|
|
|
|
|
|
| Determination of Available Boron in Soil by ICP-OES With Boiling Water Extraction |
| ZHANG Peng-peng1, 2, HU Meng-ying1, 2, XU Jin-li1, 2*, CHEN Wei-ming1, 2, GU Xue1, 2, ZHANG Ling-huo1, 2, BAI Jin-feng1, 2, ZHANG Qin1, 2 |
1. Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences, Langfang 065000, China
2. Key Laboratory of Geochemical Exploration，Ministry of Natural Resources, Langfang 065000, China |
|
|
|
|
Abstract The determination of available boron in soil is of great significance to evaluate the supply level of available boron in the soil. The content of available boron in the soil directly affects the growth process of plants, so how to extract and determine the content of available boron is very important. However, the traditional methods of boiling water extraction curcumin colorimetry and boiling water extraction methylene amine colorimetry have a long process and slow speed, which can not meet the requirements of large-scale and rapid determination of soil samples. In this study, the available boron in soil was extracted by boiling water, and the extraction solution was determined by inductively coupled plasma atomic emission spectrometry. The main purpose of this study is to compare the extraction in a closed and open environment, the best extraction time, the interference of spectral lines in the determination process and different soil types, and to optimize the conditions suitable for analyzing the available boron in soil. The results showed that the available boron in different types of soil was the closest to the certified value of reference material in the boiling water extraction for 10 minutes under the open environment. In the determination process, the content of iron raised by the boiling water extraction was relatively low, which had no effect on the content of available boron. The detection limit of this method is 0.004 9 μg·g-1, and the RSD of the results is less than 9%. The accuracy of the method is verified by 12 national standard materials for the analysis of effective soil components, and the results are consistent with the recommended values. Boiling water extraction inductively coupled plasma atomic emission spectrometry (ICP-OES) has the advantages of simple operation, short process, fast detection, accurate and reliable analysis results, avoiding boron pollution in the process of sample treatment. It can extract dozens of soil samples at a time, greatly improving the analysis efficiency, and is suitable for the determination of effective boron content in the soil.
|
|
Received: 2020-06-03
Accepted: 2020-10-05
|
|
|
|
Corresponding Authors:
XU Jin-li
E-mail: xjinli@mail.cgs.gov.cn
|
|
[1] YAO Wang，LI Juan，XIANG De-ming，et al（姚 旺, 黎 娟, 向德明，等）. Soil and Fertilizer Sciences in China（中国土壤与肥料），2019，(6)：64.
[2] WANG Zheng，LIN Yue-ping，YANG Hai-yan，et al（王 政，林跃平，杨海燕，等）. Journal of Southwest University·Natural Science Edition(西南大学学报·自然科学版)，2020，42(8)：26.
[3] LIU Hui，ZHANG Da-geng，LI Jie，et al（刘 慧，张大庚，栗 杰，等）. Journal of Shenyang Agricultural University·Social Sciences Edition（沈阳农业大学学报·社会科学版），2016，18(5)：602.
[4] XUE Jun，ZHONG Lin-sheng，GONG Sheng-fang，et al（薛 珺，钟林生，龚胜芳，等）. Chinese Journal of Spectroscopy Laboratory（光谱实验室），2010，27(5)：2080.
[5] LI Kun，ZHANG Xiang-hui，CHEN Wan-ming（李 坤，张湘晖，陈万明）. Chinese Journal of Spectroscopy Laboratory（光谱实验室），2009，26(2)：226.
[6] YI Tian-fang，PENG Li-mei，XIANG Yong（易田芳，彭礼枚，向 勇）. Physical Testing and and Chemical Analysis Part B: Chen. Anal.(理化检验：化学分册)，2019，55(8)：972.
[7] LIN Guang-xi（林光西）. Chinese Journal of Spectroscopy Laboratory（光谱实验室），2006，(3)：566.
|
| [1] |
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*. Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 69-75. |
| [2] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
| [3] |
HAN Xue1, 2, LIU Hai1, 2, LIU Jia-wei3, WU Ming-kai1, 2*. Rapid Identification of Inorganic Elements in Understory Soils in
Different Regions of Guizhou Province by X-Ray
Fluorescence Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 225-229. |
| [4] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [5] |
MENG Shan1, 2, LI Xin-guo1, 2*. Estimation of Surface Soil Organic Carbon Content in Lakeside Oasis Based on Hyperspectral Wavelet Energy Feature Vector[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3853-3861. |
| [6] |
LI Qi-chen1, 2, LI Min-zan1, 2*, YANG Wei2, 3, SUN Hong2, 3, ZHANG Yao1, 3. Quantitative Analysis of Water-Soluble Phosphorous Based on Raman
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3871-3876. |
| [7] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
| [8] |
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. |
| [9] |
AN Bai-song1, 2, WANG Xue-mei1, 2*, HUANG Xiao-yu1, 2, KAWUQIATI Bai-shan1, 2. Hyperspectral Estimation of Soil Lead Content Based on Random Frog Band Selection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3302-3309. |
| [10] |
DENG Yun1, 2, NIU Zhao-wen1, 2, FENG Qi-yao1, 2, WANG Yu1, 2*. A Novel Hyperspectral Prediction Model of Organic Matter in Red Soil Based on Improved Temporal Convolutional Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2942-2951. |
| [11] |
CAI Hai-hui1, ZHOU Ling2, SHI Zhou3, JI Wen-jun4, LUO De-fang1, PENG Jie1, FENG Chun-hui5*. Hyperspectral Inversion of Soil Organic Matter in Jujube Orchard
in Southern Xinjiang Using CARS-BPNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2568-2573. |
| [12] |
XIA Chen-zhen1, 2, 3, JIANG Yan-yan4, ZHANG Xing-yu1, 2, 3, SHA Ye5, CUI Shuai1, 2, 3, MI Guo-hua5, GAO Qiang1, 2, 3, ZHANG Yue1, 2, 3*. Estimation of Soil Organic Matter in Maize Field of Black Soil Area Based on UAV Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2617-2626. |
| [13] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
| [14] |
ZHANG Xia1, WANG Wei-hao1, 2*, SUN Wei-chao1, DING Song-tao1, 2, WANG Yi-bo1, 2. Soil Zn Content Inversion by Hyperspectral Remote Sensing Data and Considering Soil Types[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2019-2026. |
| [15] |
HU Meng-ying1, 2, ZHANG Peng-peng1, 2, LIU Bin1, 2, DU Xue-miao1, 2, ZHANG Ling-huo1, 2, XU Jin-li1, 2*, BAI Jin-feng1, 2. Determination of Si, Al, Fe, K in Soil by High Pressure Pelletised Sample and Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2174-2180. |
|
|
|
|
