1. A system for identifying groups of correlated representations of variables from a large amount of spectrometry data, comprising: a spectrometer that analyzes a plurality of samples and produces a plurality of variables from the plurality of samples; and a processor in communication with the spectrometer, wherein (a) the processor obtains the plurality of measured variables from the spectrometer, (b) the processor divides the plurality of measured variables into a plurality of measured variable subsets, (c) the processor performs principal component analysis followed by variable grouping on each measured variable subset, producing one or more group representations for each measured variable subset and a plurality of group representations for the plurality of measured variable subsets, (d) the processor calculates a total number of the plurality of group representations as a sum of a number of the one or more group representations produced for each measured variable subset, (e) if the total number is less than or equal to a maximum number of variables allowed for principal component analysis followed by variable grouping, the processor jumps to step (k), (f) the processor divides the plurality of group representations into a plurality of group representation subsets, (g) the processor performs principal component analysis followed by variable grouping on each group representation subset, producing one or more group representations for each group representation subset and a plurality of group representations for the plurality of group representation subsets, (h) the processor calculates the total number of the plurality of group representations as a sum of a number of the one or more group representations produced for each group representation subset, (i) if the total number is less than or equal to the maximum number, the processor jumps to step (k), (j) if the total number is greater than the maximum number of variables, the processor repeats steps (f)-(j), and (k) the processor performs principal component analysis followed by variable grouping on the plurality of group representations, producing a plurality of groups of correlated representations of variables.