Multivariate identification of extruded PLA samples from the infrared spectrum

Abstract

Polylactid 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 a special care must be put in the manufacturing processes involved. 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 (NMR-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 (FTIR) spectroscopy, jointly with the application of feature extraction and classification algorithms, has been applied in this study. The results of this work show the potential of the methodology proposed as a fast tool for process control.

Obtaining Active Polylactide (PLA) and Polyhydroxybutyrate (PHB) Blends Based Bionanocomposites Modified with Graphene Oxide and Supercritical Carbon Dioxide (scCO2)-Assisted Cinnamaldehyde: Effect on Thermal-Mechanical, Disintegration and Mass Transport Properties

“…A peak associated with the carbonyl group C=O, characteristic of this polymer, can be seen at approximately 1748 cm −1 [26]. While the three peaks at 1180 cm −1 , 1129 cm −1 , and 1080 cm −1 are assigned to asymmetrical vibrations of C-O-C and C-O [58]. Lastly, peaks at 749 cm −1 and 870 cm −1 are assigned to the stretching of the C-C bond, attributed to the crystalline and amorphous PLA phases [59,60].…”

Circular economy of post-consumer textile waste: Classification through infrared spectroscopy

“…Among the feature extraction algorithms, PCA, CVA (Riba et al, 2020), ECVA (Riba et al, 2013) or SVM highlight (Riba et al, 2012) To obtain a robust classification model, the calibration set must include all the variability inherent in the textile samples. To this end, it is required to have an extensive dataset of known fibers, whose origin must be known, since a supervised approach is carried out.…”

“…It is known that FTIR spectral data typically includes thousands of data points, one per wavenumber analyzed, and thus, multivariate mathematical methods are required to operate with this large number of points. Such methods include feature reduction algorithms and classifiers, the first ones designed to concentrate the relevant analytical information of the whole data set in a few latent variables, which also let partially removing most of the noise included in the original spectral data (Riba et al, 2020). To calculate the reduced set of latent variables, the principal component analysis (PCA) algorithm is applied followed by the canonical variate analysis (CVA) algorithm.…”

Post-Consumer Textile Waste Classification through Near-Infrared Spectroscopy, Using an Advanced Deep Learning Approach

“…It is a common practice to process the raw spectral data provided by the spectrometer [24] to increase the accuracy of the classification stage. It should be noted that spectral data are treated in matrix format.…”