Patent Description:
In the Article of<NPL>, it is described an ensemble averaging and digital filtering which were implemented for signal-to-noise ratio improvement in the separation techniques of size-exclusion chromatography, immunoaffinity chromatography, capillary zone electrophoresis and capillary ion analysis. The results of ensemble averaging were always greater than statistically predicted. The techniques included five to nine replicate separations and yielded signal-to-noise improvement factors of <NUM> to <NUM>. The running-average and time constant (RC)-convolution digital filters yielded increases in the signal-to-noise ratio ranging from zero to twelve. This article discusses and illustrates the usage of ensemble averaging and digital filtering in liquid-phase separation techniques.

<CIT> discloses an improved version of a capillary bridge viscometer that compensates for the effect of solvent compressibility. A novel, yet simple and inexpensive modification to a conventional capillary bridge viscometer design can improve its ability to reject pump pulses by more than order of magnitude. This improves the data quality and allows for the use of less expensive pumps. A pulse compensation volume is added such that it transmits pressure to the differential pressure transducer without sample flowing there through. The pressure compensation volume enables the cancelation of the confounding effects of pump pulses in a capillary bridge viscometer.

<CIT> discloses a method of eliminating the impulse noise, wherein said method is based on the equation of the comb filter N = Fs Fo, with Fs being the frequency of the mud piston, Fo being the multiplication of the work rate of the mud piston and N being the multiplication of the work rate of the mud piston. To eliminate the noise, a MATLAB software is used to present the mud pulse detour with a field acquisition of the mud pulse, with a determination of the signal-to-noise ratio of the comb filter based on the signal-to-noise ratio and then the width of the BW and the width of the comb filter, with the mud pulsed signal being switched so that the value of the signal is located in a predetermined area, and with the signal being loaded to the comb filter.

<CIT> discloses a method and a device for monitoring the integrity of a fluid connection between first and second fluid containing systems based on at least one time-dependent measurement signal from a pressure sensor in the first fluid containing system. The first fluid containing system comprises a first pulse generator, and the second fluid containing system comprises a second pulse generator. The pressure sensor is arranged to detect first pulses originating from the first pulse generator and second pulses originating from the second pulse generator. The integrity of the fluid connection is determined based on the presence of second pulses in the measurement signal. The second pulses may be detected by analysing the measurement signal in the time domain and/or by using timing information indicative of the timing of the second pulses in said at least one measurement signal. The analysis may be based on a parameter value that represents a distribution of signal values within a time window of the measurement signal. For example, the parameter value may be calculated as a statistical dispersion measure of the signal values, or may result from a matching of the signal values within the time window to a predicted temporal signal profile of a second pulse. The fluid connection may be established between a human blood system and an extracorporeal blood flow circuit, e.g. for extracorporeal blood treatment.

From <CIT> it is known a differential refractometer, wherein in the case where the refractive index of the solvent changes due to fluctuations in pressure or temperature, the effect of a parallel shift of the irradiated light can be eliminated.

<CIT> discloses an automatic bridge balancing device and a method for a capillary bridge viscometer.

From <CIT>, it is known a capillary bridge viscometer and a method for measuring is specific viscosity.

Liquid chromatography systems have sophisticated computer-controlled pumps that deliver solvent and sample through chromatography columns that fractionate the sample into its constituent components. The fractionated sample then flows through one or more analytical instruments, such as light scattering, refractive index, UV absorption, electrophoretic mobility, and viscosity detectors, to characterize its physical properties. During the analysis, the flow system provides a constant volume flow rate while minimizing flow and pressure variations. Modern chromatography pumps routinely supply fluid at tens to hundreds of bar with variations of <NUM> % or less. They achieve this level of performance by using a series of techniques including pressure feedback at the pump head or using nonlinear pump strokes that correct for the effect of solvent compressibility (see, for example, Agilent <NUM> Series Quaternary Pump User Manual, Agilent Technologies, Inc. , Santa Clara, California). Despite these impressive specifications the analytical instrument signals often show small periodic fluctuations in their baseline, referred herein as pump pulses. Some analytical instruments are particularly sensitive to the corrupting effect of pump pulses, which can obscure the primary measurement.

The performance of online differential viscometers is often limited by their ability to distinguish between a chromatography peak that produces a small change in viscosity that manifests as a pressure drop across a capillary, and a pump pulse that mimics one. Although differential viscometers are particularly prone towards pump pulse pickup, they are by no means the only analytical instruments that are affected. Differential refractive detectors also commonly display pressure pulses in the solvent baselines. Light scattering and UV/VIS absorption detectors tend to be relatively insensitive to pump pulses, but even so there are examples in the literature, which show that they too can be affected.

Pump pulses typically are observed as a periodic oscillation in the baseline of whatever signal the analytical instrument is measuring. When seen, the usual remedy is to reduce them at the source, either by performing maintenance on the pump to replace pistons and valve seals, or by adding external pulse dampeners such as the FlatLine™ models produced by Analytical Scientific Instruments US (Richmond, California). However even when the pump is operating correctly, there will always been be residual pressure pulses that can be transduced by the analytical instrument chain. The next step towards minimizing the effect of pump pulses is to design the analytical instruments to be as insensitive as possible to them, while retaining sensitivity to the physical effect they are intended to measure. There are a number of ways to design instruments to make them intrinsically less sensitivity to pump pulses, an example of which will be described below, but even after the pumps and the instrumentation have been optimized, the sensitivity of an instrument is often still limited by pump pulse pickup. It is a goal of this invention to correct the measured signals in software to eliminate the residual effects of pump pulses.

According to the present invention, there is provided an automated method to characterize and remove noise caused by pump pulses from data collected in a chromatography system comprising the steps of.

characterized in that step C comprises the steps of.

Preferred embodiments and modifications of the invention are defined by the dependent claims.

For definiteness consider an online viscometer that measures the change in viscosity of a fluid sample when a chromatography peak passes through the detector. One of the simplest implementations possible is the single capillary design, an example of which is shown in <FIG>. A pump <NUM> draws fluid from a reservoir <NUM> and passes it through a sensing capillary <NUM>. A differential transducer <NUM> measures the pressure across the capillary. The measured pressure is proportional to the flow rate and the sample viscosity. If one first flows solvent through the capillary and measures the baseline pressure P<NUM> , and subsequently injects a sample, the specific viscosity is simply <MAT> where Ps(t) is the pressure due to the sample. A critical problem arising from such a flowing system is that if the pump is not perfectly stable, pump pulses generate pressure signals that appear identical to changes in the sample viscosity. Since the output of the single capillary viscometer is directly proportional to the pressure, the sensitivity of such a device is limited by the quality of the pump used. High quality chromatography solvent delivery systems commonly provide solvent with pressure pulses less than <NUM>%, so the ability to measure specific viscosity is limited to roughly <NUM>% as well. One method of reducing the sensitivity to pump pulses in the instrument is taught by Haney in <CIT>) in the form of a capillary bridge viscometer.

<FIG> is a schematic of a capillary bridge viscometer, where the fluid stream splits at the top of the bridge <NUM>, and half of the sample flows through each bridge arm <NUM> and <NUM>. Since the bridge is symmetric, the differential pressure transducer in the center of the bridge measures zero when all four arms are filled with solvent. When a sample is injected it flows into both arms. One arm of the bridge <NUM> contains an additional delay volume <NUM> so that the sample enters the delay volume <NUM>, but the pure solvent that was present prior to sample injection exits, causing a pressure imbalance in the bridge, which is measured by a pressure transducer <NUM> in the center of the bridge. This imbalance pressure, combined with the inlet pressure measured by a separate pressure transducer <NUM> between the top and bottom of the bridge, gives the specific viscosity through the relation <MAT> where η is the viscosity of the sample, and ηo is the viscosity of the solvent, DP is the imbalance pressure across the bridge, and IP is the pressure from top to bottom of the bridge. This is a direct measurement of the specific viscosity that depends only on the calibrated transducers. At the end of the run, the delay volume is flushed with new solvent and a new measurement can be performed.

A primary technical advantage of a capillary bridge viscometer over a single capillary viscometer is that the bridge design naturally rejects pump pulses, since they necessarily affect both arms of the bridge nearly symmetrically. When properly implemented, the bridge can suppress the effect of pump pulses by two orders of magnitude or more. However pulse rejection is never perfect and residual pump pulses are sometimes still evident in highly sensitive viscometers such as the ViscoStar® (Wyatt Technology Corporation, Goleta, California). These sensitive instruments utilize technologies which are the subject of <CIT> and <CIT> to maximize sensitivity and can measure specific viscosity down to approximately <NUM>-<NUM> , which is up to an order of magnitude below residual noise from pump pulses, and therefore the improved sensitivity of measurements from these state-of- the-art viscometers is still limited by the pump stability.

Fortunately it is possible to determine which features of the chromatogram are due to the pump pulses and which are from the underlying measurement. The pumps that drive the pulses are computer controlled and generate piston strokes that are highly repeatable, with very accurate and stable drive frequencies. The essential observation is that the pump pulses seen in the detectors will reflect this underlying precise drive frequency. By analyzing baseline data that displays pump pulses, it is possible to determine the drive frequency of the pump and eliminate it from the data by designing a custom narrow band comb filter that blocks the pump frequency and its harmonics.

Any of the detector signals that display pump pulse contamination can be used to determine the underlying pump frequency. Once determined, the correction filter can be applied to all of the affected detectors. When the analytical instrument chain includes a single capillary or bridge viscometer, this detector is often the best reference because, by design, the IP transducer measures the pump pulses directly. IP and DP baseline signals of an aqueous solvent with a flow rate of <NUM>/min and a collection interval of <NUM> second collected by a ViscoStar® <NUM> viscometer (Wyatt Technology Corporation, Goleta California) are shown in <FIG>. The IP signal <NUM> has a mean value of <NUM>. 4psi due to the pressure drop in the bridge. The DP signal <NUM>, however, is nearly zero due to the bridge being well balanced. Shown in <FIG> is the power spectrum of the IP and DP signals shown in <FIG>. Note that the pump pulses create a periodic "picket fence" of perturbations in the power spectrum. The fundamental frequency <NUM> for the pump is <NUM> and there are spikes visible at all of the harmonics <NUM>. Since the pump signal is periodic, the harmonics are at precisely integer multiples of the fundamental frequency. It is also important to note that both detectors show spikes in the power spectrum at exactly the same frequencies. Therefore if one characterizes the pump frequency for one detector and designs a filter to remove it, the same filter can be used for all detectors so affected. There are also spikes visible that reflect (alias) off the Nyquist frequency of <NUM>. One could extend the pump pulse elimination filter to eliminate these as well, but the spectral power contained in these peaks is small compared to the fundamental and the first few harmonics. In addition there are "other peaks" <NUM> in the power spectrum that are not multiples of the fundamental. Some of these other peaks represent a second frequency from the pump, and others are from electronic pickup from other noise sources. However, all of these other peaks contain very little spectral power, and although one could use the same algorithm to eliminate them as well, they will be ignored at the present time. The downward slope in the power spectrum presented in <FIG> at high frequency is due to a combination of analog electronic filtering, the frequency response of the sensor, and digital averaging that is part of the analog to digital conversion process. The essential point is that most of the effect of pump pulses is limited to a very narrow frequency range around the fundamental and its harmonics.

<FIG> shows a zoom in of the data of <FIG> around the fundamental frequency region. This power spectrum is from a collection of baseline from pure solvent, before any sample has been injected. The noise near DC is from <NUM>/f noise, which combined with the pump pulse peak frequencies, can be seen at <NUM>. Chromatography peaks are tens of seconds wide so the spectral power associated with them will show up frequencies near <NUM> and below. They are well separated from the pump noise. Since there is a wide separation of time scales between the pump pulses and the chromatography peaks and the <NUM>/f noise, the novel use of a suitably chosen filter can eliminate the noise spikes without affecting the chromatography peaks. The filter will remove a small band of frequencies around the fundamental pump frequency along with its harmonics. Such a filter is called a comb filter and it is an objective of the present invention to construct and apply an optimized filter to eradicate the pump pulses from all of the chromatographic signals.

An appropriate comb filter is constructed to have a notch at the fundamental frequency and its harmonics. The algorithm therefore has a series of steps:.

For each step there are several variants. The following section will describe the method used in a preferred embodiment. The entire process may be automated, but since the implementation employs some heuristics, it may be of value to an end user to present the intermediates so that the user can check to make sure that the algorithm is performing correctly.

In principle, the pump frequency depends only on the model of the pump and the flow rate. One could consider generating a table of known pumps and simply computing the fundamental for each pump based on the flow rate setting. However this method would require a significant research into the pumps currently available on the market and pumps no longer in production but still in use and would have to be continually maintained in order to account for new models as well as verification that the fundamental values don't depend on internal pump parameters (such as solvent compressibility). Moreover even though the frequency of a given pump is very stable, there is no guarantee that it will be exactly the same from pump to pump, even if they are the same model.

A better method is to determine the pump frequency from measurements of the pulses in each individual system. In this case, the user waits until the system is well equilibrated and then collects a solvent baseline. The software then computes the power spectrum as shown in <FIG>. Next the software scans the power spectrum and searches for the fundamental peak. Because the pump frequency is so precise, the spectral power under the peak is much larger than the background noise, making it straightforward to identify, although there are some refinements that can improve the accuracy of the frequency measurement.

Since the pump oscillation is not synchronized with the instrument sampling, the pump period will not be commensurate with the sample frequency. The peak associated with the fundamental frequency of the pump will not fall exactly within a single bin of the power spectrum, causing the power contained to alias across several nearby bins. This effect is known as spectral leakage and does not prevent the method from working properly, but does reduce the accuracy with which the pump frequency is determined. One typically ameliorates this effect by applying a windowing function to the sample data before the fast Fourier transform (FFT) is computed. There are many windowing functions that can be used, but all of them have the characteristic that they go to zero at the start and end of the sample interval. This has the effect of convolving the power spectrum with the Fourier transform of the windowing function, reducing the aliasing artifacts. The choice of the windowing function is not critical, and for our analysis a Hann window, which is a single cycle of a sine wave, was chosen.

Another refinement that improves the accuracy of the pump frequency determination is to first fit the raw signal data to a line and then compute the power spectrum of the residuals. This removes any linear drift that would generate a large low frequency artifact and improves the performance of the windowing function.

The next subtlety is that even with the correction for drift, there is always <NUM>/f noise in the power spectrum that shows up near zero frequency, as seen in <FIG>. To avoid this artifact, the scan for the largest peak in the power spectrum does not start at zero frequency. Similarly the fundamental might not be the peak with the highest power. It sometimes happens that the second, third, or higher harmonic peak is larger than the fundamental. To address this issue, the software that finds the fundamental frequency has a start and stop frequency to limit its scan range. The positions of the start point <NUM> and the stop point <NUM>, as generated by software for the data of <FIG>, are shown graphically in <FIG>. If the start and stop frequency range does not cross the second harmonic, or if the higher harmonics decrease in amplitude, this method is reliable. The start and stop limits may be set manually, however if one does not know a priori the approximate frequency of the peak, the algorithm applies another heuristic.

Consider the case in which the algorithm accidentally chooses the second or third harmonic instead of the fundamental. Call the measured frequency wm. One can test wm/<NUM> and wm/<NUM> to determine if there is a peak present by checking if the power in this bin is much larger than its neighbors. If so, then the algorithm can determine that it has originally found the second or third harmonic and assign the fundamental frequency accordingly.

Even if the algorithm properly determines the fundamental frequency, there is some error associated with the frequency measurement that depends on the frequency resolution of the power spectrum. The longer one runs the baseline, the better is the frequency resolution and the more accurately the fundamental will be determined. Alternatively one could look explicitly for the nth harmonic and then compute the fundamental frequency as <NUM>/n times the frequency of the nth harmonic. This could improve measurement accuracy by <NUM>/n. Another technique is to fit the baseline signal S(t) to a nonlinear fit model of the form <MAT> where a, b, Ai, w<NUM>, φi are fit parameters. The fundamental is extracted directly as the w<NUM> fit parameter.

Once the fundamental frequency is determined, a comb filter is constructed to create a band-stop notch at this frequency ±Δw/<NUM> where Δw is the width of the notch. Since the error associated with the accuracy with which the fundamental frequency is unknown, when one creates the notches around the nth harmonic, the associated band-stop is increased to ±nΔw/<NUM>. The filter is defined as <NUM> when outside the band-stop notches, and <NUM> inside. <FIG> shows the resulting filter mask for the data presented in <FIG>. After the computed comb filter is generated, it may be applied to the data. The power spectrum of IP both (a) before and (b) after applying the comb filter are displayed in <FIG>. The narrow features are clipped out due to the pump pulses without affecting most of the power spectrum. As is well known in the art of signal processing, there are many variations possible in the width and phase behavior of the band pass filter, and this disclosure should not be considered limited to details set forth above.

The last step is to apply the filter. This is done by simply multiplying the Fourier transform of the signal by the mask and taking the inverse transform. Define the sampled detector signal as sn = S(nΔt). Then the discrete Fourier transform of sn is <MAT> where N is the number of samples in the collection. This can be computed efficiently by using the Fast Fourier Transform (FFT) algorithm. Call the sampled comb filter defined above as ck. Then the clipped data in the frequency space sk that eliminates the pump frequencies is simply the product <MAT> (<NUM>), which is then transformed back to the time domain by an inverse FFT as <MAT> The corrected data sn is now nearly identical to the initial sampled data sn but free from pump pulses. An example of the application of this filter to the IP data series is shown in <FIG>. As can be clearly seen, the periodic pulses of the original data <NUM> have been eliminated in the filtered data <NUM>. Once the filter has been computed, it can be applied to all of the analytical signals to achieve pulse suppression. The fundamental frequency only needs to be determined when the system is set up or when the flow rate of the HPLC pump is changed. After these settings are established, the same filter can be used for all future analyses.

A further example of the power of the inventive filtering method disclosed herein is shown in <FIG>. Here we see an injection of bovine serum albumin (BSA) into an analytical instrument chain that included a differential viscometer that measures the specific viscosity and a differential refractive index detector that measures the sample concentration. From these two detectors, the intrinsic viscosity [η] is computed through the relation <MAT> The intrinsic viscosity is shown on the left axis and the specific viscosity is on the right axis. In panel (a) the raw detector signals are used in the calculation prior to filtering. The pump pulses are seen as an oscillation of the specific viscosity signal. The intrinsic viscosity is strongly affected, especially on the edges of the peak where the signal is small. Panel (b) shows the results after the comb filter has been applied to both the differential viscometer and differential refractive index signal. Note that the measured peaks shapes are unaffected by the filter but the results are dramatically improved. As with the previous example, the pump pulses present in the unfiltered data are absent after the correction is applied.

Claim 1:
An automated method to characterize and remove noise caused by pump pulses from data collected in a chromatography system comprising the steps of
A. collecting data with an analytical instrument where said collected data contains pump pulse contamination;
B. computing the power spectrum from said collected data;
C. determining said fundamental frequency of pulses due to said pump from said power spectrum;
D. constructing a filter to remove noise around said fundamental frequency; and
E. filtering said collected data with said filter;
characterized in that step C comprises the steps of
C1. selecting a region of the power spectrum outside of the region wherein <NUM>/f noise is present;
C2. determining the frequency wm corresponding to the maximum power signal in said region;
C3. analyzing the power value of the frequency at wm/<NUM> and wm/<NUM> to determine if there is a peak in the power spectrum at these frequencies; and
C4. determining said fundamental frequency to be
a. wm if there is no peak in the power signal at wm/<NUM> or wm/<NUM>; or
b. wm/<NUM> if there is a peak in the power signal at wm/<NUM>, and therefore wm is a harmonic frequency; or
c. wm/<NUM> if there is a no peak in the power signal at wm/<NUM> and a peak is present at wm/<NUM>, and therefore wm is a harmonic frequency.