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  • br Raman spectroscopy of label free blood plasma for cervical


    Raman spectroscopy of label-free blood SYBR Green qPCR Master Mix for cervical cancer detection has not been extensively reported in the literature. In this work, we had made a pilot study on NIR Raman spectroscopic char-acterization of normal and cervical cancer using label-free blood plasma. To discriminate the normal and cancer subjects, multivariate statistical techniques including principal component analysis combined with linear discriminant analysis (PCA-LDA) and ROC were employed.
    2. Materials and methods
    2.1. Preparation of human blood plasma samples
    The blood samples of 18 cervical cancer patients were collected from the Govt. Arignar Anna Memorial Cancer Hospital, Kancheepuram, Tamilnadu, India; who were diagnosed with different stages by the pathologist are new and have not undergone any treat-ment such as chemotherapy or radiation therapy earlier. In addition to this, 30 normal samples were also collected from volunteers who were clinically confirmed that they are free from any diseases and not under any medication. They belong to the age group of 32–65 yrs of the same Indian region and have similar socioeconomic backgrounds. Prior to this study clearance and approval from the ethical committee (ref. no. 47846/E2/2012-1) has been obtained. The blood samples were col-lected by sterilized disposable syringe and transferred to Tri Potassium salt of Ethylene Diamine Tetra Acetate (K3 EDTA) coated vacutainer tube which was placed in container maintained by 4 °C. The samples were centrifuged at 1500 rpm for 10 min in a centrifuge (REMI R8C, Mumbai, India). The supernatant Plasma was pipetted out without disturbing the buffy layer or other sediments and stored in vials. The spectra were acquired on the same day.
    2.2. Raman spectroscopy and data analysis
    A Raman spectrometer Lab RAM HR 800 (Horiba Jobin Yvon, France) with an air-cooled intracavity laser point source (Model DL 785–100) of 785 nm diode laser as excitation source, 600 g/mm grating (blazed at 500 nm) and power at the sample was ˜12 ± 0.5 mw. The detection of the back-reflected light from the sample was carried out with a multi-channel air cooled peltier charge–coupled device (CCD) detector with (1024 × 256 pixels). Prior to the measurements the Raman spectrometer was calibrated as per the protocol with carbon tetrachloride (CCl4) as the standard and checked for the known Raman peaks at 218, 314 and 459 cm1.
    The individual sample from the vial was pipetted into a quartz cuvette placed in a special cuvette holder provided by the manu-facturer. The integration or exposure time was set to 60 s and the ac-quisition was carried out twice to reduce cosmic rays. The Near Infrared wavelength 785 nm, excitation the sample exhibits considerable auto-fluorescence along with noises such as instrument dark background, environmental light sources, laser-induced emissions etc. To analyze the real Raman spectra and to reduce noises, baseline correction and smoothing was done by intensity correction factor (ICS filter) followed by baseline correction of 5th degree polynomial function. The smoothening of spectra was done by Savitzky-Golay filter with 25-point  Vibrational Spectroscopy 102 (2019) 1–7
    moving average method. The Instrument control, data collection and data pre-processing were performed using inbuilt software Labspec 5.
    2.3. Statistical analysis
    2.3.1. Principle component analysis (PCA)
    PCA is acting in an unsupervised manner which is used to analyze the inherent structure of data. Finding an alternative set of coordinates PCA reduces the dimensionality of the data set. The original variables are linear combinations of the principal components; they are ortho-gonal to one another in such a way that each one successively accounts for maximum variability in that particular data set. When PC scores are plotted, it reveals relations between the samples or groups i.e. they are clustered or outlier. The PC loadings plotted as a function of different variables the highest different variable can be seen.