程存归，男，副教授

    电话：0579-82326826; 82283702

    Email：ccg@zjnu.cn

1960年3月出生，浙江师范大学副教授，硕士研究生导师。

主要经历：1980.9至今   浙江师范大学教师

研究方向及内容：有机波谱分析与化学计量学

主要科研项目：

1. 直接鉴定“浙八味”中药材品质的研究，浙江省自然科学基金项目，2002.1-2004.12，主持；

2. 根尖边缘细胞行为和胞外粘液分泌调控的大豆耐铝机制研究，国家自然科学基金，2009.1-2002.12，参与（排名第二）。

代表性论文：

1. Cungui Cheng, Jia Liu, Changjiang Zhang, Miaozhen Cai, Hong Wang, Wei Xiong. An overview of infrared spectroscopy based on continuous wavelet transform combined with machine learning algorithms: application to Chinese medicines, plant classification and cancer diagnosis. Applied spectroscopy reviews, 2010, 45(2) :148-164. (SCIⅠ区/TOP)

2. Cungui Cheng, Jia Liu, Hong Wang, and Wei Xiong. Infrared spectroscopic studies of Chinese medicines. Applied Spectroscopy Reviews, 2010, 45(3): 165-178. (SCIⅠ区/TOP)

3. Cungui Cheng, Gonglei Sun, Jieping Wan, Cuirong Sun. Facile CuI-Catalyzed arylation of azoles and amides using simple enaminones as efficient ligands. Synlett, 2009, 20(16): 2663-2668. (SCI)

4. Cheng Cun-Gui，Xiong Wei，Tian Yu-Mei. Classification of rats FTIR colon cancer data using wavelets and BPNN. Chinese Journal of Chemistry，2009，27（5）：911-914.（SCI）

5. Cheng Cun-Gui，Tian Yu-Mei, Jin Wen-Ying. A study on the early detection of colon cancer using the methods of wavelet feature extraction and SVM classifications of FTIR. A study on the early detection of colon cancer using the methods of wavelet feature extraction and SVM classifications of FTIRA study on the early detection of colon cancer using the methods of wavelet feature extraction and SVM classifications of FTIRA study on the early detection of colon cancer using the methods of wavelet feature extraction and SVM classifications of FTIR Spectroscopy-Int. J. 2008，22（5）：397-404.（SCI/EI）

6. Cungui Cheng, Wei Xiong,Yumei Tian and Changjiang Zhang. A novel recognition of three kinds of sibling plants using FTIR-CWFE combined with ANN. Spectroscopy，2009，24（2）：58-67.（SCI/EI）

7. Cungui Cheng，Gonglei Sun，Changjiang Zhang.  Early detection of gastric cancer using wavelet feature extraction and neural network classification of FTIR. Spectroscopy. 2007，22（11）：38-42.（SCI/EI）