2023
DOI: 10.1002/adom.202203104
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Recent Progresses in Machine Learning Assisted Raman Spectroscopy

Abstract: With the development of Raman spectroscopy and the expansion of its application domains, conventional methods for spectral data analysis have manifested many limitations. Exploring new approaches to facilitate Raman spectroscopy and analysis has become an area of intensifying focus for research. It has been demonstrated that machine learning techniques can more efficiently extract valuable information from spectral data, creating unprecedented opportunities for analytical science. This paper outlines tradition… Show more

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Cited by 61 publications
(40 citation statements)
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References 201 publications
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“…ML algorithms are able to learn and make predictions without being explicitly programmed and have some advantages over traditional chemometric analyses, like the ability to efficiently analyze large data sets and identify complex patterns. 128 A recent study demonstrated the use of a deep-learning model, a type of ML where algorithms create a network to store data and to learn, to denoise mass data sets. Researchers have demonstrated both supervised and unsupervised deep-learning algorithms, whereby a supervised approach requires high-quality data for training, and unsupervised techniques can be trained with noisier data.…”
Section: Data Analysis Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…ML algorithms are able to learn and make predictions without being explicitly programmed and have some advantages over traditional chemometric analyses, like the ability to efficiently analyze large data sets and identify complex patterns. 128 A recent study demonstrated the use of a deep-learning model, a type of ML where algorithms create a network to store data and to learn, to denoise mass data sets. Researchers have demonstrated both supervised and unsupervised deep-learning algorithms, whereby a supervised approach requires high-quality data for training, and unsupervised techniques can be trained with noisier data.…”
Section: Data Analysis Methodsmentioning
confidence: 99%
“…More recently, machine learning (ML) techniques have been adopted to aid in Raman data analysis. ML algorithms are able to learn and make predictions without being explicitly programmed and have some advantages over traditional chemometric analyses, like the ability to efficiently analyze large data sets and identify complex patterns . A recent study demonstrated the use of a deep-learning model, a type of ML where algorithms create a network to store data and to learn, to denoise mass data sets.…”
Section: Methodsmentioning
confidence: 99%
“…Anomaly detection methods based on autoencoders and reconstruction error were employed to identify abnormal Raman spectra, leading to promising classification results. 319 ML algorithms can also be used to analyze the large NMR data sets generated by TOCSY (total correlation spectroscopy) for identifying spin−spin coupling interactions and providing a detailed map of proton−proton connectivities, streamlining data analysis, and accelerating structure elucidation. This provides the complete structure of each compound in the mixture without having to separate the components physically or do a multivariate analysis.…”
Section: Continuum-scale Chemical Mixturesmentioning
confidence: 99%
“…This approach enables cost-effective spectrum prediction for large data sets and facilitates spectral feature identification. Anomaly detection methods based on autoencoders and reconstruction error were employed to identify abnormal Raman spectra, leading to promising classification results . ML algorithms can also be used to analyze the large NMR data sets generated by TOCSY (total correlation spectroscopy) for identifying spin–spin coupling interactions and providing a detailed map of proton–proton connectivities, streamlining data analysis, and accelerating structure elucidation.…”
Section: Continuum-scale Chemical Mixturesmentioning
confidence: 99%
“…Furthermore, an additional incentive for implementing RS in operando experiments is represented by the growing use of machine learning techniques to decode Raman data and contribute to expanding the existing spectral databases [56,57].…”
Section: Introductionmentioning
confidence: 99%