A novel strategy for rapid detection of bacteria in water by the combination of three-dimensional surface-enhanced Raman scattering (3D SERS) and laser induced breakdown spectroscopy (LIBS)

Liao, Wenlong; Lin, Qingyu; Xie, Shichen; He, Yi; Tian, Yonghui; Duan, Yixiang

doi:10.1016/j.aca.2018.06.058

Cited by 49 publications

(26 citation statements)

References 43 publications

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“…The linear dependence of the total LIBS emission intensity with concentration was confirmed in 2018 by Liao et al who demonstrated this for both E. coli and S. aureus concentrations spanning four decades, from 10 4 CFU/mL up to 10 8 CFU/ml with R 2 values greater than 0.97 [51]. Malenfant et al also reported a linear dependence of the total LIBS emission intensity on the cell concentration when a large number of cells were deposited on a nitrocellulose filter testing medium [70].…”

Section: Concentration/titermentioning

confidence: 74%

“…The use of more supervised techniques (such as LDA) upon data that has first been preprocessed by an unsupervised PCA to reduce the dimensionality of the data is now an extremely common technique, as is the use of the dendrogram output of an HCA for data classification. For example, just recently Liao et al have utilized a PCA followed by an HCA to discriminate 3D surfaceenhanced Raman spectroscopy (SERS) spectra (taken in conjunction with LIBS data) of E. coli, Staphylococcus aureus (S. aureus), and Salmonella typherium (S. typherium) [51]. An example of such a dendrogram resulting from a hierarchal cluster analysis is shown in…”

Section: Pcamentioning

confidence: 99%

“…Liao et al introduced silver nanoparticles into a suspension of S. aureus to take advantage of the wellknown surface-enhancement afforded by the nanoparticles [51]. The authors utilized a custom-built LIBS/Raman apparatus utilizing 1064 nm ns LIBS pulses and a 532 nm cw laser for Raman excitation.…”

Section: Libs Combined With Alternate Optical Modalities For Bacteriomentioning

confidence: 99%

“…A typical dendrogram plot resulting from a hierarchal cluster analysis of four bacterial strains. Reprinted from reference[51] with permission from Elsevier.…”

mentioning

confidence: 99%

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A review of the use of laser-induced breakdown spectroscopy for bacterial classification, quantification, and identification

Rehse

2019

Spectrochimica Acta Part B: Atomic Spectroscopy

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The use of laser-induced breakdown spectroscopy to determine the elemental composition of bacterial cells has been described in the peer-reviewed literature since 2003. Fifteen years on, significant accomplishments have been reported that have served to clarify and underscore the areas of bacteriological investigation that LIBS is well-suited for as well as the challenges that yet remain to be faced. This review will attempt to summarize the state of the field by surveying the available body of knowledge. The early days of these experiments, roughly from 2003 to 2007, in which many of the most fundamental experiments were initially conducted will be described. The more in-depth investigations that followed in the subsequent decade will then be detailed. Many important aspects of performing LIBS on bacterial cells were reported on and are summarized here including: the use of chemometric algorithms for statistical classification of unknown spectra; the influence of the mounting substrate on classification; the effect of the testing gas atmosphere and the choice of bacterial cell growth nutrient medium on the measured LIBS spectrum; the efficacy of a LIBS-based test as a genus-level or strainlevel discrimination test; the ability of LIBS to determine the cell titer or concentration of cells in the initial sample; the effects that possible contaminations or interferents within the sample would have on the LIBS spectrum; the influence that environmental stresses the cells may be exposed to during growth and the state of reproductive health of the cells could have on the LIBS spectrum; the use of standoff or remote apparatus to minimize the risk to the operators during bacteriological identification of unknown specimens; and the combination of other optical modalities with LIBS to enhance the sensitivity or specificity of identification. Lastly, tables are provided which summarize both every species of bacteria ever tested with LIBS as well as the major lessons learned by the community through 15 years of careful investigation. laser-induced breakdown spectroscopy; pathogens; bacteria

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Section: Concentration/titermentioning

confidence: 74%

Section: Pcamentioning

confidence: 99%

Section: Libs Combined With Alternate Optical Modalities For Bacteriomentioning

confidence: 99%

“…A typical dendrogram plot resulting from a hierarchal cluster analysis of four bacterial strains. Reprinted from reference[51] with permission from Elsevier.…”

mentioning

confidence: 99%

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A review of the use of laser-induced breakdown spectroscopy for bacterial classification, quantification, and identification

Rehse

2019

Spectrochimica Acta Part B: Atomic Spectroscopy

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“…Recently also some works focused on how to merge LIBS spectra and Raman spectra in classification. [ 30–37 ] Even though these merging methods of two kinds of spectra have been used in analyzing renal calculi, [ 30 ] geologically mixed samples, [ 31 ] plastics, [ 32 ] Mars mineralogy, [ 33,34 ] fluorine, [ 35 ] and bacteria, [ 36,37 ] parts of these works focus on using elemental or molecular information reflected by LIBS and Raman spectra separately, not the comprehensive processing of these information. Even though some methods preformed comprehensive data processing using chemometrics and machine learning methods, these works only merged the spectral information in data fusion level.…”

Section: Introductionmentioning

confidence: 99%

Feature‐level fusion of laser‐induced breakdown spectroscopy and Raman spectroscopy for improving support vector machine in clinical bacteria identification

Teng

Wang

Cui

et al. 2021

J Raman Spectroscopy

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In clinical field, the diagnosis of many diseases and their development stages depend on the detection of the corresponding bacteria. Raman spectroscopy and laser‐induced breakdown spectroscopy (LIBS) are two novel spectral diagnostic technologies for clinical bacteria identification. Both of them have been used in clinical detection combined with optimized support vector machine (SVM). In this paper, two feature‐level fusion methods (before feature selection fusion [BFSF] and after feature selection fusion [AFSF]) were proposed to improve the performance of SVM classifier and reduce the analyzing time (including the parameter tuning, model training, and testing time) simultaneously by combining data of LIBS and Raman spectroscopy. Using the most important 10 feature lines as the inputs of the optimized classifier, the analyzing time could be reduced to 1 to 2 min and the correct classification rate (CCR) achieved 95.67%. Without optimizing SVM parameters, the two proposed methods could achieve rapid and accurate classification of pathogenic bacteria further. The AFSF method showed better results with less fusion features while the BFSF method achieved higher CCR at 100% with more features. Both two methods costed around 0.2 s for analysis. These indicate that the proposed feature‐level fusion methods can improve the performance of SVM for bacteria detection. Maintaining the highest diagnostic accuracy, they can achieve the minimum analyzing time.

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Chemometric Processing of LIBS Data

Haddad¹,

Harhira²,

Képeš³

et al. 2023

Laser Induced Breakdown Spectroscopy (LIBS)

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A novel strategy for rapid detection of bacteria in water by the combination of three-dimensional surface-enhanced Raman scattering (3D SERS) and laser induced breakdown spectroscopy (LIBS)

Cited by 49 publications

References 43 publications

A review of the use of laser-induced breakdown spectroscopy for bacterial classification, quantification, and identification

A review of the use of laser-induced breakdown spectroscopy for bacterial classification, quantification, and identification

Feature‐level fusion of laser‐induced breakdown spectroscopy and Raman spectroscopy for improving support vector machine in clinical bacteria identification

Chemometric Processing of LIBS Data

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