Patent ID: 8428881

Claim:
A method for processing hyphenated chromatographic data to delineate components of a sample, the method comprising: a) obtaining hyphenated chromatographic data points for a sample that each comprise at least three dimensions, at least one of the dimensions being a continuous dimension; b) subjecting at least a portion of the data points to an algorithm that organizes the data points into discrete clusters according to the data points' continuous dimension values by starting at either a smallest or largest continuous dimension value associated with data points that are not yet associated with a discrete cluster and delineating, at the largest gap between continuous dimension values of adjacent data points within a predetermined resolution window, a boundary between discrete clusters, wherein the clusters of data points are of varying width of less than or equal to the width of the resolution window and wherein at least some of the clusters of data points are indicative of components of the sample, wherein step b) is repeated until all data points are associated with a cluster; and c) outputting to a user information describing the clusters in a user-readable format, wherein the steps a), b), and c) are performed on a suitably-configured computer and wherein the algorithm functions according to the following steps: i. creating an ordered set of the continuous dimension values of the data points; ii. selecting, as a start value, either the smallest or the largest value within the ordered set; iii. finding, as an end value, a value within the ordered set that is the closest to the start value from among the values having a distance from the start value that is greater than a designated resolution limit; iv. designating as a subset the values of the ordered set comprising the start value, the end value, and every value lying between; v. locating a largest gap between any consecutive two of the values of the subset, and designating the consecutive values as a stop value and a restart value, the stop value being the closer of the two to the start value; vi. marking as a cluster the data points having a continuous dimension value ranging from the start value up to and including the stop value; and vii. repeating steps (iii-vi) using the restart value as the start value, until each data point is contained within a marked cluster.