Reference measurement procedure for total glycerides by isotope dilution GC-MS.

BACKGROUND: The CDC's Lipid Standardization Program established the chromotropic acid (CA) reference measurement procedure (RMP) as the accuracy base for standardization and metrological traceability for triglyceride testing. The CA RMP has several disadvantages, including lack of ruggedness. It uses obsolete instrumentation and hazardous reagents. To overcome these problems the CDC developed an isotope dilution GC-MS (ID-GC-MS) RMP for total glycerides in serum. METHODS: We diluted serum samples with Tris-HCl buffer solution and spiked 200-µL aliquots with [(13)C(3)]-glycerol. These samples were incubated and hydrolyzed under basic conditions. The samples were dried, derivatized with acetic anhydride and pyridine, extracted with ethyl acetate, and analyzed by ID-GC-MS. Linearity, imprecision, and accuracy were evaluated by analyzing calibrator solutions, 10 serum pools, and a standard reference material (SRM 1951b). RESULTS: The calibration response was linear for the range of calibrator concentrations examined (0-1.24 mmol/L) with a slope and intercept of 0.717 (95% CI, 0.7123-0.7225) and 0.3122 (95% CI, 0.3096-0.3140), respectively. The limit of detection was 14.8 µmol/L. The mean %CV for the sample set (serum pools and SRM) was 1.2%. The mean %bias from NIST isotope dilution MS values for SRM 1951b was 0.7%. CONCLUSIONS: This ID-GC-MS RMP has the specificity and ruggedness to accurately quantify total glycerides in the serum pools used in the CDC's Lipid Standardization Program and demonstrates sufficiently acceptable agreement with the NIST primary RMP for total glyceride measurement.

Wavelet analysis used for spectral background removal in the determination of glucose from near-infrared single-beam spectra.

Wavelet analysis is developed as a preprocessing tool for use in removing background information from near-infrared (near-IR) single-beam spectra before the construction of multivariate calibration models. Three data sets collected with three different near-IR spectrometers are investigated that involve the determination of physiological levels of glucose (1-30 mM) in a simulated biological matrix containing alanine, ascorbate, lactate, triacetin, and urea in phosphate buffer. A factorial design is employed to optimize the specific wavelet function used and the level of decomposition applied, in addition to the spectral range and number of latent variables associated with a partial least-squares calibration model. The prediction performance of the computed models is studied with separate data acquired after the collection of the calibration spectra. This evaluation includes one data set collected over a period of more than 6 months. Preprocessing with wavelet analysis is also compared to the calculation of second-derivative spectra. Over the three data sets evaluated, wavelet analysis is observed to produce better-performing calibration models, with improvements in concentration predictions on the order of 30% being realized relative to models based on either second-derivative spectra or spectra preprocessed with simple additive and multiplicative scaling correction. This methodology allows the construction of stable calibrations directly with single-beam spectra, thereby eliminating the need for the collection of a separate background or reference spectrum.