Deep learning approach to overcome signal fluctuations in SERS for efficient On-Site trace explosives detection

Beeram, Reshma; Vendamani, V.S.; Soma, Venugopal Rao

doi:10.1016/j.saa.2022.122218

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…In addition, DL methods, such as CNNs, recurrent neural networks (RNNs), and generative adversarial networks (GANs), with their strong self-learning ability and excellent fitting ability, were gradually used in spectral analysis to obtain fast and intelligent quantitative or qualitative analysis [ 28 , 41 ]. In particular, CNNs are widely used in the modelling of spectral data by virtue of their advantages with less preprocessing and easy expansion of network architecture.…”

Section: Resultsmentioning

confidence: 99%

SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network

Qiu,

Tang,

Wang

et al. 2023

Foods

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The detection of polycyclic aromatic hydrocarbons (PAHs) on fruit and vegetable surfaces is important for protecting human health and ensuring food safety. In this study, a method for the in situ detection and identification of PAH residues on fruit and vegetable surfaces was developed using surface-enhanced Raman spectroscopy (SERS) based on a flexible substrate and lightweight deep learning network. The flexible SERS substrate was fabricated by assembling β-cyclodextrin-modified gold nanoparticles (β-CD@AuNPs) on polytetrafluoroethylene (PTFE) film coated with perfluorinated liquid (β-CD@AuNP/PTFE). The concentrations of benzo(a)pyrene (BaP), naphthalene (Nap), and pyrene (Pyr) residues on fruit and vegetable surfaces could be detected at 0.25, 0.5, and 0.25 μg/cm2, respectively, and all the relative standard deviations (RSD) were less than 10%, indicating that the β-CD@AuNP/PTFE exhibited high sensitivity and stability. The lightweight network was then used to construct a classification model for identifying various PAH residues. ShuffleNet obtained the best results with accuracies of 100%, 96.61%, and 97.63% for the training, validation, and prediction datasets, respectively. The proposed method realised the in situ detection and identification of various PAH residues on fruit and vegetables with simplicity, celerity, and sensitivity, demonstrating great potential for the rapid, nondestructive analysis of surface contaminant residues in the food-safety field.

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Section: Resultsmentioning

confidence: 99%

SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network

Qiu,

Tang,

Wang

et al. 2023

Foods

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show abstract

“…[ 369 , 370 , 371 , 372 ]; and cancer diagnosis [ 373 ], as shown in Figure 7 . In addition, machine learning was also used to improve data collection to overcome signal fluctuations and enhance the usability on site [ 374 ], to estimate the effect of scattering [ 375 ] and for the SERS signal enhancement itself [ 376 ]. In further sections, we discuss different ML techniques that were used in SERS for biology applications.…”

Section: Machine Learning In Sers-based Biosensingmentioning

confidence: 99%

“…Upon laser illumination, these hotspots are also known to diffuse or transform, thus adding further to the poor reproducibility. A substrate with homogeneous field enhancement promises good reproducibility but comes at the cost of enhancement and eventually limiting trace detection [ 374 ]. Secondly, SERS substrates majorly comprise Au or Ag nanoparticles/nanostructures.…”

Section: Conclusion and Scopementioning

confidence: 99%

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

2023

Self Cite

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Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS’s full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.

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“…However, the Raman and SERS spectra of MXenes often exhibit complex patterns, making precise determination of the flakes’ surface chemistry challenging. Nevertheless, the application of machine learning algorithms allows for the evaluation of such complex SERS spectra. − It has been demonstrated that these algorithms can extract accurate and quantitative information even from highly intricate and overlapping spectral patterns. − …”

Section: Introductionmentioning

confidence: 99%

“… 25 − 27 It has been demonstrated that these algorithms can extract accurate and quantitative information even from highly intricate and overlapping spectral patterns. 28 − 30 …”

Section: Introductionmentioning

confidence: 99%

Surface-Enhanced Raman Spectroscopy and Artificial Neural Networks for Detection of MXene Flakes’ Surface Terminations

Trelin,

Skvortsova,

Olshtrem

et al. 2024

J. Phys. Chem. C

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The properties of MXene flakes, a new class of twodimensional materials, are strictly determined by their surface termination. The most common termination groups are oxygencontaining (�O or −OH) and fluorine (−F), and their relative ratio is closely related to flake stability and catalytic activity. The surface termination can vary significantly among MXene flakes depending on the preparation route and is commonly determined after flake preparation by using X-ray photoelectron spectroscopy (XPS). In this paper, as an alternative approach, we propose the combination of surface-enhanced Raman spectroscopy (SERS) and artificial neural networks (ANN) for the precise and reliable determination of MXene flakes' (Ti 3 C 2 T x ) surface chemistry. Ti 3 C 2 T x flakes were independently prepared by three scientific groups and subsequently measured using three different Raman spectrometers, employing resonant excitation wavelengths. Manual analysis of the SERS spectra did not enable accurate determination of the flake surface termination. However, the combined SERS-ANN approach allowed us to determine the surface termination with a high accuracy. The reliability of the method was verified by using a series of independently prepared samples. We also paid special attention to how the results of the SERS-ANN method are affected by the flake stability and differences in the conditions of flake preparation and Raman measurements. This way, we have developed a universal technique that is independent of the above-mentioned parameters, providing the results with accuracy similar to XPS, but enhanced in terms of analysis time and simplicity.

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Deep learning approach to overcome signal fluctuations in SERS for efficient On-Site trace explosives detection

Cited by 8 publications

References 42 publications

SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network

SERS with Flexible β-CD@AuNP/PTFE Substrates for In Situ Detection and Identification of PAH Residues on Fruit and Vegetable Surfaces Combined with Lightweight Network

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-Enhanced Raman Spectroscopy and Artificial Neural Networks for Detection of MXene Flakes’ Surface Terminations

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