Chemotaxonomic Identification of Single Bacteria by Micro-Raman Spectroscopy: Application to Clean-Room-Relevant Biological Contaminations

Rösch, Petra; Harz, Michaela; Schmitt, Michael; Peschke, Klaus; Ronneberger, Olaf; Burkhardt, Hans; Motzkus, Hans‐Walter; Lankers, M.; Höfer, Stefan; Thiele, Hans; Popp, Jürgen

doi:10.1128/aem.71.3.1626-1637.2005

Cited by 260 publications

(202 citation statements)

References 58 publications

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“…Further investigations revealed that the single‐cell spectra could be used to differentiate between growth phases of a single species, but these differences did not obscure the overall interspecies discrimination 29. On the other hand, a previous study30 found that the Raman spectra of cells in stationary phase were characterized by sharp, distinct signals whereas the spectra of cells from other phases, e.g., early logarithmic phase, exhibited low signal‐to‐noise (S/N) ratios with broad bands. Moreover, most plant pathogens always lie in a stationary state in natural environments 28.…”

Section: Resultsmentioning

confidence: 90%

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Culture‐Free Detection of Crop Pathogens at the Single‐Cell Level by Micro‐Raman Spectroscopy

Gan

Wang

et al. 2017

Advanced Science

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The rapid and sensitive identification of invasive plant pathogens has important applications in biotechnology, plant quarantine, and food security. Current methods are far too time‐consuming and need a pre‐enrichment period ranging from hours to days. Here, a micro‐Raman spectroscopy‐based bioassay for culture‐free pathogen quarantine inspection at the single cell level within 40 min is presented. The application of this approach can readily and specifically detect plant pathogens Burkholderia gladioli pv. alliicola and Erwinia chrysanthemi that are closely related pathogenically. Furthermore, the single‐bacterium detection was able to discriminate them from a reference Raman spectral library including multiple quarantine‐relevant pathogens with broad host ranges and an array of pathogenic variants. To show the usefulness of this assay, Burkholderia gladioli pv. alliicola and Erwinia chrysanthemi are detected at single‐bacterium level in plant tissue lesions without pre‐enrichment. The results are confirmed by the plate‐counting method and a genetic molecular approach, which display comparable recognition ratios to the Raman spectroscopy‐based bioassay. The results represent a critical step toward the use of micro‐Raman spectroscopy in rapid and culture‐free discrimination of quarantine relevant plant pathogens.

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Section: Resultsmentioning

confidence: 90%

“…Spectra with an extremely poor S/N ratio or unusually large background fluorescence were discarded. The remaining spectra were subjected to quality control with the recently developed signal processing approach (rDisc)30 followed by PCA. The data processing was carried out with Matlab R2010a (The MathWorks, Inc., Natick, MA, USA).…”

Section: Methodsmentioning

confidence: 99%

Culture‐Free Detection of Crop Pathogens at the Single‐Cell Level by Micro‐Raman Spectroscopy

Gan

Wang

et al. 2017

Advanced Science

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“…Among these, vibrational techniques [3,4,5] such as Infrared [3] and Raman spectroscopy [6] showed to be especially suitable for many applications due to their non-destructive and fast methodology [7,8]. Moreover, it has been shown that very reliable recognition results can be obtained without timeconsuming cultivation steps, since it is possible to extract informative spectra from single cells [9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…Due to these advantages, Raman spectroscopy is a widespread tool for a variety of tasks such as food control [8], cancer detection [13,14,15] and the identification of microorganisms [9,11]. Equipped with powerful multivariate data analysis methods, accurate recognition rates can be achieved [11,12,16].…”

Section: Introductionmentioning

confidence: 99%

Automatic identification of novel bacteria using Raman spectroscopy and Gaussian processes

Kemmler

Rodner

Rösch

et al. 2013

Analytica Chimica Acta

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Raman spectroscopy is successfully used for the reliable classification of complex biological samples. Much effort concentrates on the accurate prediction of known categories for highly relevant tasks in a wide area of applications such as cancer detection and bacteria recognition. However, the resulting recognition systems cannot always be directly used in practice since unseen samples might not belong to classes present in the training set. Our work aims to tackle this problem of novelty detection using a recently proposed approach based on Gaussian processes. By learning novelty scores for a large bacteria Raman dataset comprising 50 different strains, we analyze the behavior of this method on an independent dataset which includes known as well as unknown categories. Our experiment reveals that Gaussian processes can be successfully applied to the task of finding unknown bacterial strains, leading to superior results compared to state-of-the-art methods such as Gaussian mixture models and Support Vector Data Description.

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“…The high level of spatial resolution achievable with modern CRM technology allowed, for example, the Raman spectroscopic characterization of microorganisms on a single cell basis [104] and enabled to establish a rapid identification technique for clinically and technically relevant pathogens [105]. Other applications of CRM of single microbial cells include the characterization of the phenotypic heterogeneity in genetically homogeneous microbial cultures [106].…”

Section: Correction Of Chromatic Aberration Artifacts In Confocal Rammentioning

confidence: 99%

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

Lasch

2012

Chemometrics and Intelligent Laboratory Systems

285

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IntroductionWith the development of modern analytical technologies such as infrared (IR) and Raman spectroscopy the capabilities of both generating and collecting data has been tremendously increased. Time-resolved vibrational spectroscopy, microspectroscopy, and vibrational hyperspectral imaging for example are now routinely employed in many areas of industry, technology development and scientific research. The advancements in IR and Raman instrumentation has led to an explosive growth in stored or transient data and has generated an urgent need for new and automated methods of spectral data analysis. Spectral data pre-processing is an important first step in the workflow of IR and Raman spectra analysis which involves specific processing procedures performed on the raw data. Pre-processing has been shown to be of crucial importance for subsequent data mining tasks. In fact, it is now widely recognized that quantitative and classification models developed on the basis of pre-processed data generally perform better than models that solely use raw data [1][2][3]. With this review it is intended to explore the concepts and techniques of pre-processing methods and to discuss the applicability of distinct pre-processing techniques in the field of biomedical IR and Raman spectroscopy. The main goals of data pre-processing can be summarized as follows: (i) Improvement of the robustness and accuracy of subsequent quantitative or classification analyses (ii) Improved interpretability: raw data are transformed into a format that will be better understandable by both humans and machines (iii) Detection and removal of outliers and trends (iv) Reduction of the dimensionality of the data mining task. Removal of irrelevant and redundant information by feature selection. For systematic reasons it is useful to subdivide data pre-processing procedures into at least eight different categories. These groups can be used to perform the following operations: 1. Exclusion (cleaning) -this class of data pre-processing methods is used to detect and eliminate spectral outliers. Tests for spectral quality are important examples and aim at the detection of outlier spectra prior to further data mining tasks. Removal of outlier spectra can be achieved by labeling them as NaNs (Not a Number), for example as "bad" pixel spectra in hyperspectral imaging applications. Another way of outlier removal in hyperspectral imaging applications is the interpolation of bad pixel spectra by using spectral data from neighboring locations. 2. Normalization -normalization is used to scale the spectra within a similar range. In vibrational spectroscopy this is useful to compensate for the differences in sample quantity or a different optical pathlength. In data mining tasks involving spectral distance measurements normalization has been shown to improve the accuracy and efficiency of the models [1][2][3]. 3. Filtering -in frequency analyses filtering is known as a group of methods that removes unwanted frequencies from the signals to be analyzed. Examples for...

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Chemotaxonomic Identification of Single Bacteria by Micro-Raman Spectroscopy: Application to Clean-Room-Relevant Biological Contaminations

Cited by 260 publications

References 58 publications

Culture‐Free Detection of Crop Pathogens at the Single‐Cell Level by Micro‐Raman Spectroscopy

Culture‐Free Detection of Crop Pathogens at the Single‐Cell Level by Micro‐Raman Spectroscopy

Automatic identification of novel bacteria using Raman spectroscopy and Gaussian processes

Spectral pre-processing for biomedical vibrational spectroscopy and microspectroscopic imaging

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