Acquisition of a series of temperature-varied sample spectra to induce characteristic structural changes of components and selection of target-descriptive variables among them for multivariate analysis to improve accuracy

Abstract

As ameans of improving the accuracy of Raman spectroscopic quantitative analysis, a strategy combining the generation of a series of temperature-varied spectra to make diverse and characteristic structural information of sample components widely available for calibration, and subsequent selection of more property-descriptive variables among these spectra, has been demonstrated. For the evaluation, Raman spectra of synthetic hydrocarbon mixtures, lube base oils (LBOs) and polyethylene (PE) pellets were acquired at regular intervals while the sample temperature gradually increased from cryogenic to near room temperature. To select target-descriptive variables from all of the snapshot (temperature-varied) spectra, a Markov blanket (MB) feature (variable) selection able to produce a minimal set of features without changing the original target distribution was adopted. The selection utilizes a conditional independence test to quickly obtain an optimal feature subset by simultaneously considering relevance to a target variable and redundancy between selected features without using any heuristic searching. When MB-selected variables were used for partial least squares (PLS) to determine the concentrations of components in the hydrocarbon mixtures, kinematic viscosity of 40 degrees C (KV@40) of LBOs, and density of PE pellets, the accuracy was improved compared to the use of either all snapshot spectra without variable selection or an optimal single snapshot spectrum. The incorporation of component-specific and property-descriptive variables without redundant information for PLS was the origin of the improvement in accuracy. The proposed method could potentially be extended to the analysis of other complex samples including petroleum-driven samples, edible oils, and other polymers.