A novel colorimetric assay coupled with clustering algorithms and genetic algorithm partial least squares can simultaneously determine cholesterol and monounsaturated/polyunsaturated fatty acids in biological samples

Current methodologies of quantifying cholesterol and monounsaturated fatty acid (MUFA)/polyunsaturated fatty acids (PUFAs) constitute techniques such as GC-MS and HPLC, which use expensive standards, are time consuming and require skilled labor. We sought to develop a simple, direct alternative method for the simultaneous determination of cholesterol and MUFA/PUFAs (linoleic (LA), alpha-linolenic (ALA), arachidonic (AA), eicosapentaenoic (EPA), docosahexaenoic (DHA), conjugated linoleic (CLA), and oleic (OA) acids) using a novel colorimetric test, the "Purdie Assay." The data were analyzed using a coded chemometric software which involves clustering algorithms, and genetic algorithm partial least squares (GAPLS). The colorimetric test with GAPLS simultaneously quantified these lipids without any separation of the analytes in human serum and vegetable oils and foods. We performed pattern recognition of biological and food samples using principal component analysis (PCA) and hierarchical clustering (HC). The assay successfully discriminated 11 clusters corresponding to different food and biological samples and also discriminated synthetic vegetable oil samples using PCA and HC corresponding to levels of prepared lipids. This study shows the wide range of possible applications of the assay as a novel, fast, and efficient tool for lipid quantification and classification. Practical applications: The novel assay coupled with GAPLS, PCA, and HC can provide an efficient tool for the direct determination and discrimination of unsaturated lipids in biological samples.