光谱学与光谱分析 |
|
|
|
|
|
Study on Estimation of Deserts Soil Total Phosphorus Content by Vis-NIR Spectra with Variable Selection |
YANG Ai-xia1,2, DING Jian-li1,2*, LI Yan-hong3,4, DENG Kai1,2 |
1. College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, China 2. Key Laboratory of Oasis Ecology (Xinjiang University) Ministry of Education, Urumqi 830046, China 3. College of Geographical Sciences and Tourism, Xinjiang Normal University, Urumqi 830054, China 4. “Xinjiang Arid Area Lakes Environment and Resources Laboratory” (A Key Laboratory of Xinjiang Uygur Autonomous Region), Xinjiang Normal University, Urumqi 830054, China |
|
|
Abstract In this paper, 300 samples of desert soil collected in the Ebinur Lake Wetland Nature Reserve of Xinjiang were used as the research subject, and the visible/near-infrared spectra data about the soil obtained with the ASD Field Spec○R 3 HR spectrometer and the data about total phosphorus in the soil obtained through chemical analysis were used as the data sources; following Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment, the combination of ant colony optimization interval partial least squares (ACO-iPLS) and genetic algorithm interval partial least squares (GA-iPLS) were employed to extract the characteristic wavelengths of the total phosphorus content in desert soil, before the partial least squares regression model for predicting the total-phosphorus content in soil was constructed; and this model was compared with the full-spectrum partial least squares model, ACO-iPLS and GA-iPLS. According to the results: through filtering with ACO-iPLS, the total-phosphorus characteristic wavebands in the desert soil were 500~700, 1 101~1 300, 1 501~1 700, and 1 901~2 100 nm; through further variable selection with GA-iPLS, 13 effective wavelengths with the minimum colinearity were selected, which were respectively: 1 621, 546, 1 259, 573, 1 572, 1 527, 564, 1 186, 1 988, 1 541, 2 024, 1 118, and 1 191 nm. According to the comparison of modeling methods, the most accurate model was the one based on the characteristic variables selected with the combination of ACO-iPLS and GA-iPLS, followed by the ones with genetic algorithm, ant colony optimization algorithm and the full spectrum method. For the total phosphorus content in soil model established with the combination of ACO-iPLS and GA-iPLS, the root mean square error of cross validation (RMSECV) and the root mean square error of prediction (RMSEP) were respectively 0.122 and 0.108 mg·g-1, and the related coefficient for cross validation (Rc) and the related coefficient for prediction (Rp) were 0.535 7 and 0.555 9, respectively. Therefore, it can be seen that the model constructed through Savizky-Golay smoothing, standard normal variation transformation and the first-order differential pretreatment and by using the combination of ACO-iPLS and GA-iPLS has simple structure, high prediction accuracy and good robustness, and can be used for estimating the total phosphorus content in desert soil.
|
Received: 2015-06-03
Accepted: 2015-10-22
|
|
Corresponding Authors:
DING Jian-li
E-mail: watarid@xju.edu.cn
|
|
[1] XUE Li-hong, ZHOU Ding-hao, LI Ying, et al(薛利红, 周鼎浩, 李 颖, 等). Acta Pedologica Sinica(土壤学报), 2014, 51(5): 993. [2] CHEN Xin, LIU Fei(陈 鑫, 刘 飞). Chinese Journal of Analysis Laboratory(分析试验室), 2013, 32(10): 50. [3] ZHOU Ding-hao, XUE Li-hong, LI Ying, et al(周鼎浩, 薛利红, 李 颖, 等). Soils(土壤), 2014, 46(1): 47. [4] SHEN Zhang-quan, LU Bi-hui, SHAN Ying-jie, et al(沈掌泉, 卢必慧, 单英杰, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(7): 1775. [5] YANG Mei-hua, ZHAO Xiao-min, FANG Qian(杨梅花, 赵小敏, 方 倩, 等). Agricultural Sciences in China(中国农业科学), 2014, 47(12): 2374. [6] LI Yan-xiao, HUANG Xiao-wei, ZOU Xiao-bo, et al(李艳肖, 黄晓玮, 邹小波, 等). Journal of Food Safety & Quality(食品安全质量检测学报), 2014, 5(6): 1679. [7] YANG Meng, SONG Jian-she, CAO Ji-ping, et al(杨 檬, 宋建社, 曹继平, 等). Computer Simulation(计算机仿真), 2009,(6): 200. [8] Allegrini F, Olivieri A C. Analytica Chimica Acta, 2011, 699(1): 18. [9] GUO Zhi-ming, HUANG Wen-qian, PENG Yan-kun, et al(郭志明, 黄文倩, 彭彦昆, 等). Chinese Journal of Analytical Chemistry(分析化学), 2014, 42(4): 513. [10] ZHOU Zhu, LI Xiao-yu, GAO Hai-long(周 竹, 李小昱, 高海龙). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2012, 43(2): 128.
|
[1] |
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. |
[2] |
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. |
[3] |
LIU Shu-hong1, 2, WANG Lu-si3*, WANG Li-sheng3, KANG Zhi-juan1, 2,WANG Lei1, 2,XU Lin1, 2,LIU Ai-qin1, 2. A Spectroscopic Study of Secondary Minerals on the Epidermis of Hetian Jade Pebbles From Xinjiang, China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 169-175. |
[4] |
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. |
[5] |
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. |
[6] |
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. |
[7] |
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. |
[8] |
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. |
[9] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[10] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[11] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[12] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[13] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[14] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[15] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
|
|
|
|