Multicomponent Assay for Human Serum Using Mid-Infrared Transmission Spectroscopy Based on Component-Optimized Spectral Region Selected by a First Loading Vector Analysis in Partial Least-Squares Regression

Abstract

Mid-infrared transmission spectroscopy with partial least-squares regression was used to determine the concentrations of blood components such as total protein, albumin, globulin, total cholesterol, HDL (high density lipoprotein) cholesterol, triglycerides, glucose, BUN (blood urea nitrogen), and uric acid in human serum. The optimal spectral region for each component was selected by first loading vector analysis. Positive peaks with positive value were assigned by first loading vector analysis. Because blood components in serum show a correlation among several components, a useful spectral region for predicting a particular component was selected such that its spectral feature was not overlapped by those of other components. Several regions with positive peaks by first loading vector were used to establish calibration models. The proposed method proved to be effective for a multicomponent assay and can also be used even when a single component spectrum in aqueous solution for all components is not known. Total protein, albumin, globulin, total cholesterol, triglycerides, and glucose have a mean percentage error of cross-validation (MPECV) of less than 5%. But HDL cholesterol, BUN, and uric acid have MPECVs between 12 and 18%. In terms of both the percentage error of cross-validation and clinically allowable error, six serum components, excepting HDL-cholesterol, BUN, and uric acid, were determined successfully.