A sustainable strategy is proposed for the valorization of solid waste from the Tequila industry through the development of bio-packaging for Tequila bottles using mycelium from Ganoderma lucidum. The fungus was isolated from Bosque de la Primavera (Jalisco, Mexico) and cultivated on lignocellulosic substrates: agave bagasse and corn stover. These agricultural residues were dried, ground, and pasteurized to optimize their performance as growth media. Their structural integration before and after fermentation was evaluated using optical microscopy. The high cellulose and hemicellulose content of both substrates supported robust mycelial development, enabling the formation of moldable materials through solid-state fermentation. After growth, the mycelium colonized the substrate, forming a functional mold adapted to the geometry of a Tequila bottle prototype. The molded parts were dried to halt fungal activity, prevent fruiting, and stabilize the structure. Physical and mechanical characterization showed competitive performance with regard to bulk density (0.11 ± 0.1 g cm⁻³), water absorption (78.1 ± 4.2%), and high impact resistance (evaluated via Solidworks simulation). A life cycle assessment revealed that mycelium packaging has a significantly lower environmental impact than expanded polystyrene. The material supports circular economy principles within the Tequila production chain.

Polymer recycling is challenging due to practical classification difficulties. Even when the polymer matrix is identified, the presence of various polymeric composites complicates their accurate classification. In this study, Fourier-transform infrared spectroscopy (ATR-FTIR) was used in combination with artificial neural networks (ANNs) to quantitatively predict the mineral filler content in polypropylene (PP) composites. Calibration curves were developed to correlate ATR-FTIR spectral features (600–1700 cm⁻¹) with the concentration (wt.%) of three mineral fillers: talc (PP-Talc), calcium carbonate (PP-CaCO₃), and glass fiber (PP-GF). ANN models developed in MATLAB 2024a achieved prediction errors below 7.5% and regression coefficients (R²) above 0.98 for all filler types. The method was successfully applied to analyze a commercial recycled pellet, and its predictions were validated by X-ray fluorescence (XRF) and energy-dispersive X-ray spectroscopy (EDX). This approach provides a simple, rapid, and non-destructive tool for non-expert users to identify both the type and amount of mineral filler in recycled polymer materials, thereby reducing misclassification in their commercialization or quality control in industrial formulations.