NIR spectroscopy in Pharmaceutical Analysis: Off‐line and At‐line PAT Applications

Romía, Marcelo Blanco; Bernàrdez, Manel Alcalà

doi:10.1002/9780470689592.ch14

Cited by 4 publications

(2 citation statements)

References 108 publications

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“…4 As a result, all practically relevant concentration modifications and changes in raw material properties and environmental conditions (e.g., humidity) must be considered when preparing the samples. [5][6][7] Moreover, achieving and maintaining robust model performance requires constantly maintaining and updating the calibration. 8,9 In the case of pharmaceutical blends, some degree of freedom exists with regard to designing a sample set for calibration.…”

Section: Introductionmentioning

confidence: 99%

Designed Blending for Near Infrared Calibration

Scheibelhofer

Grabner

Bondi

et al. 2015

Journal of Pharmaceutical Sciences

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Section: Introductionmentioning

confidence: 99%

Designed Blending for Near Infrared Calibration

Scheibelhofer

Grabner

Bondi

et al. 2015

Journal of Pharmaceutical Sciences

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“…2 Motivating examples Botonjic-Sehic, Brown, Lamontagne and Tsaparikos (2009); and Romía and Bernárdez (2010). For introductory material on the subject see Shenk and Westerhaus (1991), Brereton (2003), andBurns andCiurczac (2007).…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Model for Aggregated Functional Data

2013

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In many areas of science one aims to estimate latent sub-population mean curves based only on observations of aggregated population curves. By aggregated curves we mean linear combination of functional data that cannot be observed individually. We assume that several aggregated curves with linear independent coefficients are available. More specifically, we assume each aggregated curve is an independent partial realization of a Gaussian process with mean modeled through a weighted linear combination of the disaggregated curves. We model the mean of the Gaussian processes as a smooth function approximated by a function belonging to a finite dimensional space H K which is spanned by K B-splines basis functions. We explore two different specifications of the covariance function of the Gaussian process: one that assumes a constant variance across the domain of the process, and a more general variance structure which is itself modelled as a smooth function, providing a nonstationary covariance function.Inference procedure is performed following the Bayesian paradigm allowing experts' opinion to be considered when estimating the disaggregated curves. Moreover, it naturally provides the uncertainty associated with the parameters estimates and fitted values. Our model is suitable for a wide range of applications. We concentrate on two different real examples: calibration problem for NIR spectroscopy data and an analysis of distribution of energy among different type of consumers.

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The Future of Portable Spectroscopy

Crocombe¹

2021

Portable Spectroscopy and Spectrometry

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NIR spectroscopy in Pharmaceutical Analysis: Off‐line and At‐line PAT Applications

Cited by 4 publications

References 108 publications

Designed Blending for Near Infrared Calibration

Designed Blending for Near Infrared Calibration

A Hierarchical Model for Aggregated Functional Data

The Future of Portable Spectroscopy

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