English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This study is a comparison between the performance of five multivariate models in the determination of the unique mixture of ergotamine (ERG), metoclopramide (MET), caffeine (CAF), and paracetamol (PAR) in laboratory-prepared mixtures and in pharmaceutical formulations. Two supervised learning machine methods—artificial neural networks (PC-ANN) preceded by principle component analysis and support vector regression (SVR)—were compared with a spectral residual augmented classical least squares (SRACLS) method, multicurve resolution alternating least squares (MCR-ALS) method, and principle component based method; partial least squares (PLS). The results showed the superiority of linear learning machine methods in handling extremely noisy and complex spectral data, especially during the determination of the challenging mixture under study. ERG (the component with a close to undetectable concentration and with the lowest ratio in the studied dosage form) was only determined using three chemometric models, with root mean squared error of prediction (RMSEP) for the proposed models of 0.0879, 0.0694, and 0.0250 for PLS, SVR and PC-ANN, respectively. In addition, the results suggest that ANN is the method of choice for the determination of mixtures with extreme conditions; for example, components with a very low contribution in the overall spectra, components with narrow informative range, and extremely nonlinear spectral data.

rsc advances

Resolution and quantification challenge of modern chemometric models in the determination of anti-migraine tablets containing ergotamine, caffeine, acetaminophen, and metoclopramide