EP1448988A1 - Analyse de l'uree present dans un liquide l'aide d'une languette electronique - Google Patents

Analyse de l'uree present dans un liquide l'aide d'une languette electronique

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Publication number
EP1448988A1
EP1448988A1 EP02789086A EP02789086A EP1448988A1 EP 1448988 A1 EP1448988 A1 EP 1448988A1 EP 02789086 A EP02789086 A EP 02789086A EP 02789086 A EP02789086 A EP 02789086A EP 1448988 A1 EP1448988 A1 EP 1448988A1
Authority
EP
European Patent Office
Prior art keywords
urea
blood
liquid
electrode
sensor unit
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP02789086A
Other languages
German (de)
English (en)
Inventor
Rasmus Jansson
Per LANGÖ
Fredrik Winqvist
Kristina KRANTZ-RÜLCKER
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Otre AB
Original Assignee
Otre AB
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from SE0104006A external-priority patent/SE523961C2/sv
Priority claimed from US10/025,564 external-priority patent/US20030113933A1/en
Application filed by Otre AB filed Critical Otre AB
Publication of EP1448988A1 publication Critical patent/EP1448988A1/fr
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/483—Physical analysis of biological material
    • G01N33/487—Physical analysis of biological material of liquid biological material
    • G01N33/49—Blood
    • G01N33/492—Determining multiple analytes

Definitions

  • the present invention relates generally to the analysis of components in liquids by voltammetric methods, and in particular to the monitoring of a dialysis procedure by analysis of indicator components in dialysates.
  • Dialysis is nowadays a well established method of treating patients suffering from kidney insufficiency for various reasons. Furthermore, the number of patients requiring dialysis are believed to increase over the coming years. Dialysis is a way to partially replace the kidney function. Thereby the blood is purified or cleaned by using a special purification liquid, a solution referred to as a dialysate, and a filter.
  • the filter can be the tissues within the body (peritoneal dialysis), or an artificial kidney (heamodialysis).
  • the main function of dialysis is to remove toxic waste products produced by the body, from people suffering from impaired renal function.
  • One such waste product is urea, which is the end product of protein metabolism.
  • Urea clearance is often used as an indicator of how well the dialysis treatment is working, that is how well the blood is purified.
  • An important aspect of dialysis treatment is to ascertain when a sufficient level of urea removal has been achieved, i.e. when the treatment procedure can be stopped without jeopardizing the patient's health.
  • In-line measurement of e.g. blood components i.e. continuously measuring analytes in the blood path is a potentially efficient method.
  • the risks associated with the introduction of measuring probes in the blood path are potentially large, in that no such measurement method leaves the blood unaffected.
  • the object of the present invention is to provide a real-time, on-line system (in view of the potential hazards, in-line methods are not the subject of the present invention) for the direct or indirect monitoring of urea in liquids, in particular blood, other urea containing body fluids, dialysate, or in any combination thereof, by voltammetric analysis using a so-called electronic tongue.
  • it should provide means for monitoring the efficiency of dialysis in a dialysis apparatus, as a stand-alone unit or integrated in a peritoneal dialysis process, by continuously monitoring the level of urea in said liquid, thereby enabling control of the level of urea in blood.
  • the method comprises in its broadest embodiment bringing a liquid in the form of blood, other urea containing body fluids, dialysate, or a combination thereof, in contact with an electronic tongue comprising at least one working electrode.
  • a voltage pulse sequence is applied to the electrodes.
  • the pulse amplitudes can be different for different electrodes, and suitably the amplitudes are varied between negative potentials and positive potentials.
  • the liquid to be analyzed can be blood that is diverted from the vascular system of a patient, and discarded when the analysis is completed.
  • the liquid to be analyzed is a dialysate that has been used for the purification of blood in a dialysis apparatus, said liquid having passed a filter where the purification takes place.
  • An example of another urea containing body fluid that can be analyzed is urine.
  • the method preferably comprises measuring urea concentration before and after the filter responsible for the removal of urea from the liquid.
  • the measurement system is defined in claim 8, and is based on a voltammetric electronic tongue, the response of which is analyzed by suitable multivariate methods to provide a result in the form of a concentration of the analyte of interest or any other relevant measure.
  • Fig. 1 is an example of a SAPV step function
  • Fig. 2 is an example of a LAPV step function
  • Fig. 3 is an example of a SUPERLAPV step function
  • Fig. 4 is a response curve for the SUPERLAPV step function
  • Fig. 5 is a schematic view of an electronic tongue
  • Fig. 6 is a schematic view of a device with flowing sample liquid
  • Fig. 7 is a schematic picture of response patterns of non-selective sensors
  • Fig. 8 is an ODP plot for urea measurements with a Pt electrode
  • Fig. 9 is an ODP plot of information contribution from an Rh electrode in a urea measurement
  • Fig. 10 shows signal energy over time for an electronic tongue
  • Fig. 11 is an ODP score plot of stabilization measurements and a normal concentration measurement series
  • Fig. 12 is a PLS score plot of validation data
  • Fig. 13 is an ODP score plot of the data from Fig. 12;
  • Fig. 14 is an ODP score plot of training and validation data from different measurement series;
  • Fig. 15 is a graph showing CRA predictions of the validation data of Fig. 14;
  • Fig. 16 is a non-calibrated ODP score plot of urea variation in buffer and in dialytic liquid, respectively; and Fig. 17 is a calibrated version of Fig. 16.
  • the inventive method and system is based on the use of a kind of sensor referred to as an electronic tongue, which is based on voltammetry.
  • the non-selectivity of this sensor technology generates large amounts of data which are interpreted using multivariate methods.
  • potentiometry the voltage over a charged membrane is measured.
  • voltammetry a predefined voltage function - typically a step function with different amplitudes, positive and/or negative - is applied between a catalytically active working electrode and a counter electrode.
  • a reference electrode can be used.
  • the voltage causes a specific current response which is measured. The result is a characteristic response profile for the measured medium.
  • SAPV voltage function
  • LAPV low-amplitude pulse voltammetry
  • the present invention we use a voltage function, which will be referred to as the SUPERLAPV, where the voltage oscillates between positive and negative amplitudes.
  • An example of a SUPERLAPV step function is shown in Figure 3.
  • the three step functions presented here were deliberately made as similar as is possible, to make the actual differences stand out and not be exaggerated by other variations. At a first glance, they appear rather similar because they have the same maximum amplitudes and the same difference between neighboring peak values. However, by virtue of the switching polarity of the SUPERLAPV, it makes possible much larger step-to-step voltage differences than can be obtained with SAPV and twice that of LAPV.
  • SUPERLAPV was shown to be superior to the other two voltage functions (SAPV and LAPV) for measuring the redox activity of urea, probably because this activity is not as easily triggered by the smaller voltage oscillations of SAPV and LAPV.
  • Figure 5 shows a schematic picture of the electronic tongue
  • Figure 6 shows a schematic picture of the device used to control the exchange of samples, and the details of each will be given below. It is to be understood that the shown embodiments, although completely functional for the purpose of demonstrating the invention, are not designed nor optimized for commercial implementation as a final product, and many variations are possible within the inventive concept.
  • the illustrated system in the form of an electronic tongue basically consists of an electrode unit, suitably but not necessarily comprising a plurality of electrodes, in the shown embodiment four electrodes.
  • the tubular housing in which the four working electrodes are located, in an insulating matrix material constitutes the counter electrode.
  • the electronic tongue further comprises a potentiostat and a PC (or a suitable microprocessor) for data processing.
  • the term "electronic tongue”, as used herein, refers rather to the entire system than to the actual sensor unit (i.e. the counter electrode unit).
  • the sensor unit is immersed in a sample liquid in a suitable vessel, which could be of metal and serve as a counter electrode if the sensor unit is made entirely of an insulating material.
  • a suitable vessel which could be of metal and serve as a counter electrode if the sensor unit is made entirely of an insulating material.
  • the potentiostat can be conventional and will not be discussed further herein.
  • the system comprises a flow cell, in the form of a cap covering the working electrode of the sensor unit, and also exposing the counter electrode (i.e. the housing rim).
  • the sample is introduced into the flow cell via an inlet, e.g. by means of a suitable pump (a syringe pump is illustrated), and is expelled after measurement through an outlet into a waste vessel.
  • a suitable pump a syringe pump is illustrated
  • Other components are the same as in the embodiment of Fig. 5, but are not shown in Fig. 6.
  • the liquid to be analyzed can in a preferred embodiment comprise a dialysate.
  • the sensor unit is arranged in a dialysate flow path after a filter unit of said dialysis apparatus.
  • the system comprises a further sensor unit, arranged before said filter unit. In this case it may be advantageous to collect samples from the dialysate and perform the measurements on said samples in order to avoid any contamination of the dialysate.
  • the liquid to be analyzed is blood, derived from a patient and said sensor unit is arranged to measure the desired component in a sample of said blood.
  • the blood can thereby be sampled by continuously withdrawing blood from a patient, and said sensor unit is suitably arranged in the diverted flow path of the blood. Since this blood should be collected as waste and disposed of, thereby causing a fairly extensive withdrawal of blood from a patient, it may be advantageous to take samples intermittently and carry out the measurements on such samples for this embodiment.
  • An ideal, selective sensor is only sensitive to one physical property or chemical compound. This is the preferable sensor type when one wants to measure a specific, pre-defined quality, such as pH, conductivity, or light intensity.
  • Non-selective sensors respond to more than one stimulus and thus give ambiguous information by themselves. In reality, few sensors are completely selective (reacting to only one stimulus) and none is totally non- selective (reacting to all stimuli). Still, these terms are used to describe sensors with high and low selectivity, respectively.
  • Fig. 7 shows a schematic picture of response patterns from non-selective sensors. By combining several non-selective sensors, a unique 'fingerprint' can be obtained for the sample. The picture is not based on real measurements.
  • Non-selective sensors are particularly useful when the variables of the measurement either are not known beforehand (latent variables) or are difficult to measure directly with existing, selective sensors.
  • the main drawback is that the use of non-selective sensors requires the use of more advanced mathematical tools for data processing.
  • PCA Principal Component Analysis
  • PLS Partial Least Squares
  • ODP Optimal Discriminative Projection
  • PCA and PLS projections are to find the directions, called principal components, in the multidimensional measurement space that contain the largest variance. These directions, typically two to three for clarity, form the plane (or hyperplane) onto which all measurement points are projected.
  • the PCA method is unsupervised, meaning that no information about the measured samples is used to find the latent variable of interest. This causes the method to be intrinsically very sensitive to noise and sensor drift; whenever the drift or noise is significant, it will be represented by one of the first principal components.
  • ODP ODP (see Doctoral Thesis by Per Spangeus, "New Algorithms for General Sensors or How to Improve Electronic Noses", Link ⁇ pings Tekniska H ⁇ gskola, 2001, incorporated herein in its entirety by reference) is a supervised projection method used to find the directions in the multidimensional measurement space that best separate between different classes of samples. Because of this, ODP, unlike PCA and PLS, requires the samples to belong to discrete classes. It is thus a classification algorithm rather than a quantification algorithm. The continuous concentration problem, which is essentially the subject matter in this application, can still be solved by ODP by measuring a sufficiently large number of discrete concentrations, for example 0,1,2, ,10 mM, and letting a regression model interpolate between the clusters.
  • RMSEP Root Mean Square Error of Prediction
  • RMSEP The definition is analogous to that of standard deviation and may be interpreted in a similar way, only the deviation is calculated as the difference between each measured value and its 'true' value instead of the average of the whole set.
  • a RMSEP of, say, 0.75 mM would mean that the predictions of the regression model are 0.75 mM off the 'true' value on average.
  • this RMSEP value corresponds to a relative error of 7.5 %. There is no definite maximum level of the relative error that differentiates between a good and a bad model, though a value of less than 10 % is often considered acceptable.
  • step time On a scale from 25 to 700 ms, the most favorable step time was shown to be 25 ms. However, 50 ms was selected for the particular research setup in order to obtain increased hardware stability. For future reference this indicates that there are potential benefits in shortening the step time further, provided there is new hardware to deal with a faster sampling process in a more efficient way. Thus, there is no specific lower limit identified for the step time.
  • step amplitudes Of the investigated step amplitudes, the largest, ranging from -2 to 2 V, proved most beneficial. This is considered to be a relatively high voltage difference seen from a voltammetric perspective. Still larger amplitudes may work even better, and are not excluded from the invention.
  • each measurement consisted of five cycles (i.e. consecutive measurements on the same sample), of which the first two were disregarded most of the time to decrease the influence of the transient.
  • the urea molecule is uncharged and does not contribute to the conductivity of the solution. Still, the conductivity of the different samples was measured 'just in case' to exclude the possibility of synergistic effects that might affect the conductivity. As expected, the measurements showed no differences in conductivity (If there were a difference in conductivity, it could be possible that the electronic tongue measured this and nothing else. The result would still be valid but render the method superfluous, as there are much simpler means to measure conductivity).
  • the tongue used in this project had one reference electrode and four working electrodes: gold, iridium, platinum, and rhodium.
  • Gold is known to be sensitive to chloride ions and iridium is known to deteriorate easily in general, hence both were excluded from the beginning. However, they both do contribute with information, and a more careful selection of voltage function would still render these materials useful, and are therefore not excluded from the invention as claimed.
  • Figures 8 and 9 show ODP score plots for platinum and rhodium, respectively, for one measurement series of 0—10 mM urea in dialytic liquid. The large difference clearly shows platinum to be superior to rhodium. Because of this, the rhodium electrode was excluded from all subsequent measurements. However, it cannot be ruled out that rhodium and the other electrodes may be useful if one employs different step functions. However other metals than the ones mentioned here may also be useful, as well as alloys thereof and other types of electrode materials.
  • a key signifies the amplitude of key number i and n is the total number of keys in the step function.
  • the tongue was polished before the first measurement. 1100 measurements were made on 5 mM urea in dialytic liquid over six different occasions during one week. One minute separated each measurement and the liquid was renewed between each measurement. The variation in signal energy as a function of time is shown in Figure 10. Except for the obvious noise, three things are interesting:
  • ODP is outstanding in neglecting drift directions.
  • the part of the signal that signifies urea content is superimposed upon a 'base signal', the latter of which is intimately coupled to the electrode surface condition.
  • FIG. 12 shows a PLS score plot of the validation data when 50 % of the data set was used for training and the other half for validation.
  • the RMSEP value for these predictions was 0.75 mM, i.e. a relative error of 7.5 %.
  • Figure 13 shows the corresponding ODP score plot, which in turn gives an RMSEP of 0.17 mM or 1.7 %. This result from the ODP/CRA predictions is outstanding but should not be generalized as the predictions are made within one measurement series.
  • the purpose was mainly to test the difference between PLS and ODP for a situation that involves some long-term drift.
  • the tongue was polished before the first series started. No stabilization time was allowed for the tongue and all data was included in the analysis, i.e. a worst possible scenario for the algorithms given the experimental setup.
  • the ODP score plot in Figure 14 shows both training and validation data for the predictions of measurement series 5 from a model based on measurement series 1-4. Plotting both training and validation data is interesting in this case when predicting measurements from a set not included in the building of the model; As is seen in the figure there is a direction of bias (a systematic difference between training and validation data), probably because the model was not built on data containing this drift direction.
  • the RMSEP was 0.96 mM for PLSR and 0.65 mM for ODP/CRA.
  • additional improvements are to be expected if higher-order terms can be successfully included in the model.
  • such an effort would probably prove futile compared to including more data in the model and should in any case be made after such an inclusion.
  • Figure 16 shows an ODP score plot of urea measurements in both dialytic liquid and phosphate buffer.
  • the data from the dialytic liquid has been used for training and the data from the phosphate buffer for validation. Since the phosphate buffer that was used in this experiment was very different from the dialytic liquid, for example by having 50 % higher conductivity and phosphate rather than carbonate as a buffer, it is natural that the two separate in measurement space. What is interesting, however, is that the urea direction seems to be constant not only over time, as was shown in Section Stability, but also for two chemically different liquids. This positive result made plausible that a rather simple calibration algorithm should be able to compensate for differences in liquids.
  • a first tentative calibration algorithm was based on the idea that the urea content information is superimposed upon a 'base signal', as has already been discussed in Section Stability.
  • the base signal is calculated whenever a new measurement starts — though allowing for some initial stabilization — and then subtracted from all the subsequent measurements.
  • this is a linear translation and should, theoretically, 'bring down' the urea variation around the origin in the multidimensional measurement space.
  • the base signal is estimated by taking a row vector average over a certain number of measurements.
  • Figure 17 shows the same thing as Figure 16 with the only difference that the data has been calibrated according to the algorithm described above.
  • the match is not perfect but satisfactory, especially in the interval 1 - 6 mM.
  • the present system is an on-line, real-time monitoring system, it is very well adapted for automatic control of the status of a treatment, such as dialysis.
  • a continuous output of concentration values of the analyte under observation, e.g. urea onto a display, in the form of a graph that gives a visual and readily comprehensible indication of the progress of the treatment.
  • an indicator lamp shining red as long as a predetermined level of the analyte has not been reached, and as soon as the set value is reached, it can turn green, indicating complete treatment.

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Abstract

L'invention porte sur un procédé ayant trait dans son exécution la plus large à l'analyse de l'urée présent dans un liquide, pouvant être du sang, d'autres fluides corporels, des dialysats ou leur mélange, par mise en contact du liquide avec une languette électronique comportant une ou plusieurs électrodes auxquelles on applique une suite d'impulsion de tension dont l'amplitude peut différer pour chacune des électrodes et dont le potentiel peut varier entre + et -.
EP02789086A 2001-11-29 2002-11-22 Analyse de l'uree present dans un liquide l'aide d'une languette electronique Withdrawn EP1448988A1 (fr)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
US25564 1998-02-18
SE0104006A SE523961C2 (sv) 2001-11-29 2001-11-29 Metod för detektion av ureakoncentration i en kroppsvätska samt ett system i form av en elektronisk tunga
SE0104006 2001-11-29
US10/025,564 US20030113933A1 (en) 2001-12-18 2001-12-18 Analysis of components in liquids
PCT/SE2002/002134 WO2003046554A1 (fr) 2001-11-29 2002-11-22 Analyse de l'uree present dans un liquide l'aide d'une languette electronique

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Publication Number Publication Date
EP1448988A1 true EP1448988A1 (fr) 2004-08-25

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EP02789086A Withdrawn EP1448988A1 (fr) 2001-11-29 2002-11-22 Analyse de l'uree present dans un liquide l'aide d'une languette electronique

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EP (1) EP1448988A1 (fr)
AU (1) AU2002353714A1 (fr)
WO (1) WO2003046554A1 (fr)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101339153B (zh) * 2008-07-28 2013-03-13 浙江工商大学 一种示踪食品致病性细菌生长的检测方法
EP2578292B1 (fr) 2011-10-07 2018-12-26 General Electric Technology GmbH Procédé pour contrôler une colonne de lavage par voie humide utile pour supprimer le dioxyde de soufre d'un gaz
EP2579032B1 (fr) 2011-10-07 2015-06-03 Alstom Technology Ltd Capteur de sulfite et procédé pour mesurer la concentration de sulfite dans une substance
EP3104171B1 (fr) 2015-06-12 2018-08-22 General Electric Technology GmbH Capteur d'acide dibasique et procédé pour mesurer en continu la concentration d'acide dibasique dans une substance

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE3129988A1 (de) * 1981-07-29 1983-02-17 Siemens AG, 1000 Berlin und 8000 München "verfahren und vorrichtung zur bestimmung von harnstoff"
SE518437C2 (sv) * 1997-09-07 2002-10-08 Appliedsensor Sweden Ab Elektronisk tunga

Non-Patent Citations (1)

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Title
See references of WO03046554A1 *

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Publication number Publication date
AU2002353714A1 (en) 2003-06-10
WO2003046554A1 (fr) 2003-06-05

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