SERS Sensor for Human Glycated Albumin Direct Assay Based on Machine Learning Methods

SERS Sensor for Human Glycated Albumin Direct Assay Based on Machine Learning Methods

In this study, a non-labeled sensor system for direct determining human glycated albumin levels for medical application is proposed. Using machine learning methods applied to surface-enhanced Raman scattering (SERS) spectra of human glycated albumin and serum human albumin enabled the avoidance of c...

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Bibliographic Details
Main Authors: Ekaterina A. Slipchenko, Irina A. Boginskaya, Robert R. Safiullin, Ilya A. Ryzhikov, Marina V. Sedova, Konstantin N. Afanasev, Natalia L. Nechaeva, Ilya N. Kurochkin, Alexander M. Merzlikin, Andrey N. Lagarkov
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Chemosensors
Subjects:
human serum albumin
surface-enhanced Raman spectroscopy
protein mixtures
machine learning methods
non-labeled sensor system
medical sensor system
Online Access:https://www.mdpi.com/2227-9040/10/12/520

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