[1] [1] YU Zh F, QIU Q, GUO Y. Glucose concentration detection by dou-ble-modulation optical polarization method [J]. Acta Optica Sinica, 2016, 36(1): 117001(in Chinese).

[2] [2] CHEN W, CHEN Y Q. Fabrication of flexible continuous glucose monitoring sensor by non-patterning method [J]. Chinese Journal of Analytical Chemistry, 2016, 44(4): 654-659(in Chinese).

[3] [3] FAN Y Q, GAO F, WANG M, et al. Recent development of wearable microfluidics applied in body fluid testing and drug delivery[J]. Chinese Journal of Analytical Chemistry, 2017, 45(3): 455-463(in Chinese).

[4] [4] SHOKREKHODAEI M, QUINONES S. Review of non-invasive glucose sensing techniques: Optical, electrical and breath acetone [J]. Sensors, 2020, 20(5): 1251.

[5] [5] LARIN K V, ELEDRISI M S, MOTAMEDI M, et al. Noninvasive blood glucose monitoring with optical coherence tomography: A pilot study in human subjects [J]. Diabetes Care, 2002, 25(12): 2263-2267.

[6] [6] KURANOV R V, SAPOZHNIKOVA V V, PROUGH D S, et al. Prediction capability of optical coherence tomography for blood glucose concentration monitoring [J]. Journal of Diabetes Science and Technology, 2007, 1(1): 470-477.

[7] [7] LARIN K V, MOTAMEDI M, ASHITKOV T V, et al. Specificity of noninvasive blood glucose sensing using optical coherence tomography technique: A pilot study [J]. Physics in Medicine and Biology, 2003, 48(10): 1371-1390.

[8] [8] HE R Y, WEI H J, GU H M, et al. Effects of optical clearing agents on noninvasive blood glucose monitoring with optical coherence tomography: A pilot study [J]. Journal of Biomedical Optics, 2012, 17(10): 101513.

[9] [9] MARUO K, OOTA T, TSURUGI M, et al. Noninvasive near-infrared blood glucose monitoring using a calibration model built by a numerical simulation method: Trial application to patients in an intensive care unit [J]. Applied Spectroscopy, 2006, 60(12): 1423-1431.

[10] [10] RAMASAHAYAM S, ARORA L, CHOWDHURY S R, et al. FPGA based system for blood glucose sensing using photo plethysmography and online motion arifact correction using adaline [C]//Proceedings of the 9th International Conference on Sensing Technology. New York, USA: IEEE, 2015: 1-21.

[11] [11] KATRUI N B, NUR A T, MOHD H H, et al. PLS predictive model for in-vivo non-invasive finger touch blood glucose NIR spectrosensor [C]//Regional Symposium on Micro and Nanoelectronics (RSM). New York, USA: IEEE, 2021: 88-91.

[12] [12] ARIF A Y, NOREHA A M, ZAHIRUL A H M, et al. Continuous non-invasive blood glucose level measurement using near-infrared LEDs [C]//8th International Conference on Computer and Communication Engineering (ICCCE). New York, USA: IEEE, 2021: 32-37.

[13] [13] YU Y, HUANG J P, ZHU J, et al. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework [J]. IEEE Sensors Journal, 2021, 21(3): 3506-3519.

[14] [14] GOETZ M J, COTE G L, ERCKENS R, et al. Application of a multivariate technique to Raman spectra for quantification of body chemicals [J]. IEEE Transactions on Biomedical Engineering, 1995, 42(7): 728-731.

[15] [15] ENEJDER A M K, SCECINA T G, OH J, et al. Raman spectroscopy for noninvasive glucose measurements [J]. Journal of Biomedical Optics, 2005, 10(3): 031114.

[16] [16] GOLPARVAR A, BOUKHAYMA A, LOAYZA T, et al. Very selective detection of low physiopathological glucose levels by spontaneous Raman spectroscopy with univariate data analysis [J]. Biological Nano Science, 2021, 11: 871-877.

[17] [17] DEEPAK K P, HARDIK L K, PARIDHI S, et al. Overview of Raman spectroscopy: Fundamental to applications [J]. Modern Techniques of Spectroscopy, 2021, 13: 145-184.

[18] [18] GLADKOVA N D, PETROVA G A, NIKULIN N K, et al. In vivo optical coherence tomography imaging of human skin: Norm and pathology [J]. Skin Research and Technology, 2000, 6(1): 6-16.

[19] [19] DREZEK R, DUNN A, RICHARDS K R. Light scattering from cells: Finite-difference time-domain simulations and goniometric measurements [J]. Applied Optics, 1999, 38(16): 3651-3661.

[20] [20] SU Y, YAO X S, LI Z, et al. Measurement of the thermal coefficient of optical attenuation at different depth regions of in vivo human skins using optical coherence tomography: A pilot study[J]. Biomedical Optics Express, 2015, 6(2): 500-513.

[21] [21] SU Y, YAO X S, WEI C J, et al. Determination of the pressure coefficient of optical attenuation in differernt layers of in-vivo human skins with optical coherence tomography[J]. IEEE Photonics Journal, 2016, 8(1): 3800110.

[22] [22] SOLANKI J, SEN P, ANDREWS J T, et al. Blood glucose monitoring in human subjects using optical coherence tomography [J]. Journal of Optics, 2012, 41(3): 127-133.

[23] [23] KURANOV R V, SPAOZHNIKOVA V V, PROUGH D S, et al. In vivo study of glucose-induced changes in skin properties assessed with optical coherence tomography[J]. Physics in Medicine and Biology, 2006, 51(16): 3885-3900.

[24] [24] YASUAKI H, YOSHIAKI Y. Automatic characterization and segmentation of human skin using three-dimensional optical coherence tomography [J]. Optics Express, 2006, 14(5): 1862-1877.

[25] [25] BHANDARI A, HAMRE B, FRETTE B, et al. Modeling optical properties of human skin using Mie theory for particles with different size distributions and refractive indices[J]. Optics Express, 2011, 19(15): 14549-14567.

[26] [26] SU Y, MENG Zh, WANG L Zh, et al. Correlation analysis and ca-libration of noninvasive blood glucose monitoring in vivo with optical coherence tomography [J]. Chinese Journal of Lasers, 2014, 41(7): 0704002(in Chinese).

[27] [27] SU Y, MENG Zh, YU H M, et al. Study on blood glucose lag time in noninvasive measurement using optical coherence tomography [J]. Laser Technology, 2015, 39(1): 19-22(in Chinese).