Rapid detection of beer spoilage bacteria based on label-free SERS technology

Shang, Lindong; Xu, Lei; Wang, Yu; Liu, Kunxiang; Peng, Liang; Zhou, Shuangjun; Chen, Fuyuan; Peng, Hao; Zhou, Chunyang; Lü, Zhen-Ming; Li, Bei

doi:10.1039/d2ay01221a

Cited by 9 publications

(5 citation statements)

References 32 publications

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“…AgNPs were synthesized using a modified method of Leopold and LendI . A solution of silver nitrate was prepared by diluting 1 mL (0.1 mM) of silver nitrate solution with 9 mL of deionized water to make 10 mL (0.01 mM) of silver nitrate solution.…”

Section: Methodsmentioning

confidence: 99%

“…The AgNPs produced by this method can be stored for about 1 month. The Raman spectra of the AgNPs are relatively clean, with no obvious Raman peak positions and only a bulge resembling a fluorescent background, which does not affect the subsequent results 26 (Figures S1 and S2).…”

Section: Agnp Colloid Productionmentioning

confidence: 98%

“…AgNPs were synthesized using a modified method of Leopold and LendI. 26 A solution of silver nitrate was prepared by diluting 1 mL (0.1 mM) of silver nitrate solution with 9 mL of deionized water to make 10 mL (0.01 mM) of silver nitrate solution. Then, 11.6 mg (0.17 mM) of hydroxylamine hydrochloride, 3.3 mL (0.1 mM) of sodium hydroxide solution, and 0.3 mL of KCl solution (0.03 mM) were added to 100 mL of deionized water and mixed well.…”

Section: Agnp Colloid Productionmentioning

confidence: 99%

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Stable SERS Detection of Lactobacillus fermentum Using Optical Tweezers in a Microfluidic Environment

Shang,

Liang,

et al. 2023

Anal. Chem.

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Rapid identification of fermented lactic acid bacteria has long been a challenge in the brewing industry. This study combined label-free surface-enhanced Raman scattering (SERS) and optical tweezer technology to construct a test platform within a microfluidic environment. Six kinds of lactic acid bacteria common in industry were tested to prove the stability of the SERS spectra. The results demonstrated that the utilization of optical tweezers to securely hold the bacteria significantly enhanced the stability of the SERS spectra. Furthermore, SVM and XGBoost machine learning algorithms were utilized to analyze the obtained Raman spectra for identification, and the identification accuracies exceeded 95% for all tested lactic acid bacteria. The findings of this study highlight the crucial role of optical tweezers in improving the stability of SERS spectra by capturing bacteria in a microfluidic environment, prove that this technology could be used in the rapid identification of lactic acid bacteria, and show great significance in expanding the applicability of the SERS technique for other bacterial testing purposes.

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Section: Methodsmentioning

confidence: 99%

Section: Agnp Colloid Productionmentioning

confidence: 98%

Section: Agnp Colloid Productionmentioning

confidence: 99%

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Stable SERS Detection of Lactobacillus fermentum Using Optical Tweezers in a Microfluidic Environment

Shang,

Liang,

et al. 2023

Anal. Chem.

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“…The Raman scattering that occurs between matter and photons can be reflected in the received Raman spectrum, and thus, the molecular movement of matter can be visualized. In recent years, Raman spectroscopy has been widely used in the fields of microbial identification, species identification, food authentication, microplastic identification, pharmaceutical analysis and tumour diagnosis [11] , [12] , [13] , [14] , [15] , [16] .…”

Section: Introductionmentioning

confidence: 99%

Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy

Liu¹,

Liu²,

Zhang³

et al. 2023

Computational and Structural Biotechnology Journal

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“…The currently prevalent methods for identifying cell lines, such as short tandem repeat (STR) analysis, often require complex operations such as staining or culturing of cells [4]. Given the successful applications of Raman spectroscopy in various fields, such as microbial identification, species identification, food identification, microplastic identification, drug analysis, and tumor diagnosis [5][6][7][8][9][10][11][12], we utilized Raman spectroscopy and a self-built SL-Raman model to accurately distinguish between normal and gastric cancer cell lines in our previous study [13].…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based ultra‐fast identification of Raman spectra with low signal‐to‐noise ratio

Liu,

Chen,

Shang

et al. 2023

Journal of Biophotonics

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Ensuring the correct use of cell lines is crucial to obtaining reliable experimental results and avoiding unnecessary waste of resources. Raman spectroscopy has been confirmed to be able to identify cell lines, but the collection time is usually 10‐30s. In this study, we acquired Raman spectra of five cell lines with integration times of 0.1s and 8s, respectively, and the average accuracy of using long‐short memory neural network to identify the spectra of 0.1s was 95%, and the average accuracy of identifying the spectra of 8s was 99.8%. At the same time, we performed data enhancement of 0.1s spectral data by real‐valued non‐volume preserving method, and the recognition average accuracy of long‐short memory neural networks recognition of the enhanced spectral data was improved to 96.2%. With this method, we shorten the acquisition time of Raman spectra to 1/80 of the original one, which greatly improves the efficiency of cell identification.This article is protected by copyright. All rights reserved.

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Rapid detection of beer spoilage bacteria based on label-free SERS technology

Abstract: Beer spoilage bacteria have been a headache for major breweries. In order to rapidly identify spoilage bacteria and improve the sensitivity and signal-to-noise ratio of bacterial SERS detection. Using the...

Cited by 9 publications

References 32 publications

Stable SERS Detection of Lactobacillus fermentum Using Optical Tweezers in a Microfluidic Environment

Stable SERS Detection of Lactobacillus fermentum Using Optical Tweezers in a Microfluidic Environment

Building an ensemble learning model for gastric cancer cell line classification via rapid raman spectroscopy

Deep learning‐based ultra‐fast identification of Raman spectra with low signal‐to‐noise ratio

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