基于主成分分析和模糊聚类法的细菌后向散射光谱分类方法

A Method of Backscattering Micro-Spectrum Classification Based on Principal Component Analysis and Fuzzy Cluster Analysis

食源性致病菌的快速检测是解决食品安全问题最有效的途径之一。为了实现对食源性致病菌的快速、高效、无标记检测和分类,提升了原有的光纤共聚焦后向散射光谱系统的性能,将其光场直径减小到适合较小生物样品的水平,即达到单菌水平检测。在无标记条件下,测定了三种常见的形态相近的食源性致病菌(肠炎沙门氏菌、大肠杆菌、鼠伤寒沙门氏菌)的后向散射光谱。选取500~800 nm范围的特征波段,将主成分分析和模糊聚类分析相结合,建立多元分析模型。主成分分析结果表明,所得的前5个主成分已经包含80.41%的特征区光谱信息。将前5个主成分分量作为模糊聚类分析的变量。由所求得的隶属度矩阵可知,三种细菌聚类结果的准确率均为100%。该结果说明光纤共聚焦后向散射光谱方法结合主成分分析和聚类分析法能够快速、高效、无标记地对单个细菌进行分析和分类。

Rapid detection of foodborne pathogens is one of the most effective ways to overcome food safety problems. To realize a rapid, efficient and label-free detection and classification of foodborne pathogens, this study aims to improve the performance of existing optical fiber confocal backscattering spectrum system. Through this process, the light field diameter is reduced to fit small biological samples, and single spectrum level detection can be achieved. Furthermore, the backscattering micro-spectrum of three categories of common foodborne pathogens(Salmonella enteritidis, Escherichia coli, and Salmonella typhimurium) with similar morphology is measured without labels. A multivariate analysis model is established by combining principal component analysis(PCA) and fuzzy cluster analysis(FCA) at the characteristic wavelength range of 500--800 nm. Results show that the top five principal components contain 80.41% characteristic spectral information. The scores of the top five principal components are taken as the variables for the FCA. The accuracy of 100%, according to the degree matrix of membership, is achieved for the clustering results of three kinds of bacteria. Also, results show that optical fiber confocal backscattering spectroscopy, combined with PCA and FCA, can be used to analyze and classify a single spectrum rapidly, efficiently, and without labels.