New augmented classical least squares methods for improved quantitative spectral analyses

Haaland, David M.; Melgaard, David K.

doi:10.1016/s0924-2031(01)00199-0

Cited by 53 publications

(46 citation statements)

References 16 publications

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“…Melgaard et al [11] introduced CRACLS algorithm that can update the model during prediction without recalibration. The CRACLS algorithm is based on CLS so it retains the qualitative benefits of CLS, and the flexibility of PCR and PLS modeling when spectrally active components are not included in the calibration.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Neural Networks and Concentration Residual Augmented Classical Least Squares for the Simultaneous Determination of Diphenhydramine, Benzonatate, Guaifenesin and Phenylephrine in their Quaternary Mixture

Darwish¹,

Metwally²,

Bayoumi³

et al. 2015

Trop. J. Pharm Res

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102.21 ± 1.34, 100.30 ± 1.17, 99.31 ± 2.00 and 98.50 ± 1.27, respectively. On the other hand, % recovery of the four analytes by CRACLS was 99.84 ± 2.22, 100.07 ± 0.63, 98.37 ± 1.42 and 97.99 ± 0.96, respectively. Conclusion: The proposed methods can be applied for the quantitative determination of the four components without interference from excipients, thus obviating the need for preliminary extraction of analytes from the pharmaceutical formulation. The ability of the methods to deconvolute the highly overlapped UV spectra of the four components' mixtures using low-cost and easy-to-handle instruments such as UV spectrophotometer is also an advantage.

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Section: Discussionmentioning

confidence: 99%

“…The choice of these two methods was based on their high quantitative predictive power. The principles and theories of these two models are discussed in the literature [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks and Concentration Residual Augmented Classical Least Squares for the Simultaneous Determination of Diphenhydramine, Benzonatate, Guaifenesin and Phenylephrine in their Quaternary Mixture

Darwish¹,

Metwally²,

Bayoumi³

et al. 2015

Trop. J. Pharm Res

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“…14 Multivariate NAS was first implemented using pure component projection 11 and classical regression. 15 Implementation of NAS via pure component or classical regression methods is potentially cumbersome 10 ; this problem was solved by Lorber et al 10 by using inverse regression instead. 15 Both classical and inverse regression mathematics are suitable for the determination of NAS; however, the remaining descriptions employ inverse regression.…”

Section: Introductionmentioning

confidence: 99%

Determination of figures of merit for near-infrared and raman spectrometry by net analyte signal analysis for a 4-component solid dosage system

2007

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Process analytical technology has elevated the role of sensors in pharmaceutical manufacturing. Often the ideal technology must be selected from many suitable candidates based on limited data. Net analyte signal (NAS) theory provides an effective platform for method characterization based on multivariate figures of merit (FOM). The objective of this work was to demonstrate that these tools can be used to characterize the performance of 2 dissimilar analyzers based on different underlying spectroscopic principles for the analysis of pharmaceutical compacts. A fully balanced, 4-constituent mixture design composed of anhydrous theophylline, lactose monohydrate, microcrystalline cellulose, and starch was generated; it consisted of 29 design points. Six 13-mm tablets were produced from each mixture at 5 compaction levels and were analyzed by near-infrared and Raman spectroscopy. Partial least squares regression and NAS analyses were performed for each component, which allowed for the computation of FOM. Based on the calibration error statistics, both instruments were capable of accurately modeling all constituents. The results of this work indicate that these statistical tools are a suitable platform for comparing dissimilar analyzers and illustrate the complexity of technology selection.

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“…the loading vectors from PCA on the spectral residuals E A (SRACLS), known pure component spectra [14], or a priori known other variation not included in the calibration 23 set (PACLS) [15][16]. The addition of the spectral shapes both changes and corrects the concentration…”

mentioning

confidence: 99%

“…Spectra were collected in the 10000-5700 cm -1 region with a resolution of 8 cm -1 and averaged over 12 16 scans. The effective sample size was approximately 11% of the tablet.…”

mentioning

confidence: 99%

Robust calibrations on reduced sample sets for API content prediction in tablets: Definition of a cost-effective NIR model development strategy

Pieters

Saeys

Kerkhof³

et al. 2013

Analytica Chimica Acta

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New augmented classical least squares methods for improved quantitative spectral analyses

Cited by 53 publications

References 16 publications

Artificial Neural Networks and Concentration Residual Augmented Classical Least Squares for the Simultaneous Determination of Diphenhydramine, Benzonatate, Guaifenesin and Phenylephrine in their Quaternary Mixture

Artificial Neural Networks and Concentration Residual Augmented Classical Least Squares for the Simultaneous Determination of Diphenhydramine, Benzonatate, Guaifenesin and Phenylephrine in their Quaternary Mixture

Determination of figures of merit for near-infrared and raman spectrometry by net analyte signal analysis for a 4-component solid dosage system

Robust calibrations on reduced sample sets for API content prediction in tablets: Definition of a cost-effective NIR model development strategy

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