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Volume 22, Issue 2
  • ISSN: 1570-1794
  • E-ISSN: 1875-6271

Abstract

Background

In China, the traditional method for analyzing soil available phosphorus is inadequate for large-scale soil assessment and nationwide soil formulation demands. To address this, we propose a rapid and reliable method for soil-available phosphorus detection. The setup includes an on-site rapid pre-treatment device, a non-contact conductivity detection device, and a capillary electrophoresis buffer solution system composed of glacial acetic acid and hydroxypropyl-β-cyclodextrin.

Methods

The on-site rapid pre-treatment process includes fresh soil moisture content detection (moisture rapid detector), weighing (handheld weighing meter), stirring (handheld rapid stirrer), and filtration (soil rapid filter) to obtain the liquid sample, and direct injection (capillary electrophoresis detector). The phosphate ion detection parameters include capillary size, separation voltage, injection parameters, and electric injection. We used Liaoning brown soil, Henan yellow tidal soil, Heilongjiang black soil, and Anhui tidal soil as standard samples. Additionally, we used mathematical modeling methods and machine learning algorithms to analyze and process research data.

Results and Conclusion

Following calibration with standard samples, the experimental blind test samples demonstrated conformity with the national standard method, exhibiting a relative standard deviation of less than 3%. The proposed pre-treatment device and non-contact conductivity detector are powered by lithium-ion batteries, rendering them ideal for extended field operations. The non-contact conductivity detector obviates the need for direct contact with test samples, mitigating environmental pollution. Furthermore, the neural network model exhibited the highest level of goodness of fit in chemical data analysis.

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2025-06-16

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References

  1. SeghouaniM. BravinM.N. MollierA. Crop response to nitrogen-phosphorus colimitation: theory, experimental evidences, mechanisms, and models. A review.Agron. Sustain. Dev.2024441310.1007/s13593‑023‑00939‑z
     [Google Scholar]
  2. MoylesI.R. DonohueJ.G. FowlerA.C. Quasi-steady uptake and bacterial community assembly in a mathematical model of soil-phosphorus mobility.J. Theor. Biol.202150911053010.1016/j.jtbi.2020.110530 33129953
     [Google Scholar]
  3. RupngamT. MessigaA.J. KaramA. Solubility of soil phosphorus in extended waterlogged conditions: An incubation study.Heliyon202392e1350210.1016/j.heliyon.2023.e13502 36825191
     [Google Scholar]
  4. QuinteroC.E. Dynamic of Phosphorus in Soils Fertilized with different Phosphorus Sources and Phosphorus Acquisition by Lotus Corniculatus.Journal of Ecology & Natural Resources20226400030710.23880/jenr‑16000307
     [Google Scholar]
  5. LiuY.F. WenZ.F. BianY. ZhouY. LiuZ.F. ZhangY. FengX.S. A review on recent innovations of pretreatment and analysis methods for sulfonylurea herbicides.Crit. Rev. Anal. Chem.202213010.1080/10408347.2022.2116694 36045570
     [Google Scholar]
  6. KruseJ. AbrahamM. AmelungW. BaumC. BolR. KühnO. LewandowskiH. NiederbergerJ. OelmannY. RügerC. SantnerJ. SiebersM. SiebersN. SpohnM. VestergrenJ. VogtsA. LeinweberP. Innovative methods in soil phosphorus research: A review.J. Plant Nutr. Soil Sci.20151781438810.1002/jpln.201400327 26167132
     [Google Scholar]
  7. ReisJ.V. Víctor Hugo AlvarezV. DuriganR.D. PaulucioR.B. CantaruttiR.B. Interpretation of soil phosphorus availability by Mehlich-3 in soils with contrasting phosphorus buffering capacity.Rev. Bras. Ciênc. Solo202044e019011310.36783/18069657rbcs20190113
     [Google Scholar]
  8. HuD. ZhangC. ZhangY. Comparison of different pretreatment methods on phosphorus release and recovery as struvite from excess sludge.Environ. Technol.202344216116910.1080/09593330.2021.1967459 34432613
     [Google Scholar]
  9. ParkH.J. LeeS.Y. HanC.W. KweonG. Pretreatment of Soil Samples for Rapid Soil Phosphorus Measurement.Journal of Agriculture & Life Science201650319320310.14397/jals.2016.50.3.193
     [Google Scholar]
  10. WankeD.J. HeichelJ. ZikeliS. MüllerT. HartmannT.E. Comparison of soil phosphorus extraction methods regarding their suitability for organic farming systems.J. Plant Nutr. Soil Sci.2023186559960810.1002/jpln.202300129
     [Google Scholar]
  11. Sánchez-EstevaS. KnadelM. KucheryavskiyS. de JongeL.W. RubækG.H. HermansenC. HeckrathG. Combining laser-induced breakdown spectroscopy (LIBS) and visible near-infrared spectroscopy (Vis-NIRS) for soil phosphorus determination.Sensors (Basel)20202018541910.3390/s20185419 32967345
     [Google Scholar]
  12. GuoP. LiT. GaoH. ChenX. CuiY. HuangY. Evaluating calibration and spectral variable selection methods for predicting three soil nutrients using Vis-NIR spectroscopy.Remote Sens. (Basel)20211319400010.3390/rs13194000
     [Google Scholar]
  13. ShiriJ. KeshavarziA. KisiO. KarimiS.M. KarimiS. NazemiA.H. Rodrigo-CominoJ. Estimating soil available phosphorus content through coupled Wavelet–data-driven models.Sustainability (Basel)2020125215010.3390/su12052150
     [Google Scholar]
  14. SouzaM.F. FrancoH.C.J. AmaralL.R. Estimation of soil phosphorus availability via visible and near-infrared spectroscopy.Sci. Agric.2020775e2018029510.1590/1678‑992x‑2018‑0295
     [Google Scholar]
  15. XieZ. LiS. TangS. HuangL. WangG. SunX. HuZ. Phosphorus leaching from soil profiles in agricultural and forest lands measured by a cascade extraction method.J. Environ. Qual.201948356857810.2134/jeq2018.07.0285 31180433
     [Google Scholar]
  16. HartmannT.E. WollmannI. YouY. MüllerT. Sensitivity of three phosphate extraction methods to the application of phosphate species differing in immediate plant availability.Agronomy (Basel)2019912910.3390/agronomy9010029
     [Google Scholar]
  17. PrasadA. SahuS.P. Figueiredo StofelaS.K. ChaichiA. HasanS.M.A. BamW. MaitiK. McPeakK.M. LiuG.L. GartiaM.R. Printed electrode for measuring phosphate in environmental water.ACS Omega2021617112971130610.1021/acsomega.1c00132 34056285
     [Google Scholar]
  18. ZhuX. WangK. YanH. LiuC. ZhuX. ChenB. Microfluidics as an emerging platform for exploring soil environmental processes: a critical review.Environ. Sci. Technol.202256271173110.1021/acs.est.1c03899 34985862
     [Google Scholar]
  19. LiuK. WangM.W. LinW.Y. PhungD.L.D. GirgisM.D. WuA.M. TomlinsonJ.S. ShenC.K. Molecular imaging probe development using microfluidics.Curr. Org. Synth.20118447348710.2174/157017911796117205 22977436
     [Google Scholar]
  20. JiaX.X. LiS. HanD.P. ChenR. YaoZ.Y. NingB. GaoZ.X. FanZ.C. Development and perspectives of rapid detection technology in food and environment.Crit. Rev. Food Sci. Nutr.202262174706472510.1080/10408398.2021.1878101 33523717
     [Google Scholar]
  21. KweonG. LundE.D. MaxtonC. LeeW.S. MengelD.B. Comparison of soil phosphorus measurements.Trans. ASABE201558240541410.13031/trans.58.10903
     [Google Scholar]
  22. McColeM. BradleyM. McCaulM. McCruddenD. A low-cost portable system for on-site detection of soil pH and potassium levels using 3D printed sensors.Results in Engineering20232010156410.1016/j.rineng.2023.101564
     [Google Scholar]
  23. NawaraS. Van DaelT. MerckxR. AmeryF. ElsenA. OdeursW. VandendriesscheH. McgrathS. RoisinC. JouanyC. PellerinS. DenoroyP. Eichler-LöbermannB. BörjessonG. GoosP. AkkermansW. SmoldersE. A comparison of soil tests for available phosphorus in long‐term field experiments in Europe.Eur. J. Soil Sci.201768687388510.1111/ejss.12486
     [Google Scholar]
  24. TianH. QiaoJ. ZhuY. JiaX. ShaoM. Vertical distribution of soil available phosphorus and soil available potassium in the critical zone on the Loess Plateau, China.Sci. Rep.2021111315910.1038/s41598‑021‑82677‑4 33542419
     [Google Scholar]
  25. WeiY. WangR. ZhangJ. GuoH. ChenX. Partition management of soil nutrients based on capacitive coupled contactless conductivity detection.Agriculture202313231310.3390/agriculture13020313
     [Google Scholar]
  26. PaulP. DuchateauT. Sänger-van de GriendC. AdamsE. Van SchepdaelA. Capillary electrophoresis with capacitively coupled contactless conductivity detection method development and validation for the determination of azithromycin, clarithromycin, and clindamycin.J. Sep. Sci.201740173535354410.1002/jssc.201700560 28683179
     [Google Scholar]
  27. EldeebM.A. DhamuV.N. PaulA. MuthukumarS. PrasadS. Electrochemical Soil Nitrate Sensor for In Situ Real-Time Monitoring.Micromachines (Basel)2023147131410.3390/mi14071314 37512625
     [Google Scholar]
  28. ElbashirA.A. ElgorasheR.E.E. AlnajjarA.O. Aboul-EneinH.Y. Application of capillary electrophoresis with capacitively coupled contactless conductivity detection (CE-C4D): 2017–2020.Crit. Rev. Anal. Chem.202252353554310.1080/10408347.2020.1809340 32835492
     [Google Scholar]
  29. DoY.N. KieuT.L.P. DangT.H.M. NguyenQ.H. DangT.H. TranC.S. VuA.P. DoT.T. NguyenT.N. DinhS.L. NguyenT.M.T. PhamT.N.M. HoangA.Q. PhamB. NguyenT.A.H. Green analytical method for simultaneous determination of glucosamine and calcium in dietary supplements by capillary electrophoresis with capacitively coupled contactless conductivity detection.J. Anal. Methods Chem.2023202311010.1155/2023/2765508 36760655
     [Google Scholar]
  30. DasB. HuthN. ProbertM. CondronL. SchmidtS. Soil phosphorus modeling for modern agriculture requires balance of science and practicality: A perspective.J. Environ. Qual.20194851281129410.2134/jeq2019.05.0201 31589725
     [Google Scholar]
  31. SaidiS. AyoubiS. ShirvaniM. AziziK. ZhaoS. Digital mapping of soil phosphorous sorption parameters (PSPs) using environmental variables and machine learning algorithms.Int. J. Digit. Earth20231611752176910.1080/17538947.2023.2210314
     [Google Scholar]
  32. MeuwlyM. Machine learning for chemical reactions.Chem. Rev.202112116102181023910.1021/acs.chemrev.1c00033 34097378
     [Google Scholar]
  33. LiuY. LuZ. HeW. WuY. LiJ. SunC. A novel portable microchip electrophoresis system for rapid on-site detection of soil nutrient ions.Meas. Sci. Technol.202435707510410.1088/1361‑6501/ad3bdb
     [Google Scholar]
  34. KubáňP. HauserP.C. 20th anniversary of axial capacitively coupled contactless conductivity detection in capillary electrophoresis.Trends Analyt. Chem.201810231132110.1016/j.trac.2018.03.007
     [Google Scholar]
  35. HauserP.C. KubáňP. Capacitively coupled contactless conductivity detection for analytical techniques – Developments from 2018 to 2020.J. Chromatogr. A2020163246161610.1016/j.chroma.2020.461616 33096295
     [Google Scholar]
  36. DinhL.M. HoangQ.A. Pham ThiN.M. Nguyen ThiA.H. HuongN.T.A. Capillary electrophoresis with capacitively coupled contactless conductivity detection: Recent applications in food control. Heavy metals and arsenic concentrations in water, agricultural soil, and rice in Ngan Son district, Bac Kan province, Vietnam20194426627610.47866/2615‑9252/vjfc.3848
     [Google Scholar]
  37. GrafH.G. RudischB.M. ManegoldJ. HuhnC. Advancements in capacitance‐to‐digital converter‐based C 4 D technology for detection in capillary electrophoresis using amplified excitation voltages and comparison to classical and open‐source C 4 Ds.Electrophoresis20214212-131306131610.1002/elps.202000394 33710630
     [Google Scholar]
  38. ZhangX. WangW. NordinA.N. LiF. JangS. VoiculescuI. The influence of the electrode dimension on the detection sensitivity of electric cell–substrate impedance sensing (ECIS) and its mathematical modeling.Sens. Actuators B Chem.201724778079010.1016/j.snb.2017.03.047
     [Google Scholar]
  39. ZhangJ. GaoJ. ChenX. WangR. ZhangZ. WeiY. Design and experiment of capacitively-coupled contactless conductivity detection device for rapid measurement of soil potassium ion.Nongye Jixie Xuebao201849S136036410.6041/j.issn.1000‑1298.2018.S0.048
     [Google Scholar]
  40. El-AttugM.N. LutumbaB. HoogmartensJ. AdamsE. Van SchepdaelA. Method development and validation for trifluoroacetic acid determination by capillary electrophoresis in combination with capacitively coupled contactless conductivity detection (CE-C4D).Talanta201083240040310.1016/j.talanta.2010.09.051 21111152
     [Google Scholar]
  41. NingZhiqiang Liu Jiaxiang; Wu Yue; Tao Mengqi; Fang Yonghua, Infrared spectrum baseline correction method based on improved iterative polynomial fitting.Jiguang Yu Guangdianzixue Jinzhan202057303300110.3788/LOP57.033001
     [Google Scholar]
  42. BateniA. SusnarS.S. AmirfazliA. NeumannA.W. A high-accuracy polynomial fitting approach to determine contact angles.Colloids Surf. A Physicochem. Eng. Asp.20032191-321523110.1016/S0927‑7757(03)00053‑0
     [Google Scholar]
  43. Najwa Mohd RizalN. HayderG. MnzoolM. ElnaimB.M.E. MohammedA.O.Y. KhayyatM.M. Comparison between regression models, support vector machine (SVM), and artificial neural network (ANN) in river water quality prediction.Processes (Basel)2022108165210.3390/pr10081652
     [Google Scholar]
  44. DeringerV.L. BartókA.P. BernsteinN. WilkinsD.M. CeriottiM. CsányiG. Gaussian process regression for materials and molecules.Chem. Rev.202112116100731014110.1021/acs.chemrev.1c00022 34398616
     [Google Scholar]
  45. ChenY. SongL. LiuY. YangL. LiD. A review of the artificial neural network models for water quality prediction.Appl. Sci. (Basel)20201017577610.3390/app10175776
     [Google Scholar]
  46. TongY. YuL. LiS. LiuJ. QinH. LiW. Polynomial fitting algorithm based on neural network.ASP Transactions on Pattern Recognition and Intelligent Systems202111323910.52810/TPRIS.2021.100019
     [Google Scholar]
  47. TangW. LiY. YuY. WangZ. XuT. ChenJ. LinJ. LiX. Development of models predicting biodegradation rate rating with multiple linear regression and support vector machine algorithms.Chemosphere202025312666610.1016/j.chemosphere.2020.126666 32289603
     [Google Scholar]
  48. DeissL. MargenotA.J. CulmanS.W. DemyanM.S. Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy.Geoderma202036511422710.1016/j.geoderma.2020.114227
     [Google Scholar]
  49. García NietoP.J. CombarroE.F. del Coz DíazJ.J. MontañésE. A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): A case study.Appl. Math. Comput.2013219178923893710.1016/j.amc.2013.03.018
     [Google Scholar]
  50. Suárez SánchezA. García NietoP.J. Riesgo FernándezP. del Coz DíazJ.J. Iglesias-RodríguezF.J. Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain).Math. Comput. Model.2011545-61453146610.1016/j.mcm.2011.04.017
     [Google Scholar]
  51. YuanH. YangG. LiC. WangY. LiuJ. YuH. FengH. XuB. ZhaoX. YangX. Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of RF, ANN, and SVM regression models.Remote Sens. (Basel)20179430910.3390/rs9040309
     [Google Scholar]
  52. SalemO. GuerassimovA. MehaouaA. MarcusA. FurhtB. Anomaly detection in medical wireless sensor networks using SVM and linear regression models.Int. J. E-Health Med. Commun.2014512045[IJEHMC]10.4018/ijehmc.2014010102
     [Google Scholar]
  53. GaoD. LiuY. MengJ. JiaY. FanC. Estimating significant wave height from SAR imagery based on an SVM regression model.Acta Oceanol. Sin.201837310311010.1007/s13131‑018‑1203‑7
     [Google Scholar]
  54. SwilerL.P. GulianM. FrankelA.L. SaftaC. JakemanJ.D. A survey of constrained Gaussian process regression: Approaches and implementation challenges.Journal of Machine Learning for Modeling and Computing20201211915610.1615/JMachLearnModelComput.2020035155
     [Google Scholar]
  55. NawazM.N. KhanM.H.A. HassanW. JaffarS.T.A. JafriT.H. Utilizing undisturbed soil sampling approach to predict elastic modulus of cohesive soils: a Gaussian process regression model.Multiscale and Multidisciplinary Modeling, Experiments and Design.Springer202411610.1007/s41939‑024‑00458‑8
     [Google Scholar]
  56. YuanZ. PengX. MaC. ZhangA. ChenZ. JiangZ. ZhangY. Prediction of mechanical properties of LPBF built part based on process monitoring and Gaussian process regression.Meas. Sci. Technol.202435808560310.1088/1361‑6501/ad4383
     [Google Scholar]
  57. SchulzE. SpeekenbrinkM. KrauseA. A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions.J. Math. Psychol.20188511610.1016/j.jmp.2018.03.001
     [Google Scholar]
  58. BachocF. GamboaF. LoubesJ.M. VenetN. A Gaussian process regression model for distribution inputs.IEEE Trans. Inf. Theory201864106620663710.1109/TIT.2017.2762322
     [Google Scholar]
  59. Nguyen-TuongD. SeegerM. PetersJ. Model learning with local gaussian process regression.Adv. Robot.200923152015203410.1163/016918609X12529286896877
     [Google Scholar]
  60. HoangN.D. PhamA.D. NguyenQ.L. PhamQ.N. Estimating compressive strength of high performance concrete with Gaussian process regression model.Adv. Civ. Eng.201620161810.1155/2016/2861380
     [Google Scholar]
  61. KimM.K. KimY.S. SrebricJ. Predictions of electricity consumption in a campus building using occupant rates and weather elements with sensitivity analysis: Artificial neural network vs. linear regression.Sustain Cities Soc.20206210238510.1016/j.scs.2020.102385
     [Google Scholar]
  62. ZhangX.C. GongJ.G. XuanF.Z. A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures.Eng. Fract. Mech.202125810813010.1016/j.engfracmech.2021.108130
     [Google Scholar]
  63. DaoD.V. JaafariA. BayatM. Mafi-GholamiD. QiC. MoayediH. PhongT.V. LyH-B. LeT.T. TrinhP.T. LuuC. QuocN.K. ThanhB.N. PhamB.T. A spatially explicit deep learning neural network model for the prediction of landslide susceptibility.Catena202018810445110.1016/j.catena.2019.104451
     [Google Scholar]
  64. RuanX. ZhuY. LiJ. ChengY. Predicting the citation counts of individual papers via a BP neural network.J. Informetrics202014310103910.1016/j.joi.2020.101039
     [Google Scholar]
  65. AbhishekK. SinghM.P. GhoshS. AnandA. Weather forecasting model using artificial neural network.Procedia Technol.2012431131810.1016/j.protcy.2012.05.047
     [Google Scholar]
  66. KumarR.L. KhanF. DinS. BandS.S. MosaviA. IbekeE. Recurrent neural network and reinforcement learning model for COVID-19 prediction.Front. Public Health2021974410010.3389/fpubh.2021.744100 34671588
     [Google Scholar]
  67. WenL. YuanX. Forecasting CO2 emissions in Chinas commercial department, through BP neural network based on random forest and PSO.Sci. Total Environ.202071813719410.1016/j.scitotenv.2020.137194 32088474
     [Google Scholar]
  68. PalR. SekhA.A. KarS. PrasadD.K. Neural network based country wise risk prediction of COVID-19.Appl. Sci. (Basel)20201018644810.3390/app10186448
     [Google Scholar]
/content/journals/cos/10.2174/0115701794295930240902050855
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Rapid Detection of Soil Available Phosphorus using Capacitively Coupled Contactless Conductivity Detection
Curr. Org. Synth. 22, 169 (2025); https://doi.org/10.2174/0115701794295930240902050855
/content/journals/cos/10.2174/0115701794295930240902050855
/content/journals/cos/10.2174/0115701794295930240902050855
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  • Article Type:     Research Article
Keyword(s): back propagation neural network model; capacitively coupled contactless conductivity detection; capillary electrophoresis; detection; on-site rapid pretreatment; Soil available phosphorus

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