Pattern Recognition Analysis for Classification of Hypertensive Model Rats and Diurnal Variation Using ^1H-NMR Spectroscopy of Urine
Pattern Recognition Analysis for Classification of Hypertensive Model Rats and Diurnal Variation Using ^1H-NMR Spectroscopy of Urine
复制标题
使用^1H-NMR 尿液光谱进行高血压模型大鼠分类和昼夜变化的模式识别分析
作者：
Masako Fujiwara;Kazunori Arifuku;Itiro Ando;T. Nemoto
自发性高血压模型大鼠和未给药的正常大鼠在白天和夜间采集尿样。测量他们尿液样本的^1H NMR谱，并通过模式识别方法进行分析，称为主成分分析（PCA）和类类比软独立建模（SIMCA）。在PCA评分图中，由于昼夜变化（白天和夜间）以及两种大鼠品系之间的差异，实现了尿液数据的分离。通过SIMCA方法有效地提取了各个分离中的尿液特征的差异作为标记变量。NMR测量加上模式识别方法提供了一种直接的方法来检查疾病的代谢状态和初步筛选工具的标记候选人，以进一步发展。
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.