Department of Molecular Biology and Genetics, Bilecik S.E. University, 11230 Bilecik, Turkey; Department of Biochemistry, Middle East Technical University, 06800 Ankara, Turkey.
Department of Biological Sciences, Middle East Technical University, 06800 Ankara, Turkey.
Spectrochim Acta A Mol Biomol Spectrosc. 2018 Jan 15;189:282-290. doi: 10.1016/j.saa.2017.08.038. Epub 2017 Aug 15.
Rapid, cost-effective, sensitive and accurate methodologies to classify bacteria are still in the process of development. The major drawbacks of standard microbiological, molecular and immunological techniques call for the possible usage of infrared (IR) spectroscopy based supervised chemometric techniques. Previous applications of IR based chemometric methods have demonstrated outstanding findings in the classification of bacteria. Therefore, we have exploited an IR spectroscopy based chemometrics using supervised method namely Soft Independent Modeling of Class Analogy (SIMCA) technique for the first time to classify heavy metal-exposed bacteria to be used in the selection of suitable bacteria to evaluate their potential for environmental cleanup applications. Herein, we present the powerful differentiation and classification of laboratory strains (Escherichia coli and Staphylococcus aureus) and environmental isolates (Gordonia sp. and Microbacterium oxydans) of bacteria exposed to growth inhibitory concentrations of silver (Ag), cadmium (Cd) and lead (Pb). Our results demonstrated that SIMCA was able to differentiate all heavy metal-exposed and control groups from each other with 95% confidence level. Correct identification of randomly chosen test samples in their corresponding groups and high model distances between the classes were also achieved. We report, for the first time, the success of IR spectroscopy coupled with supervised chemometric technique SIMCA in classification of different bacteria under a given treatment.
快速、经济、敏感且准确的细菌分类方法仍在开发中。标准微生物学、分子和免疫学技术的主要缺点需要可能使用基于红外（IR）光谱的有监督化学计量技术。基于 IR 的化学计量学方法的先前应用已经在细菌分类方面取得了出色的发现。因此，我们首次利用基于 IR 的化学计量学和有监督方法，即软独立建模分类相似性（SIMCA）技术，对暴露于重金属的细菌进行分类，以便选择合适的细菌用于评估其在环境清理应用中的潜力。在这里，我们展示了暴露于生长抑制浓度的银（Ag）、镉（Cd）和铅（Pb）的实验室菌株（大肠杆菌和金黄色葡萄球菌）和环境分离株（戈登氏菌和微杆菌）的强大分化和分类。我们的结果表明，SIMCA 能够以 95%的置信水平将所有暴露于重金属的组和对照组彼此区分开来。还实现了随机选择的测试样品在其相应组中的正确识别和类别之间的高模型距离。我们首次报道了在给定处理下，IR 光谱与有监督化学计量技术 SIMCA 结合成功用于不同细菌的分类。
