pH sensing using whispering gallery modes of a silica hollow bottle resonator.

The limitations of electrochemical pH sensors have stimulated the development of optical pH sensing methods. In the method reported here, swellable pH-sensitive polymer particles are deposited on the interior surface of a silica hollow bottle resonator. As the pH of the buffer solution in contact with the particles increases, the refractive index of the particles decreases. As a result, whispering gallery modes with internal evanescent components shift in frequency as a function of pH. This shift is monitored by the throughput of tunable diode laser light coupled into the whispering gallery modes using a tapered fiber. Plots of selected mode frequencies vs. pH yielded sigmoid shaped titration curves similar to those obtained using turbidity to monitor refractive index changes of the particles as a function of pH. The response time of 10-15 s and best resolution of 0.06 pH unit represent improvements over previous optical pH sensing methods.

Pattern recognition-assisted infrared library searching of automotive clear coats.

Pattern recognition techniques have been developed to search the infrared (IR) spectral libraries of the paint data query (PDQ) database to differentiate between similar but nonidentical IR clear coat paint spectra. The library search system consists of two separate but interrelated components: search prefilters to reduce the size of the IR library to a specific assembly plant or plants corresponding to the unknown paint sample and a cross-correlation searching algorithm to identify IR spectra most similar to the unknown in the subset of spectra identified by the prefilters. To develop search prefilters with the necessary degree of accuracy, IR spectra from the PDQ database were preprocessed using wavelets to enhance subtle but significant features in the data. Wavelet coefficients characteristic of the assembly plant of the vehicle were identified using a genetic algorithm for pattern recognition and feature selection. A search algorithm was then used to cross-correlate the unknown with each IR spectrum in the subset of library spectra identified by the search prefilters. Each cross-correlated IR spectrum was simultaneously compared to an autocorrelated IR spectrum of the unknown using several spectral windows that span different regions of the cross-correlated and autocorrelated data from the midpoint. The top five hits identified in each search window are compiled, and a histogram is computed that summarizes the frequency of occurrence for each selected library sample. The five library samples with the highest frequency of occurrence are selected as potential hits. Even in challenging trials where the clear coat paint samples evaluated were all the same make (e.g., General Motors) within a limited production year range, the model of the automobile from which the unknown paint sample was obtained could be identified from its IR spectrum.