We propose novel and real-time smart software tools to process spectroscopy data. Material abundance or compositional maps will be generated for rover guidance, sample selection, and other scientific missions. First, we propose a novel anomaly detector called clustered kernel Reed-Xiaoli (CKRX) algorithm. This tool was developed by us, is fast, and can achieve very high anomaly detection rate in hyperspectral images from the Air Force. This is important in planetary missions because we may need to look for some anomalous regions in a scene. Second, if target material signatures are available, then we propose a fast matched signature identification algorithm called Adaptive Subspace Detector (ASD). We compared ASD with several other tools and found that ASD outperformed other methods. Third, if target material signatures are not available, then we propose a new technique called minimum volume constrained non-negative matrix factorization (MVCNMF) to perform unsupervised material identification. In a recent comparative study by using hyperspectral images from the Air Force, the MVCNMF performed better than some conventional unsupervised methods. Fourth, the above tools can be implemented in a parallel processing architecture, in which the computations are distributed to multiple cores. We have applied it to speech processing and genomic processing recently. Real-time performance is achievable.