| 光谱学与光谱分析 |
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| Fast Determination of Heavy Metal Cu in Ludwigia Prostrata Leaves Using Near Infrared Diffuse Spectroscopy |
| LIU Yan-de, SHI Yu, CAI Li-jun |
| College of Mechanical Engineering, East China Jiaotong University, Nanchang 330013, China |
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Abstract Heavy metal ions in plants can be determined by using the near-infrared spectral (NIRS) technique, because they combine with the organic molecular groups that have NIRS absorptions. The present article analyzed the fast detection of heavy metal Cu in Ludwigia prostrata leaves by near infrared diffuse spectral technology. Different preprocessing methods were compared, combined with partial least squares (PLS), and the fast detection models of heavy metal Cu in Ludwigia prostrata leaves were established. The results showed that the best model was obtained by PLS with the preprocessing method of average smoothing. The correlation coefficient (r) and root mean square error of calibration(RMSECV) was 0.950 and 5.99 respectively; External validation correlation coefficient (r) and root mean square error of prediction(RMSEP) was 0.923 and 7.38 respectively. The study shows that fast determination of heavy metal Cu in Ludwigia prostrata leaves using near infrared diffuse spectroscopy is feasible.
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Received: 2012-06-01
Accepted: 2012-09-18
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Corresponding Authors:
LIU Yan-de
E-mail: jxliuyd@163.com
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[1] Bhupinder Dhir, Sheela Srivastava. Ecological Engineering, 2011, 37(6): 893.
[2] JIANG Jian-jun, DENG Lin, LI Hua(江建军, 邓 林, 李 华). Food and Machinery(食品与机械), 2011, 27(6): 40.
[3] Ghosh M, Singh S P. Environ. Pollut, 2005, 133(2): 365.
[4] Tandy S, Schulin R, Nowack B. Chemosphere, 2006, 62(9): 1454.
[5] Sarma H. Journal of Environmental Science and Technology, 2011, 4(2): 118.
[6] WANG Hai-hui, HUAN Heng-fu, LUO Ying, et al(王海慧, 郇恒福, 罗 瑛, 等). Chinese Agricultural Science Bulletin(中国农学通报), 2009, 25(11): 210.
[7] QU Ran, MENG Wei, LI Jun-sheng, et al(屈 冉, 孟 伟, 李俊生, 等). Chinese Journal of Ecology(生态学杂志), 2008, 27(4): 626.
[8] Daniel Mirgorodsky, Lukasz Jablonski, Delphine Ollivier, et al. Earth and Environmental Science, 2012, 3: 433.
[9] Li Gengfei. Northern Horticulture, 2012, 06: 72.
[10] WANG Guang-lin, ZHANG Jin-chi, WANG Li, et al(王广林, 张金池, 王 丽, 等). Journal of Nanjing Forestry University(Natural Sciences Edition)(南京林业大学学报·自然科学版), 2009, 33(4)：43.
[11] Song S B, Hwang J B, Hong Y K, et al. Korean Journal of Weed Science, 2008, 28(3): 214.
[12] Lesteur M, Latrille E, Bellon Maurel V, et al. Bioresource Technology, 2011, 102(3): 2280.
[13] Lee H W, Christie A, Liu J J, et al. Biotechnalogy Progress, 2012, 28(3): 824.
[14] XU Qing-xian, SHEN Heng-sheng, LIN Bin(徐庆贤, 沈恒胜, 林 斌). Fujian Journal of Agricultural Sciences(福建农业学报), 2011, 26(3): 440.
[15] LU Wan-zhen, YUAN Hong-fu, XU Guang-tong, et al(陆婉珍, 袁洪福, 徐广通, 等). Modern Near Infrared Spectral Analysis Technology(现代近红外光谱分析技术). Beijing: China Petrochemical Press(北京: 中国石化出版社), 1999.
[16] LIU Ge-yu, CHAI Tuan-yao, SUN Tao(刘戈宇, 柴团耀, 孙 涛). Chinese Journal of Biotechnology(生物工程学报), 2010, 26(5): 561.
[17] ZHANG Zhong-chun, QIU Bao-sheng(张中春，邱保胜). Plant Physiology Journal(植物生理学报), 2012, 48(5): 425.
[18] FU Xiao-ping, DOU Chang-ming, HU Shao-ping, et al(傅晓萍, 豆长明, 胡少平，等). Chinese Journal of Plant Ecology(植物生态学报), 2010, 34(11): 1354.
[19] DING Zhu-hong, HU Xin, YIN Da-qiang(丁竹红, 胡 忻, 尹大强). Ecology and Environmental Sciences(生态环境学报), 2009, 18(2): 777. |
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