Multivariate calibration techniques applied to derivative spectroscopy data for the analysis of pharmaceutical mixtures

Luca, Michele De; Oliverio, Filomena; Ioele, Giuseppina; Ragno, Gaetano

doi:10.1016/j.chemolab.2008.10.009

Cited by 63 publications

(42 citation statements)

References 36 publications

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“…[23][24][25][26][27] CAF and MEP have been also quantified in pharmaceuticals by multivariate procedures. [28][29][30] UV analysis of the quaternary mixture showed an extensive overlap of the drug spectra. The spectral interference resulted very critical for PPA and PNA, because of their low absorptivity and, at the same time, the relatively higher absorptivity of the other two components.…”

Section: Artificial Neural Network Combined With Principal Component mentioning

confidence: 99%

Artificial Neural Network Combined with Principal Component Analysis for Resolution of Complex Pharmaceutical Formulations

et al. 2011

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A chemometric approach based on the combined use of the principal component analysis (PCA) and artificial neural network (ANN) was developed for the multicomponent determination of caffeine (CAF), mepyramine (MEP), phenylpropanolamine (PPA) and pheniramine (PNA) in their pharmaceutical preparations without any chemical separation. The predictive ability of the ANN method was compared with the classical linear regression method Partial Least Squares 2 (PLS2). The UV spectral data between 220 and 300 nm of a training set of sixteen quaternary mixtures were processed by PCA to reduce the dimensions of input data and eliminate the noise coming from instrumentation. Several spectral ranges and different numbers of principal components (PCs) were tested to find the PCA-ANN and PLS2 models reaching the best determination results. A two layer ANN, using the first four PCs, was used with log-sigmoid transfer function in first hidden layer and linear transfer function in output layer. Standard error of prediction (SEP) was adopted to assess the predictive accuracy of the models when subjected to external validation. PCA-ANN showed better prediction ability in the determination of PPA and PNA in synthetic samples with added excipients and pharmaceutical formulations. Since both components are characterized by low absorptivity, the better performance of PCA-ANN was ascribed to the ability in considering all non-linear information from noise or interfering excipients.

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Section: Artificial Neural Network Combined With Principal Component mentioning

confidence: 99%

Artificial Neural Network Combined with Principal Component Analysis for Resolution of Complex Pharmaceutical Formulations

et al. 2011

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“…Approaches such as principal component regression (PCR) and partial least squares (PLSs) have been widely applied both for zero-or higher-order spectra. A combination of MVA and derivative spectral data is highly beneficial where features such as easiness of application and reliability of obtained results are greatly improved [55][56][57][58].…”

Section: Spectroscopic Analyses -Developments and Applicationsmentioning

confidence: 99%

Factorial Design and Machine Learning Strategies: Impacts on Pharmaceutical Analysis

El-Azazy¹

2017

Spectroscopic Analyses - Developments and Applications

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Pharmaceutical analysis is going through an expeditious progress as the perception of 'multivariate data analysis' (MVA) becomes gradually more assimilated. Pharmaceutical analysis comprises a range of processes that covers both chemical and physical assessment of drugs and their formulations employing different analytical techniques. With the revolution in instrumental analysis and the huge amount of information produced, there must be an up-to-date data processing tool. The role of chemometrics then comes up. Multivariate analysis (MVA) has the capability of effectively drawing a complete picture of the investigated process. Moreover, MVA reproduces the arithmetic influence of variables and their interactions through a smaller number of trials, keeping both efforts and capitals. Spectrophotometry is among the most extensively used techniques in pharmaceutical analysis either direct (single component) or derivative (multicomponent). In addition to these recognized benefits, using chemometrics in conjunction with spectrophotometry affects three vital characteristics: accuracy, precision and robustness. The impact of hyphenation of spectrophotometric analytical techniques to chemometrics (experimental design and support vector machines) on analytical laboratory will be revealed. A theoretical background on the different factorial designs and their relevance is provided. Readers will be able to use this chapter as a guide to select the appropriate design for a problem.

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“…First direction of development is a combination of derivative spectra with chemometric methods [28,36,39,42]. Procedures based on derivatisation of ratio spectra [24,25,28,30,40,44,47] belong to the same group.…”

Section: New Trends In Derivative Spectrophotometrymentioning

confidence: 99%

Basic Principles and Analytical Application of Derivative Spectrophotometry

Karpińska¹

2012

Macro to Nano Spectroscopy

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Multivariate calibration techniques applied to derivative spectroscopy data for the analysis of pharmaceutical mixtures

Cited by 63 publications

References 36 publications

Artificial Neural Network Combined with Principal Component Analysis for Resolution of Complex Pharmaceutical Formulations

Artificial Neural Network Combined with Principal Component Analysis for Resolution of Complex Pharmaceutical Formulations

Factorial Design and Machine Learning Strategies: Impacts on Pharmaceutical Analysis

Basic Principles and Analytical Application of Derivative Spectrophotometry

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