Optical and embedded ML driven computational optimization of portable micro-volume spectrophotometer for biochemical analysis in resource-limited environment

Abia Moiz; Ayesha Faiz Ur Rasool; Muhammad Aamir; Irfan Ahmed Usmani; Abia Moiz; Ayesha Faiz Ur Rasool; Muhammad Aamir; Irfan Ahmed Usmani

doi:10.3934/bioeng.2026005

AIMS Bioengineering

2026, Volume 13, Issue 1: 96-114. doi: 10.3934/bioeng.2026005

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Research article

Optical and embedded ML driven computational optimization of portable micro-volume spectrophotometer for biochemical analysis in resource-limited environment

Department of Biomedical Engineering, Salim Habib University, NC-24, Deh Dih, Salim Habib Road, Karachi, 74900, Pakistan

Received: 01 December 2025 Revised: 07 February 2026 Accepted: 24 February 2026 Published: 06 March 2026

Comprehensive biochemical analysis is an essential part of clinical diagnostics, pharmaceutical development, and biomedical research. The gold-standard technique for such analysis is spectrophotometry, which has demonstrated excellent reliability, quantitative accuracy, and broad biomolecular sensitivity. Commercial micro-volume spectrophotometers, which are instrumental to applications with limited sample volumes, are often restricted to well-equipped laboratories due to the high cost and portability limitations especially in resource-limited settings. To address this gap, we propose a proof-of-concept, 3D-printed micro-volume spectrophotometer that integrates precision optics, microfluidic sample handling (8–15 µL), combined with spectral reconstruction and machine learning-based concentration estimation algorithm in a compact and affordable format. Preliminary validation of the proposed system based on bovine serum albumin (BSA) protein quantification via Biuret assay demonstrates strong agreement with commercial spectrophotometers (R² > 0.91). With a production cost of approximately $36 and rapid measurement, the architecture provides a framework that can be extended to other absorbance-based biochemical assays ensuring accessible spectrophotometric systems for point-of-care and resource-limited applications, overcoming traditional limits of cost, sample volume, and portability.
- tiny ML,
- portable spectrophotometry,
- regression AI,
- embedded neural networks,
- point of care devices,
- optoelectronics
Citation: Abia Moiz, Ayesha Faiz Ur Rasool, Muhammad Aamir, Irfan Ahmed Usmani. Optical and embedded ML driven computational optimization of portable micro-volume spectrophotometer for biochemical analysis in resource-limited environment[J]. AIMS Bioengineering, 2026, 13(1): 96-114. doi: 10.3934/bioeng.2026005

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Abstract

Comprehensive biochemical analysis is an essential part of clinical diagnostics, pharmaceutical development, and biomedical research. The gold-standard technique for such analysis is spectrophotometry, which has demonstrated excellent reliability, quantitative accuracy, and broad biomolecular sensitivity. Commercial micro-volume spectrophotometers, which are instrumental to applications with limited sample volumes, are often restricted to well-equipped laboratories due to the high cost and portability limitations especially in resource-limited settings. To address this gap, we propose a proof-of-concept, 3D-printed micro-volume spectrophotometer that integrates precision optics, microfluidic sample handling (8–15 µL), combined with spectral reconstruction and machine learning-based concentration estimation algorithm in a compact and affordable format. Preliminary validation of the proposed system based on bovine serum albumin (BSA) protein quantification via Biuret assay demonstrates strong agreement with commercial spectrophotometers (R² > 0.91). With a production cost of approximately $36 and rapid measurement, the architecture provides a framework that can be extended to other absorbance-based biochemical assays ensuring accessible spectrophotometric systems for point-of-care and resource-limited applications, overcoming traditional limits of cost, sample volume, and portability.

Conflict of interest

The authors declare no conflict of interest.

Author contributions

Abia Moiz: Design, Methodology, Formal Analysis, Writing. Ayesha Faiz Ur Rasool: Design, Methodology, Data collection, Validation, Writing. Muhammad Aamir: Design, Supervision, Review & Editing. Irfan A. Usmani: Validation, Supervision, Review & Editing.

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