Issue 16, 2026, Issue in Progress

Green manufacturing of amoxicillin trihydrate: a malic acid-assisted crystallization framework enhanced by Taguchi–ANN optimization

Abstract

Industrial production of amoxicillin trihydrate (AMCT) often suffers from low yield, impurity inclusion, and inconsistent crystal morphology. This study introduces a scalable green crystallization strategy using malic acid as a biodegradable habit modifier, developed as part of an improved eco-friendly AMCT manufacturing framework. A hybrid optimization approach integrating Taguchi design with Artificial Neural Network (ANN) modeling was employed to capture both linear and nonlinear interactions among critical process variables. Multi-technique characterization (XRD, FTIR, DSC, BET, LC-MS) confirmed that malic acid preserves lattice integrity while substantially refining particle attributes, reducing crystallite size from 85.9 to 66.4 nm and increasing specific surface area from 5.27 to 11.07 m2 g−1. This significant increase in surface area is a key physical factor theoretically favoring improved dissolution kinetics. The ANN model exhibited excellent predictive performance (R2 > 0.99) for both purity and yield. Under optimized conditions (2.5 M malic acid, pH 5.5, 60 min, 1500 rpm), AMCT crystals were obtained with 99.21% purity and 61.82% yield. These results demonstrate a robust, data-driven framework for sustainable AMCT production, providing a high-performance alternative to conventional mineral-acid-based crystallization methods.

Graphical abstract: Green manufacturing of amoxicillin trihydrate: a malic acid-assisted crystallization framework enhanced by Taguchi–ANN optimization

Supplementary files

Article information

Article type
Paper
Submitted
22 Dec 2025
Accepted
10 Mar 2026
First published
17 Mar 2026
This article is Open Access
Creative Commons BY license

RSC Adv., 2026,16, 14793-14805

Green manufacturing of amoxicillin trihydrate: a malic acid-assisted crystallization framework enhanced by Taguchi–ANN optimization

M. F. Ergin, H. Yaşa and H. Çelik Onar, RSC Adv., 2026, 16, 14793 DOI: 10.1039/D5RA09898J

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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