Peer-Reviewed Publication

Spectrochim Acta A Mol Biomol Spectrosc2026;365128775.September 11, 2026Journal Article

Machine learning-assisted optimization of a BK7/Ag/As₂S₃/graphene surface plasmon resonance biosensor for high-sensitivity urine glucose detection.

U Arun Kumar¹, A Alavudeen Basha², Hashim Elshafie³, Azath Mubarakali⁴, P Parthasarathy⁵, Ngaira Mandela⁶

¹Department of Electrical and Electronics Engineering, Faculty of Engineering, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore 641021, Tamil Nadu, India; Centre for Energy and Environment, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore, Tamil Nadu 641021, India. Electronic address: arunkumar.udayakumar@kahedu.edu.in.

²Department of Biomedical Engineering, Excel Engineering College, Namakkal, Tamilnadu, India.

³Department of Computer Engineering, College of Computer Science, King Khalid University, Main Campus Al Farah, Abha 61421, Saudi Arabia. Electronic address: helshafie@kku.edu.sa.

⁴Department of Informatics and Computer Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia. Electronic address: aabdurrahman@kku.edu.sa.

⁵Department of ECE, CMR Institute of Technology, Bengaluru 560037, Karnataka, India. Electronic address: parthasarathy.p@cmrit.ac.in.

⁶School of Science and Technology, Open University of Kenya, The Cradle, Silicon Savanna, Konza Technopolis, P. O. Box 2440, Nairobi, Kenya.

PubMed DOI

Abstract

Blood glucose monitoring remains challenging due to the invasiveness of finger-prick methods, enzyme instability in electrochemical sensors, and the limitations of laboratory-based techniques for continuous real-time detection. This study presents a theoretically optimized multilayer surface plasmon resonance (SPR) biosensor based on a BK7 prism/silver/arsenic trisulfide/graphene structure operati…

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