Abstract

The direct combination of chemometrics and two-dimensional (2D) correlation spectroscopy is considered. The use of a reconstructed data matrix based on the signifi cant scores and loading vectors obtained from the principal component analysis (PCA) of raw spectral data is proposed as a method to improve the data quality for 2D correlation analysis. The synthetic noisy spectra were analyzed to explore the novel possibility of the use of PCA-reconstructed spectra, which are highly noise suppressed. 2D correlation analysis of this reconstructed data matrix, instead of the raw data matrix, can significantly reduce the contribution of the noise component to the resulting 2D correlation spectra.

Comparison of principal component analysis and biochemical component analysis in Raman spectroscopy for the discrimination of apoptosis and necrosis in K562 leukemia cells

Yi Hong Ong, Mayasari Lim, and Quan Liu
Opt. Express 20(20) 22158-22171 (2012)

Comparison of principal component analysis and biochemical component analysis in Raman spectroscopy for the discrimination of apoptosis and necrosis in K562 leukemia cells: errata

Yi Hong Ong, Mayasari Lim, and Quan Liu
Opt. Express 20(22) 25041-25043 (2012)

Modeling of gas absorption cross sections by use of principal-component-analysis model parameters

Jimmy Bak
Appl. Opt. 41(15) 2840-2846 (2002)

Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging

Jaime Zabalza, Jinchang Ren, Jie Ren, Zhe Liu, and Stephen Marshall
Appl. Opt. 53(20) 4440-4449 (2014)

Utilization of laser-induced breakdown spectroscopy, with principal component analysis and artificial neural networks in revealing adulteration of similarly looking fish fillets

Omnia Hamdy, Zienab Abdel-Salam, and Mohamed Abdel-Harith
Appl. Opt. 61(34) 10260-10266 (2022)