Systems and methods to process data in chromatographic systems

Patent metadata

Field	Value
Publication number	US-10488377-B2
Application number	US-201214004450-A
Country	US
Kind code	B2
Filing date	Mar 12, 2012
Priority date	Mar 11, 2011
Publication date	Nov 26, 2019
Grant date	Nov 26, 2019

Abstract

A system and method for processing data in chromatographic systems is described. In an implementation, the system and method includes processing data generated by a chromatographic system to generate processed data, analyzing the processed data, and preparing and providing results based on the processed data.

First claim

What is claimed is:

1. A method of identifying information indicative of the presence of ions in a chromatography, mass spectrometry system, the method comprising:
passing ions through a mass spectrometer and generating data associated therewith in a data acquisition system;
receiving the data at processing modules;
processing the data with the processing modules associated with the mass spectrometer to generate processed data, wherein the data includes long clusters and short clusters and the processing step comprises (i) separating, by the processing modules, the long clusters from the short clusters; (ii) filtering, by the processing modules, the data to smooth the data thereby yielding filtered clusters; (iii) dividing, by the processing modules, the filtered clusters into sub-clusters; and (iv) qualifying, by the processing modules, the sub-clusters to extract undesired sub-clusters therefrom;
analyzing, by the processing modules, the processed data to extract noise therefrom and to group together one or more constituents of the mass spectra for one or more eluting compounds to aid in the elucidation of each of such compounds, wherein the one or more constituents are isotopes, adducts and fragments;
obtaining, by the processing modules, ion information associated with the processed data such that the ion information is free of noise;
preparing and providing, by the processing modules, results relating to the processed data, wherein the results include the ion information.

2. The method of claim 1 further comprising:
reincorporating information associated with at least one of the isotopes and adducts that may have been extracted as noise.

3. The method of claim 1 , wherein the separating step further comprises:
separating the data into blocks;
estimating an intensity of a baseline in the center of each block;
linearly interpolating between equidistant quartile points of each block to yield a baseline estimation;
clipping the data above the baseline level and preserving the data below the baseline; and
smoothing the clipped data to yield an improved version of the baseline.

4. The method of claim 3 , wherein a length of each block is a multiple of an expected full-width half-height of the data.

5. The method of claim 3 , wherein a length of each block is estimated as five times an expected full-width half-height of the data.

6. The method of claim 3 , wherein the smoothing step involves the application of a Savitzky-Golay smoothing algorithm.

7. The method of claim 3 , wherein estimation of the intensity of a baseline in the center of a block is based on an intensity of the baseline in the lower quartile of the block.

8. The method of claim 1 , wherein the qualification step comprises at least one of:
selecting sub-clusters that have a signal-to-noise ratio that is greater than a threshold signal-to-noise ratio
selecting sub-clusters that have a peak shape that is greater than a threshold quality, and
selecting sub-clusters that have a minimum cluster length.

9. The method of claim 8 , wherein the threshold signal-to-noise ratio is 10.

10. The method of claim 8 , wherein the noise is the pre-defined acquisition noise of one-fourth (¼) ion area.

11. The method of claim 8 , wherein the noise is the standard deviation of the residual between the original cluster data and the smoothed cluster data.

12. The method of claim 8 , wherein sub-clusters with a signal-to-noise ratio that is less than the threshold signal-to-noise ratio are still used in the factor analysis if they are isotopes or adducts.

13. The method of claim 8 , further comprising the step of:
trimming the baseline of a sub-cluster from a left and a right side of a peak.

14. The method of claim 13 , wherein the trimming step further comprises:
scanning raw-data within the sub-cluster from the ends to the center;
identifying where the intensities rise above a threshold on each end as a new end point;
discarding the data outside of the new end points.

15. The method of claim 14 , wherein the threshold is four times the standard deviation of the sub-cluster.

16. The method of claim 8 , wherein the threshold quality is based on a correlation between a fitting of the sub-cluster and a pre-defined curve.

17. The method of claim 16 , wherein the pre-defined curve is a bi-Gaussian curve.

18. The method of claim 17 , wherein the threshold correlation is 0.8.

19. The method according to claim 18 , wherein the defined intensity is at or around one-half of the intensity of one or both of the two peaks.

20. The method of claim 16 , wherein the threshold correlation is 0.6.

21. The method of claim 8 , wherein the minimum cluster length is 5 sticks.

22. The method of claim 1 , wherein the filtering step utilizes an infinite impulse response filter.

23. The method of claim 1 , wherein the filtering step comprises:
identifying the largest peak within the data;
estimating the full-width half-height of the identified peak;
matching the estimated full-width half-height against a look-up table to identify one or more optimized filter coefficients;
smoothing the data based on the optimized filter coefficients; and
identifying a noise figure for each cluster.

24. The method of claim 23 , wherein the optimized filter coefficients are a set of forward and reverse second-order infinite impulse response filter coefficients.

25. The method of claim 24 , wherein the noise figure is the standard deviation of the residual between the smooth data and the raw data.

26. The method of claim 25 , wherein the noise figure is assigned to each of the sub-clusters that are derived from a cluster.

27. The method of claim 24 , wherein the optimized coefficients are calculated according to the following steps:
forming Gaussian peaks at each expected full-width half-height;
adding noise to the Gaussian peaks thereby yielding noisy Gaussian peaks; and
optimizing the Gaussian peaks to adjust the filter coefficients in a manner that substantially minimizes the residual between the noise Gaussian peaks and the Gaussian peaks.

28. The method of claim 27 , wherein the optimizing step utilizes a non-linear Levenburg-Marquardt process.

29. The method of claim 1 , wherein the clusters have peaks and valleys and the dividing step further comprises:
identifying each instance within a filtered cluster wherein a valley situated between two peaks has a minimum point that is less than a defined intensity of the two peaks; and
separating the cluster into sub-clusters based on each identified instance, if any.

30. The method according to claim 1 , where the analyzing step further comprises:
determining significant factors for factor analysis; and
providing initial seed estimates of those factors.

31. The method according to claim 30 , further comprising:
eliminating lower quality peaks.

32. The method according to claim 1 , wherein the analyzing step further comprises:
selecting a base peak among the data;
evaluating and correlating all local data with the base peak;
combining local data having a predetermined minimum correlation value with the base peak to create a factor; and
estimating the spectra for the factor.

33. The method according to claim 32 , wherein the base peak is selected ma

Assignees

• Wang Jihong
• Willis Peter Markel
• Leco Corp

Inventors

• Wang Jihong
• Willis Peter Markel

Classifications

• G06F2218/14

  by matching peak patterns · CPC title

• G01N30/8696

  Details of Software · CPC title

• G01N30/72

  Mass spectrometers {(mass spectrometers per se H01J49/00)} · CPC title

• H01J49/0036

  Step by step routines describing the handling of the data generated during a measurement · CPC title

• G01N30/8675 Primary

  Evaluation, i.e. decoding of the signal into analytical information (for analysis of specific compounds see also G01N30/88 and subgroups of G01N33/00; chemical libraries per se C40B) · CPC title