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Bahreini M, Hosseinzadegan A, Rashidi A, Mirzaei H, Hajian P. Quantitative analysis of the blood serum based on Raman spectroscopy method using partial least squares regression. ICOP & ICPET _ INPC _ ICOFS 2018; 24 :225-228
URL: http://opsi.ir/article-1-1544-en.html
URL: http://opsi.ir/article-1-1544-en.html
1- Applied Spectroscopy Laboratory, Laser and Optics Science and Technology Center, Tehran
2- Laser and Plasma Research Institute, Shahid Beheshti University, Tehran
3- Clinical Pathology Laboratory, Shohadae Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran
4- Cancer Research Center, Shohadae Tajrish Hospital, Department of Radiation Oncology, Shahid Beheshti University of Medical Sciences, Tehran
2- Laser and Plasma Research Institute, Shahid Beheshti University, Tehran
3- Clinical Pathology Laboratory, Shohadae Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran
4- Cancer Research Center, Shohadae Tajrish Hospital, Department of Radiation Oncology, Shahid Beheshti University of Medical Sciences, Tehran
Abstract: (2231 Views)
Blood tests are one of the most important tools in most medical diagnosis. Therefore, the methods of measuring blood components are very important. Raman spectroscopy is a suitable tool for measuring blood components, because while it needs a small amount of sample, it can measure the concentration of the components in the blood with a single test, and in a short time. In this work, cholesterol, glucose, high density lipoprotein (HDL), low density lipoprotein (LDL) and triglycerides concentrations were investigated quantitatively for 40 serum samples using Raman spectroscopy whit partial least square (PLS) regression. In all measurements, the 532 nm excitation wavelength is used. For all quantities, a correlation coefficient was significant and more than 94%. In addition, for the first time, the correlation coefficient for LDL was obtained more than about 95%. The results show that Raman spectroscopy can measure the concentration of blood serum components in a single test with an acceptable accuracy.
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