Multivariate data analysis-NMR and LCMS - Khatib, Alfi (2019)
Metabolomics is becoming a trending approach in herbal science. It requires a statistical tool covering multi-variables correlating to signals from analytical instruments. This statistical calculation is called multivariate data analysis. It extracts information from data with multiple variables of which are calculated simultaneously. Multivariate data analysis produces an underlying trend or latent variable in order to reduce a statistical error. In another word, it reduces the dimensional of the variables by projection. This approach avoids the loss of information or missing data, therefore it develops a more stable model. In this presentation, a basic theory of multivariate data analysis is explained as well as examples of its application in NMR and LC-MS based metabolomics.