Near-infrared spectroscopy and machine-learning evaluation of PVA/β-TCP biomaterial composites

Abstract

A composite of polyvinyl alcohol (PVA) and beta-tricalcium phosphate (β-TCP) was synthesized as a biomaterial filament for 3D printers and its analytical and chemical evaluation was performed. PVA powder and β-TCP were mixed in the range of 0–20 wt% and hot-melt extruded at 200 °C using a single-screw extruder. Comprehensive material characterization of the synthesized filament was performed by powder X-ray diffraction (XRD), near-infrared spectroscopy (NIR), and scanning electron microscopy (SEM). XRD analysis confirmed that the amorphous nature of PVA and the crystalline nature of β-TCP coexisted and the physical mixture state was well maintained. In near-infrared spectroscopy, concentration-dependent spectral changes were observed by normalization, and principal component analysis showed that the first principal component explained 85.6% of the variance. In machine learning regression analysis, partial least squares regression (PLS), random forest (RF), and support vector machine (SVM) were compared, and SVM achieved the best prediction accuracy (R² = 0.910). SEM observations confirmed streaky structures along the extrusion direction and uniform dispersion of β-TCP particles. This study demonstrated that a combined NIR spectroscopy and machine learning approach is effective as a non-destructive quality evaluation technique for composite filaments for 3D printing. This technique enables real-time composition monitoring and quality control of biomaterial filaments, and is expected to be applied to the manufacturing of patient-specific biomedical devices.