Preliminary physicochemical characterization of biowaste from small restaurants in Costa Rica
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Wendy Villalobos-González
Abstract
Increased interest in waste valorization underscores the potential of biowaste (BW) from small-scale food establishments as a resource within the circular economy. This study aimed to characterize the physicochemical properties of BW generated by three small restaurants in Costa Rica to evaluate its suitability for value-added applications. Eighteen samples from each restaurant were randomly collected, processed, and analyzed for moisture, fiber, starch, protein, calorific value, and other properties following standardized laboratory protocols. Statistical analysis, including ANOVA, was applied to identify significant differences between the waste profiles of the establishments. The results demonstrated variability in BW composition, with significant differences in fiber, starch, and carbohydrate content across the three locations, likely due to differences in menu offerings and food preparation practices. High fiber and carbohydrate content in certain samples indicated their potential for bio-based material production and energy recovery applications. The study concluded that BW from small restaurants presents valuable opportunities for integration into sustainable waste management systems, supporting Costa Rica’s initiatives toward circular economy practices and environmental sustainability. These findings underscore the importance of tailored waste valorization strategies that align with the unique waste profiles of small food service establishments.
Acknowledgments
We would like to extend our gratitude to Geographer Benjamín Álvarez Garay from the Universidad Estatal a Distancia for his invaluable collaboration in the preparation of the map featured in Fig. 1. Also, we would like to thank Universidad Estatal a Distancia and the Instituto Tecnológico de Costa Rica for providing technical and institutional support for this research.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All the authors actively participated in the research process, contributing to laboratory experiments, data analysis, validation, and the preparation of the original manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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