Discrimination of bacteria using pyrolysis-gas chromatography-differential mobility spectrometry (Py-GC-DMS) and chemometrics
Discrimination of bacteria using pyrolysis-gas chromatography-differential mobility spectrometry (Py-GC-DMS) and chemometrics
复制标题
DOI：
10.1039/b812666f
复制
发表时间：
2009-01-01
期刊：
影响因子：
4.2
通讯作者：
Goodacre, Royston
中科院分区：
文献类型：
--
作者：
Cheung, William;Xu, Yu;Goodacre, Royston
采用热解-气相色谱-差分迁移率谱法（Py-GC-DMS）对细菌进行了鉴别。研究了芽孢杆菌属的3株芽孢杆菌，其中包括2株枯草芽孢杆菌和1株巨芽孢杆菌。选择这些菌株是为了评估使用Py-GC-DMS进行菌株鉴别的可能性。该仪器是在内部构建的，并在60天内使用苏格兰威士忌质量控制来评估仪器的长期可重复性。为了进一步评估再现性，每个细菌培养6次，每个培养重复分析，形成3个分析重复。DMS数据以正、负两种模式生成，每种模式下的数据相互独立分析。在归一化之前，Py-GC-DMS数据通过相关优化翘曲（COW）和非对称最小二乘（ALS）进行预处理，以对齐DMS色谱并消除任何不可避免的基线偏移。处理后的色谱分析使用主成分分析（PCA），然后使用偏最小二乘法进行判别分析（PLS-DA）的监督学习方法。用主成分分析法可以很容易地观察到枯草芽孢杆菌和巨型芽孢杆菌的分离；然而，两种枯草芽孢杆菌之间的菌株区分只能使用监督学习。由于分析了多个生物重复，因此对训练集和测试集进行了详尽的分离，从而可以评估3375个测试集的正确分类率（ccr）。在PLS-DA中，负离子模式的DMS数据比正离子模式的DMS数据更具歧视性。
Discrimination of bacteria was investigated using pyrolysis-gas chromatography-differential mobility spectrometry (Py-GC-DMS). Three strains belonging to the genus Bacillus were investigated and these included two strains of Bacillus subtilis and a single Bacillus megaterium. These were chosen so as to evaluate the possibility of bacterial strain discrimination using Py-GC-DMS. The instrument was constructed in-house and the long-term reproducibility of the instrument was evaluated over a period of 60 days using a Scotch whisky quality control. To assess the reproducibility further each bacterium was cultured six times and each culture was analysed in replicate to give three analytical replicates. The DMS data were generated in both positive and negative modes, and the data in each mode were analysed independently of each other. The Py-GC-DMS data were pre-processed via correlation optimised warping (COW) and asymmetric least square (ALS) to align the DMS chromatograms and to remove any unavoidable baseline shifts, prior to normalisation. Processed chromatograms were analysed using principal component analysis (PCA) followed by supervised learning methodology using partial least squares for discriminant analysis (PLS-DA). It was found that the separations between B. subtilis and B. megaterium can be readily observed by PCA; however, strain discrimination within the two B. subtilis was only possible using supervised learning. As multiple biological replicates were analysed an exhaustive splitting of the training and test sets was undertaken and this allowed correct classification rates (CCRs) to be assessed for the 3375 test sets. It was found that with PLS-DA the negative ion mode DMS data were more discriminatory than the positive mode data.