This paper demonstrates the potential of a two-dimensional (2D) gradient mapping technique that utilized the eigenvalue manipulating transformation (EMT) of the spectral data set. The EMT technique, by lowering the power of a set of eigenvalues associated with the original data, enhances the contributions of minor principle components (PCs). The operation converts the original spectral data set to the one with subtle differences among the responses of the system being exaggerated. Small shoulders and obscure minor features may become much more visible, because such small differentiating features are often captured only by the minor PCs enlarged by the EMT treatment. This improvement for 2D mapping is potentially very important to determine the transition temperatures, which are not readily detected in convention spectral analysis.