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Big (Bio)Chemical Data Mining Using Chemometric Methods: A Need for Chemists.

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This review explores analyzing big (bio)chemical data (BBCD) using multivariate chemometric methods. It addresses challenges in modern analytical research and highlights applications in omics sciences for reliable results.

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Data Science

Background:

  • Modern analytical research generates vast amounts of big (bio)chemical data (BBCD).
  • Multivariate chemometric methods offer powerful tools for analyzing complex datasets.
  • Challenges exist in applying these methods to the scale and complexity of BBCD.

Purpose of the Study:

  • To review the application of multivariate chemometric methods for analyzing BBCD.
  • To highlight the potential and limitations of chemometrics in addressing BBCD challenges.
  • To provide insights into obtaining reliable qualitative and quantitative results from BBCD.

Main Methods:

  • Review of chemometric techniques applied to chromatographic, spectroscopic, and hyperspectral imaging data.
  • Emphasis on applications within omics sciences.
  • Discussion of theoretical background and practical considerations for BBCD analysis.

Main Results:

  • Chemometrics can effectively solve BBCD problems across various analytical measurements.
  • Applications span diverse chemical disciplines, including omics.
  • The review outlines procedures for enhancing data reliability.

Conclusions:

  • Multivariate chemometrics are crucial for unlocking the potential of BBCD in (bio)chemistry.
  • Addressing current limitations is key to advancing BBCD analysis.
  • This review provides a comprehensive overview of current applications and future perspectives.