1. A method of analyzing signal-vector data from at least one first order sensor, comprising: providing a training data set that includes signal-vector data from at least one first order sensor, wherein the training data set includes background data and sample data collected under known conditions; adjusting the sample data of the training data set to account for variations between the background data and the sample data using a background adjustment technique; normalizing the training data set; transforming the normalized training data set into wavelet coefficients using a wavelet transformation technique; using an automated analysis of variance feature selection technique to automatically select desirable wavelet coefficients for classification; using a pattern recognition technique to classify the training data set; and outputting a classification model generated from the pattern recognition technique.