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[通过可见和近红外光谱的多变量分析对慢性疲劳综合征进行光谱诊断]

[Spectroscopic diagnosis of chronic fatigue syndrome by multivariate analysis of visible and near-infrared spectra].

作者信息

Sakudo Akikazu, Kuratsune Hirohiko, Hakariya Yukiko, Kobayashi Takanori, Ikuta Kazuyoshi

机构信息

Department of Virology, Research Institute for Microbial Diseases, Osaka University.

出版信息

Nihon Rinsho. 2007 Jun;65(6):1051-6.

Abstract

We have recently evaluated the possibility of visible and near-infrared (Vis-NIR) spectroscopy for diagnosis of chronic fatigue syndrome(CFS). Vis-NIR spectra in the 600-1,100 nm region for sera from CFS patients and healthy donors were subjected to principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) to develop multivariate models to discriminate between CFS patients and healthy donors. The PCA and SIMCA model predicted successful prediction of the masked samples. Furthermore, taking advantage of Vis-NIR spectroscopy to enable noninvasive analysis, our preliminary results have shown that SIMCA model from Vis-NIR spectra of thumb has achieved 70-80% correct determinations. In this review, we will introduce the potential of the Vis-NIR spectroscopy for CFS diagnosis.

摘要

我们最近评估了利用可见和近红外(Vis-NIR)光谱诊断慢性疲劳综合征(CFS)的可能性。对慢性疲劳综合征患者和健康捐赠者血清在600 - 1100 nm区域的Vis-NIR光谱进行主成分分析(PCA)和类类比软独立建模(SIMCA),以建立多变量模型来区分慢性疲劳综合征患者和健康捐赠者。PCA和SIMCA模型对盲样进行了成功预测。此外,利用Vis-NIR光谱能够进行无创分析,我们的初步结果表明,来自拇指Vis-NIR光谱的SIMCA模型已实现70 - 80%的正确判定。在本综述中,我们将介绍Vis-NIR光谱在慢性疲劳综合征诊断中的潜力。

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