Bonn-Rhein-Sieg University of Applied Sciences, Institute of Safety and Security Research, von Liebig-Straße 20, 53359 Rheinbach, Germany.
Bonn-Rhein-Sieg University of Applied Sciences, Institute of Safety and Security Research, von Liebig-Straße 20, 53359 Rheinbach, Germany.
Talanta. 2019 May 1;196:325-328. doi: 10.1016/j.talanta.2018.12.094. Epub 2018 Dec 27.
Raman-Microspectroscopy with subsequent chemometric evaluation was used for the rapid and non-destructive differentiation of seven important spoilage related microorganisms, namely Brochothrix thermosphacta DSM 20171, Pseudomonas fluorescens DSM 4358, Pseudomonas fluorescens DSM 50090, Micrococcus luteus, Escherichia coli HB101, Escherichia coli TOP10 and Bacillus thuringiensis israelensis DSM 5724. Therefore fast collected spectra directly from rapid surface blots without any pretreatments like purification or singulation steps were used. To estimate and classify the Raman-spectroscopic data at genera and strain level an adequate preprocessing together with a subsequent chemometric evaluation consisting of principal component analysis and discriminant analysis was used. Thereby, importance was attached to a balanced data set, as this makes the multivariate analysis of the data significantly more resilient and meaningful. The analysis showed that the differentiation of spoilage related microorganisms on genera and strain level was successful and the classification of independent test data showed only an error rate of 3.5%.
拉曼微光谱分析结合后续化学计量学评估用于快速无损地区分七种重要的与变质相关的微生物，分别是热死环丝菌 DSM 20171、荧光假单胞菌 DSM 4358、荧光假单胞菌 DSM 50090、藤黄微球菌、大肠杆菌 HB101、大肠杆菌 TOP10 和苏云金芽孢杆菌以色列亚种 DSM 5724。因此，直接从快速表面印迹中快速收集未经任何预处理（如纯化或单细胞分离步骤）的光谱数据。为了估计和分类拉曼光谱数据在属和菌株水平上，使用了适当的预处理以及随后的化学计量学评估，包括主成分分析和判别分析。因此，重要的是要获得平衡的数据集，因为这使得数据的多元分析更加稳健和有意义。分析表明，成功地对变质相关微生物进行了属和菌株水平的区分，独立测试数据的分类仅显示出 3.5%的错误率。
