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Chemometric algorithms for the direct determination of lipids in synthetic mixtures and human serum

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Abstract:

Although polyunsaturated fatty acids (PUFAs) can be detected by chromatographic methods, a low cost method to quantify PUFAs in serum samples with little to no separation or sample preparation would enable PUFAs to be monitored and detected during routine clinical serum analysis. The goal of this research project was to develop a simple, direct alternative method for the determination of the PUFAs in addition to cholesterol in human serum. The simple colorimetric assay used is rapid, rugged, inexpensive, and specific to the -C=CH-CH2- group that accomplishes, in a single assay the simultaneous quantitation of cholesterol, ω-3 (methyl esters of linolenic, eicosapentaenoic (EPA) and docosahexaenoic (DHA)) fatty acids, and ω-6 (methyl esters of linoleic, conjugated linoleic (CLA), and arachidonic) fatty acids. Several chemometric models consisting of K-matrix using ordinary least squares (OLS) and non-negative least squares regression (NNLS), ridge regression K-matrix (RR), P-matrix regression (PM), principal component regression (PCR), and partial least squares (PLS) were introduced and applied for the direct determination of lipids in synthetic human serum models. The principal outcome was that the RR, PM, PCR, and PLS algorithms successfully out-performed the K-matrix regression approach when applied to the study of prepared mixtures (synthetic sera) in chloroform solutions. In the case of assays for intact human serum specimens, the same PLS model yielded results for ω-3 and ω-6 polyunsaturated fatty acids (PUFAs). These data compared very well for the same samples when measured using the gas chromatography-mass spectrometry (GC-MS) gold standard method. Similar results were also derived from the between-methods ω-6/ ω-3 ratios. This study has also demonstrated how chemometric algorithms might provide alternatives to separations methods for the direct determination of lipids in human serum and its synthetic models. The obvious profits from this accomplishment are the reductions in time and costs.

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