The feasibility of latent variables applied to GC-MS data

This review starts with a brief introduction to gas chromatography—mass spectrometry (GC-MS) and multivariate analysis. It proceeds with a description of the general strategy of extracting relevant quantitative information from GC-MS instrumentation by latent variables in particular. espect to mass spectra and data analysis, several thorough reviews have been written within the two major fields of classification and curve resolution techniques. As a consequence this review will focus less on these two fields, and put more emphasis to latent variables applied to GC-MS data in environmental field studies and spectrum—structure/property modelling. erstand and operate a GC-MS system of today, the chemist must be knowledgeable in gas chromatography, mass spectrometry, vacuum technology and computer science. It is my hope that this review can aid both the skilled mass spectrometrist within the latter subject, as well as any scientist to achieve relevant information from supported chemical data in their own field of study.