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Fuzzy Optimal Associative Memory for Background Prediction of Near-Infrared Spectra

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Abstract:

A fuzzy optimal associative memory (FOAM) has been devised for background correction of near-infrared spectra. The FOAM yields improved predicted background scans for calculation of near-IR absorbance spectra of glucose in plasma matrices from single-beam data. The FOAM is an enhanced optimal associative memory (OAM) that uses a fuzzy function for encoding the spectra. The FOAM can predict a matching reference spectrum for a near-IR absorbance spectrum with low glucose absorbances by using second-derivative spectra. Glucose concentrations were predicted from calibration models furnished by partial least-squares (PLS). The FOAM stored reference spectra obtained from either water/ phosphate buffer or plasma/glucose solutions. Both of these associative memories were evaluated. The standard error of prediction (SEP) for glucose concentration from an optimal PLS calibration model based on FOAM-corrected spectra was 0.60 mM for the water/phosphate buffer spectra. For FOAM-corrected spectra from plasma/glucose reference spectra, the SEP was 0.68 mM. The SEP of conventionally corrected double-beam second-derivative spectra was 0.81 mM. FOAM-corrected spectra generally furnish improved calibration models.

Keywords: Background prediction; Fuzzy optimal associative memories; Glucose; Near-infrared spectroscopy

Document Type: Research Article

DOI: http://dx.doi.org/10.1366/0003702963906708

Affiliations: 1: Center for Intelligent Chemical Instrumentation, Department of Chemistry, Clippinger Laboratories, Ohio University, Athens, Ohio 45701-2979; present address: Biocontrol Technology, Inc., 300 Indian Springs Rd., Indiana, PA 15701. 2: Center for Intelligent Chemical Instrumentation, Department of Chemistry, Clippinger Laboratories, Ohio University, Athens, Ohio 45701-2979

Publication date: January 1, 1996

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