Mass Spectrometry is a widely used method to determine the mass of proteins and other small molecules. It analyzes the ratio of mass to charge for small molecules. An important part of the method, which vastly improves the range of molecules that mass spec can distinguish is how large protein can be separated into component sub-domains which can then be analyzed for their mass content. The process requires a creation of a calibration curve between the absorption of light to the concentration of the analyte, or molecule to be measured. The accuracy with which the analyte can be measured depends upon the accuracy of the calibration curve.

Because there are only so many parts that each protein can be broken into, and sometimes those component parts are very similar in weight and are difficult to distinguish from each other. There is, however, a very useful statistical tool that can be used to make sense of the resultant data: principal component analysis or PCA.

PCA is a multivariate method of analysis that is useful with large, multidimensional data sets because it allows for the reduction of the number of variables in the set by reducing the number of variables to only the “principal components” that are most relevant to retaining the variability in the set.

Sources:

http://neon.otago.ac.nz/chemlect/chem306/pca/index.html

http://neon.otago.ac.nz/chemlect/chem306/pca/Spectroscopy_PCA/index.html

http://en.wikipedia.org/wiki/Principal_components_analysis

http://neon.otago.ac.nz/chemlect/chem306/pca/Theory_PCA/index.html