Pattern recognition analysis for classification of hypertensive model rats and diurnal variation using 1H-NMR spectroscopy of urine.

Urine samples were collected during the daytime and nighttime from spontaneously hypertensive model rats and normal rats without dosing. The 1H NMR spectra were measured for their urine samples, and analyzed by a pattern recognition method, known as Principal Component Analysis (PCA) and Soft Independent Modeling of Class Analogy (SIMCA). The separation of urinary data due to the diurnal variation (daytime and nighttime) and also to the difference between the two strains of rat was achieved in the PCA score plot. Differences of the urinary profiles in the respective separation were effectively extracted as marker variables by the SIMCA method. NMR measurements coupled with pattern recognition methods provide a straightforward approach to inspect the disease metabolic status and the preliminary screening tool of marker candidates for further development.