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Single-Molecule Identification and Quantification of Steviol Glycosides with a Deep Learning-Powered Nanopore Sensor.

Minmin Li1, Jing Wang1,2, Chen Zhang3

  • 1State Key Laboratory of Medical Proteomics, National Chromatographic R. & A. Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, P. R. China.

ACS Nano
|August 27, 2024
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Summary

A novel nanopore sensing method accurately identifies diverse steviol glycosides (SGs) by analyzing their unique molecular interactions. This approach, combined with AI, enables rapid, single-molecule analysis for quality control of these natural sweeteners.

Keywords:
deep learningglycansglycosidesnanoporesingle-molecule detection

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Area of Science:

  • Biotechnology
  • Analytical Chemistry
  • Nanotechnology

Background:

  • Steviol glycosides (SGs) are complex natural sweeteners with structural variations that challenge precise identification.
  • Current methods struggle to differentiate between SG isomers, limiting understanding of their biological activities and use in mixtures.

Purpose of the Study:

  • To develop a method for precise detection and discrimination of diverse steviol glycoside species.
  • To enable rapid, automated, and accurate single-molecule identification and quantification of SGs.

Main Methods:

  • Utilized a wild-type aerolysin nanopore to detect and discriminate SGs based on electro-osmotic flow effects at varied voltages.
  • Analyzed molecular binding and translocation events within the nanopore.
  • Developed a deep learning-based artificial intelligence (AI) model using nanopore data from 15 SGs.

Main Results:

  • The nanopore method successfully detected and discriminated various SG species, with binding events at low voltages identifying most SGs.
  • Higher voltages enabled unambiguous identification of SGs differing by a single hydroxyl group.
  • The AI model achieved rapid, automated, and precise single-molecule identification and quantification of SGs in real samples.

Conclusions:

  • Nanopore sensing offers a valuable tool for the precise structural analysis of complex glycosides.
  • This technology, enhanced by AI, holds significant potential for sensitive and rapid quality assurance of glycoside products.