Laser tweezers Raman spectroscopy combined with deep learning to classify marine bacteria

Liu, Bo; Liu, Kunxiang; Wang, Nan; Ta, Kaiwen; Peng, Liang; Yin, Huabing; Li, Bei

doi:10.1016/j.talanta.2022.123383

Cited by 26 publications

(16 citation statements)

References 27 publications

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“…This technique has been successfully employed to study the effect of thalassemia on hemoglobin deformability [ 217 ]. Moreover, RT was also used to trap and chemically analyze individual tire and road wear particles in liquid environments [ 218 ], detect microplastic polymers in seawater [ 219 ] and aid in the identification and subsequent classification of marine bacteria based on their cell phenotypes [ 220 ]. Recently, RT was also used to investigate single grains of cosmic dust [ 196 ].…”

Section: Discussion and Future Perspectivesmentioning

confidence: 99%

Investigation of Soft Matter Nanomechanics by Atomic Force Microscopy and Optical Tweezers: A Comprehensive Review

Magazzù

Marcuello

2023

Nanomaterials

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Soft matter exhibits a multitude of intrinsic physico-chemical attributes. Their mechanical properties are crucial characteristics to define their performance. In this context, the rigidity of these systems under exerted load forces is covered by the field of biomechanics. Moreover, cellular transduction processes which are involved in health and disease conditions are significantly affected by exogenous biomechanical actions. In this framework, atomic force microscopy (AFM) and optical tweezers (OT) can play an important role to determine the biomechanical parameters of the investigated systems at the single-molecule level. This review aims to fully comprehend the interplay between mechanical forces and soft matter systems. In particular, we outline the capabilities of AFM and OT compared to other classical bulk techniques to determine nanomechanical parameters such as Young’s modulus. We also provide some recent examples of nanomechanical measurements performed using AFM and OT in hydrogels, biopolymers and cellular systems, among others. We expect the present manuscript will aid potential readers and stakeholders to fully understand the potential applications of AFM and OT to soft matter systems.

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Section: Discussion and Future Perspectivesmentioning

confidence: 99%

Investigation of Soft Matter Nanomechanics by Atomic Force Microscopy and Optical Tweezers: A Comprehensive Review

Magazzù

Marcuello

2023

Nanomaterials

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“…Due to the small characteristic differences among Raman spectra, computer methods such as machine learning and deep learning are often used to differentiate the spectra when analysing them. Algorithms such as the principal component analysis-linear discriminant analysis (PCA-LDA), K-nearest neighbour (KNN), partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) algorithms are widely used [20] , [26] , [27] . PCA can perform linear dimensionality reduction by orthogonal transformation [28] and reduce the difficulty of data analysis.…”

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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“…[13][14][15][16] Successful combination of ANN and Raman spectroscopy has been witnessed in recent years. [17][18][19][20][21][22] Compared with the support vector machines (SVM) algorithm, ANN shows more stable classification performance when dealing with a large number of classes and huge datasets. [22,23] Therefore, Raman spectroscopy combined with ANN is expected to provide rapid and accurate identification of microorganisms.…”

Section: Introductionmentioning

confidence: 99%

“…Training the network through a large amount of data can extract effective features from complex spectral data so that the network has the ability to accurately classify spectra [13–16] . Successful combination of ANN and Raman spectroscopy has been witnessed in recent years [17–22] . Compared with the support vector machines (SVM) algorithm, ANN shows more stable classification performance when dealing with a large number of classes and huge datasets [22,23] .…”

Section: Introductionmentioning

confidence: 99%

Optofluidic identification of single microorganisms using fiber‐optical‐tweezer‐based Raman spectroscopy with artificial neural network

Lin

et al. 2023

BMEMat

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Rapid and accurate detection of microorganisms is critical to clinical diagnosis.As Raman spectroscopy promises label-free and culture-free detection of biomedical objects, it holds the potential to rapidly identify microorganisms in a single step. To stabilize the microorganism for spectrum collection and to increase the accuracy of real-time identification, we propose an optofluidic method for single microorganism detection in microfluidics using opticaltweezing-based Raman spectroscopy with artificial neural network. A fiber optical tweezer was incorporated into a microfluidic channel to generate optical forces that trap different species of microorganisms at the tip of the tweezer and their Raman spectra were simultaneously collected. An artificial neural network was designed and employed to classify the Raman spectra of the microorganisms, and the identification accuracy reached 94.93%. This study provides a promising strategy for rapid and accurate diagnosis of microbial infection on a lab-on-a-chip platform.Chenghong Lin and Xiaofeng Li contributed equally to this work.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Laser tweezers Raman spectroscopy combined with deep learning to classify marine bacteria

Cited by 26 publications

References 27 publications

Investigation of Soft Matter Nanomechanics by Atomic Force Microscopy and Optical Tweezers: A Comprehensive Review

Investigation of Soft Matter Nanomechanics by Atomic Force Microscopy and Optical Tweezers: A Comprehensive Review

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

Optofluidic identification of single microorganisms using fiber‐optical‐tweezer‐based Raman spectroscopy with artificial neural network

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