tetano
Editor, Senior Moderator
J Appl Spectrosc
. 2023 Jan 26;1-9.
doi: 10.1007/s10812-023-01487-w. Online ahead of print.
Classification of Coronavirus Spike Proteins by Deep-Learning-Based Raman Spectroscopy and its Interpretative Analysis
Wenbo Mo[SUP] 1 2 [/SUP], Jiaxing Wen[SUP] 1 2 [/SUP], Jinglin Huang[SUP] 1 [/SUP], Yue Yang[SUP] 1 [/SUP], Minjie Zhou[SUP] 1 [/SUP], Shuang Ni[SUP] 1 [/SUP], Wei Le[SUP] 1 [/SUP], Lai Wei[SUP] 1 [/SUP], Daojian Qi[SUP] 1 [/SUP], Shaoyi Wang[SUP] 1 [/SUP], Jingqin Su[SUP] 1 [/SUP], Yuchi Wu[SUP] 1 [/SUP], Weimin Zhou[SUP] 1 [/SUP], Kai Du[SUP] 1 [/SUP], Xuewu Wang[SUP] 2 [/SUP], Zongqing Zhao[SUP] 1 [/SUP]
Affiliations
Abstract
The outbreak of COVID-19 has spread worldwide, causing great damage to the global economy. Raman spectroscopy is expected to become a rapid and accurate method for the detection of coronavirus. A classification method of coronavirus spike proteins by Raman spectroscopy based on deep learning was implemented. A Raman spectra dataset of the spike proteins of five coronaviruses (including MERS-CoV, SARS-CoV, SARS-CoV-2, HCoVHKU1, and HCoV-OC43) was generated to establish the neural network model for classification. Even for rapidly acquired spectra with a low signal-to-noise ratio, the average accuracy exceeded 97%. An interpretive analysis of the classification results of the neural network was performed, which indicated that the differences in spectral characteristics captured by the neural network were consistent with the experimental analysis. The interpretative analysis method provided a valuable reference for identifying complex Raman spectra using deep-learning techniques. Our approach exhibited the potential to be applied in clinical practice to identify COVID-19 and other coronaviruses, and it can also be applied to other identification problems such as the identification of viruses or chemical agents, as well as in industrial areas such as oil and gas exploration.
Keywords: Raman spectroscopy; coronavirus; deep learning; interpretative analysis; spike protein.
. 2023 Jan 26;1-9.
doi: 10.1007/s10812-023-01487-w. Online ahead of print.
Classification of Coronavirus Spike Proteins by Deep-Learning-Based Raman Spectroscopy and its Interpretative Analysis
Wenbo Mo[SUP] 1 2 [/SUP], Jiaxing Wen[SUP] 1 2 [/SUP], Jinglin Huang[SUP] 1 [/SUP], Yue Yang[SUP] 1 [/SUP], Minjie Zhou[SUP] 1 [/SUP], Shuang Ni[SUP] 1 [/SUP], Wei Le[SUP] 1 [/SUP], Lai Wei[SUP] 1 [/SUP], Daojian Qi[SUP] 1 [/SUP], Shaoyi Wang[SUP] 1 [/SUP], Jingqin Su[SUP] 1 [/SUP], Yuchi Wu[SUP] 1 [/SUP], Weimin Zhou[SUP] 1 [/SUP], Kai Du[SUP] 1 [/SUP], Xuewu Wang[SUP] 2 [/SUP], Zongqing Zhao[SUP] 1 [/SUP]
Affiliations
- PMID: 36718373
- PMCID: PMC9876753
- DOI: 10.1007/s10812-023-01487-w
Abstract
The outbreak of COVID-19 has spread worldwide, causing great damage to the global economy. Raman spectroscopy is expected to become a rapid and accurate method for the detection of coronavirus. A classification method of coronavirus spike proteins by Raman spectroscopy based on deep learning was implemented. A Raman spectra dataset of the spike proteins of five coronaviruses (including MERS-CoV, SARS-CoV, SARS-CoV-2, HCoVHKU1, and HCoV-OC43) was generated to establish the neural network model for classification. Even for rapidly acquired spectra with a low signal-to-noise ratio, the average accuracy exceeded 97%. An interpretive analysis of the classification results of the neural network was performed, which indicated that the differences in spectral characteristics captured by the neural network were consistent with the experimental analysis. The interpretative analysis method provided a valuable reference for identifying complex Raman spectra using deep-learning techniques. Our approach exhibited the potential to be applied in clinical practice to identify COVID-19 and other coronaviruses, and it can also be applied to other identification problems such as the identification of viruses or chemical agents, as well as in industrial areas such as oil and gas exploration.
Keywords: Raman spectroscopy; coronavirus; deep learning; interpretative analysis; spike protein.