Automatic peak identification method

• US 20060217938A1
• Filed: 03/22/2006
• Published: 09/28/2006
• Est. Priority Date: 03/22/2005
• Status: Active Grant

• Current Assignee: College of William & Mary
• Original Assignee: College of William & Mary
• Inventors: Cooke, William E., Tracy, Eugene R., Chen, Haijian
• Granted Patent: US 7,219,038 B2
• Time in Patent Office: Days
• US Class Current: 702/189
• CPC Class Codes: G06F 18/00 Pattern recognition
• CPC Class Codes: G06F 2218/10 by analysing the shape of a waveform, e.g. extracting parameters relating to peaks

Abstract

The invention described herein details a protocol to improve analysis and peak identification in spectroscopic data. Bayesian methods are used to automatically identify peaks in data sets. After identifying peak shapes, the method tests the hypothesis that a given number of peaks is found within any given data window. If a peak is identified within a given window, then the likelihood function is maximized in order to estimate peak position and amplitude. This process yields a spectrum with high resolution and minimal artifacts. The method described herein is particularly useful for identifying peaks in data sets obtained from spectroscopy.

Claims

1. An automated method of data processing and evaluation comprising the steps of:

(a) selecting a plurality of peak shapes within a data set;

(b) developing a hypothesis which predicts a specified number of peaks present within a given window;

(c) testing the hypothesis that the specified number of peaks is present within the given window; and

(d) maximizing a likelihood function to estimate the position and amplitude of said peaks present within the given window.

2. The method of claim 1, wherein said maximizing of said likelihood function is performed only within one or more windows identified as having one or more peaks present.

3. The method of claim 1, wherein said data set is obtained from a mass spectra.

4. The method of claim 3, wherein said data set is obtained from time-of-flight mass spectra.

5. The method of claim 3, wherein said peak shapes are derived from an evolution of probability density for a single ion.

6. The method of claim 5, wherein said evolution of probability density maps a probability density function from an input to a flight tube into a probability flux striking a detector.

7. The method of claim 3, wherein said data set is derived from one or more complex biological mixtures.

8. The method of claim 1, wherein said data set is obtained from analysis of an image.

9. A software program for data processing and evaluation comprising:

(a) a first program code for selecting a plurality of peak shapes within a data set;

(b) a second program code for developing a hypothesis which predicts a specified number of peaks present within a given window;

(c) a third program code for testing the hypothesis that the specified number of peaks is present within the given window; and

(d) a fourth program code for maximizing a likelihood function to estimate the position and amplitude of said peaks present within the given window.