Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach

Erzina, Mariia; Trelin, Andrii; Guselnikova, Olga; Skvortsova, A.; Strnadov, K.; Švorčı́k, Václav; Lyutakov, Oleksiy

doi:10.1016/j.snb.2022.132057

Cited by 21 publications

(6 citation statements)

References 42 publications

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“…52 Their work sheds light on the intricacies of interactions between distinct sugars and proteins, a paradigm integral to understanding carbohydrate-mediated cellular recognition and signaling cascades. In a distinct vein, Erzina and associates wielded SERS to interrogate glycoproteins within human plasma, bestowing insights pivotal for gauging the roles of glycoproteins in health and disease trajectories 53 (see Fig. 6B).…”

Section: Probing Carbohydrate Structures Via Sersmentioning

confidence: 99%

Deciphering biomolecular complexities: the indispensable role of surface-enhanced Raman spectroscopy in modern bioanalytical research

Xia,

Huang,

Wang

et al. 2024

Analyst

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Section: Probing Carbohydrate Structures Via Sersmentioning

confidence: 99%

Deciphering biomolecular complexities: the indispensable role of surface-enhanced Raman spectroscopy in modern bioanalytical research

Xia,

Huang,

Wang

et al. 2024

Analyst

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“…ResNet, [ 115] ANN, [ 116] CNN, [117][118][119][120][121] PCA [ 122] Quantify the abundance of certain molecules RF, [44] PCA+LR, [123] ANN, [ 47,[123][124][125][126] CNN, [127][128][129][130] PCA+SVM, [ 131] PLS, [124,125] SVR, [124] PLS+GA, [132] SVM [ 133] Discover the multiplexed variation in the whole profile PCA, [ 134] Autoencoder, [ 135] CNN, [ 136,137] PCA+LDA [138] Early disease diagnosis ResNet, [ 115] RF, [139] KNN, [ 139] naïve Bayes, [ 139] PLS+SVM [140] SERS spectrum with microRNAs Early disease diagnosis RF, [ 141] LR, [141] naïve Bayes [ 141] Covariance matrices of SERS spectrum…”

Section: Molecular Graphmentioning

confidence: 99%

“…As for the ML model is usually trained upon the dataset collected from one specific detection system, which can be extended by transfer learning to other systems without additional large dataset. As the result, the built model can be applied to a wider range of molecules [128] under different background conditions, [129,133] gearing toward broad applications (Figure 6b). Unfortunately, in the complex mixtures, the detectability and the discriminability of the target molecules may be varied (unpredictable increase or reduction) by other coexisting molecules due to competitive adsorption [44] and spectral feature overlapping, [47,283] causing unreliable quantification.…”

Section: Ai For Sers-based Applicationsmentioning

confidence: 99%

“…When encountering insufficient training data, transfer learning is a desirable strategy, [301] which has exhibited its effectiveness in SERS domain. [128,129,136,302] For example, Tseng et al reused the Gram-positive species classifier (pretrained with ≈4k data) to perform antibiotic-resistant strain identification with 200 data only, which achieved a high accuracy of 98.5%. [302] The main idea is to transfer knowledge from task A to task B.…”

Section: Ai For Sers-based Applicationsmentioning

confidence: 99%

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Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Bi,

Lin,

Chen

et al. 2023

Small Methods

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Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever‐sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high‐level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above‐mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

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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%

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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Quantitative detection of α1-acid glycoprotein (AGP) level in blood plasma using SERS and CNN transfer learning approach

Cited by 21 publications

References 42 publications

Deciphering biomolecular complexities: the indispensable role of surface-enhanced Raman spectroscopy in modern bioanalytical research

Deciphering biomolecular complexities: the indispensable role of surface-enhanced Raman spectroscopy in modern bioanalytical research

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

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

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