Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy

Weng, Shizhuang; Yuan, Hecai; Zhang, Xueyan; Pan, Li; Zheng, Ling; Zhao, Jing; Huang, Liusheng

doi:10.1039/d0an00492h

Cited by 76 publications

(37 citation statements)

References 32 publications

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“… 170 Recently, ML methods have been trained to recognize features in Raman (or SERS) spectra for the identity of an analyte by applying DL networks, including ANN, CNN, and fully convolutional network for feature engineering. 171 For example, Leong et al. designed a machine-learning-driven “SERS taster” to simultaneously harness useful vibrational information from multiple receptors for enhanced multiplex profiling of five wine flavor molecules at ppm levels.…”

Section: Ai In Chemistrymentioning

confidence: 99%

Artificial intelligence: A powerful paradigm for scientific research

et al. 2021

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Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

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Section: Ai In Chemistrymentioning

confidence: 99%

Artificial intelligence: A powerful paradigm for scientific research

et al. 2021

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“…Weng, et al [45] shows that deep learning networks perform better than the common machine learning methods (including kNN, SVM, RF, LR, and PLS) and provide feasible alternatives for the recognition and quantitation of SERS. In their s t udy, deep learning networks were used as fully connected networks, convolutional neural networks (CNN), fully convolutional networks (FCN), and principal component analysis networks (PCANet) to determine their abilities to recognize drugs in human urine and measure pirimiphos-methyl in wheat extract in the two input forms of a one-dimensional vector or a two-dimensional matrix.…”

Section: Recognition and Quantitation Of Drugs In Human Urine By Pcanetmentioning

confidence: 99%

Deep Learning Approach for Raman Spectroscopy

Jinadasa¹,

Kahawalage²,

Halstensen³

et al. 2022

Recent Developments in Atomic Force Microscopy and Raman Spectroscopy for Materials Characterization

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Raman spectroscopy is a widely used technique for organic and inorganic chemical material identification. Throughout the last century, major improvements in lasers, spectrometers, detectors, and holographic optical components have uplifted Raman spectroscopy as an effective device for a variety of different applications including fundamental chemical and material research, medical diagnostics, bio-science, in-situ process monitoring and planetary investigations. Undoubtedly, mathematical data analysis has been playing a vital role to speed up the migration of Raman spectroscopy to explore different applications. It supports researchers to customize spectral interpretation and overcome the limitations of the physical components in the Raman instrument. However, large, and complex datasets, interferences from instrumentation noise and sample properties which mask the true features of samples still make Raman spectroscopy as a challenging tool. Deep learning is a powerful machine learning strategy to build exploratory and predictive models from large raw datasets and has gained more attention in chemical research over recent years. This chapter demonstrates the application of deep learning techniques for Raman signal-extraction, feature-learning and modelling complex relationships as a support to researchers to overcome the challenges in Raman based chemical analysis.

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“…Their reported accuracy of the device was above

. Weng et al [ 19 ] proposed some deep learning (DL) models for drug recognition in urine using fully connected neural network (FCNN), and convolution neural network (CNN). They have compared the accuracy of their model with conventional ML models such as random forest (RF), K-nearest neighbor (KNN)-based classifier, and SVM.…”

Section: Introductionmentioning

confidence: 99%

“…Several applications of machine learning have been reported in the fields of the SERS signal acquisition and data analysis [ 18 , 19 ]. However, there was not enough discussion about the performance of the machine learning models according to the SERS preprocessing methods and the reproducibility according to the batch-effect .…”

Section: Introductionmentioning

confidence: 99%

SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network

et al. 2021

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Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are lacking. Towards this end, we provide the SERS spectral benchmark dataset of Rhodamine 6G (R6G) for a molecule detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. Our best model, coined as the SERSNet, robustly identifies R6G molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task.

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Deep learning networks for the recognition and quantitation of surface-enhanced Raman spectroscopy

Abstract: Surface-enhanced Raman spectroscopy (SERS) based on machine learning methods has been applied in material analysis, biological detection, food safety, and intelligent analysis.

Cited by 76 publications

References 32 publications

Artificial intelligence: A powerful paradigm for scientific research

Artificial intelligence: A powerful paradigm for scientific research

Deep Learning Approach for Raman Spectroscopy

SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network

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