Chromatography principal components analysis applications

Principal component analysis is most easily explained by showing its application on a familiar type of data. In this chapter we show the application of PCA to chromatographic-spectroscopic data. These data sets are the kind produced by so-called hyphenated methods such as gas chromatography (GC) or high-performance liquid chromatography (HPLC) coupled to a multivariate detector such as a mass spectrometer (MS), Fourier transform infrared spectrometer (FTIR), or UV/visible spectrometer. Examples of some common hyphenated methods include GC-MS, GC-FTIR, HPLC-UV/Vis, and HLPC-MS. In all these types of data sets, a response in one dimension (e.g., chromatographic separation) modulates the response of a detector (e.g., a spectrum) in a second dimension. 

Due to the absence of hydrogen donor capabilities [31], cyanopropyl silica phases are less retentive in normal-phase liquid chromatography than under-ivatized silica or other NP packing materials. Therefore, very few applications have been reported that utilize cyanopropyl-bonded silica in the HILIC mode [32,33]. The limited number of applications may also be attributed to the mechanical instabiUty of cyanopropyl-bonded silica when operated with solvents of intermediate polarity. This instabihty is caused by a decrease in the adhesion of particles to each other that maintain the integrity of the column bed in either nonpolar or highly polar solvents [25]. Dinh et al. [34] performed a multivariate modeling of column selectivity by principal component analysis of chromatographic data from polar compounds of various structures on 20 commercially available HILIC columns and verified the low potential of cyanopropyl-bonded silica columns due to insufficient hydrophilicity. 

Otto, M., Chemometrics Statistics and Computer Application in Analytical Chemistry. Wiley-VCH, Weinheim, Germany, 1999. Chapter 5.2. Principal component analysis, pp. 125—132. Heberger, K., Evaluation of polarity indicators and stationary phases hy principal component analysis in gas-liquid chromatography. Chemom. Intell. Lab. Syst., 47, 41-49 (1999). 

A principal components multivariate statistical approach (SIMCA) was evaluated and applied to interpretation of isomer specific analysis of polychlorinated biphenyls (PCBs) using both a microcomputer and a main frame computer. Capillary column gas chromatography was employed for separation and detection of 69 individual PCB isomers. Computer programs were written in AMSII MUMPS to provide a laboratory data base for data manipulation. This data base greatly assisted the analysts in calculating isomer concentrations and data management. Applications of SIMCA for quality control, classification, and estimation of the composition of multi-Aroclor mixtures are described for characterization and study of complex environmental residues. 

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