Application of simca multivariate data analysis to the classification of gas chromatographic profiles of human brain tissues

Sixteen samples of three types (classes) of brain tissue were characterized by capillary gas chromatography (g.c.). Each sample is thus characterized by the peak heights of 105 peaks in each g.c. profile. SIMCA pattern recognition is used to analyze the 16 × 105 data matrix in order to differentiate between the three classes on the basis of the g.c. data only. The SIMCA method is therefore applicable even when the number of variables (105) exceeds the number of objects (16). The results indicate that g.c. profiles are useful for the identification of brain tissue type.