Partial Least Squares (PLS1) Algorithm for Quantitating Cholesterol and Polyunsaturated Fatty Acids in Human Serum

We have previously exploited various chemometric algorithms for the direct determination of cholesterol and polyunsaturated fatty acid (PUFA) molar concentrations in synthetic mixtures and human serum. The simple colorimetric assay used is rapid, rugged, inexpensive, and specific to the -CH=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). Previously, ridge regression (RR), P-matrix regression (PM), principal component regression (PCR), and partial least squares (PLS2) successfully out-performed the K-matrix regression (KM) approach when applied to the study of prepared mixtures (synthetic sera) in chloroform solutions. In this paper, partial least squares in the form of PLS1 is investigated and applied to quantify molar concentrations of cholesterol and PUFAs in actual human serum samples. Results show that PLS1 yielded lesser root mean square errors of prediction in the calibration model, and molar concentrations comparing quite equally well with the gas chromatography-mass spectrometry (GC-MS) procedures.