热裂解质谱法和人工神经网络快速鉴定犬源痤疮丙酸杆菌。

R Goodacre, M J Neal, D B Kell, L W Greenham, W C Noble, R G Harvey
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引用次数: 87

摘要

获得了参考菌株丙酸杆菌和犬分离株的居里点热解质谱。通过监督学习(使用反向传播算法)训练人工神经网络(ann)从这些菌株的热解质谱中识别这些菌株;所有犬源分离株均鉴定为人类野生型痤疮假体。这是一项重要的分类学发现,表明热解质谱与人工神经网络的结合提供了一种客观、快速、准确的鉴定技术。从同一只狗的不同活检标本中分离出的细菌被发现是不同的痤疮假单胞杆菌菌株,这表明动物体内的微生物群存在差异。将Kohonen人工神经网络(kann)对犬分离物的分类与经典的多变量分析(canonical variate analysis)和层次聚类分析(hierarchical clustering analysis)进行比较,发现两者的分类结果相似。这是第一次证明,在微生物学中,kann作为一种无监督聚类技术,具有自动和相对客观地分组热解质谱的潜力。
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Rapid identification using pyrolysis mass spectrometry and artificial neural networks of Propionibacterium acnes isolated from dogs.

Curie-point pyrolysis mass spectra were obtained from reference Propionibacterium strains and canine isolates. Artificial neural networks (ANNs) were trained by supervised learning (with the back-propagation algorithm) to recognize these strains from their pyrolysis mass spectra; all the strains isolated from dogs were identified as human wild type P. acnes. This is an important nosological discovery, and demonstrates that the combination of pyrolysis mass spectrometry and ANNs provides an objective, rapid and accurate identification technique. Bacteria isolated from different biopsy specimens from the same dog were found to be separate strains of P. acnes, demonstrating a within-animal variation in microflora. The classification of the canine isolates by Kohonen artificial neural networks (KANNs) was compared with the classical multivariate techniques of canonical variates analysis and hierarchical cluster analysis, and found to give similar results. This is the first demonstration, within microbiology, of KANNs as an unsupervised clustering technique which has the potential to group pyrolysis mass spectra both automatically and relatively objectively.

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