Analysis of such large multidimensional datasets is difficult without multi-variate statistical analysis techniques to reduce dimensionality. To visualize the effects of reaction variables, we employed principal component analysis (PCA) , an unsupervised data analysis method that allows patterns in data with multiple variables to be observed in scatter plots with minimal loss of information. This is achieved by a rotation of the multi-dimensional axes, where each axis corresponds to a different variable, in our case the integrated intensities of identified peaks from LC-MS analysis. The rotation preserves the orthogonality of the axes but the first new axis, or principal component, is chosen as the direction of maximum variance in the data, the second principal component corresponds to the next most variance (orthogonal to the first) and so on. In this way, a small subset of the new axes, or principal components, provides most of the information in the data and pairwise scatter plots showing the coordinates of the observations in relation to even just the first two principal components can reveal meaningful patterns in the data. As these new axes are obtained by a rotation, each principal component is a linear combination of the original variables, or peak intensities, and the rotation matrix provides the coefficients, or loadings, of each in the linear combinations. The magnitudes of the loadings for a particular component therefore show the importance of each species to any patterns observed in the direction of that component.