title: [The preliminary study on noninvasive detection using NIR diffusion reflectance spectrum for monitoring blood glucose].
abstract: The noninvasive detection method using near-infrared diffusion reflectance spectrum for monitoring blood glucose was described. A group of healthy volunteers(2 young male and 2 young female) was tested twenty-six times under changing of both the blood glucose concentration and spectrum region during various states. In order to set up a model, the set was chosen from these samples, the set of prediction was not belong to the set of the model. The results from different selected spectrum regions and reprocessing methods show as follows: 1) In the spectrum region of 9,000-12,000 cm-1 using MIN-MAX normalization reprocessing method to set up a model and predict, the errors of prediction were more than 36 mg.dL-1; the model errors were under 16 mg.dL-1 and predictive errors were less than 25 mg.dL-1 by using smoothing and second derivatives in the same region; in the another region of 4,000-5,000 cm-1 with smoothing and first derivatives both error of model and prediction were more than 25 mg.dL-1. 2) In the region of 4,000-9,000 cm-1 with smoothing and second derivatives, the errors from model were less than 15 mg.dL-1, the errors of prediction were less than 31 mg.dL-1; selecting the spectrum regions of 6,100-7,500 cm-1 and 4,200-4,700 cm-1 by a quantitative software OPU VERSION 3.01 with smoothing, first derivatives and vector normalization, the errors of model were no more than 11 mg.dL-1, the errors of predication were no more than 22 mg.dL-1. 3) Choosing the sample from the subject itself to establish the model in 9,000-12,000 cm-1 with smoothing and second derivatives, the errors of model and predication were less than 15 mg.dL-1 and 11 mg.dL-1 respectively. The results showed that the method was feasible to monitoring the blood glucose concentration by selecting an appropriate spectrum region and reprocessing methods with partial least square algorithm which set up a mathematical model. Excepting the number of sample, selected spectrum region and reprocessing methods, the other influencing factors such as the variant from the subject were discussed also.
authors.affiliation: Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China.
authors.forename: Lan
authors.initials: L
authors.lastname: Huang
authors.forename: Hai-shu
authors.initials: HS
authors.lastname: Ding
authors.forename: Guang-zhi
authors.initials: GZ
authors.lastname: Wang
mesh_terms: D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation*; D003920:Diabetes Mellitus / Q000097:blood; D006801:Humans; D019265:Spectroscopy, Near-Infrared / Q000379:methods*