Breuch René, Klein Daniel, Siefke Eleni, Hebel Martin, Herbert Ulrike, Wickleder Claudia, Kaul Peter
Hochschule Bonn-Rhein-Sieg, University of Applied Sciences, Institute of Safety and Security Research, von-Liebig-Str. 20, 53359, Rheinbach, Germany.
Hochschule Bonn-Rhein-Sieg, University of Applied Sciences, Institute of Safety and Security Research, von-Liebig-Str. 20, 53359, Rheinbach, Germany.
Talanta. 2020 Nov 1;219:121315. doi: 10.1016/j.talanta.2020.121315. Epub 2020 Jul 1.
Surface-enhanced Raman spectroscopy (SERS) with subsequent chemometric evaluation was performed for the rapid and non-destructive differentiation of seven important meat-associated microorganisms, namely Brochothrix thermosphacta DSM 20171, Pseudomonas fluorescens DSM 4358, Salmonella enterica subsp. enterica sv. Enteritidis DSM 14221, Listeria monocytogenes DSM 19094, Micrococcus luteus DSM 20030, Escherichia coli HB101 and Bacillus thuringiensis sv. israelensis DSM 5724. A simple method for collecting spectra from commercial paper-based SERS substrates without any laborious pre-treatments was used. In order to prepare the spectroscopic data for classification at genera level with a subsequent chemometric evaluation consisting of principal component analysis and discriminant analysis, a data pre-processing method with spike correction and sum normalisation was performed. Because of the spike correction rather than exclusion, and therefore the use of a balanced data set, the multivariate analysis of the data is significantly resilient and meaningful. The analysis showed that the differentiation of meat-associated microorganisms and thereby the detection of important meat-related pathogenic bacteria was successful on genera level and a cross-validation as well as a classification of ungrouped data showed promising results, with 99.5% and 97.5%, respectively.
采用表面增强拉曼光谱（SERS）并进行后续化学计量学评估，以快速、无损地区分七种重要的与肉类相关的微生物，即嗜热栖热菌DSM 20171、荧光假单胞菌DSM 4358、肠炎沙门氏菌肠炎亚种肠炎血清型DSM 14221、单核细胞增生李斯特菌DSM 19094、藤黄微球菌DSM 20030、大肠杆菌HB101和苏云金芽孢杆菌以色列亚种DSM 5724。使用了一种简单的方法，无需任何繁琐的预处理即可从商用纸质SERS底物收集光谱。为了通过由主成分分析和判别分析组成的后续化学计量学评估来准备用于属水平分类的光谱数据，进行了一种带有尖峰校正和总和归一化的数据预处理方法。由于采用了尖峰校正而非排除法，因此使用了平衡数据集，数据的多变量分析具有显著的弹性和意义。分析表明，在属水平上成功区分了与肉类相关的微生物，从而检测出了重要的肉类相关病原菌，交叉验证以及对未分组数据的分类均显示出了良好的结果，分别为99.5%和97.5%。
