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The combination of unfolded partial least-squares (U-PLS) with residual bilinearization (RBL) has not been properly exploited to process experimental second-order spectroscopic information, although it is able to achieve the important second-order advantage. Among other desirable properties, the technique can handle incomplete calibration information, i.e., when only certain analyte concentrations are known in the training set. It can also cope with analyte spectral changes from sample to sample, due to its latent variable structure. In this work, U-PLS/RBL has been successfully applied to experimental fluorescence excitation-emission matrix data aimed at the quantitation of analytes in complex samples: these were the antibiotic tetracycline and the anti-inflammatory salicylate, in both cases in the presence of human serum, where significant analyte-background interactions occur. The interactions of the analyte with the serum proteins modify their spectral fluorescence properties, making it necessary to employ training sets of samples where the biological background is present, possibly causing analyte spectral changes from sample to sample. The predictive ability of the studied model has been compared with that of parallel factor analysis (PARAFAC), as regards test samples containing different sera, and also other pharmaceuticals which could act as potential interferents.

An advanced analytical chemistry laboratory experiment concerning the determination of the mucolitic bromhexine in a commercial syrup is described. It involves the following steps: (i) preparing nine calibration mixtures and recording their absorption spectra in the region 285-348 nm, (ii) recording spectrophotometric data for four synthetic unknowns and two commercial samples, and (iii) processing them with the multivariate calibration technique of principal component regression (PCR). The theory of PCR is discussed, and a Visual Basic 5.0 for Windows 95/98 program is made available to students for data processing. The program allows students to perform cross-validation and to obtain and save relevant statistical information (root mean square deviation, correlation coefficient, and relative error of prediction), as well as calibration spectral factors and spectral residuals for each test sample, all of which illustrate the PCR technique in detail. The reagents used are low-cost and nontoxic; the experiment is simple and gives students an insight into a real practice that integrates chemistry, instrumentation, and computer techniques.

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