Use of PCA with hierarchical analysis could give sufficient desired information..

PCA and CA are the most widely used methods.PCA and CA can be useful in studies regarding bioactive compounds in foods. PCA is the process of reducing high dimensions into a few layers of key features. After dimension reduction, we only have to deal with a small number of features and leave others out.PCA - creates a low-dimensional representation of the samples from a data set which is optimal in the sense that it contains as much of the variance in the original data set as is possible. PCA also provides a variable representation that is directly connected to the sample representation, and which allows the user to visually find variables that are characteristic for specific sample groups.Hierarchical clustering - builds a tree-like structure (a dendrogram) where the leaves are the individual objects (samples or variables) and the algorithm successively pairs together objects showing the highest degree of similarity.

As both answer above have mentioned, Using PCA would give the ideal predictor factors. Looking at the loading (higest) would also enable one to identify the MDS for further analysis and correlation. Regards