光谱学与光谱分析 |
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Based on the LS-SVM Modeling Method Determination of Soil Available N and Available K by Using Near-Infrared Spectroscopy |
LIU Xue-mei1, 2, LIU Jian-she1* |
1. College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China 2. School of Civil Engineering, East China Jiaotong University, Nanchang 330013, China |
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Abstract Visible infrared spectroscopy (Vis/SW-NIRS) was investigated in the present study for measurement accuracy of soil properties,namely, available nitrogen(N) and available potassium(K). Three types of pretreatments including standard normal variate (SNV), multiplicative scattering correction (MSC) and Savitzky-Golay smoothing+first derivative were adopted to eliminate the system noises and external disturbances. Then partial least squares (PLS) and least squares-support vector machine (LS-SVM) models analysis were implemented for calibration models. Simultaneously, the performance of least squares-support vector machine (LS-SVM) models was compared with three kinds of inputs, including PCA(PCs), latent variables(LVs), and effective wavelengths (EWs). The results indicated that all LS-SVM models outperformed PLS models. The performance of the model was evaluated by the correlation coefficient (r2) and RMSEP. The optimal EWs-LS-SVM models were achieved, and the correlation coefficient (r2) and RMSEP were 0.82 and 17.2 for N and 0.72 and 15.0 for K, respectively. The results indicated that visible and short wave-near infrared spectroscopy (Vis/SW-NIRS)(325~1 075 nm) combined with LS-SVM could be utilized as a precision method for the determination of soil properties.
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Received: 2012-03-29
Accepted: 2012-06-10
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Corresponding Authors:
LIU Jian-she
E-mail: liujianshe@dhu.edu.cn
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[1] CEN Yi-lang, SONG Tao, HE Yong, et al(岑益郎,宋 韬,何 勇). Journal of Zhejiang University(Agric & Life Sci)(浙江大学学报·农业与生命科学版), 2011, 37(3): 300. [2] LI Jie, ZHANG Xiao-chao, YUAN Yan-wei, et al(李 颉,张小超,苑严伟,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(2): 176. [3] Vohlanda M, Emmerling C. European Journal of Soil Science, August, 2011, 62: 598. [4] Summers D, Lewis M, Ostendorf B, et al. Ecological Indicators, 2011, 11: 123. [5] Kuang B, Mouazen A M. European Journal of Soil Science, August, 2011, 62: 629. [6] SONG Hai-yan, HE Yong(宋海燕,何 勇). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2007, 38(12): 113. [7] Chen Huazhou, Pan Tao, Chen Jiemei, et al. Chemometrics and Intelligent Laboratory Systems, 2011, 107: 139. [8] Valentin H Klaus, Till Kleinebecker, Steffen Boch, et al. Ecological Indicators, 2012, 14: 82. [9] LU Yan-li, BAI You-lu, WANG Lei, et al(卢艳丽,白由路,王 磊,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2010, 26(1): 256. [10] Viscarra RRossel A, Chapell A, De Caritat P, et al. European Journal of Soil Science, June 2011, 62: 442. [11] Summers D, Lewis M, Ostendorf B, et al. Ecological Indicators, 2011, 11: 123. [12] YAO Yan-min, WEI Na, TANG Peng-qin, et al(姚艳敏,魏 娜,唐鹏钦,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(8): 95. [13] Mouazen A M, Kuang B, et al. Geoderma, 2010, 158(1-2): 23. [14] Liang Xiuying, L Xiaoyu, Lei Tingwu, et al. Measurement, 2011, 44: 2200. [15] YUAN Shi-lin, MA Tian-yun, SONG Tao, et al(袁石林, 马天云, 宋 韬, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2009, 9(40): 150. [16] Shao Yongni, He Yong. Soil Research, 2011, 49:166. [17] BAO Shi-dan(鲍士旦). Soil Agricultural Chemistry Analysis(土壤农化分析). Beijing: China Agriculture Press(北京: 中国农业出版社), 1999. 30. [18] LIU Fei, WANG Li, HE Yong(刘 飞,王 莉,何 勇). Journal of Zhejiang University:Engineering Science(浙江大学学报·工学版), 2010, 44(3): 619.
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