Molecular factor computing for predictive spectroscopy

Bin Dai, Aaron Urbas, Craig C. Douglas, Robert A. Lodder

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Purpose. The concept of molecular factor computing (MFC)-based predictive spectroscopy was demonstrated here with quantitative analysis of ethanol-in-water mixtures in a MFC-based prototype instrument. Methods. Molecular computing of vectors for transformation matrices enabled spectra to be represented in a desired coordinate system. New coordinate systems were selected to reduce the dimensionality of the spectral hyperspace and simplify the mechanical/electrical/computational construction of a new MFC spectrometer employing transmission MFC filters. A library search algorithm was developed to calculate the chemical constituents of the MFC filters. The prototype instrument was used to collect data from 39 ethanol-in-water mixtures (range 0-14%). For each sample, four different voltage outputs from the detector (forming two factor scores) were measured by using four different MFC filters. Twenty samples were used to calibrate the instrument and build a multivariate linear regression prediction model, and the remaining samples were used to validate the predictive ability of the model. Results. In engineering simulations, four MFC filters gave an adequate calibration model (r2=0.995, RMSEC=0.229%, RMSECV=0.339%, p=0.05 by f test). This result is slightly better than a corresponding PCR calibration model based on corrected transmission spectra (r2=0.993, RMSEC=0.359%, RMSECV=0.551%, p=0.05 by f test). The first actual MFC prototype gave an RMSECV=0.735%. Conclusion. MFC was a viable alternative to conventional spectrometry with the potential to be more simply implemented and more rapid and accurate.

Original languageEnglish
Pages (from-to)1441-1449
Number of pages9
JournalPharmaceutical Research
Volume24
Issue number8
DOIs
StatePublished - Aug 2007

Bibliographical note

Funding Information:
This work was supported in part by the National Science Foundation through CNS-0540178, the Kentucky Science and Education Fund, and by the National Institutes of Health through N01AA 33003 and T32 HL072743.

Funding

This work was supported in part by the National Science Foundation through CNS-0540178, the Kentucky Science and Education Fund, and by the National Institutes of Health through N01AA 33003 and T32 HL072743.

FundersFunder number
National Institutes of Health (NIH)
Kentucky Science and Education Fund
National Institute on Alcohol Abuse and AlcoholismN01AA033003
National Institute on Alcohol Abuse and Alcoholism
U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China0540178
U.S. Department of Energy Chinese Academy of Sciences Guangzhou Municipal Science and Technology Project Oak Ridge National Laboratory Extreme Science and Engineering Discovery Environment National Science Foundation National Energy Research Scientific Computing Center National Natural Science Foundation of China
National Heart, Lung, and Blood Institute Family Blood Pressure ProgramT32HL072743
National Heart, Lung, and Blood Institute Family Blood Pressure Program

    Keywords

    • Chemometrics
    • Genetic algorithm
    • Multivariate analysis
    • Near infrared spectroscopy (NIR)
    • Optical computing

    ASJC Scopus subject areas

    • Biotechnology
    • Molecular Medicine
    • Pharmacology
    • Pharmaceutical Science
    • Organic Chemistry
    • Pharmacology (medical)

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