Supervised Distance Preserving Projections: Applications in the quantitative analysis of diesel fuels and light cycle oils from NIR spectra

• Supervised dimensionality reduction using the SDPP for the quantitative analysis of materials properties from light absorbance spectra.
• The aim is to design light regression models for industrial use.
• Focus on a comprehensive comparison with state-of-the-art and reference methods from the research and the industrial literature.
• The estimation accuracies obtained in two diverse case studies show the potential of the SDPP in on-line and laboratory implementations.

In this work, we discuss a recently proposed approach for supervised dimensionality reduction, the Supervised Distance Preserving Projection (SDPP) and, we investigate its applicability to monitoring material's properties from spectroscopic observations. Motivated by continuity preservation, the SDPP is a linear projection method where the proximity relations between points in the low-dimensional subspace mimic the proximity relations between points in the response space. Such a projection facilitates the design of efficient regression models and it may also uncover useful information for visualisation. An experimental evaluation is conducted to show the performance of the SDPP and compare it with a number of state-of-the-art approaches for unsupervised and supervised dimensionality reduction. The regression step after projection is performed using computationally light models with low maintenance cost like Multiple Linear Regression and Locally Linear Regression with k-NN neighbourhoods. For the evaluation, a benchmark and a full-scale calibration problem are discussed. The case studies pertain the estimation of a number of chemico-physical properties in diesel fuels and in light cycle oils, starting from near-infrared spectra. Based on the experimental results, we found that the SDPP leads to parsimonious projections that can be used to design light and yet accurate estimation models.