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

Masako Fujiwara, Kazunori Arifuku, Itiro Ando, Tadashi Nemoto

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

Urine samples were collected during the daytime and nighttime from spontaneously hypertensive model rats and normal rats without dosing. The 1 H 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. 2005

Original languageEnglish
Pages (from-to)1259-1262
Number of pages4
Journalanalytical sciences
Volume21
Issue number11
DOIs
Publication statusPublished - 2005 Nov

ASJC Scopus subject areas

  • Analytical Chemistry

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