Probabilistic partial least squares regression for quantitative analysis of Raman spectra.

Area of Science:

• Spectroscopy
• Biomedical Imaging
• Chemometrics

Background:

• Surface-Enhanced Raman Scattering (SERS) shows promise for in vivo molecular imaging.
• Quantitative analysis of SERS spectra is crucial for accurate molecular detection.
• Current methods like Partial Least Squares Regression (PLSR) have limitations in incorporating complex domain knowledge.

Purpose of the Study:

• To develop a novel probabilistic Partial Least Squares Regression (PPLSR) model.
• To enhance the quantitative analysis of SERS spectra for in vivo applications.
• To provide a probabilistic framework for future Bayesian nonparametrics models.

Main Methods:

• Proposed a probabilistic PLSR (PPLSR) model integrating probabilistic Principal Component Analysis (PCA) and probabilistic curve fitting.
• Developed an Expectation-Maximization (EM) algorithm for parameter estimation in the PPLSR model.
• Evaluated the PPLSR model using two real-world Raman spectra datasets.

Main Results:

• The PPLSR model offers a probabilistic interpretation of PLSR, clarifying its underlying principles.
• Experimental results demonstrate the effectiveness of the proposed PPLSR model.
• The model facilitates the incorporation of complex domain knowledge into quantitative SERS analysis.

Conclusions:

• The developed PPLSR model represents a significant advancement for quantitative SERS analysis in molecular imaging.
• This probabilistic approach lays the groundwork for more sophisticated Bayesian modeling techniques.
• The PPLSR model shows strong potential for improving in vivo molecular imaging applications.