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An approach coupling signal processing and partial least-squares regression analysis (PLS) is described in which raw spectral data are processed with a multiple band-pass filter and the filtered spectra
are used in a PLS to build a calibration model for the analyte of interest. The multiple band-pass filter is specifically designed for a desired analyte based on the Fourier frequency characteristics of
the pure spectrum of the desired analyte and the spectra of the interference background. It maximizes the ratio of signal to background. This combined multiple band-pass filtering and PLS method (MFPLS)
was evaluated by determining clinically relevant levels of glucose, urea, ethanol, and acetaminophen in simulated human sera, in which triglyceride was simulated with triacetin; bovine serum albumin and
globulin were used to model protein molecules in the serum. The results demonstrate that MFPLS produces better accuracy of prediction than PLS in all instances.
The Society publishes the internationally recognized, peer reviewed journal, Applied Spectroscopy, which is available both in print and online. Subscriptions are included with membership or can be purchased by institutional or corporate organizations. Abstracts may be viewed free of charge. Previously published as Bulletin (Society for Applied Spectroscopy)