Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level

Wang, Yu; Peng, Hao; Liu, Kunxiang; Shang, Lindong; Xu, Lei; Lü, Zhen-Ming; Li, Bei

doi:10.1016/j.talanta.2022.124112

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…Outstretched scan spectra were set from 400 cm −1 to 2000 cm −1 , with an integration time of 3 s, and one accumulation. To reduce the effect of intracellular spatial heterogeneity on the detection of biomolecular abundance, the Raman signal was measured at three equally spaced points along the length of the E. coli and their average spectra were taken to represent the whole-cell Raman fingerprint spectra of E. coli [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting

Li,

2023

Molecules

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Biomolecular abundance detection of fermentation microorganisms is significant for the accurate regulation of fermentation, which is conducive to reducing fermentation costs and improving the yield of target products. However, the development of an accurate analytical method for the detection of biomolecular abundance still faces important challenges. Herein, we present a non-invasive biomolecular abundance detection method based on Raman spectra combined with target extraction and multimodel fitting. The high gain of the eXtreme Gradient Boosting (XGBoost) algorithm was used to extract the characteristic Raman peaks of metabolically active proteins and nucleic acids within E. coli and yeast. The test accuracy for different culture times and cell cycles of E. coli was 94.4% and 98.2%, respectively. Simultaneously, the Gaussian multi-peak fitting algorithm was exploited to calculate peak intensity from mixed peaks, which can improve the accuracy of biomolecular abundance calculations. The accuracy of Gaussian multi-peak fitting was above 0.9, and the results of the analysis of variance (ANOVA) measurements for the lag phase, log phase, and stationary phase of E. coli growth demonstrated highly significant levels, indicating that the intracellular biomolecular abundance detection was consistent with the classical cell growth law. These results suggest the great potential of the combination of microbial intracellular abundance, Raman spectra analysis, target extraction, and multimodel fitting as a method for microbial fermentation engineering.

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Section: Methodsmentioning

confidence: 99%

Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting

Li,

2023

Molecules

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“…The technique measures the inelastically scattered radiation (the Raman effect) obtained after the illumination of samples with a monochromatic laser source [ 29 ]. Many commercially available Raman devices operate in a microscopic mode, allowing access to molecular information at the micrometric level under confocal conditions [ 30 , 31 ]. While Raman spectroscopy has been extensively studied in research for the mapping or profiling of biological tissues and cells for disease diagnosis and other biomedical applications [ 32 , 33 , 34 , 35 , 36 , 37 , 38 ] or even subcellular analysis [ 39 , 40 , 41 ], it remains a powerful analytical technique.…”

Section: Introductionmentioning

confidence: 99%

Label-Free Quantification of Nanoencapsulated Piperonyl Esters in Cosmetic Hydrogels Using Raman Spectroscopy

et al. 2023

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Raman spectroscopy is a well-established technique for the molecular characterisation of samples and does not require extensive pre-analytical processing for complex cosmetic products. As an illustration of its potential, this study investigates the quantitative performance of Raman spectroscopy coupled with partial least squares regression (PLSR) for the analysis of Alginate nanoencapsulated Piperonyl Esters (ANC-PE) incorporated into a hydrogel. A total of 96 ANC-PE samples covering a 0.4% w/w–8.3% w/w PE concentration range have been prepared and analysed. Despite the complex formulation of the sample, the spectral features of the PE can be detected and used to quantify the concentrations. Using a leave-K-out cross-validation approach, samples were divided into a training set (n = 64) and a test set, samples that were previously unknown to the PLSR model (n = 32). The root mean square error of cross-validation (RMSECV) and prediction (RMSEP) was evaluated to be 0.142% (w/w PE) and 0.148% (w/w PE), respectively. The accuracy of the prediction model was further evaluated by the percent relative error calculated from the predicted concentration compared to the true value, yielding values of 3.58% for the training set and 3.67% for the test set. The outcome of the analysis demonstrated the analytical power of Raman to obtain label-free, non-destructive quantification of the active cosmetic ingredient, presently PE, in complex formulations, holding promise for future analytical quality control (AQC) applications in the cosmetics industry with rapid and consumable-free analysis.

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A review of innovative electrochemical strategies for bioactive molecule detection and cell imaging: Current advances and challenges

Zhang,

Jiang,

Liu

et al. 2024

Analytica Chimica Acta

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Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level

Cited by 6 publications

References 32 publications

Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting

Non-Invasive Detection of Biomolecular Abundance from Fermentative Microorganisms via Raman Spectra Combined with Target Extraction and Multimodel Fitting

Label-Free Quantification of Nanoencapsulated Piperonyl Esters in Cosmetic Hydrogels Using Raman Spectroscopy

A review of innovative electrochemical strategies for bioactive molecule detection and cell imaging: Current advances and challenges

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