[1] Kanick S C, Davis S C, Zhao Y, et al.. Dual-channel red/blue fluorescence dosimetry with broadband reflectance spectroscopic correction measures protoporphyrin IX production during photodynamic therapy of actinic keratosis[J]. Journal of Biomedical Optics, 2014, 19(7): 075002.

[2] Bradley R S, Thorniley M S. A review of attenuation correction techniques for tissue fluorescence[J]. Journal of the Royal Society Interface, 2006, 3(6): 1-13.

[3] Brooks S, Hoy C L, Amelink A, et al.. Sources of variability in the quantification of tissue optical properties by multidiameter single-fiber reflectance and fluorescence spectroscopy[J]. Journal of Biomedical Optics, 2015, 20(5): 057002.

[4] Liu C B, Rajaram N, Vishwanath K, et al.. Experimental validation of an inverse fluorescence Monte Carlo model to extract concentrations of metabolically relevant fluorophores from turbid phantoms and a murine tumor model[J]. Journal of Biomedical Optics, 2012, 17(7): 077012.

[5] 贾浩, 陈斌, 李东, 等. 模拟皮肤组织中光传播的非结构化网格蒙特卡罗法[J]. 中国激光, 2015, 42(4): 0404001.

    Jia Hao, Chen Bin, Li Dong, et al.. Unstructured grid based Monte Carlo method for the simulation of light propagation in skin tissues[J]. Chinese J Lasers, 2015, 42(4): 0404001.

[6] Valdés S P, Leblond F, Kim A, et al.. A spectrally constrained dual-band normalization technique for protoporphyrin IX quantification in fluorescence-guided surgery[J]. Optics Letters, 2012, 37(11): 1817-1819.

[7] 甄洁, 王颖, 王成铭, 等. 应用光学相干层析术研究光动力治疗鲜红斑痣前后皮肤结构和光学参数的变化[J]. 中国激光, 2015, 42(6): 0604001.

    Zhen Jie, Wang Ying, Wang Chengming, et al.. Research on the structure and optical parameter changes of port wine stain after photodynamic therapy based on optical coherence tomography[J]. Chinese J Lasers, 2015, 42(6): 0604001.

[8] 潘津津, 焦学军, 焦典, 等. 利用功能性近红外光谱法研究大脑皮层血氧情况随任务特征变化规律[J]. 光学学报, 2015, 35(8): 0817001.

    Pan Jinjin, Jiao Xuejun, Jiao Dian, et al.. Study on variation in cortex oxygen with task features using functional near-infrared spectroscopy[J]. Acta Optica Sinica, 2015, 35(8): 0817001.

[9] Kanick S C, Robinson D J, Sterenborg H J, et al.. Extraction of intrinsic fluorescence from single fiber fluorescence measurements on a turbid medium[J]. Optics Letters, 2012, 37(5): 948-950.

[10] Jacques S L. Optical properties of biological tissues: A review[J]. Physics in Medicine and Biology, 2013, 58(11): R37-R61.

[11] Poli R, Kennedy J, Blackwell T. Particle swarm optimization[J]. Swarm Intelligence, 2007, 1(1): 33-57.

[12] Kennedy J. Particle swarm optimization[M]. //Encyclopedia of machine learning. Heidelberg: Springer, 2010: 760-766.

[13] 王泽兵, 杨卫, 秦丽. 基于粒子群算法的动态热释电目标跟踪[J]. 光学学报, 2014, 34(10): 1004001.

    Wang Zebing, Yang Wei, Qin Li. Target tracking based on particle swarm optimization using dynamic pyroelectric infrared sensor[J]. Acta Optica Sinica, 2014, 34(10): 1004001.

[14] 王书涛, 陈东营, 魏蒙, 等. 荧光光谱法和PSO-BP神经网络在山梨酸钾浓度检测中的应用[J]. 中国激光, 2015, 42(5): 0515004.

    Wang Shutao, Chen Dongying, Wei Meng, et al.. Application of fluorescence spectroscopy and PSO-BP neural network in the detection of potassium sorbate concentration[J]. Chinese J Lasers, 2015, 42(5): 0515004.

[15] Hull E, Ediger M, Unione A, et al.. Noninvasive, optical detection of diabetes: Model studies with porcine skin[J]. Optics Express, 2004, 12(19): 4496-4510.

[16] Meerwaldt R, Links T, Graaff R, et al.. Simple noninvasive measurement of skin autofluorescence[J]. Annals of the New York Academy of Sciences, 2005, 1043(1): 290-298.

[17] Colagiuri S, Hussain Z, Zimmet P, et al.. Screening for type 2 diabetes and impaired glucose metabolism: The Australian experience[J]. Diabetes Care, 2004, 27(2): 367-371.

[18] Smit A J, Smit J M, Botterblom G J, et al.. Skin autofluorescence based decision tree in detection of impaired glucose tolerance and diabetes[J]. PLoS ONE, 2013, 8(6): e65592.

[19] Zhang Y Z, Zhu L, Wang Y K, et al.. Classification of skin autofluorescence spectrum using support vector machine in type 2 diabetes screening[J]. Journal of Innovative Optical Health Sciences, 2013, 6(4): 1350036.

[20] 李飞, 王贻坤, 朱灵, 等. 基于神经网络模式识别的糖尿病无创风险评估方法研究[J]. 光谱学与光谱分析, 2014, 34(5): 1327-1331.

    Li Fei, Wang Yikun, Zhu Ling, et al.. Research on noninvasive risk evaluation of diabetes mellitus based on neural network pattern recognition[J]. Spectroscopy and Spectral Analysis, 2014, 34(5): 1327-1331.