Deep learning-based component identification for the Raman spectra of mixtures

Fan, Xiaqiong; Wen, Ming; Zeng, Huitao; Zhang, Zhimin; Lü, Hongmei

doi:10.1039/c8an02212g

Cited by 157 publications

(101 citation statements)

References 43 publications

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“…In contrast to an artificial neural network, the convolution layer in CNN allows the model to extract small details and to be trained on the extracted details of the input data, which improves its prediction accuracy . Since then, CNNs have been widely used for image recognition or image classification, and there were several trials to use artificial intelligence algorithm to study Raman spectral data as well as different types of spectroscopic data . In this study, we suggest a platform for Raman signature classification of EVs based on CNN.…”

Section: Introductionmentioning

confidence: 99%

Classifying Raman spectra of extracellular vesicles based on convolutional neural networks for prostate cancer detection

Lee

Lenferink

Otto

et al. 2019

J Raman Spectroscopy

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Since early 2000s, machine learning algorithms have been widely used in many research and industrial fields, most prominently in computer vison. Lately, many fields of study have tried to use these automated methods, and there are several reports from the field of spectroscopy. In this study, we demonstrate a classification model based on machine learning to classify Raman spectra. We obtained Raman spectra from extracellular vesicles (EVs) to find tumor derived EVs. The convolutional neural network (CNN) was trained on preprocessed Raman data and raw Raman data. We compare the result from CNN with results from principal component analysis that is widely used among in spectroscopy. The new model classifies EVs with an accuracy of >90%. Moreover, the new model based on CNN is also suitable for classifying the raw Raman data directly without preprocessing with a minimum accuracy of 93%.

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Section: Introductionmentioning

confidence: 99%

Classifying Raman spectra of extracellular vesicles based on convolutional neural networks for prostate cancer detection

Lee

Lenferink

Otto

et al. 2019

J Raman Spectroscopy

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“…Thereby, N one‐component identification models were trained with data composed of spectra of a pure component, negative and positive samples in terms of this pure component. The N models could successfully solve the un‐mixing task at the end .…”

Section: Deep Learning For Vibrational Spectroscopymentioning

confidence: 99%

Deep learning a boon for biophotonics?

Pradhan

Guo

Ryabchykov

et al. 2020

Journal of Biophotonics

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This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.

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“…Typically, retention time, accurate mass and mass spectra acquired from various analytical platforms are searched against reference databases [57] , [58] , [59] such as HMDB [60] , METLIN [61] and MassBank [62] to name a few. Similarities between unknow and reference compounds’ data are typically estimated based on correlation [63] , weighted cosine similarity [64] and Euclidean distance [65] which are used to rank the matching candidate hits [66] . This approach is limited by availability of known compounds and their spectral coverage in the reference databases [67] .…”

Section: In Ms Spectra Processing and Interpretationmentioning

confidence: 99%