Rapid, sensitive detection of ganciclovir, penciclovir and valacyclovir-hydrochloride by artificial neural network and partial least squares combined with surface enhanced Raman spectroscopy

Li, Dongguo; Zhang, Qinyan; Deng, Banglin; Chen, Yu; Ye, Liming

doi:10.1016/j.apsusc.2020.148224

Cited by 14 publications

(6 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It works by finding a hyperplane that distinguishes two or more classes using a kernel function [ 362 ]. If the data set is small and the number of variables is large, PLS is useful for its ability to still extract useful information and is often used for quantitative studies [ 363 , 364 ]. DTs are widely used for classification of the data using a method bootstrapping [ 365 ].…”

Section: Machine Learning In Sers-based Biosensingmentioning

confidence: 99%

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

2023

View full text Add to dashboard Cite

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.

show abstract

Section: Machine Learning In Sers-based Biosensingmentioning

confidence: 99%

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

2023

View full text Add to dashboard Cite

show abstract

“…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%

“…[282] To quantify Sudan-1 (a carcinogenic food additive), Cheung et al used multiple regression models including PLS, 𝜖-support vector and ANNs, which were trained on the SERS spectra of different concentrations of Sudan-1 124 . Similar approaches were adopted to predict the concentration of drug molecules (e.g., ganciclovir, penciclovir, valacyclovir-hydrochloride, [47] acyclovir [125] ), environmental pollutants (e.g., PAHs [132] ), blood metabolites (e.g., glucose, [130] caffeine, theobromine, paraxanthine [126] ) among the others. All the above have showed improved limit of detection than the conventional manual calculations.…”

Section: Ai For Sers-based Applicationsmentioning

confidence: 99%

“…[45,46] Differently, Artificial Neural Networks (ANNs) mimic the human brain with an input layer, a hidden layer, and an output layer. [47] Neurons are connected between layers and send their data to the next layer once activated. ANNs with more than one hidden layer are termed Deep Neural Networks (DNNs), where Multilayer Perceptron (MLP) is a special case of DNN stacking fully connected layers and activation layers to learn nonlinear mappings.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Bi,

Lin,

Chen

et al. 2023

Small Methods

View full text Add to dashboard Cite

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.

show abstract

“…7 The advent of machine learning has provided a new scope for identication and quantication in SERS. Techniques such as partial least squares regression (PLSR), [8][9][10] articial neural networks (ANNs), convolutional neural networks (CNNs), 8,9,[11][12][13] and support vector regression (SVR) have been widely used. [14][15][16] Table 1 presents the summary of recent studies performed for quantication in SERS using machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for rapid quantification of trace analyte molecules using SERS and flexible plasmonic paper substrates

et al. 2022

View full text Add to dashboard Cite

show abstract

Rapid, sensitive detection of ganciclovir, penciclovir and valacyclovir-hydrochloride by artificial neural network and partial least squares combined with surface enhanced Raman spectroscopy

Cited by 14 publications

References 40 publications

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

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

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Machine learning for rapid quantification of trace analyte molecules using SERS and flexible plasmonic paper substrates

Contact Info

Product

Resources

About