Chun J, Ward A C, Kang S O, Hah Y C, Goodfellow M
Department of Microbiology, Medical School, Newcastle upon Type, UK.
Zentralbl Bakteriol. 1997 Jan;285(2):258-66. doi: 10.1016/s0934-8840(97)80033-3.
Sixteen reference strains and thirteen fresh isolates of three putatively novel Streptomyces species were examined six times over twenty months using pyrolysis mass spectrometry to examine the long-term reproducibility of the procedure. The reference strains and new isolates were correctly identified using information in each of the datasets and operational fingerprinting, but direct statistical comparison of the datasets for strain identification was unsuccessful between datasets. Artificial neural networks were also used to identify the strains held in the datasets. Neural networks trained with pyrolysis mass spectra from a single dataset were found to successfully identify the reference strains and fresh isolates in that dataset but were unable to identify many of the strains in the other datasets. However, a neural network trained on representative pyrolysis mass spectra from each of the first three datasets were found to identify the reference strains and fresh isolates in those three datasets and in the three subsequent datasets. Therefore, artificial neural network analysis of pyrolysis mass spectrometric data can provide a rapid, cost-effective, accurate and long-term reproducible way of identifying and typing microorganisms.
在二十个月的时间里，使用热解质谱法对16株参考菌株和三种可能的新型链霉菌的13株新鲜分离株进行了六次检测，以检验该方法的长期重现性。利用每个数据集中的信息和操作指纹图谱，参考菌株和新分离株得到了正确鉴定，但在不同数据集之间，直接对用于菌株鉴定的数据集进行统计比较未成功。人工神经网络也被用于鉴定数据集中保存的菌株。发现用单个数据集的热解质谱训练的神经网络能够成功鉴定该数据集中的参考菌株和新鲜分离株，但无法鉴定其他数据集中的许多菌株。然而，发现用前三个数据集中每个数据集的代表性热解质谱训练的神经网络能够鉴定这三个数据集中以及随后三个数据集中的参考菌株和新鲜分离株。因此，对热解质谱数据进行人工神经网络分析可以提供一种快速、经济高效、准确且长期可重现的微生物鉴定和分型方法。
