[Application of chemometric methods in three-dimensional fluorescence spectral analysis]

Area of Science:

• Analytical Chemistry
• Data Science

Context:

• Traditional spectral analysis methods struggle with increasingly complex analytical data.
• Chemometrics offers advanced data processing techniques for spectral analysis.

Purpose:

• To illustrate common three-dimensional fluorescence spectral types analyzed by chemometrics.
• To present an overview of chemometric methods including statistical, pattern recognition, and neural network approaches.
• To discuss the application and future prospects of chemometrics in multidimensional spectral analysis.

Summary:

• This paper reviews chemometric methods for analyzing multidimensional spectral data, particularly three-dimensional fluorescence spectra.
• It covers statistical methods, pattern recognition, neural networks, and second-order calibration techniques.
• The discussion includes current applications and future directions for chemometrics in spectral analysis.

Impact:

• Provides a comprehensive overview of chemometric applications in spectral analysis.
• Highlights the importance of chemometrics in extracting maximum information from complex spectral data.
• Informs researchers about the potential of chemometric methods for future advancements in multidimensional spectral analysis.