FUZZY OPTIMAL ASSOCIATIVE MEMORY FOR BACKGROUND PREDICTION OF NEAR-INFRARED SPECTRA

A fuzzy optimal associative memory (FOAM) has been devised for backgro und correction of near-infrared spectra. The FOAM yields improved pred icted background scans for calculation of near-IR absorbance spectra o f glucose in plasma matrices from single-beam data. The FOAM is an enh anced optimal associative memory (OAM) that uses a fuzzy function for encoding the spectra. The FOAM can predict a matching reference spectr um for a near-IR absorbance spectrum with low glucose absorbances by u sing second-derivative spectre. Glucose concentrations were predicted from calibration models furnished by partial least-squares (PLS). The FOAM stored reference spectra obtained from either water/phosphate buf fer or plasma/glucose solutions. Both of these associative memories we re evaluated. The standard error of prediction (SEP) for glucose conce ntration 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-derivat ive spectra was 0.81 mM. FOAM-corrected spectra generally furnish impr oved calibration models.