Interesting features finder (IFF): Another way to explore spectroscopic imaging data sets, giving minor compounds and traces a chance to express themselves

Abstract

Today, we acquire larger and larger spectroscopic imaging data sets on complex samples. Even before proceeding with a spectroscopic analysis, we often have information concerning for example the presence of major compounds. However, we are not in the same position regarding the presence and location of minor compounds and traces in the sample, while this is generally a more interesting piece of information. Modern spectroscopic imaging uses chemometric tools that allow the simultaneous exploration of the entire spectral range available. These well-tested tools, such as principal component analysis, often exploit the variance contained in the data set to extract maximum chemical information. In general, we can say that these approaches are quite efficient but they are not completely adapted to the characteristics of the data acquired during a spectral imaging experiment. Indeed, such data sets have generally a limited signal to noise ratio and minor compounds and traces are often present on a small number of pixels compared to the total number in the considered data set. It is then quite possible that these compounds are present in a sample but not detected by the chosen multivariate tool. The goal of this work is to introduce an approach called Interesting Features Finder (IFF) able to retrieve minor compounds and traces independently of the variance they may express in the spectral data set. Like other state of the art methods, it uses the notion of convex hull but with the great advantage of working directly on the raw data, without prior projection to reduce their dimensionality. By means of a synthetic spectral data set and a micro-LIBS (laser-induced breakdown spectroscopy) imaging one acquired on a sample taken in Antarctica, we will show the potential of this approach in terms of sensitivity of detection of compounds but also of robustness against noise.