In this paper, we present a different way to the standard methods to classify Raman spectra whose grouping process is based on a phenomenon of clustering observed in nature at the atomic level and correctly described by the statistical physics model known as the Potts model, which represents the interacting spins on a crystalline lattice. This clustering method is known as the super paramagnetic clustering (SPC), which allows identifying hierarchical structures in data banks. In this novel method, we assigned a Potts spin to each data point (Raman spectrum) and introduced an interaction between neighboring points whose coupling strength is a decreasing function of the distance between the nearest neighboring sites. We found a hierarchical tree structure in our data bank of Raman spectra allowing us to discriminate between the spectra from control and diabetes patients. The sensitivity and specificity of the diabetes detection technique by Raman spectroscopy were calculated directly because the SPC method achieves an accurate determination of the members of each cluster. As a cross-check, SPC results were compared with published results of multivariate analysis, observing excellent agreements; however, the SPC method allows determining the members of all identified clusters explicitly.

中文翻译：

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糖尿病患者拉曼光谱的超顺磁聚类

在本文中，我们提出了一种与标准方法不同的方法来对拉曼光谱进行分类，该方法的分组过程基于自然界在原子水平上观察到的聚集现象，并且被称为Potts模型的统计物理模型正确描述，相互作用在晶格上旋转。这种聚类方法称为超顺磁性聚类（SPC），它可以识别数据库中的层次结构。在这种新颖的方法中，我们将Potts自旋分配给每个数据点（拉曼光谱），并引入相邻点之间的相互作用，其耦合强度是最近的相邻站点之间距离的递减函数。我们在我们的拉曼光谱数据库中发现了一个分层的树状结构，使我们能够区分对照和糖尿病患者的光谱。直接通过拉曼光谱法检测糖尿病检测技术的灵敏度和特异性是因为SPC方法可以准确确定每个簇的成员。作为一项交叉检查，将SPC结果与已发表的多元分析结果进行了比较，观察到了极好的一致性；但是，SPC方法允许显式确定所有已识别群集的成员。将SPC结果与已发表的多元分析结果进行比较，观察到极好的一致性；但是，SPC方法允许显式确定所有已识别群集的成员。将SPC结果与已发表的多变量分析结果进行比较，观察到极好的一致性；但是，SPC方法允许显式确定所有已识别群集的成员。