Advancing Glycan Analysis: A New Platform Integrating SERS, Boronic Acids, and Machine Learning Algorithms Academic Article uri icon


  • AbstractGlycans are the most abundant fundamental biomolecules, but profiling glycans is challenging due to their structural complexity. To address this, a novel glycan detection platform is developed by integrating surfaceenhanced Raman spectroscopy (SERS), boronic acid receptors, and machine learning tools. Boronic acid receptors bind with glycans, and the reaction influences molecular vibrations, leading to unique Raman spectral patterns. Unlike prior studies that focus on designing a boronic acid with high binding selectivity toward a target glycan, this sensor is designed to analyze overall changes in spectral patterns using machine learning algorithms. For proofofconcept, 4mercaptophenylboronic acid (4MBA) and 1thianthrenylboronic acid (1TBA) are used for glycan detection. The sensing platform successfully recognizes the stereoisomers and the structural isomers with different glycosidic linkages. The collective spectra that combine the spectra from both boronic acid receptors improve the performance of the support vector machine model due to the enrichment of the structural information of glycans. In addition, this new sensor could quantify the mole fraction of sialic acid in lactose background using the machine learning regression technique. This lowcost, rapid, and highly accessible sensor will provide the scientific community with another option for frequent comparative glycan screening in standard biological laboratories.

published proceedings

  • Advanced Sensor Research

author list (cited authors)

  • Hu, Q., Kuai, D., Park, H., Clark, H., Balbuena, P. B., Kwon, J. S., & Wu, H.

complete list of authors

  • Hu, Qiang||Kuai, Dacheng||Park, Hyundo||Clark, Haley||Balbuena, Perla B||Kwon, Joseph Sang‐Il||Wu, Hung‐Jen

publication date

  • December 2023