EventsThe 4th International Electronic Conference on Processes
Published
This submission belongs to the session S1. Environmental and Green Processes of the event The 4th International Electronic Conference on Processes
Published date
17 Oct, 2025
Academic Editor
author-avatarYoung-Cheol Chang
Citation
Mourad Kharbach, Huiwen Yu, Precision Eggshell Valorization: Optimizing Biomaterial Production with NIR/HSI and Machine Learning, in Proceedings of The 4th International Electronic Conference on Processes, 20 October–22 October 2025, MDPI: Basel, Switzerland
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Precision Eggshell Valorization: Optimizing Biomaterial Production with NIR/HSI and Machine Learning

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1. Circular Economy/Sustainable Solutions, LAB University of Applied Sciences, Mukkulankatu 19, 15101 Lahti, Finland, Finland
2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA, USA
Abstract

Introduction: The substantial global generation of eggshell waste, estimated at millions of metric tons annually, presents a significant environmental challenge, contributing to landfill burden and associated pollution. This necessitates innovative strategies for waste valorization, transforming this abundant byproduct into high-value biomaterials.

Methods: The valorization process involves initial cleaning and drying of eggshells to remove organic contaminants and moisture. Subsequently, eggshells undergo mechanical processing (crushing, grinding, sieving) to achieve desired particle sizes, and thermal treatments like calcination to convert calcium carbonate into calcium oxide or hydroxyapatite precursors. For real-time process control and quality assurance, advanced non-destructive analytical techniques such as Near-Infrared (NIR) spectroscopy and Hyperspectral Imaging (HSI) are integrated. These optical methods, coupled with machine learning algorithms, enable rapid and accurate assessment of critical parameters including chemical composition, moisture content, and purity, ensuring optimal material characteristics for specific applications.

Results: The application of these controlled processes, rigorously monitored by NIR/HSI and machine learning, yields high-purity eggshell-derived biomaterials. These materials find diverse applications, including advanced bone regeneration scaffolds and dental implants in biomedical engineering, natural exfoliants and skin mineralizers in cosmetics , calcium fortification in food and nutraceuticals, and adsorbents for environmental remediation.

Conclusions: Eggshell valorization represents a compelling example of circular economy principles in action, transforming a low-value waste stream into high-value products. The integration of advanced analytical techniques like NIR spectroscopy and HSI, powered by machine learning, is pivotal for ensuring the quality, consistency, and safety of these biomaterials, thereby facilitating their broader commercialization and contributing to a more sustainable future.

Keywords
Eggshell Valorization
Biomaterials
NIR Spectroscopy
Hyperspectral Imaging
Machine Learning
Circular Economy
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