The prediction of bacteria type and culture growth phase by an electronic nose with a multi-layer perceptron network
The prediction of bacteria type and culture growth phase by an electronic nose with a multi-layer perceptron network
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DOI：
10.1088/0957-0233/9/1/016
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发表时间：
1998-01-01
影响因子：
2.4
通讯作者：
Hines, EL
中科院分区：
文献类型：
--
作者：
Gardner, JW;Craven, M;Hines, EL
本文研究了利用电子鼻预测两种潜在致病微生物--大肠杆菌（Eschericha coli，E. coli）的种类和生长阶段。Coli)和金黄色葡萄球菌(S.金黄色）。为了实现这一点，我们开发了一种自动化系统，用于对在标准营养培养基中生长的细菌培养物的顶部空间进行取样，具有高度的可重复性。通过使用六种不同的金属氧化物半导体气体传感器的阵列来检查顶部空间，并通过具有反向传播（BP）学习算法的多层感知器（MLP）来分类。基于9种不同的传感器参数和4种不同的归一化技术，研究了36种不同的预处理算法的性能。最佳的MLP被找到，以成功地100%的未知S。金黄色葡萄球菌样品和92%的未知E.大肠杆菌样品，基于取自滞后、对数和稳定生长期的一组360个训练载体和360个测试载体。细菌的真实的生长期由光学细胞计数确定，并由顶空样品以81%的准确度预测。我们的结论是，这些结果显示了相当大的希望，因为正确预测病原菌的类型和生长期可能有助于更快速地治疗细菌感染和更有效地测试新的抗生素药物。
An investigation into the use of an electronic nose to predict the class and growth phase of two potentially pathogenic micro-organisms, Eschericha coli (E. coli) and Staphylococcus aureus (S. aureus), has been performed. In order to do this we have developed an automated system to sample, with a high degree of reproducibility, the head space of bacterial cultures grown in a standard nutrient medium. Head spaces have been examined by using an array of six different metal oxide semiconducting gas sensors and classified by a multi-layer perceptron (MLP) with a back-propagation (BP) learning algorithm. The performance of 36 different pre-processing algorithms has been studied on the basis of nine different sensor parameters and four different normalization techniques. The best MLP was found to classify successfully 100% of the unknown S. aureus samples and 92% of the unknown E. coli samples, on the basis of a set of 360 training vectors and 360 test vectors taken from the lag, log and stationary growth phases. The real growth phase of the bacteria was determined from optical cell counts and was predicted from the head space samples with an accuracy of 81%. We conclude that these results show considerable promise in that the correct prediction of the type and growth phase of pathogenic bacteria may help both in the more rapid treatment of bacterial infections and in the more efficient testing of new anti-biotic drugs.