Multivariate identification of extruded PLA samples from the infrared spectrum

Abstract

Polylactic acid (PLA) is a biodegradable thermoplastic polymer that is presented as a good alternative to petroleum-derived plastics. Some of the major drawbacks of this material are its lack of thermal stability and rapid degradation in large-scale production; thus, special care must be taken during processing. To improve their properties, a reactive extrusion with a multi-epoxy chain extender (SAmfE) has been performed at pilot plant scale. The induced topological modifications produce a mixture of several types of non-uniform structures. Conventional chromatographic (SEC—static light scattering) or spectroscopic (nuclear magnetic resonance) techniques usually fail in characterizing non-uniform structures. A method for the classification of modified PLA samples based on a multivariate treatment of the spectral data obtained by Fourier-transform infrared spectroscopy, jointly with the application of feature extraction and classification algorithms, was applied in this study. The results of this work show the potential of the methodology proposed to improve quality control during manufacturing.