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A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

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Models and methods for quantitative analysis of surface-enhanced Raman spectra.

Shuo Li, James O Nyagilo, Digant P Dave

IEEE Journal of Biomedical and Health Informatics

|September 24, 2013

Summary

Latent variable regression (LVR) models offer superior quantitative analysis for surface-enhanced Raman spectra in molecular imaging. Partial least square regression (PLSR) excels by integrating calibration and feature extraction for accurate nanotag concentration determination.

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Area of Science:

Spectroscopy and Imaging
Biomedical Engineering
Chemometrics

Background:

Surface-enhanced Raman spectroscopy (SERS) shows promise for in vivo molecular imaging.
Quantitative analysis of SERS data is crucial but current methods lack clear understanding.
Existing methods include classical least squares and various multivariate calibration models.

Purpose of the Study:

To elucidate the theoretical foundations of commonly used quantitative analysis models for SERS.
To demonstrate the suitability of latent variable regression (LVR) models for SERS quantitative analysis.
To compare the performance and underlying principles of different LVR methods.

Main Methods:

Theoretical analysis of direct classical least squares, full spectrum, and selected multivariate calibration models.
Comparative analysis of LVR methods including principal component regression, reduced-rank regression, partial least square regression (PLSR), canonical correlation regression, and robust canonical analysis.
Evaluation of model performance using SERS datasets and diverse criteria.

Main Results:

LVR models are theoretically better suited for Raman spectra quantitative analysis.
Partial least square regression (PLSR) is identified as a hybrid model combining multivariate calibration and feature extraction.
PLSR effectively relates nanotag concentrations to spectrum intensity by optimizing latent variables.

Conclusions:

A deeper understanding of LVR models, particularly PLSR, is provided for SERS quantitative analysis.
PLSR's unique approach explains its superior performance in determining nanotag concentrations from SERS data.
The study validates the effectiveness of various models through empirical testing on SERS datasets.