Prediction and interpretation of the antioxidant capacity of green tea from dissimilar chromatographic fingerprints

摘要

Previously, multivariate calibration techniques have been successfully applied to model and predict the antioxidant activity of green tea from its chromatographic fingerprint. Since the selectivity differences between dissimilar chromatographic systems have already been valuably used in several applications, in this paper it is studied whether combining the complementary information contained in two dissimilar fingerprints can improve the predictive capacity of the multivariate calibration model. The simplest way of combining the data is concatenating both fingerprints for each sample. The resulting matrix can then be subjected to Orthogonal Projections to Latent Structures (O-PLS). Unfortunately, this approach resulted in a more complex model with a prediction error of about the average of the errors obtained with the individual fingerprints. Secondly, only the peaks with high loading and low orthogonal loading from both chromatograms were included in the O-PLS model. This resulted in a reduced complexity, but not in better predictions, probably due to a lack of complementarity of the information concerning the antioxidant capacity. Finally, the concatenated fingerprints were subjected to stepwise multiple linear regression (MLR) in order to build a model based on the variables most correlated with the antioxidant capacity. The obtained prediction error was lower than those of both previous approaches, but still higher than the error of the model based on a single analysis. This is probably again caused by a lack of complementarity in the variables. Nevertheless, it was advantageous to develop fingerprints on dissimilar system, because it enables to choose the most suited chromatographic profile to build a multivariate calibration model for the considered purpose. In contrast to what was expected, the study showed that the most simple (so the worst separated) fingerprints resulted in the best predictions. On the other hand, a more complex fingerprint in which more compounds are separated is still important to improve the interpretability of the model.