| 光谱学与光谱分析 |
|
|
|
|
|
| Study on the Identification of Six Kinds of Bee Pollens by Three-Step Infrared Macro-Fingerprint Method |
| WU Jie1, 3, ZHOU Qun2, WU Li-ming1，AN Jian-dong1, SUN Su-qin2*, HU Fu-liang3 |
1. Institute of Apicultural Research, the Chinese Academy of Agricultural Sciences, Beijing 100093, China
2. Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology (Ministry of Education), Department of Chemistry, Tsinghua University, Beijing 100084, China
3. College of Animal Science, Zhejiang University, Hangzhou 310029, China |
|
|
|
|
Abstract Six kinds of bee pollens, including apricot pollen, lotus pollen, rape pollen, camellia pollen, watermelon pollen and corn poppy pollen, were identified non-destructively by Fourier transform infrared spectroscopy (FTIR) combined with derivative spectra and two-dimensional correlation spectroscopy (2D) in the present article. Compared with conventional IR spectra of samples, some certain differences were found in the characteristic peaks of proteins, lipids and carbohydrates. Obvious differences of the six kinds of bee pollens were found in the second derivative spectra. And in the 2D-IR correlation spectra, the samples presented the differences in the position and intensity of the autopeaks and correlation peak clusters. Therefore, the three-step IR macro-fingerprint provides a more rapid and effective method for the identification of different kinds of bee pollens.
|
|
Received: 2009-03-10
Accepted: 2009-06-20
|
|
|
|
Corresponding Authors:
SUN Su-qin
E-mail: sunsq@tsinghua.edu.cn; apiswuwu@126.com
|
|
[1] Loidl A, Crailsheim K. Journal of Comparative Physiology B, 2001, 171: 313.
[2] Hannelie H, Sue W N. Phytochemistry, 2006, 67: 1486.
[3] Leja M, Mareczek A, Wyzgolik G, et al. Food Chemistry, 2007, 100: 237.
[4] Almeida M L B, Lmeida M, Lucila C P, et al. Journal of Food Composition and Analysis, 2005, 18: 105.
[5] Silva T M S, Camara C A, Da Silva L, et al. Journal of Food Composition and Analysis, 2006, 19: 507.
[6] ZHANG Hong-cheng, DONG Jie, LI Hui, et al (张红城，董 捷，李 慧，等). Food Science(食品科学), 2007，28(9): 500.
[7] TANG Lin, LI Qian, ZHAI Jin-liang, et al(唐 琳，李 倩，翟金亮，等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2008，8(1): 17.
[8] LIANG Bi-yan, LI Shu-yuan, SUN Su-qin(梁碧燕，李书渊，孙素琴). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2009，29(2): 313.
[9] WU Li-ming, XUE Xiao-feng, ZHOU Xiao, et al(吴黎明，薛晓锋，周 骁，等). Food Science(食品科学), 2008，29(6): 335.
|
| [1] |
CHENG Jia-wei1, 2，LIU Xin-xing1, 2*，ZHANG Juan1, 2. Application of Infrared Spectroscopy in Exploration of Mineral Deposits: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 15-21. |
| [2] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
| [3] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
| [4] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [5] |
LIU Jia, ZHENG Ya-long, WANG Cheng-bo, YIN Zuo-wei*, PAN Shao-kui. Spectra Characterization of Diaspore-Sapphire From Hotan, Xinjiang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 176-180. |
| [6] |
BAO Hao1, 2，ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
| [7] |
GUO Ya-fei1, CAO Qiang1, YE Lei-lei1, ZHANG Cheng-yuan1, KOU Ren-bo1, WANG Jun-mei1, GUO Mei1, 2*. Double Index Sequence Analysis of FTIR and Anti-Inflammatory Spectrum Effect Relationship of Rheum Tanguticum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 188-196. |
| [8] |
LI Xiao-dian1, TANG Nian1, ZHANG Man-jun1, SUN Dong-wei1, HE Shu-kai2, WANG Xian-zhong2, 3, ZENG Xiao-zhe2*, WANG Xing-hui2, LIU Xi-ya2. Infrared Spectral Characteristics and Mixing Ratio Detection Method of a New Environmentally Friendly Insulating Gas C5-PFK[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3794-3801. |
| [9] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
| [10] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
| [11] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [12] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
| [13] |
DANG Rui, GAO Zi-ang, ZHANG Tong, WANG Jia-xing. Lighting Damage Model of Silk Cultural Relics in Museum Collections Based on Infrared Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3930-3936. |
| [14] |
SUN Wei-ji1, LIU Lang1, 2*, HOU Dong-zhuang3, QIU Hua-fu1, 2, TU Bing-bing4, XIN Jie1. Experimental Study on Physicochemical Properties and Hydration Activity of Modified Magnesium Slag[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3877-3884. |
| [15] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
|
|
|
|
