| 光谱学与光谱分析 |
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| Near Infrared Reflectance Spectroscopy (NIRS): a Novel Approach to Reconstructing Historical Changes of Primary Productivity in Antarctic Lake |
| CHEN Qian-qian1, LIU Xiao-dong1*, LIU Wen-qi2, JIANG Shan1 |
1. Institute of Polar Environment, University of Science and Technology of China, Hefei 230026, China
2. Instruments’ Center for Physical Science, University of Science and Technology of China, Hefei 230026, China |
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Abstract Compared with traditional chemical analysis methods, reflectance spectroscopy has the advantages of speed, minimal or no sample preparation, non-destruction, and low cost. In order to explore the potential application of spectroscopy technology in the paleolimnological study on Antarctic lakes, we took a lake sediment core in Mochou Lake at Zhongshan Station of Antarctic, and analyzed the near infrared reflectance spectroscopy (NIRS) data in the sedimentary samples. The results showed that the factor loadings of principal component analysis (PCA) displayed very similar depth-profile change pattern with the S2 index, a reliable proxy for the change in historical lake primary productivity. The correlation analysis showed that the values of PCA factor loading and S2 were correlated significantly, suggesting that it is feasible to infer paleoproductivity changes recorded in Antarctic lakes using NIRS technology. Compared to the traditional method of the trough area between 650 and 700 nm, the authors found that the PCA statistical approach was more accurate for reconstructing the change in historical lake primary productivity. The results reported here demonstrate that reflectance spectroscopy can provide a rapid method for the reconstruction of lake palaeoenviro nmental change in the remote Antarctic regions.
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Received: 2011-01-17
Accepted: 2011-04-22
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Corresponding Authors:
LIU Xiao-dong
E-mail: ycx@ustc.edu.cn
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[1] ZHANG Zi-kong, CHEN Xue-xiu, REN Peng(张子倥, 陈雪秀, 任 鹏). Near Infrared Spectroscopy Technology(近红外光谱分析技术). Beijing: China Agricultural Science and Technology Press(北京：中国农业科技出版社), 1992.
[2] LIU Xiao-dong, SUN Li-guang, XIE Zhou-qing, et al(刘晓东, 孙立广, 谢周清, 等). Quaternary Sciences(第四纪研究), 2002, 22(5): 483.
[3] SUN Jing, LIU Xiao-dong, SUN Li-guang, et al(孙 静, 刘晓东, 孙立广, 等). Chinese Journal of Polar Research(极地研究), 2007, 19(3): 203.
[4] Michelutti N, Wolfe A P, Vinebrooke R D, et al. Geophysical Research Letters, 2005, 32: L19715.
[5] Das B, Vinebrooke R D, Sanchez-Azofeifa A, et al. Canadian Journal of Fisheries and Aquatic Sciences, 2005, 62(5): 1067.
[6] Das B. Aquatic Ecology, 2007, 41(2): 209.
[7] Wolfe A P, Vinebrooke R D, Michelutti N, et al. Journal of Paleolimnology, 2006, 36(1): 91.
[8] Michelutti N, Blais J M, Cumming B F, et al. Journal of Paleolimnology, 2010, 43(2): 205.
[9] Liu X D, Sun L G, Xie Z Q, et al. Boreas, 2007, 36(2): 182.
[10] Cen H Y, He Y. Trends in Food Science & Technology, 2006, 18(2): 72.
[11] Butkuté B, lepetiené A. Chemija, 2004, 15(2): 12.
[12] XU Bin-bin(徐彬彬). Soils(土壤), 2000, 32(6): 281.
[13] Sanei H, Goodarzi F. Appl. Geochem, 2006, 21(11): 1900.
[14] LI Su-ju, WU Qian, WANG Xue-jun, et al(李素菊, 吴 倩, 王学军, 等). Journal of Lake Science(湖泊科学), 2002, 14: 228.
[15] LIU Xiao-dong, SUN Li-guang, XIE Zhou-qing, et al(刘晓东, 孙立广, 谢周清, 等). Chinese Journal of Polar Research(极地研究), 2004, 16(4): 295.
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