Detection of Whole Blood Glucose Concentration by Fourier-Transform Raman Spectroscopy and Artificial Neural Networks
- DOI
- 10.2991/icaset-17.2017.3How to use a DOI?
- Keywords
- Whole blood, Glucose, Raman spectroscopy, Artificial neural network, Diabetes diseases
- Abstract
Objectives: Diabetes, which is caused by the disorder of blood glucose, has been one of the most important metabolic diseases worldwide. In order to avoid the diabetes-related complications, such as blindness and loss of limbs, the diet adjustment and insulin therapy are need to check glucose level at least four to five times a day. The aim of this paper was to investigate the potential of Fourier-Transform Raman spectroscopy as a diagnostic tool to monitor the blood glucose, triglycerides and total cholesterol levels. Methods: Raman spectroscopy were acquired from 116 individuals with diabetes patients, patients and healthy patients in order to gain an insight into the determination of biochemical changes for the diabetes diagnose. The human whole blood was examined at 1064nm excitation laser source. Results: A new method with modified sample selection algorithm named (K-Means) KM is developed and applied to the Raman spectroscopy of whole blood. The algorithms have been successfully applied to improve the prediction accuracy. This method can detect the glucose concentration in the human whole blood without complicated sample preparation procedures. Conclusions: The experimental results show that the Raman spectroscopy technology has enormous clinical potential as a rapid diagnostic tool for diabetes diseases.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Qiaoyun Wang AU - Guangfei Wu AU - Peng Shan AU - Zhigang Li AU - Sheng Hu AU - Zhenhe Ma PY - 2017/05 DA - 2017/05 TI - Detection of Whole Blood Glucose Concentration by Fourier-Transform Raman Spectroscopy and Artificial Neural Networks BT - Proceedings of the 2017 7th International Conference on Applied Science, Engineering and Technology (ICASET 2017) PB - Atlantis Press SP - 18 EP - 22 SN - 2352-5401 UR - https://doi.org/10.2991/icaset-17.2017.3 DO - 10.2991/icaset-17.2017.3 ID - Wang2017/05 ER -