A Novel Calibration-Minimum Method for Prediction of Mole Fraction in Non-Ideal Mixture

Shibayama, Shojiro; Kaneko, Hiromasa; Funatsu, Kimito

doi:10.1208/s12249-016-0547-6

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…Funatsu et al identified this weakness in the method and proposed two ways to mitigate it. One is to limit the region(s) of the spectra to be included into the analysis using of a genetic algorithm for variable selection, and the other is the modification of the inner relationship in BLL adding a nonlinear term. , The latter extension requires the estimation of a nonlinear pure spectrum and an exponent for the molar fractions.…”

Section: Introductionmentioning

confidence: 99%

Supervised Extended Iterative Optimization Technology for Estimation of Powder Compositions in Pharmaceutical Applications: Method and Lifecycle Management

García‐Muñoz

Torres²

2020

Ind. Eng. Chem. Res.

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Further enhancements to the extended iterative optimization technology (EIOT) method are presented. These are meant to include the effects of nonchemical interferences of known origin in the EIOT parameters and to manage the lifecycle of the method in a pharmaceutical manufacturing application. A derivation of EIOT using projection to latent structures (PLS) is also presented and shown to be mathematically equivalent to the EIOT model. A structured methodology to apply the method is proposed and illustrated with a real pharmaceutical case study. Two diagnostics are proposed to diagnose the performance of an EIOT model in real-time. The M and the SSR are described and shown to be mathematical equivalences to the established Hotelling’s T2 and the orthogonal distance to the model plane (or SPE), used in a traditional solution based on a PLS model.

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Section: Introductionmentioning

confidence: 99%

Supervised Extended Iterative Optimization Technology for Estimation of Powder Compositions in Pharmaceutical Applications: Method and Lifecycle Management

García‐Muñoz

Torres²

2020

Ind. Eng. Chem. Res.

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“…This approach implies the additional estimation of the nonlinear spectral signatures ( s nonlinear,n ) and the order ( p n ) of the nonlinear component per ingredient. None of these proposals addresses the fact that the estimate of r n is a molar fraction

{\overset{false^}{boldd}}^{new} = \sum_{n} ((), r_{n} s_{n} + r_{n}^{p_{n}} s_{italicnonlinear, n})

…”

Section: Introductionmentioning

confidence: 99%

Estimation of mass‐based composition in powder mixtures using Extended Iterative Optimization Technology (EIOT)

2018

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The Extended Iterative Optimization Technology (EIOT) method is proposed as an extension to Muteki's [I&ECR 2013;52 (35):12258-12268] Iterative Optimization Technology to address deviations from Beer-Lambert's law in powders. The new method estimates the apparent spectrum for the pure species, rather than using the measured spectrum and augments Beer-Lambert's law with a bilinear term to capture the signature and strength of the nonchemical interferences. The proposed method has exhibited acceptable performance in spite of using a lean data set to estimate its parameters. The method provides robust and coherent estimates within the physical boundaries of the system and exhibits robustness to instrument transfer. The lean effort needed to build the EIOT method positions it as an attractive option in early stages of pharmaceutical drug product development. Its robustness to distinguish chemical from nonchemical signals implies a potential to lower the total cost of ownership for an EIOT-based solution in manufacturing.

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“…However, the predictive accuracy of nonlinear IOT largely depends on training samples and the wavelength regions used. IOT with virtual molecular interaction spectra (IOT-VIS) has been developed [14]. It can express spectral change as nonlinear terms and requires few training samples.…”

Section: Introductionmentioning

confidence: 99%

Iterative optimization technology combined with wavelength selection based on excess absorption for a process analytical technology calibration–minimum approach

Shibayama

Kaneko

Funatsu

2016

Chemometrics and Intelligent Laboratory Systems

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a b s t r a c tProcess analytical technology (PAT) plays an important role in the pharmaceutical industry. Calibration-free/ minimum methods in PAT are expected to aid in a deeper understanding of processes in the early development stage of new drugs. Iterative optimization technology (IOT), an existing calibration-free method, is not able to predict the compositions of nonideal mixtures because the Beer-Lambert law does not hold in some wavelength regions. In this paper, we propose IOT with wavelength selection based on excess absorption (WLSEA), which is available with at least one calibration sample. Excess absorption (EA) is the residual between the measured and ideal spectra of a mixture, and includes noise and spectral change related to molecular interactions. WLSEA determines a threshold of EA that separates noise and spectral change by minimizing prediction errors of IOT. Consequently, WLSEA selects a set of regions where predictive accuracy of IOT is high. WLSEA-IOT can be applied to predict compositions of both ideal and nonideal mixtures that have ideal regions. The performance of the proposed IOT is verified by analyses with three types of mixture spectra. The proposed wavelength selection method will enhance both development of quantitative methods and analyses of molecular interactions with infrared spectroscopy.

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A Novel Calibration-Minimum Method for Prediction of Mole Fraction in Non-Ideal Mixture

Cited by 6 publications

References 38 publications

Supervised Extended Iterative Optimization Technology for Estimation of Powder Compositions in Pharmaceutical Applications: Method and Lifecycle Management

Supervised Extended Iterative Optimization Technology for Estimation of Powder Compositions in Pharmaceutical Applications: Method and Lifecycle Management

Estimation of mass‐based composition in powder mixtures using Extended Iterative Optimization Technology (EIOT)

Iterative optimization technology combined with wavelength selection based on excess absorption for a process analytical technology calibration–minimum approach

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