Abstract:
A method for developing an algorithm for quantifying the hydrocarbon content of aqueous media includes: a) irradiating aqueous test samples containing hydrocarbons and particulates with light so that fluorescent emissions and scattered light signals are emitted from the test samples; b) detecting fluorescent emissions and the scattered light signals emitted from the test samples; c) generating first data signals representing the intensities of the fluorescent emissions, and second data signals representing the intensities and scatter angles of the second data signals; d) storing representations of the first and second data signals to create a data set; e) dividing the data set into training, test, and validation data sets; f) selecting input parameters from the data set; g) defining and training a neural network having hidden node using the training and the test data sets; and h) validating the neural network using the validation data set.

Description:
This is a continuation-in-part of application Ser. No. 09/814,089, entitled A System For Quantifying the Hydrocarbon Content of Aqueous Media, filed 21 Mar. 2001. 

   BACKGROUND OF THE INVENTION 
   Many industrial processes utilize an oil-content-monitor (OCM) to provide a real-time on-line measure of the amount of petroleum hydrocarbons present in process water or wastewater streams. Bilge discharge monitoring is a common example of OCM usage. Ships at sea treat bilge water to remove oily contaminants prior to discharging the bilge into the surrounding environment. Environmental regulations specify that bilge water may not be pumped overboard if the oil content exceeds 15 part-per-million (ppm) within the coastal zone, or 100 ppm at sea. Shipboard OCMs provide on-line measurements of the amount of fuel or oil present in the treated bilge water. The ship&#39;s crew utilizes this information to make ongoing decisions as to whether the processed bilge may be lawfully discharged or requires further treatment. Examples of other OCM applications include on-line monitoring of: oil well process water discharge, car/aircraft wash facilities, power plant effluent, engine cooling water, desalination plant intake, boiler condensate, storm water runoff, and reclaimed groundwater. 
   Many existing OCM systems use optical methods to measure oil content. OCM sensors based on ultraviolet (UV) fluorescence, optical scattering, or optical transmission/absorption methods are common. Optical techniques have a “stand-off” advantage over other methods in that direct physical contact with the sample is unnecessary. 
   Most optically based OCMs are single-channel (zero order) instruments, i.e. they utilize one measured parameter to determine hydrocarbon content. The single parameter these instruments measure may include fluorescence emission at a single wavelength band, or suspended-particle scattering at a single angle, or optical absorption at a single wavelength band, or the ratio of single-angle scattering to single wavelength-band transmission, etc. Instrument calibration is performed by applying a mathematical transformation of the single measured datum in order to relate the raw signal to actual oil content. Single channel instruments offer the benefit of a simple univariate calibration model, e.g. the calibration is typically implemented as a linear function of system response. 
   Accurate quantification when the hydrocarbon species and matrix are not known a priori is simply not possible with single-channel (univariate calibration) methods. Single-channel (univariate calibration) instruments are adequate for applications where the hydrocarbon analyte, aqueous matrix, and mixing conditions are all well characterized and do not vary over time. However, as single-channel instruments they cannot provide accurate oil content measurements when any of the following conditions exist: a) when the type of hydrocarbon analyte is unknown or changing, b) when the background signal is varying, c) when matrix effects are present (i.e. when the sensitivity of the analyte is dependent upon the presence of other species), or d) when physical factors that effect emulsification, e.g. mechanical stirring, temperature, etc. vary. The inaccuracies are due to the fact that a single data point provides insufficient information to resolve multiple unknown parameters. If the instrumental sensitivity is significantly different for two or more types of petroleum products, for example diesel fuel and lube oil, and both are potentially present in the sample, then a given instrumental response cannot be uniquely associated with a single “overall” oil content. Single-channel instruments are also incapable of distinguishing between a signal arising from target analytes and background interference. Signal changes brought about by spectral or physical interferences, common in many applications, cannot be differentiated from signal changes arising from a change in oil content. In a dynamic environment, this leads to erroneous determinations of oil content. 
   Accurate measurement of small quantities of oil in water (e.g. low mg L −1 ) is extremely difficult when the hydrocarbon type and/or matrix is changing or when physical and chemical interferences are present. Therefore, a need exists for an accurate and reliable method for determining the concentration of oil droplets in aqueous media. 
   SUMMARY OF THE INVENTION 
   A method for developing an algorithm for quantifying the hydrocarbon content of aqueous media includes: a) irradiating aqueous test samples containing hydrocarbons and particulates with light so that fluorescent emissions and scattered light signals are emitted from the test samples; b) detecting fluorescent emissions and the scattered light signals emitted from the test samples; c) generating first data signals representing the intensities of the fluorescent emissions, and second data signals representing the intensities and scatter angles of the second data signals; d) storing representations of the first and second data signals to create a data set; e) dividing the data set into training, test, and validation data sets; f) selecting input parameters from the data set; g) defining and training a neural network having hidden nodes using the training and the test data sets; and h) validating the neural network using the validation data set. 
   Advantages of the invention will become more apparent upon review of the accompanying drawings and specification, including the claims. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  shows a system for quantifying the petroleum content of aqueous media that embodies features of the present invention. 
       FIG. 2  illustrates the process of developing an algorithm for transforming sample data into a value representing hydrocarbon contamination. 
       FIG. 3  is an example of a data vector that associates levels of hydrocarbon contamination to actual spectral and particulate size data for several liquid test samples. 
       FIG. 4  shows a second embodiment of the sample cell. 
       FIG. 5  shows an embodiment of the present invention that includes a single excitation optical energy source. 
       FIG. 6  is a flow chart that illustrates an embodiment of a process for developing a calibration algorithm using a neural network. 
   

   Throughout the several views, like elements are referenced using like references. 
   DESCRIPTION OF THE PREFERRED EMBODIMENT 
   The present invention is directed to the development of a calibration algorithm that is used in a system  10  for quantifying the hydrocarbon content of aqueous media. Referring to  FIG. 1 , system  10  includes a sample cell  12 , which may be implemented as a transparent tube, in which is present an aqueous sample  13 . The sample cell  12  may be made of a transparent material such as quartz or glass that is generally chemically inert to hydrocarbons. The sample cell  12  may also be implemented in aluminum, Teflon®, or stainless steel and have transparent windows, not shown, for allowing the penetration and emission of light between the interior and exterior of the cell. A light source  14  generates a light signal  16  having ultraviolet components. Light signal  16  is directed into sample cell  12  to stimulate fluorescent emissions  17  from any hydrocarbons  18  that may be present in the aqueous sample  13  that are irradiated by light signal  16 . Light source  14  may be economically implemented as a broadband light source, such as a xenon flashlamp, in which case light signal  16  is a broadband light beam having multi-spectral, including ultraviolet, components. An optical filter or monochrometer  20  may be used to limit the spectral bandpass of light signal  16 . However, light source  16  may also be implemented using any UV source such as a deuterium lamp, light emitting diode, or laser. Multiple light sources as well as multiple excitation energies could be used to enhance selectivity. In response to detecting fluorescent emission signals  17 , a spectral detector  22  generates electrical signals  24  that represent one or more spectral components of signal  17  having different wavelengths for characterizing fluorescent emission signals  17 . Spectral detector  22  may be implemented as a spectrograph (or monochrometer or optical filter array) coupled photodiode, photodiode array, CCD, photomultiplier tube (PMT), or multianode PMT. In the preferred embodiment, the field of view of the spectral detector  22  is oriented so that the path of light signal  16  does not excite the spectral detector  22 . For example, the path of light signal  16  may be orthogonal to a vector that is normal to the light sensing surface of detector  22 . 
   System  10  relies on multi-angle optical scattering techniques for determining the size-distribution of droplets  34  in aqueous test sample  13 . Droplets  34  may be emulsified hydrocarbons, generally in the form of oil droplets, in aqueous test sample  13 . Hereinafter, the references to hydrocarbons and oil droplets may be used interchangeably. Analysis of the sizes of droplets  34  is achieved by measuring the intensity of scattered light signals  36  at many (32) angles, as for example, between 0.1 and 20 degrees. Coherent light source  30  generates a coherent light signal  32  that is directed into sample cell  12 . If light signal  32  irradiates any emulsified oil droplets  34  that may be suspended in aqueous test sample  13 , the interaction between the light signal  32  and the droplets or particulates  34  causes the light signal  32  to become divided into scattered light signals  36 . Scattering refers to the transformation of coherent light signal  32  into many light signals  36  that propagate at an angle with respect to the direction of coherent light signal  32 . Scattered light signals  36  are detected by multi-angle photodetector  38 , such as a ring detector, charge couple device (CCD), or photodiode array, from which is determined the intensity and angle of scatter for each detected scattered light signal  36  with respect to the direction of coherent light signal  32 . The multi-angle photodetector  38  generates electrical signals, collectively referenced as signal  40  from locations on the photodetector  38  where the scattered light signals  36  irradiate light detecting elements  39 . Signal  40  represents the intensities and scatter angles of light signals  36  that are detected by light scattering detector  38 . Software executed in processor  42  employs the information encoded in signal  40  to determine the size distribution of oil droplets  34  in aqueous test sample  13  using well known techniques. Software for determining particle size distribution from optical intensities and scatter angles of light passing through a liquid is available from Sequoia Scientific, Inc. 
   Processor  42  implements a calibration algorithm that uses the particle size distribution of oil droplets  34  previously determined by processor  42  using intensity and scatter angle information encoded in signal  40 , and the spectral component information from flourescent emission signals  17  represented in signals  24  to determine a value representing the hydrocarbon concentration content H c  of hydrocarbons  34  present in the aqueous test sample  13 . Implementation of the algorithm by processor  42  results in the generation of an output signal  44  representing the hydrocarbon concentration content H c  of oil in test sample  34  that is provided to display  46 . Display  46  presents H c  in human readable form, as for example, “Oil content=25 ppm.” By way of example, display  46  may be implemented as a video monitor, a printer, a strip chart recorder, and the like. 
   The development of the calibration algorithm is described with reference to  FIGS. 2 and 6 . First, the types of fluids, degrees of contamination, and matrix conditions are established for a particular monitoring application at step  50 .  FIG. 3  illustrates a table of example data for various mixtures and types of fluids that have predetermined levels of hydrocarbon contamination and particle size contamination that encompass the scope of the levels of contamination of test samples  13  likely to be examined by sensor  10 . For example, the range of hydrocarbon contamination, typically measured in parts per million (ppm), in test sample  13  may range from 0 parts per million (ppm) to 25 ppm, and the size-distribution particulates  34  may range from 0 to 20 microns. 
   Next, at step  52 , and still referring to  FIGS. 2 and 6 , various test samples  13  (which may include a water and/or sea water matrix) having different types and levels of hydrocarbon contamination and particle size distributions that span the defined scope of such characteristics are formulated. Also at step  52 , spectral component data and particle size distribution data are generated for each test sample using system  10 . The test samples  13  are placed in test cell  12  and irradiated by light signal  16 . Any fluorescent or “spectral” emission signals  17  generated by irradiation of the test samples  13  are detected by spectral detector  22  as described above. Data that represents spectral emission signals  17  are provided by signal  24  to processor  42  for storage. Also, the test samples  13  are irradiated by coherent light signal  32  that is generated by coherent light source  30 . Any particulates  34  that are suspended in test samples  13  cause light signals  32  to scatter and be transformed into scattered light signals  36  which are detected by multi-angle detector  38 . Data representing the locations where scattered light signals  36  irradiate the multi-angle detector  38  are provided as signal  40  to processor  42  for storage. Software implemented in processor  42  determines the particle size distribution of each test sample  13 . 
   For example, data record number one of the data set shown in  FIG. 3  represents a sample mixture of sea water contaminated with 5 ppm of marine diesel fuel (DFM). Data record numbers  3  and  4  represent mixtures of sea water contaminated with jet fuel (JP5). Data record number  1  is characterized by spectra data, i.e., fluorescent emission intensity at different wavelengths, and the volume concentrations or size distributions of oil droplets  34 . More specifically, data record number one represents an actual sample of seawater contaminated with 5 parts per million (ppm) of DFM. Measured fluorescent relative intensities for sample  1  at 280 nm, 300 nm, and 320 nm are  35 ,  56 , and  17 , respectively. For purposes of illustration which are presented by way of example only,  FIG. 3  shows 5 data records associated with 5 different test samples of contaminated water or sea water that are used to derive the calibration algorithm. Fluorescent data generated by each aqueous sample  13  is used to construct data records such as the ones shown in  FIG. 3 . However, the actual development of the calibration algorithm may employ any suitable number of data records, the number of which may be much greater than five. In general, the calibration algorithm will more accurately relate spectral characteristics and particle sizes to values of hydrocarbon contamination when hundreds, and even thousands of data records are used to derive the algorithm. In addition, each data record may contain many more fluorescence intensity values than the three shown, as well as a greater number of volume concentrations (i.e., size distributions) than as shown in  FIG. 3 . In general, the hydrocarbons detected by embodiments described herein and used to develop the calibration algorithm include diesel fuel, jet fuel, and lubricating oils such as 9250 and 2190 lubricating oils. However, the hydrocarbons detected by embodiments described herein may also include aviation fuel, gasoline, hydraulic fluid, crude oil, and fuel oil. 
   At step  54 , raw data such as shown in  FIG. 3  is converted to a more usable form through normalization, scaling, and/or mean centering to create more robust calibration models for development of the calibration algorithm. At step  56 , the data shown in  FIG. 2  is divided into three independent groupings. One data record is used to train or develop the calibration algorithm, the second set is used during the training process to test the algorithm, and the third set is used to validate the calibration algorithm. The derivation of the calibration algorithm assumes that the hydrocarbon concentration content H c  of aqueous sample  13  is a function of the spectral components of fluorescent emission signals  17  and particle size distribution of oil droplets  34 . It is to be noted that particle size distribution may be derived using standard techniques such as optical scattering or image analysis. 
   Next at step  58  shown in  FIG. 2 , the calibration algorithm is derived. There are many mathematical techniques suitable for developing the calibration algorithm. Examples of suitable techniques include multiple linear regression, multiple nonlinear regression, principle components regression, partial least squares regression, and recursive least squares regression. The preferred method for developing the calibration algorithm includes the use of artificial neural networks. Artificial neural networks, such as back propagation, provide a convenient and powerful means of fusing multivariate, generally nonlinear, spectral and droplet size data, such as found in the data vector presented by way of example in  FIG. 3 , and transforming the data into an oil content value. Still other methods include using a look-up table, or a nearest-neighbor classifier. 
   The calibration algorithm may be developed using a three layer back-propagation neural network (BPNN) using NeuralWorks Professional II Plus by NeuralWare, Inc. and a Pentium II 450-MHz PC with 128 MB-RAM. Although the calibration algorithm was developed using a three-layer back-propagation neural network, it is to be understood that other types of neural networks may also be used. By way of example, the BPNN may include input, hidden, and output layers. The output layer of the BPNN has a single node (neuron) corresponding to oil content in parts per million (ppm). The input layer has a single node for each of the input values that comprise each data record in the data vector as shown in  FIG. 3 . For example, in  FIG. 3 , each data record includes three fluorescent emission data and three volume concentrations for three droplet size ranges. Thus, using the example of the data vector in  FIG. 3 , BPNN has six input nodes (neurons) corresponding to the fluorescence intensities at three different wavelengths and the volume concentrations for three droplet size ranges for each data record. Although not shown, the input layer may also include additional nodes or neurons for optical transmission and the scattering intensity at the same angle as the fluorescent detection for each data record. The BPNN transfer function used by each node is preferably a sigmoid or hyperbolic tangent, although other functions may also be used. The optimal number of nodes in the hidden layer is determined during the training process by trial and error and by iterative improvement, as for example, by trying different numbers of nodes in the hidden layers until the user is satisfied with the results. Seven hidden layers were found to provide satisfactory results for developing the calibration algorithm. The data set in  FIG. 3  is split into a training data set, a test data set, and a validation data set, as exemplified in  FIG. 2 . The BPNN is trained using standard, well known methodology for adjusting weight parameters ultimately selected for the final version of the calibration algorithm. 
   Once the weights are determined so that BPNN provides satisfactory results, the BPNN is considered to be “trained,” whereupon the calibration algorithm then is defined as a sequence of mathematical functions that employ the weights developed by the BPNN generally as coefficients. Satisfactory results are defined where the absolute value of the difference between the calculated value of H c-calc  and the actual value of H c  for a particular data record is less than some acceptable limit δ, i.e., |H c-calc −H c |≦δ, where H c-calc  represents the calculated hydrocarbon concentration content of aqueous sample  13  that is determined by the calibration algorithm. The calibration algorithm is characterized as a multivariate calibration algorithm because it employs multiple data inputs, as for example six inputs from each data record of  FIG. 3 , in order to determine the level of contamination of test sample  13 . However, it is to be understood that the calibration algorithm may employ any number of data inputs as required to suit the requirements of a particular application. The algorithm may be implemented in software for execution by processor  42 , or may alternatively be implemented in hardware if faster performance is desired. 
   Once the calibration algorithm has been successfully tested, then the third data set may be inserted into the algorithm to validate the algorithm, and thereby provide a separate, independent check of the validity of the algorithm. The difference between testing and validation data is that validation data is not used to develop the model. If a calibration model does not perform to the desired degree of accuracy with the validation set, then the model is improved using the training and test data records. After being validated, the calibration algorithm may be implemented in processor  42  for processing data. 
     FIG. 6  shows step  52  in greater detail where at step  100  the test samples  13  are formulated. Each of the test samples may include different concentrations of hydrocarbon contaminants and different particle size distributions, which are all known. Next, as described above, a particular test sample  13  is irradiated by light signal  16  so that spectral emission data and particle size distribution data are generated. At step  104 , spectral emission data and particle size distribution data are stored by processor  42 . A determination is made at step  106  as to whether spectral component and particle distribution data have been generated and stored for all of the test samples  13 . If the determination at step  106  is NO, i.e., that spectral component and particle distribution data have not been generated and stored for all of the test samples  13 , the process returns to step  102 . If however, the determination at step  106  is YES, i.e., that spectral component and particle distribution data have been generated and stored for all of the test samples  13 , the process continues to step  54 . 
   Still referring to  FIG. 6 , steps  58  and  60  (collectively illustrated in  FIG. 6  as  58 / 60 ), in applications where a neural network is used to develop the calibration algorithm, may include step  110  where input parameters are selected from the data generated at step  52 . Such parameters may include particular fluorescence intensities and particular size volume concentrations, rather than others. For example, with regard to Data Record No.  1  in  FIG. 3A , input parameters for DFM may be selected for fluorescence intensities at 280 nm and 320 nm, and particle size distribution volume concentrations at 2–5 microns and 5–10 microns rather than fluorescence intensity at 300 nm and a particle size volume concentration of 10–20 microns. Then at step  112 , the number of hidden nodes in the back propagation algorithm is selected. For example, in one embodiment, seven hidden nodes have been found to provide satisfactory results. Proceeding next to step  114 , the nerual network is trained using the training set provided from step  52 A and the test set provided from step  52 B. The neural network employs iterative techniques as described above in an attempt to provide a calibration algorithm that provides satisfactory results. A determination as to whether satisfactory results are obtained from the neural network is made at step  116  where the results of comparing the calculated output H c-calc  of the calibration algorithm with the corresponding values H c  of the test set. training set. If |H c −H c-calc  (α, then the calibration algorithm is determined to be acceptable and the process continues to step  62 , where α represents an error value. If however, |H c −H c-calc |≧α, then the calibration algorithm is not deemed to be adequately trained and the process returns to step  110 . 
   An embodiment of step  62  shown in  FIG. 2  is exemplified in more detail in  FIG. 6  and as described below. The validation data set from step  52 C ( FIG. 2 ) is provided as input into the neural network at step  118 . The at step  120 , the validation data set is compared with the results R obtained by inputting the validation set into the neural network trained at steps  58 / 60 . If /R/&lt;δ, the validity of the neural network is confirmed, δ represents an error value, and the process ends at step  124 . If, however, /R/≧δ, then the neural network has not been validated, whereupon the process continues to step  100 . 
   In the operation of sensor  10 , data signals  24  and  40  are input into processor  42  whereupon processor  42  determines the particle size distribution of oil droplets in aqueous sample  13  from information encoded in signal  40 . Then, the calibration algorithm is executed by processor  42  to calculate H c-calc  which estimates the actual hydrocarbon concentration content H c  in aqueous sample  13  from spectral data of fluorescent emission signals  17  encoded in signal  24  and the particle size distributions. Processor  42  generates an output signal  44  that represents H c-calc  which is provided to display  46 . Then display  46  presents the calculated level of hydrocarbon concentration content H c-calc  in human readable form, such as in a textual and/or alpha/numeric format. 
   In practice, sensor  10  may be placed so that the aqueous sample  13  flows through the sample cell  12 . For example, for bilge monitoring applications the sample stream  13  is typically the aqueous effluent of an oil-water separator. The user determines the data acquisition rate, i.e. the number of measurements per unit time. The detection of light signals  17  and  36  and processing of signals  24  and  40  may be performed by processor  42  on a time scale of a few seconds or less, whereupon the presentation of display  46  is updated after each measurement. 
   In another embodiment, shown in  FIG. 4 , sample cell  12  may be implemented as a tube  70  in which are mounted windows  72 ,  74 ,  76 , and  78 , which may be made of quartz or glass. Fluorescent excitation light signal  16  enters window  72  and if any hydrocarbons that are present in test sample  13  are irradiated by light signal  16 , the fluorescent light signals  17  will be emitted out of tube  70  through window  74  for detection by spectral detector  22 . Windows  72  and  74  are preferably offset radially by an angle θ, such as 90° with respect to reference axes a and b so that the propagation direction of fluorescent excitation light signal  16  does not transect both windows  72  and  74  in order to prevent light signal  16  from entering the field of view of spectral detector  22 . Coherent light signal  32  enters window  76  and if signal  32  irradiates any emulsified oil droplets  34 , light signal  32  is transformed into scattered light signals  37  which are emitted from tube  70  through window  78 . Windows  76  and  78  may preferably be mounted diametrically opposed to each other in tube  70  as shown in  FIG. 4 , however, such a configuration is not necessary. Tube  70  may be made of stainless steel because stainless steel has excellent chemical resistance to the types of contaminants likely to be found in test sample  13 . 
     FIG. 5  illustrates another embodiment of system  10  which includes sample cell  12  through which flows aqueous sample  13 . An ultraviolet coherent light source  80  generates an ultraviolet coherent light signal  81  that is directed into sample cell  12 . UV light signal  81  stimulates fluorescent emission signals  17  from any hydrocarbons  18  that may be present in the aqueous sample  13  that are irradiated by light signal  81 . Spectral photo detector  82 , such as a CCD array having optical sensing elements  86 , detects fluorescent emission signals  17  and the intensities and locations where scatter light signals  37  irradiate photo detector  82 . Scatter light signals  37  are produced as a result of UV light signal  81  irradiating particular oil droplets  36  in aqueous sample  13 . Photo detector  82  generates a signal  24  that represents selected spectral characteristics of fluorescent emission signals  17 . In the embodiment of sensor  10  shown in  FIG. 5 , the field of view of photo detector  82  may detect UV light signal  81 . The effect of signal  81  irradiating photo detector  82  may be nulled because the optical sensing elements  86  that are irradiated by UV signal  81  may be taken off-line or by appropriate use of algorithms executed in processor  42 . Photodetector  82  generates electrical signals, collectively referenced as signal  40  from locations on the photodetector  82  where the scattered light signals  37  irradiate light detecting elements  86  of photo detector  82 . Signal  40  represents the intensities and scatter angles of light signals  37  that are detected by photo detector  82 . Software executed in processor  42  employs the information encoded in signal  40  to determine the size distribution of oil droplets  34  in aqueous test sample  13  using well known techniques. Because photo detector  82  detects both fluorescent emission signals  17  and scattered light signals  37 , only one optical energy source is required. 
   Processor  42  implements an algorithm that uses the particle size distribution of oil droplets  34  previously determined by processor  42  using intensity and scatter angle information encoded in signal  40 , and the spectral component information from flourescent emission signals  17  represented in signal  24  to determine a value H c-calc  representing the hydrocarbon concentration content H c  of hydrocarbons  34  present in the aqueous test sample  13 . Implementation of this algorithm by processor  42  results in the generation of an output signal  44  representing the hydrocarbon concentration content H c-calc  of oil in test sample  34  that is provided to display  46 . Display  46  presents H c-calc  in human readable form, as for example, “Oil content=25 ppm.” 
   In another embodiment of sensor  10 , raw data encoded in signal  40 , which represents intensity and location data corresponding to the locations on detector  38  that are irradiated by scattered light signals  37 , may be directly input into the BPNN to develop the calibration algorithm in lieu of using the particle size distribution data as inputs into the BPNN. Therefore, processor  42  may execute the algorithm to determine the hydrocarbon content H c-calc  of aqueous test sample  13  directly from information encoded in signals  24  and  40 , without having to determine the particle size distribution of particulates  34 . In this embodiment, the calibration algorithm estimates the hydrocarbon content H c-calc  based on the fluorescent emission signals  17  and the scattering light signals  37 , and does not employ the particle size distribution to estimate H c-calc . 
   Obviously, many modifications and variations of the present invention are possible in light of the above teachings. For example, particle size distributions may also be determined based on optical imaging and other techniques. It is therefore to be understood that within the scope of the appended claims, the invention may be practiced otherwise than as specifically described.