Machine learning for substance analysis
INDUSTRY PROJECT
Aim of the project
Creation of a product in the field of optical biosensing, which analyzes spectral data and determines the composition of protein substances using artificial intelligence.
Main advantages of the product
  • 1
    Fast data preparation
    There is no need for long and complex sample preparation to collect data during physical experiments.
  • 2
    Data uniqueness
    The optical method used for data collection, Raman spectroscopy, allows determining and recording the unique distinctive features of various protein substances.
  • 3
    No destructive effects
    The properties and structure of the analyzed substance remain unchanged.
  • 4
    Use of machine learning
    The time required to obtain results is reduced. Analysis of mixtures of substances with numerous components becomes possible.
Areas of application
  • Creation of biosensors
    Easy-to-manufacture, compact, portable devices can be developed, leveraging the high sensitivity and fast response of the optical method used for data acquisition.
  • Analysis of materials
    Studying the physical properties of various materials, including artificial ones: minerals, plastics, metamaterials, and their classification.
  • Industrial Аgriculture
    Faster and simpler monitoring of soil fertility and fertilizer quality, studying and preventing diseases of plant crops.
  • Medicine and diagnostics
    Diagnosis of various diseases, including cancer. Improving the healthcare level. The used approach follows the trend of miniaturization in the production of medical devices.
Project Team
  • Ekaterina Ponkratova
    4th year PhD student
  • Dmitry Zuev
    Team leader
  • Artem Shtumpf
    Student
  • Elena Petrova
    Student
FEEL FREE TO CONTACT US
hns@metalab.ifmo.ru
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