Fourier transform infrared (FT-IR) spectroscopy in bacteriology: towards a reference method for bacteria discrimination
Fourier transform infrared (FT-IR) spectroscopy in bacteriology: towards a reference method for bacteria discrimination
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DOI：
10.1007/s00216-006-0851-1
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发表时间：
2007-03-01
影响因子：
4.3
通讯作者：
Menezes, Jose C.
中科院分区：
文献类型：
--
作者：
Preisner, Ornella;Lopes, Joao Almeida;Menezes, Jose C.
在临床相关的病原微生物之间进行快速和可靠的区分是微生物学中的一项重要任务。微生物对抗菌剂的耐药性增加了感染的流行率。傅里叶变换红外（FT-IR）光谱法以非破坏性方式评估微生物细胞的整体分子组成的可能性反映在不同微生物高度典型的特定光谱指纹中。为了使用FT-IR光谱法在常规基础上区分不同的微生物物种和菌株，需要应用广泛的化学计量学技术。使用FT-IR进行细菌表征的主要问题仍然是光谱预处理方法。我们分析了不同的光谱预处理方法及其对减少光谱变异性和增加化学计量学模型鲁棒性的影响。根据脉冲场凝胶电泳（PFGE）产生的染色体DNA限制性图谱，对屎肠球菌菌株的不同类型进行了分类。从人类患者中收集样品。收集的FT-IR光谱用于验证是否获得相同的分类。为了进一步优化细菌分类，我们研究了最具鉴别力的光谱区域的选定组合是否可以改善结果。两种不同的变量选择方法（遗传算法（GAs）和自举）进行了研究，并通过与使用整个光谱获得的结果进行比较，其相对优点的细菌分类报告。判别偏最小二乘（二-PLS）模型的基础上校正的光谱表现出更好的预测能力高达40%相比，等效模型使用整个光谱范围。与所有波长的模型相比，估计分数的不确定性降低了约50%。概述了用于屎肠球菌细菌鉴别的光谱范围和相关化学信息。
Rapid and reliable discrimination among clinically relevant pathogenic organisms is a crucial task in microbiology. Microorganism resistance to antimicrobial agents increases prevalence of infections. The possibility of Fourier transform infrared (FT-IR) spectroscopy to assess the overall molecular composition of microbial cells in a non-destructive manner is reflected in the specific spectral fingerprints highly typical for different microorganisms. With the objective of using FT-IR spectroscopy for discrimination between diverse microbial species and strains on a routine basis, a wide range of chemometrics techniques need to be applied. Still a major issue in using FT-IR for successful bacteria characterization is the method for spectra pre-processing. We analyzed different spectra pre-processing methods and their impact on the reduction of spectral variability and on the increase of robustness of chemometrics models. Different types of the Enterococcus faecium bacterial strain were classified according to chromosomal DNA restriction patterns produced by pulsed-field gel electrophoresis (PFGE). Samples were collected from human patients. Collected FT-IR spectra were used to verify if the same classification was obtained. In order to further optimize bacteria classification we investigated whether a selected combination of the most discriminative spectral regions could improve results. Two different variable selection methods (genetic algorithms (GAs) and bootstrapping) were investigated and their relative merit for bacteria classification is reported by comparing with results obtained using the entire spectra. Discriminant partial least-squares (Di-PLS) models based on corrected spectra showed improved predictive ability up to 40% when compared to equivalent models using the entire spectral range. The uncertainty in estimating scores was reduced by about 50% when compared to models with all wavelengths. Spectral ranges with relevant chemical information for Enterococcus faecium bacteria discrimination were outlined.