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Evaluation of Fourier transform infrared spectroscopy for the rapid identification of glycopeptide-intermediate Staphylococcus aureus

机译:傅里叶变换红外光谱技术用于快速鉴定糖肽中间金黄色葡萄球菌的评估

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摘要

Objectives To evaluate Fourier transform infrared (FTIR) spectroscopy as a rapid method for distinguishing glycopeptide-intermediate Staphylococcus aureus (GISA) from glycopeptide-susceptible methicillin-resistant S. aureus (MRSA) and to compare three data analysis methods. Methods First-derivative normalized spectra of dried films of bacterial growth on Que-Bact® Universal Medium No. 2 were examined by singular value decomposition to identify key spectral regions. Region selection was analysed by principal component analysis (PCA), self-organizing maps (SOMs) and the K-nearest neighbour (KNN) algorithm. The initial data set included 35 GISA (including GISA Mu50 and heterogeneous GISA Mu3) and 25 epidemic MRSA. The regions were then tested using enlarged data sets that included 22 sporadic and 85 additional epidemic MRSA. Results Epidemic MRSA and GISA/hGISA were separated into two distinct clusters on the basis of spectral data from regions 1352-1315 and 1480-1460 cm−1, the former providing 100% correct classification by all three analyses and the latter providing 96.67% correct by PCA, 98.34% by SOM and 100% by KNN. The 1480-1460 cm−1 region was more effective for distinguishing GISA/hGISA from a set combining sporadic and epidemic MRSA, with two GISA/hGISA and four sporadic MRSA misclassified by PCA and SOM (92.69% correct), while the KNN method misclassified three of the four sporadic MRSA (93.90% correct). The addition of 85 other epidemic MRSA this set increased the fraction of correctly classified isolates to 96.41% and 97.01% by PCA, SOM and KNN, respectively. Conclusions As only 6 of 167 isolates were misclassified, FTIR spectroscopy may provide means of rapid and accurate identification of GISA and hGISA among isolates of MRSA
机译:目的评估傅立叶变换红外(FTIR)光谱作为区分糖肽中间性金黄色葡萄球菌(GISA)和糖肽敏感性耐甲氧西林金黄色葡萄球菌(MRSA)的快速方法,并比较三种数据分析方法。方法通过奇异值分解检查在2号通用培养基上细菌生长的干膜的一阶导数归一化光谱,以识别关键光谱区域。通过主成分分析(PCA),自组织映射(SOM)和K最近邻(KNN)算法分析区域选择。初始数据集包括35个GISA(包括GISA Mu50和异构GISA Mu3)和25个流行性MRSA。然后使用扩大的数据集对区域进行测试,其中包括22个散发性MRSA和85个流行性MRSA。结果根据来自区域1352-1315和1480-1460 cm-1的光谱数据,流行性MRSA和GISA / hGISA被分为两个不同的簇,前三种方法均提供100%正确的分类,后者提供96.67%的正确分类PCA,SOM占98.34%,KNN占100%。 1480-1460 cm-1区域对于区分散发性和流行性MRSA的集合更有效地区分了GISA / hGISA,其中两个GISA / hGISA和四个散发性MRSA被PCA和SOM错误分类(正确率为92.69%),而KNN方法被错误分类四个散发性MRSA中的三个(正确率为93.90%)。通过此方法添加的其他85种流行性MRSA,PCA,SOM和KNN分别将正确分类的分离株的比例分别提高到96.41%和97.01%。结论由于167个分离株中只有6个被错误分类,因此FTIR光谱法可提供快速,准确鉴定MRSA分离株中GISA和hGISA的方法。

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