Chemometrical Contributions Extending the Application of Near-Infrared and Raman Spectroscopy

Abstract

Raman and near-infrared (NIR) reflectance spectroscopy are increasingly being applied in industry and laboratories. Examples are: investigation of interactions between DNA molecules, characterizing polymer properties, and separating demolition waste. These applications demand robust systems and require methodologies to collect, maintain, and analyze the large amounts of measurement data. NIR reflectance spectroscopy suffers from wavelength-dependent interactions between particle size and NIR radiation. Raman and NIR measurements are influenced by instrumental disturbances and limited spectral regions contain relevant information. Specific preprocessing techniques reduce the disturbances and wavelength selection provides the most relevant regions. The performance of combining wavelength selection and preprocessing is unknown and validating qualitative models requires identifying and assessing the factors that influence the model performance. Object selection and model validation are related because the selected objects determine the model performance. The Autosort project, financially supported by the EU, investigated the separation of demolition waste facing these challenges. Combining linear discriminant analysis with the Mahalanobis distance successfully separated the demolition waste utilizing six wavelength regions and NIR specific preprocessing techniques. Water peaks are excluded, good discrimination characteristics are obtained, and the model performance and robustness is improved. Kennard-Stone object selection works best because uniformly selected objects are obtained. The validation showed that sensor modifications were necessary during development and that the separation is only acceptable if dry objects are measured in the middle of the conveyor belt. Utilizing SERS, Raman spectra can be measured on very small surfaces (100 nanometer). Chemometrics facilitated the removal of outlier spectra and the detection of interesting spectra. Raman and NIR spectroscopy are complementary techniques and their combination might improve predictive power. Despite solving offset and peak intensity differences before concatenating these spectra, the prediction performance was reduced. For this reason, wavelength selection was applied and a substantial effect was found for the NIR spectra.