Application of lasso for identification of functional groups with significant contributions to antioxidant activities of Centella asiatica

Abstract

High-dimensional data has more variables than observations (p>>n). In this case, modeling with regression analysis becomes ineffective because it will violate the multicollinearity assumption. The least absolute shrinkage and selection operator (LASSO) can handle high-dimensional data and multicollinearity because LASSO works by reducing the parameters of variables with significant effects and selecting variables with minor effects. In its application, several variables have the same characteristics. Reducing and selecting variables in the form of groups can solve the problem so that the group LASSO can be used as a solution. This study used data on antioxidant activity in C. asiatica. It is a plant that contains antioxidants. The spectroscopic technique can find important information about antioxidants, namely the Fourier transformed infrared spectrophotometer (FTIR). FTIR is a spectroscopic technique based on molecular vibrations subjected to infrared so that it can characterize molecules with functional groups. FTIR data has large dimensions and multicollinearity. This study has 1866 explanatory variables (p) and 15 predictor variables (n). So, this study aimed to implement LASSO and the group LASSO to identify functional groups that major affect the antioxidant. This study concluded that group LASSO was better than the LASSO with modification of the LAR algorithm in identifying functional groups that had a major contribution to antioxidant activity. The results showed that the functional groups that had a major effect on the antioxidant activity of C. asiatica were –NH –OH, and C –O. In general, the functional groups that had a major effect on the antioxidant activity of C. asiatica came from phenolic compounds.