On a black hole effect in bilinear curve resolution based on least squares

Article Chemistry, Analytical

Selection of essential spectra to improve the multivariate curve resolution of minor compounds in complex pharmaceutical formulations

Laureen Coic et al.

Summary: This study demonstrates the importance of reducing the hyperspectral imaging data matrix through the selection of essential spectra in the analysis of complex pharmaceutical formulations. The results show that more accurate decomposition can be achieved when only essential spectra are analyzed, compared to analyzing the full dataset.

ANALYTICA CHIMICA ACTA (2022)

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Article Chemistry, Analytical

Joint selection of essential pixels and essential variables across hyperspectral images

Mahdiyeh Ghaffari et al.

Summary: A method is proposed for the joint selection of essential samples and variables in a data matrix for spectral unmixing, leading to a highly-reduced dataset with benefits such as minimized computational effort, meticulous data mining, easier model building, and better problem understanding or interpretation. The approach allows for reduction rates of over 99%, easy application of multivariate curve resolution - alternating least squares (MCR-ALS) on reduced data sets, and ready acquisition of full distribution maps and spectral profiles.

ANALYTICA CHIMICA ACTA (2021)

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Article Chemistry, Analytical

Pixel-based Raman hyperspectral identi fi cation of complex pharmaceutical formulations

Laureen Coic et al.

Summary: The study introduces a pixel-based identification (PBI) approach for identifying spectral signatures in Raman hyperspectral imaging data. By reducing the number of spectral signatures through the identification of essential spectral pixels (ESP), direct database matching can be performed efficiently and rapidly, enabling the analysis of complex pharmaceutical formulations.

ANALYTICA CHIMICA ACTA (2021)

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Article Automation & Control Systems