micro-organisms, Biophysical Joumal 1971 11 341, Lovely and Dahlquist, Statistical measures of bacterial motility and chemo taxis, Joumal of Theoretical Biology 1975 50 477, and Shaefer et al, Intensity fluctuation spectroscopy of motile micro- organisms, Nature 1974 248 162).

This frequency shift may depend on the particular way that the bacteria cell moves (Holtz et al, Rotational translational models for interpretation of quasi- elastic light scattering spectra of motile bacteria, Applied Optics 1978 17 3197).

That is to say combination of rotational motion followed by either helical variation, as in E. coli. Br, or followed by straight lines at random angles, as in S. typhus.

Furthermore, number fluctuations from different bacteria concentrations will produce an additional frequency shift in the optical spectrum. Consequently, not only is there a shift of the maxima/minima of the intensity signature towards smaller or larger angles according to larger or smaller cell sizes and their internal o structure variation, but also a frequency shift according to motional effects. These phenomena may be 'short lived'. It is therefore desirable to detect the beginning and the end of the event of bacteria passing through the optical path of the laser.

Cell Models Picturing a cell as being composed of distinct uniform objects of proper geometrical shape is generally speaking not correct. Moreover, in many studies it has been indicated that each part of the cell has a refractive index different from that of its surroundings. However, viewing a cell as an object with randomly varying refractive index is also incorrect. Therefore, in order to generate a more so accurate representation of the cell, a cell can be modelled as having various compartments within its volume and within these compartments the refractive index is different from that of the surrounding objects.

P1111 spec For example a spherical cell (for example cocci) could be represented as that of a n layered concentric spherical compartments where each layer has a different average refractive index. A model of a cell is represented schematically in Figure 2. The model shows a cell wall 201, a plasma membrane 202, cytoplasm 203 and a nucleoid region 204. If the dominant features of the cell are found' to be the cell wall 201, the plasma membrane 202 and cytoplasm 203 then n = 3.

In real applications, for example in water treatment, an unavoidable issue is To that of very large aggregates of particles. That is to say large chains, quadruplets and so on of particles may form. Each of these aggregates may comprise a larger structure of self-similar nature (e.g. multiple chains of different length) or a random structure, if the process of growth that generated them dictates a multiple plane division. The latter is of great interest since it may fully model the appearance of configurations such as staphylococci.

It is important that the best model for a particle type is selected to isolate particular contributions to scattering from different components of each cell. For example, recognising contributions arising from the cell wall would provide an o indication of the Gram reaction of the cell, an all important issue in microbiological testing; whilst recognising contributions from internal structures such as spore inclusions may render identification of specific genera.

Summarv of the Invention The inventors have realised the limitations and problems associated with prior art methods and apparatus for detecting particles and accordingly devised a method and apparatus for detecting particles that reduces these problems and limitations.

According to a first aspect there is provided apparatus for particle analysis comprising: a radiation source; P1111 spec a sample chamber configured to contain a sample, said sample comprising a plurality of particles; a first array of radiation detectors; a second array of radiation detectors; said device configured to collect data from said radiation detectors at least at a first time and a second time, wherein said second array occupies a first position at said first time and a second position at said second time; a processor configured to process said collected data, said processing determining at least one predetermined parameter of said particles.

Preferably, said radiation source comprises a laser; and said arrays of radiation detectors are configured to detect visible light.

go Preferably, each said array of radiation detectors comprises at least one photo-multiplier tube.

Preferably, each said array of radiation detectors comprises: an array of optical fibres; a photo-multiplier tube; means to sequentially connect each optical fibre of said array of optical o fibres to said photomultiplier tube, and collect data at said photo-multiplier tube from each said optical fibre.

P11 1 1.spec Preferably, said first array of radiation detectors is mounted on a first support, and each radiation detector of said first array of radiation detectors is located at the same distance from said sample chamber; said second array of radiation detectors is mounted on a second support, and each radiation detector of said second array of radiation detectors is located at the same distance from said sample chamber; Preferably, said second array of radiation detectors is movable relative to To said sample chamber.

Preferably, said second array of radiation detectors is rotatable about said sample chamber.

Preferably, said optical fibres comprise non-coherent polymer fibres.

Preferably, said predetermined parameter comprises information relating to any one of the following: 2 o particle shape particle size particle species particle refractive index refractive index of at least one inferred layer of said particle particle motion P1111 spec Preferably, said processing determining at least one predetermined parameter of said particles comprises removing collected data arising from non- motile particles.

Preferably, said processing determining at least one predetermined parameter of said particles comprises comparing a measured predetermined parameter value with a series of predefined parameter values from a database.

Preferably, said processing determining at least one predetermined o parameter of said particles comprises: obtaining measured intensity data from said first and second array of radiation detectors fitting said measured intensity data to a predicted function of said particles Preferably, said radiation source is configured to generate radiation at a range of different wavelengths.

Preferably, said radiation source is configured to generate radiation at a range of different intensities.

According to a second aspect there is provided a method of particle analysis comprising: illuminating a sample with radiation, said sample comprising a plurality of particles; collecting a first data set from a first array of radiation detectors; collecting a second data set from a second array of radiation detectors; P1111 spec collecting said first data set and said second data set at least at a first time and a second time, wherein said second array occupies a first position at said first time and a second position at said second time; processing said collected data to determine at least one predetermined parameter of said particles Preferably, said method further comprises: To providing said illumination using a laser; and said arrays of radiation detectors are configured to detect visible light.

Preferably, each said array of radiation detectors comprises at least one photo-multiplier tube.

Preferably, each said array of radiation detectors comprises: an array of optical fibres; a photo-multiplier tube; and said method further comprises sequentially connecting each optical fibre of said array of optical fibres to said photo-multiplier tube, and collecting data at said photo-multiplier tube from each said optical fibre.

Preferably, said first array of radiation detectors is mounted on a first support, and each radiation detector of said first array of radiation detectors is 3 o located at the same distance from said sample chamber; P1111 spec said second array of radiation detectors is mounted on a second support, and each radiation detector of said second array of radiation detectors is located at the same distance from said sample chamber; Preferably, said method further comprises: moving said second array of radiation detectors relative to said sample chamber.

To Preferably, said method furthercomprises: rotating said second array of radiation detectors about said sample chamber.

Preferably, said optical fibres comprise non-coherent polymer fibres.

Preferably, said predetermined parameter comprises information relating to any one of the following: 2 o particle shape particle size particle species particle refractive index refractive index of at least one inferred layer of said particle o particle motion P11 1 1.spec Preferably, said processing determining at least one predetermined parameter of said particles comprises removing collected data arising from non- motile particles.

Preferably, said processing determining at least one predetermined o parameter of said particles comprises: obtaining measured intensity data from said first and second array of radiation detectors fitting said measured intensity data to a predicted function of said particles Preferably, said method comprises illuminating said sample with radiation, said radiation being generated at least at two different wavelengths.

Preferably, said method comprises illuminating said sample with radiation, said radiation being generated at least at two different intensities.

According to a third aspect there is provided a device for particle analysis comprising: a laser source; a sample chamber configured to contain a sample comprising a plurality of particles; a first ring, a second ring; P1111 spec a plurality of radiation detectors disposed on said first ring; a plurality of radiation detectors disposed on said second ring; wherein said second ring is rotatable about said sample chamber; means to collect light intensity data from said plurality of radiation detectors To means to process said collected light intensity data to infer information about said particles.

Brief Description of the Drawings

For a better understanding of the invention and to show how the same may be carried into effect, there will now be described by way of example only, specific embodiments, methods and processes according to the present invention with reference to the accompanying drawings in which: Figure 1 illustrates schematically the prior art apparatus of the homodyne 2 o experiment.

Figure 2 illustrates a prior art model of a cell.

Figure 3 illustrates schematically the apparatus for particle analysis.

Figure 4 illustrates a flow diagram showing the measurement procedure.

Figure 5 illustrates schematically the structure of one array of radiation detectors.

Figure 6 illustrates a flow diagram showing the data collection process for the apparatus.

P1111.spec Figure 7 illustrates illustrated a graph showing the form of a signal using a 2-layer Mie solution for radius of 1.1pm and average relative refractive index of 1.3 Figure 8 illustrates schematically the hardware used for data acquisition from the optical fibres.

Figure 9 illustrates schematically the data flow processing.

JO Detailed Description

There will now be described by way of example a specific mode contemplated by the inventors. In the following description numerous specific details are set forth in order to provide a thorough understanding. It will be apparent however, to one skilled in the art, that the present invention may be practiced without limitation to these specific details. In other instances, well known methods and structures have not been described in detail so as not to

unnecessarily obscure the description.

Referring to figure 3 herein, there is illustrated schematically the apparatus for particle analysis. The apparatus comprises a light source 301, a sample chamber 302 configured to hold a fluid sample, a first detector array 303 and a second detector array 304. The first detector array 303 comprises a plurality of radiation detectors 305-307, and the second detector array 304 comprises a plurality of radiation detectors 308-310.

The light source 301 comprises a laser. Although not essential, it is preferred that the light source 301, the sample chamber 302 and the first detector array 303 lie in substantially the same plane.

So Each radiation detector 305-307 on the first array 303 is disposed at a scattering angle v on the first array 303 relative to the sample chamber 302.

P1111 spec Each radiation detector 308-310 on the second array is disposed at a scattering angle v on the second array 303 relative to the sample chamber 302.

Furthermore, the second array 304 is rotatable about the sample chamber 302 by angle 6. The rotation is effected by a motor (not shown), although the rotation may also be effected manually.

In use, a sample containing particles such as bacteria is placed in the sample chamber 302. The light source 301 illuminates the sample with a laser Jo emission 311. Light is scattered by the particles and detected at the radiation detectors 305-307, 308-310. The second array 304 is in a first position. Once measurements of light intensity have been obtained, the second array is moved to a second position and a further series of measurements is taken. Each array extends from 3 to 176 angular position, relative to the direction of the incident :s laser light beam 311. The angular range has been calculated for a sample chamber 302 comprising a circular sample cuvette with flat entry and exit windows and for a 2 mm source laser. It will be evident to one skilled in the art that the invention may be practiced without limitation to these parameters.

o Referring to Figure 4 herein, there is illustrated a flow diagram showing the measurement procedure. The second array 304 is in an initial position 401. The light source is turned on and the sample is illuminated 402 by a laser. Scattered light intensity data is collected at each radiation detector on both the first array 303 and the second array 304. If a further angle of the second array is required : 404, then the second array 304 is moved to a further position 405, and further data are collected from each radiation detector. If no further angle of the second array 304 is required 404 then the collected data is processed 406.

Although only three radiation detectors are shown on each array 303, 304, it o will be apparent that the more radiation detectors there are, the more detailed the data collected will be. Ideally there are at least 100 radiation detectors on each array.

P11 1 1.spec Referring to Figure 5 herein, there is illustrated schematically the structure of one array of radiation detectors. Each radiation detector comprises an optical fibre 501-503. The fibres 501-503 are non-coherent polymer fibres, as these allow higher signal transmission in the visible side of the spectrum, with a diameter of 0.5 - - 0.75 mm where both ends are coated to improve uniformity.

Thus the light scattered by a particle in the chamber 302 and in the field of view of the array 303 or 304 will enter the fibres and emerge from N number of output ends.

The output faces of the fibres are scanned using a rotating disc 504 allowing light from only one fibre to pass through a slit at any time. By controlling the speed of rotation, the scattering angle v is known withrespect to the sample chamber 302, thereby avoiding alignment and calibration problems. Furthermore only one sensor module is required reducing the overall cost of the device.

The scattered light is then passed to the detector 505. A Photo Multiplier Tube module (PMT) is used, which supplies a time dependent output electrical signal which can be related to the scattering angle v from the timing pulses o produced by optical switches attached to the scanning disc. A hollow reflector can be used in order to maximize optical transfer by directing the scattered light to the PMT's sensing window.

The duration of the complete angular scan is controlled by setting the speed : of a DC motor (not shown) to which the scanning disc is attached. As a result, and assuming that the scanning time lower limit is at 10 ms then for n = 100 optical fibres, then the time dependent portion of the output electrical signal corresponding to each angle (the scattering angle of each optical fibre) is, on average, of 0.1 ms duration.

Referring to figure 6 herein, there is illustrated a flow diagram showing the data collection process for the apparatus comprising optical fibres. The second P11 1 1.spec array is located in its first position 601. The light source 301 illuminates 602 the sample in the sample chamber 302. Data is collected 603 from optical fibre n on both the first array 303 and the second array 304. If further measurements are required 604, then the disc rotates to allow light from the next optical fibre to the PMT, such that light intensity measurements can be obtained at optical fibre n=n+1 605. Further data are collected 603 and the process repeats. If no further measurements are required 604, and light intensity measurements have been taken from each optical fibre, then the procedure moves to the next step. If a further angle of the second array 304 is required 606 then the second array is JO moved 607 to a further position and the process repeated from collecting data 603. If no further angle of the second array 304 is required 606 then no further measurements are required and the collected data may be processed 608.

The scattered light intensity from each optical fibre is collected by the PMT 505 which in turn 'converts' it to an electrical signal (current or voltage), to be amplified by a logarithmic amplifier giving a better resolution at lower signal levels. The advantage of using a PMT module is such that even from a single particle the resulting light scattering signal can be predicted to be high enough so as not to violate the detector's responsivity/sensitivity characteristic curve. The o analogue signal obtained is then outputted to the data acquisition module or card and converted to a digital signal for further analysis.

From the data obtained from the two detector arrays, information can be obtained about the shape of the particles or aggregates to assist in characterizing z the bacteria species present. It will be appreciated by one skilled in the art that this information can equally be used for other particles suspended in fluid, such as inorganic particles.

Referring to Figure 7, there is illustrated a graph showing the form of the so signal using a 2-layer Mie solution for radius of 1.1,um and average relative refractive index of 1.3 (water medium is assumed). The graph shows the log of light intensity 701 plotted against the scattering angle v 702. The solid line 703 P1111.spec represents the graph with incident light beam of wavelength 532 nm and power of mW at perpendicular polarization. The dotted line 704 represents the same signal with added Gaussian noise (30db).

Whilst the second array 304 moves to obtain measurements, the first array 303 remains in a fixed position relative to the sample chamber 302. In this way, the first array measures the scattering at different angles v, whereas the second array 304 measures both the scattering at different angles v and angle of elevation relative to the sample chamber 302. Direct comparisons can be JO made from data measured by the first array 303 and the second array 304 and related with each increment of the elevation angle which can be incremented in small steps by using a microstepper motor under computer control. This allows capture of data such that a 3 dimensional picture of scattering in the form of a 3 dimensional surface can be obtained. The processing and analysis of such surfaces allows the characterization and discrimination of sample content.

Light intensity measurements obtained from each optical fibre are both time and frequency dependent. The intensity of light scattered is measured at different sampling intervals for each scattering angle. Synchronisation between 2 o measurements obtained by the first array and the second array readings for each scattering angle is therefore desirable.

The apparatus allows capture of scattering data that is of particular use in the characterization of non-spherical particles or when any configuration of : aggregates/agglomerates of particles is present (e.g. linear chains, quadruplets and so on). Models can be used to predict and validate the intensity of scattered light and to determine whether a particular sample configuration is present or not.

This allows for instance, to scan a sample of water for the presence of spherical and non-spherical (rod-like) shaped particles with given optical properties and so size distributions such as conforms. This is one way that the apparatus can be used to discriminate the species of particle present.

P1111 spec In order to generate a more accurate representation of the cell, it is modelled as having various compartments within its volume and within these compartments the refractive index is different from that of the surrounding objects. In this way, each of the structures internal or external to the plasma membrane are modelled as a different layer in an elayered spherically or ellipsoidal symmetric inhomogeneous particle. Information is obtained about each part of the cell that can answer many fundamental questions (e.g. dormancy, gram reaction). The theory of RDG is modified and extended to spherically symmetrical and ellipsoidal particles/cells with an arbitrary number of layers and o corresponding relative refractive indices. Population variations in size are accounted for. The modified RGD theory (mRDG) is applied to account for contributions resulting from the radiation field inside the particle that is analysed.

This improves the prediction ability from larger structures that fall outside the RDG conditions. The so-called 'inverse problem' is approximated by the use of an e-layer mRDG model I(v,5) (see Figure 7, solid line 703) in conjunction with incoming experimental data I(v, 5) _ (so)) , where the processed signal is derived from the processing algorithms that follow, described in Figure 9 (Sk-(S())T, denoted as 915 in Fig.9). For an example of the expected post processed signal, and for = 0, thenI(v,O)=I(v), refer to Figure 7 dashed line 2 0 704.

An approximation of the 'inverse problem' is computed by fitting the experimental data on the e-layer function and computed by means of minimization of the objective function according to equation 4: | log I(v, is) - log I(v, is)| (N + 1) (log I(O, O) - log I(Vo'do))

A

where I(O,O) is the forward scattering intensity, that is to say the maximum expected intensity magnitude; and I(vo,/So) is the minimum observed intensity.

P111 1 spec The conditions, to which the objective function (E) is subjected, will be defined according to the domain to be handled.

Asymmetry (positive or negative skew-ness of a size distribution) is also accounted for. The model allows verification of important cell properties such as spore inclusions and gram reaction leading to better characterization and discrimination of a sample.

Referring to Figure 8 herein, there is illustrated schematically the hardware To used for data acquisition from the optical fibres from one array. Each input 801- 806 corresponds to a different scattering angle v, and each input is scanned at different frequencies f: Each input 801-805 is fed to a corresponding channel 807-811. Each channel produces corresponding scanned data 813-817 each time the input detects light from the corresponding optical fibre. A complete set :s of scanned data 818 at every measured scattering angle v is then made from the individual scanned data from individual scattering angles v. Alternatively, where the scanned data is continuously measured, for example where a series of PMTs are used, the continuous input 806 is fed into a channel 807 to produce scanned data 819.

From the acquired data it is desirable to determine an intensity signature by analysing frequency shift to help to characterize the particles present. It has been found that that the higher the scattering angle, the lower the intensity of the measured scattered light. As a result a motion analysis is best performed for angles of 90 .

Referring to Figure 9 herein, there is illustrated schematically the data flow processing for a single channel input at scattering angle v. Channel 1 receives a signal 901. This is processed using a wavelet analysis algorithm 902. Non-linear o signal fragmentation 903 is performed on the signal to split the signal into a series of signal portions 904,905,906, 907. Data fragment analysis 908 is performed on each signal portion. Timefrequency localization is accomplished by altering a P1111 spec scale and position of a transform basis function (also known as the mother wavelet). The data set is segmented in packets of detailed coefficients. If the scale is small then rapid changes are detected; and high frequencies are included. The same procedure applies for low frequencies analysis, with the only difference being that the scale is now large.

Each portion is separated by down-sampling 909 the portion. Down- sampling splits the signal portion into high frequency portions 910 and low frequency portions 911.

When a low frequency component appears, at a relatively high intensity level, then it is a Brownian particle. If this 'common' frequency (0.10 to 10 Hz) is observed at several instances, then Brownian motion within the fluid is evident.

Low frequency portions 911 arise from dead cells or non-bacteria particles, whereas high frequency portions 910 arise from motile species such as bacteria.

For the input data path that continuously monitors intensity fluctuations at a small forward angle a signal is recorded that produces a bandwidth of about 5 kHz. As a result a Nyquist sampling frequency of at least 10 kHz is used.

So Consequently in a time period of one second, at least 10000 samples are received and subsequently fed into the analysis algorithm. It has been verified that at this concentration multiple scattering effects are minimised and pose no threat on data or signal analysis. At concentrations close to say 103 living cells per ml of solution it is expected that an occasional frequency shift away from the :!5 frequency of non-motile particles (Lorentz peak) will appear in the details (dams) 912 of a wavelet analysis algorithm. In that case, a time-frequency localization has been achieved and as a result, the start and beginning of the event of living cells passing through the laser beam is recorded. In the algorithm, it follows that if mother wavelet being ark and scaling function Aid have the relationships 3 o (I) = H 2): 2) (5) P11 1 1.spec where (P is an approximate continuation function of a time series corresponding to the mother wavelet H is a fixed function. and

+()) = G)))( ) (6) where is an approximate continuation function of a time series corresponding to the mother wavelet, G is a fixed function.

then the decomposition (down-sample) is achieved by calculating On = Mock +th(k-2n) (7 Katz where h is a function of the number of sampled data points 5(c(,m) ) iS a time dependent function k is a constant dn = ckm+'g(k-2n) (8) Katz where g is a function of the number of sampled data points (dams) is a time dependent function 2 0k is a constant where On = lick h(n-2k) + dk g(n-2k) (9 Katz 25It is noted that the power spectrum obtained from a Fourier Transform of air is acting as a low pass filter whilst avid is a band pass filter (where S6nm and {V,m are time series corresponding to the mother wavelet).

P11 1 1.spec By obtaining detailed and approximate (cm')coefficients, it is possible to: 1. suppress the low frequency elements of Brownian particles and reconstruct only for the time windows relevant to the higher frequency content (away from the Lorentzian peak), thereby reducing noise from impurities and only analysing data from motile species such as bacteria.

2. observe and analyse motional effects of the particles, according to their frequency components.

The signal is de-noised in this way to remove data arising from nonbacteria species, unwanted signals from the detectors and so on, and reconstructed 913 to leave only information arising from motile species such as bacteria.

By using the de-noised signal is possible to establish an intensity profile, or optical signature of the bacteria over different angles. That is to say, since the wavelet analysis is performed only in a single 'forward' solid angle, high amplitude intensity fluctuations from motile bacteria are detected, as opposed to smaller angles. The reconstructed signal 914 is free of liquid impurities (background So noise with fL 10 Hz) and is used as a time guide for the remaining channels in order to obtain the optical signature of scattered light intensity versus angle of detection.

The signal representation 913 also gives information about the motion of the bacteria, in the form of a motion pattern 916. As different bacteria species move in different ways, by comparing the motion pattern obtained with motion pattern data stored in a library, the bacteria can be further characterized. The library contains motion patterns for the types of bacteria that are to be detected in order for them to be compared against a particular sample.

The algorithm for one channel of data can be summarised as follows (the algorithm is repeated for each channel): P1111 spec lnput data sinnal from photodectors: 1. Get data 901 from channel at a scattering angle v; 2. Calculate 903-910 the coefficients (cams) and (dams) through Equations 6 and 7; APPIV a De-noising function to the signal: a. Apply hard- threshold at (c<,m' ) b. Combine 915 (cm')and (dm')to reconstruct noise- free signal s(t); C. For segments of reconstructed signal s(t) that present appreciable variability in amplitude: I. Get data at other scattering angles for time periods where s(t) was found with higher entropy; II. Get average intensity value for specific t (time periods).

d. Obtain light intensity signature IsCa( ,t).

2 5 APPIY a motion exploration function to the optical signature: e. Get frequencies where s(t) is maximum; f. Save to memory; vector saved at library; g. Compare similarity from other signatures (known cases) in library; P1111 spec h. Infer type of motion.

Interference and Sensitivity Issues Some output signal may be produced even without any illumination, producing noise in the detected signals. This sets a lower limit in the detectable light levels since below that level the signal is lost in the dark current background noise. By producing several patterns such as those illustrated in Figure 7 herein and identifying a portion containing significant information in the dynamic range, the detector characteristics required can be matched. A PMT module with a JO sensitivity range in the visible range of the radiation spectrum and with a high response time overcomes this problem.

A further sensitivity issue arises from the laser source used. Even though there are virtually no wavelength shifts in the laser, the intensity varies in time s mainly due to thermal effects and aging; hence, introducing some extra noise due to intensity instabilities. This problem is overcome by monitoring the input laser light using a beam splitter and assigning different weights to the fibre output ends signal. Calibration weights are assigned once and monitored on a periodic basis.

An optical filter is not essential.

The sample chamber 302 carries the sample to be examined in a buffer liquid that is water-based. Impurities in the sample or buffer liquid may introduce some noise in the signal. However their contribution in the time/frequency domain is at the DC to 20Hz range and is eliminated using a signal processing algorithm.

: Problems of internal reflections in the scattering volume are minimized by using of a Witnauer-Scherr type cuvette, known in the art. However, there is still an issue arising from the cone of reception of the fibre input. To compensate for this effect a volume correction factor is adopted given in equation 10.

sin() (10) so where VO denotes the true detected volume. As a result all scattering patterns are multiplied by sin( ). Finally, the light exiting from the flat end of the P1111 spec sample chamber 302 is introduced to a light trap so as to remove any reflections from the body of the apparatus.

It will be apparent to one skilled in the art that the invention is not limited to having 2 arrays of radiation detectors, with one array being rotatable. Other configurations are possible, provided that light intensity data can be obtained over at least two elevation angles with respect to time.

Furthermore, the invention is not limited to the use of visible light. Visible To light is used to detect the presence of bacteria. Other forms of radiation may be found to be suitable to detect other types of particle, depending on their size and configuration.

Furthermore, multiple wavelength studies are feasible by the use of the apparatus as presented here. A source is required that can generate radiation such as light at different wavelengths and different illumination intensities.

Alternatively, a series of sources may be used, each source being configured to generate radiation with a wavelength or intensity different to the other sources in the series.

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Claims

Claims: 1. Apparatus for particle analysis comprising: a radiation source;

a sample chamber configured to contain a sample, said sample comprising a plurality of particles; a first array of radiation detectors; a second array of radiation detectors; said device configured to collect data from said radiation detectors at least at a first time and a second time, wherein said second array occupies a first position at said first time and a second position at said second time; a processor configured to process said collected data, said processing determining at least one predetermined parameter of said particles.

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2. A device for particle analysis as claimed in claim 1 wherein said radiation source comprises a laser; and said arrays of radiation detectors are configured to detect visible light.

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3. A device for particle analysis as claimed in claim 1 or claim 2 wherein each said array of radiation detectors comprises at least one photo multiplier tube.
4. A device for particle analysis as claimed in any preceding claim o wherein each said array of radiation detectors comprises: an array of optical fibres; P1111 spec a photo-multiplier tube; means to sequentially connect each optical fibre of said array of optical fibres to said photomultiplier tube, and collect data at said photo-multiplier tube from each said optical fibre.
5. A device for particle analysis as claimed in any preceding claim wherein said first array of radiation detectors is mounted on a first support, and each radiation detector of said first array of radiation detectors is located at the same distance from said sample chamber; said second array of radiation detectors is mounted on a second support, and each radiation detector of said second array of radiation detectors is located at the same distance from said sample chamber;
6. A device for particle analysis as claimed in any preceding claim wherein said second array of radiation detectors is movable relative to said sample chamber.

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7. A device for particle analysis as claimed in any preceding claim wherein said second array of radiation detectors is rotatable about said sample chamber.
8. A device for particle analysis as claimed in claim 4 wherein said optical fibres comprise non-coherent polymer fibres.
9. A device for particle analysis as claimed in any preceding claim wherein said predetermined parameter comprises information relating to any one of the following: particle shape particle size P1111 spec particle species particle refractive index refractive index of at least one inferred layer of said particle particle motion To
10. A device for particle analysis as claimed in any preceding claim wherein said processing determining at least one predetermined parameter of said particles comprises removing collected data arising from non-motile particles.
11. A device for particle analysis as claimed in any preceding claim wherein said processing determining at least one predetermined parameter of said particles comprises comparing a measured predetermined parameter value with a series of predefined parameter values from a database.
12. A device for particle analysis as claimed in any preceding claim wherein said processing determining at least one predetermined parameter of said particles comprises: obtaining measured intensity data from said first and second array of radiation detectors fitting said measured intensity data to a predicted function of said particles
13. A device for particle analysis as claimed in any preceding claim so wherein said radiation source is configured to generate radiation at a range of different wavelengths.

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14. A device for particle analysis as claimed in any preceding claim wherein said radiation source is configured to generate radiation at a range of different intensities.
15. A method of particle analysis comprising: illuminating a sample with radiation, said sample comprising a plurality of particles; o collecting a first data set from a first array of radiation detectors; collecting a second data set from a second array of radiation detectors; collecting said first data set and said second data set at least at a first time and a second time, wherein said second array occupies a first position at said first time and a second position at said second time; processing said collected data to determine at least one predetermined parameter of said particles
16. A method of particle analysis as claimed in claim 15 further comprising: providing said illumination using a laser; and said arrays of radiation detectors are configured to detect visible light.
17. A method of particle analysis as claimed in claim 15 or claim 16 wherein each said array of radiation detectors comprises at least one photo o multiplier tube.
18. A method of particle analysis as claimed in any one of claims 15 to 17 wherein each said array of radiation detectors comprises: P1111 spec an array of optical fibres; a photo-multiplier tube; and said method further comprises sequentially connecting each optical fibre of said array of optical fibres to said photo-multiplier tube, and collecting data at said photo-multiplier tube from each said optical flare.
19. A method of particle analysis as claimed in any one of claims 15 to 1 8 comprising: mounting said first array of radiation detectors on a first support such that s each radiation detector of said first array of radiation detectors is located at the same distance from said sample; mounting said second array of radiation detectors is on a second support, ; such that each radiation detector of said second array of radiation detectors is o located at the same distance from said sample;
20. A method of particle analysis as claimed in any one of claims 15 to 1 9 comprising: moving said second array of radiation detectors relative to said sample.
21. A method of particle analysis as claimed in any one of claims 15 to comprising: 3 0 rotating said second array of radiation detectors about said sample.
22. A method of particle analysis as claimed in claim 18 wherein said optical fibres comprise non-coherent polymer fibres.

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23. A method of particle analysis as claimed in any one of claims 15 to 22 wherein said predetermined parameter comprises information relating to any one of the following: particle shape particle size particle species particle refractive index refractive index of at least one inferred layer of said particle particle motion
24. A method of particle analysis as claimed in any one of claims 15 to 23 wherein said processing determining at least one predetermined parameter of o said particles comprises removing collected data arising from non-motile particles.
25. A method of particle analysis as claimed in any one of claims 15 to 24 wherein said processing determining at least one predetermined parameter of said particles comprises: comparing a measured predetermined parameter value with a series of predefined parameter values from a database.

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26. A method of particle analysis as claimed in any one of claims 15 to wherein said processing determining at least one predetermined parameter of said particles comprises: P111 1 spec obtaining measured intensity data from said first and second array of radiation detectors fitting said measured intensity data to a predicted function of said particles
27. A method of particle analysis as claimed in any one of claims 15 to 26 comprising illuminating said sample with radiation, said radiation being generated at least at two different wavelengths.
28. A method of particle analysis as claimed in any one of claims 15 to 27 comprising illuminating said sample with radiation, said radiation being generated at least at two different intensities.
29. A device for particle analysis comprising: a laser source; a sample chamber configured to contain a sample comprising a plurality of particles; a first ring, a second ring; a plurality of radiation detectors disposed on said first ring; a plurality of radiation detectors disposed on said second ring; wherein said second ring is rotatable about said sample chamber; means to collect light intensity data from said plurality of radiation detectors P1111 spec means to process said collected light intensity data to infer information about said particles.

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