Data and code on serum Raman spectroscopy as an efficient primary screening of coronavirus disease in 2019 (COVID-19) YinGang LiLintao LuShun YinYu SuYuanzhang ZengYilan LuoMei MaMaohua ZhouHongyan YaoDezhong LiuGang LangJinyi 2020 <p><b>Please note that there is no peer-reviewed publication associated with this data record.</b><br></p><p><br></p><p>This fileset consists of 13 data files, 1 code file and 2 ReadMe files.</p><p>The dataset <b>data.mat</b> is in .mat file format and therefore not openly-accessible. The following datasets, are an openly-accessible version of the .mat file:</p><p><br></p><p><b>Fig2_1.txt</b> in .txt file format</p><p><b>Fig2_2.txt</b> in .txt file format</p><p><b>Fig2_3.txt</b> in .txt file format</p><p><b>Fig2_4.txt</b> in .txt file format</p><p><b>Fig2_5.txt </b>in .txt file format</p><p><b>Fig2_6.txt</b> in .txt file format</p><p><b>raw_COVID.txt</b> in .txt file format</p><p><b>raw_Helthy.txt</b> in .txt file format</p><p><b>raw_Suspected.txt</b> in .txt file format</p><p><b>raw_Tube.txt </b>in .txt file format</p><p><b>table2_data.txt </b>in .txt file format</p><p><b>wave_number.txt</b> in .txt file format</p><p>The code file is the following: <b>code.m</b> in .m file format</p><p>The two ReadMe files are the following: <b>readme.txt </b>in .txt file format and <b>readme.m</b> in .m file format.</p><p><br></p><p>Data in Fig2_1.txt, Fig2_2.txt, Fig2_3.txt, Fig2_4.txt, Fig2_5.txt and Fig2_6.txt were used to plot Figure 2 in the related manuscript.</p><p>raw_COVID.txt contains the raw Raman spectroscopy data from the serum samples obtained from the 53 confirmed COVID-19 patients.</p><p>raw_Helthy.txt contains the raw Raman spectroscopy data from the serum samples obtained from healthy individuals.</p><p>raw_Suspected.txt contains the raw Raman spectroscopy data from the serum samples obtained from suspected cases (individuals suspected of COVID-19 infection)</p><p>raw_Tube.txt contains the raw spectra data from cryopreservation tubes with saline solution inside.</p><p>wave_number.txt contains data of the Raman Spectrum shift.</p><p>table2_data.txt was used to generate Table 2 in the related manuscript.</p><p>The code code.m was used for data processing.</p><p><br></p><p><b>Software needed to access data</b>: data.mat can only be accessed using the Matlab software. Running the code code.m also requires Matlab.</p><p><br></p><p><b>Study aims and methodology</b>: The recommended diagnosis method for the coronavirus disease (COVID-19 is a qPCR-based technique, however, it is a time consuming, expensive, and a sample dependent procedure with relative high false negative ratio. The aim of this study was to develop a widely available, cheap and quick method to diagnose COVID-19 disease based on Raman spectroscopy.</p><p>A total of 157 serum samples were collected from 53 confirmed patients, 54 suspected cases (fever but not COVID-19) and 50 healthy controls. Raman spectroscopy was used to analyse these samples and the machine learning support vector machine (SVM) method were applied to the spectral dataset to build a diagnostic algorithm.<br></p><p>The experimental set up consisted of a Volume Phase Holographic (VPH) spectrograph, deep-cooled CCD camera, and a Raman probe and laser. </p><p>A total of 2355 spectra from 157 individuals were imported to MATLAB (R2013a) software (Math-200 works, Inc.).</p><p>For more details on the methodology, please read the related article.</p><div><br></div>