A SOM-based approach to estimating product properties from spectroscopic measurements

In this work, the problem of real-time monitoring of products’ properties from spectrophotoscopic measurements is presented. Light absorbance spectra are used as inputs to software sensors that estimate outputs otherwise difficult to measure on-line. We approached the problems associated to calibrating the estimation models from very high-dimensional inputs and a reduced number of observations by selecting only a subset of relevant inputs emerging from the topological structure of the data. The topologically preserving representation is performed using the self-organizing map (SOM) where the input significance to the output is computed with the measure of topological relevance (MTR on SOM). As a result, we found that spectral inputs with a topology that is close to the output's are also associated to the wavelengths that chemically explain the influence of the spectra to the property of interest. Being based on a selection of original spectral variables, the resulting models retain the chemical interpretability of the underlying system. Moreover, the selection approach is independent on the regression model to be embedded in the soft sensors. To support the presentation, the utility of the MTR on SOM is discussed on full-scale problems from pharmaceutical and refining industry. Based on our results, the approach leads to accurate and parsimonious models that can be efficiently implemented in industrial settings.