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

Content Provider	Springer Nature Link
Author	Shibayama, Shojiro Kaneko, Hiromasa Funatsu, Kimito
Copyright Year	2016
Abstract	This article proposes a novel concentration prediction model that requires little training data and is useful for rapid process understanding. Process analytical technology is currently popular, especially in the pharmaceutical industry, for enhancement of process understanding and process control. A calibration-free method, iterative optimization technology (IOT), was proposed to predict pure component concentrations, because calibration methods such as partial least squares, require a large number of training samples, leading to high costs. However, IOT cannot be applied to concentration prediction in non-ideal mixtures because its basic equation is derived from the Beer–Lambert law, which cannot be applied to non-ideal mixtures. We proposed a novel method that realizes prediction of pure component concentrations in mixtures from a small number of training samples, assuming that spectral changes arising from molecular interactions can be expressed as a function of concentration. The proposed method is named IOT with virtual molecular interaction spectra (IOT-VIS) because the method takes spectral change as a virtual spectrum x $_{nonlin,i }$ into account. It was confirmed through the two case studies that the predictive accuracy of IOT-VIS was the highest among existing IOT methods.
Starting Page	595
Ending Page	604
Page Count	10
File Format	PDF
Journal	AAPS PharmSciTech
Volume Number	18
Issue Number	3
e-ISSN	15309932
Language	English
Publisher	Springer US
Publisher Date	2016-05-11
Publisher Institution	American Association of Pharmaceutical Scientists
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Pharmacology/Toxicology Biotechnology Biochemistry Pharmacy
Content Type	Text
Resource Type	Article

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