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Analysis of bovine milk proteins is crucial in many food and non-food industrial applications, nevertheless labour-intensive wet-chemical, low-throughput methods are still routinely used. In this work, external cavity-quantum cascade laser (EC-QCL) mid-infrared spectroscopy is employed as a rapid method for protein analysis of commercial bovine milk. Combined analysis of the amide I and II bands enabled quantitation of individual proteins (casein, β-lactoglobulin, α-lactalbumin) and total protein content. IR spectra of spiked and diluted milk samples were employed for calibration of the target analytes in the presence of a complex matrix by partial least squares (PLS) regression modelling. A sample set of different milk types (pasteurized; differently processed extended shelf life, ESL; ultra-high temperature, UHT) was analysed, and results agreed well with reference methods. Quantitation of temperature sensitive proteins enables detailed distinction between milk types experiencing different heat loads during processing, and discrimination between diverse bovine milk types is successfully demonstrated. [ABSTRACT FROM AUTHOR]

Three different experimental systems have been studied regarding the determination of analytes in complex samples, using non-linear second-order instrumental data, which are intrinsically able to provide the second-order advantage. This permits the quantitation of calibrated analytes in the presence of unexpected sample components, although a suitable algorithm is required. The recently described combination of artificial neural networks with post-training residual bilinearization has been applied to the three data sets, with successful results concerning prediction accuracy and precision, as well as profile recovery for the potential interferents in test samples. The studies involve: (1) the determination of two pharmaceuticals in the presence of an unexpected excipient by absorbance-pH matrix measurements, (2) the quantitation of iron(II) by its catalytic effect on the kinetics of the bromate oxidation of a colorant in the presence of a second interfering organic dye, and (3) the analysis of the antibiotic amoxicillin by fluorescence excitation-emission matrices in the presence of a fluorescent anti-inflammatory. The prediction results were compared and shown to be significantly better than those yielded by the unfolded partial least-squares/residual bilinearization model, due to the non-linear nature of the studied data.

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.