We shall introduce Kemeny–Snell distance (KSD) metric on metabolomics and validate results with partial least squares discriminant analysis (PLS-DA). KSD metric allows principally to identify the most relevant chemical shift ranges directly from spectra without metabolite library-limited signature search and quantitation. The one-dimensional proton nuclear magnetic resonance spectra of the serum of ischemic heart disease (IHD) patients (n = 19) and controls (n = 19) showed the statistical significance by the first latent variable (LV1; 35.18%) of PLS-DA. The significance between ischemic heart disease (IHD) patients and controls was tested and confirmed by KSD metric. We used PLS-DA and KSD metric for the interpretation of serum NMR spectra of IHD patients and healthy controls and both methods show a significant deviation from the controls. KSD is a robust to spectral artifacts and potentially useful as a diagnostic tool to assess the likelihood of many pathologies simultaneously.

我们将介绍代谢组学的Kemeny-Snell距离（KSD）指标，并使用偏最小二乘判别分析（PLS-DA）验证结果。KSD度量原则上可以直接从光谱中确定最相关的化学位移范围，而无需代谢物库限制的特征搜索和定量。缺血性心脏病（IHD）患者（n = 19）和对照（n的血清）的一维质子核磁共振波谱= 19）通过PLS-DA的第一个潜在变量（LV1; 35.18％）显示了统计显着性。缺血性心脏病（IHD）患者和对照之间的显着性已通过KSD度量标准进行了测试和确认。我们使用PLS-DA和KSD度量标准来解释IHD患者和健康对照的血清NMR谱，两种方法均显示出与对照的显着差异。KSD对频谱伪影具有很强的鲁棒性，并且有可能用作诊断工具以同时评估许多病理的可能性。