Chemometric Strategies for Simultaneous Quantification of Co-Existing Pollutants in Competitive and Multicomponent Adsorption Systems: A Critical Review

  • Maha Salah Nasr College of Food Science, AL-Qasim Green University, Babylon, Iraq

Abstract

Adsorption studies are still, in the great majority of cases, reported for a single adsorbate, yet the effluents they claim to treat almost never contain one pollutant. Moving from one solute to two or three changes the experiment in a way that is easy to underestimate, because the analytical step itself becomes the limiting factor: the absorbance recorded at one wavelength can no longer be assigned to one species. This review examines how chemometric tools have been used, and occasionally misused, to recover individual concentrations of co-existing pollutants during competitive adsorption. We first set out the artefacts peculiar to adsorption supernatants: spectral overlap, adsorbent-derived colour, residual turbidity and pH-driven speciation. We then follow the methodological ladder from univariate remedies such as derivative and ratio-spectra techniques and the H-point standard addition method, through first-order multivariate calibration by principal component regression and partial least squares with wavelength selection and net analyte signal theory, up to second-order and multiway approaches such as curve resolution and parallel factor analysis, which retain accuracy in the presence of uncalibrated interferents. Neural networks and machine-learning regressors are discussed separately, since they address a genuinely nonlinear problem but carry their own validation burden. Throughout we insist on a point that most application papers pass over: competition factors, extended Langmuir constants and ideal adsorbed solution predictions inherit the uncertainty of the quantification step, so an unvalidated calibration propagates quietly into the thermodynamic conclusions. A minimum reporting set is proposed to make multicomponent removal data comparable between laboratories.

Keywords: Competitive adsorption, Multicomponent systems, Chemometrics, Derivative spectrophotometry, Partial least squares, Multivariate curve resolution, Analytical figures of merit

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References

1. Bayuo J, Rwiza MJ, Sillanpää M, Mtei KM. Removal of heavy metals from binary and multicomponent adsorption systems using various adsorbents – a systematic review. RSC Adv. 2023; 13(19): 13052-13093. https://doi.org/10.1039/D3RA01660A
2. Akhtar N, Aslam Z, Shawabkeh RA, Baig N, Aslam U, Ihsanullah, et al. Decolorization of multicomponent dye-laden wastewater by modified waste fly ash: a parametric analysis for an anionic and cationic combination of dyes. Environ Sci Pollut Res. 2023; 30(31): 77165-77180. https://doi.org/10.1007/s11356-023-27671-1
3. Amrutha, Jeppu G, Girish CR, Prabhu B, Mayer K. Multi-component adsorption isotherms: review and modeling studies. Environ Process. 2023; 10(2): 38. https://doi.org/10.1007/s40710-023-00631-0
4. Sharma K, Dalai AK, Vyas RK. Removal of synthetic dyes from multicomponent industrial wastewaters. Rev Chem Eng. 2018; 34(1): 107-134. https://doi.org/10.1515/revce-2016-0042
5. Gao JF, Wang JH, Yang C, Wang SY, Peng YZ. Binary biosorption of Acid Red 14 and Reactive Red 15 onto acid treated okara: simultaneous spectrophotometric determination of two dyes using partial least squares regression. Chem Eng J. 2011; 171(3): 967-975. https://doi.org/10.1016/j.cej.2011.04.047
6. Hajati S, Ghaedi M, Barazesh B, Karimi F, Sahraei R, Daneshfar A, et al. Application of high order derivative spectrophotometry to resolve the spectra overlap between BG and MB for the simultaneous determination of them: ruthenium nanoparticle loaded activated carbon as adsorbent. J Ind Eng Chem. 2014; 20(4): 2421-2427. https://doi.org/10.1016/j.jiec.2013.10.022
7. Turabik M. Adsorption of basic dyes from single and binary component systems onto bentonite: simultaneous analysis of Basic Red 46 and Basic Yellow 28 by first order derivative spectrophotometric analysis method. J Hazard Mater. 2008; 158(1): 52-64. https://doi.org/10.1016/j.jhazmat.2008.01.033
8. Besegatto SV, Martins ML, Lopes TJ, da Silva A. Multivariated calibration as a tool for resolution of color from mandarin peel and dyes in aqueous solution for bioadsorption studies. J Environ Chem Eng. 2021; 9(1): 104605. https://doi.org/10.1016/j.jece.2020.104605
9. Xia H, Yang Y, Zhang W, Yang Z, Dai Z. Spectrophotometric determination of p-nitrophenol under ENP interference. J Anal Methods Chem. 2021; 2021: 6682722. https://doi.org/10.1155/2021/6682722
10. Antony A, Mitra J. Refractive index-assisted UV/Vis spectrophotometry to overcome spectral interference by impurities. Anal Chim Acta. 2021; 1149: 238186. https://doi.org/10.1016/j.aca.2020.12.061
11. Islam MA, Choudhury JT, Hossain F, Uddin MT. Competitive Langmuir model for dye mixture adsorption: disagreement between theoretical ground and application condition. Langmuir. 2025; 41(1): 216-230. https://doi.org/10.1021/acs.langmuir.4c03340
12. Karpińska J. Derivative spectrophotometry – recent applications and directions of developments. Talanta. 2004; 64(4): 801-822. https://doi.org/10.1016/j.talanta.2004.03.060
13. Gao JF, Zhang Q, Su K, Wang JH. Competitive biosorption of Yellow 2G and Reactive Brilliant Red K-2G onto inactive aerobic granules: simultaneous determination of two dyes by first-order derivative spectrophotometry and isotherm studies. Bioresour Technol. 2010; 101(15): 5793-5801. https://doi.org/10.1016/j.biortech.2010.02.091
14. Zolgharnein J, Bagtash M, Shariatmanesh T. Simultaneous removal of binary mixture of Brilliant Green and Crystal Violet using derivative spectrophotometric determination, multivariate optimization and adsorption characterization of dyes on surfactant modified nano-γ-alumina. Spectrochim Acta A Mol Biomol Spectrosc. 2015; 137: 1016-1028. https://doi.org/10.1016/j.saa.2014.08.115
15. Rastgordani M, Zolgharnein J, Mahdavi V. Derivative spectrophotometry and multivariate optimization for simultaneous removal of Titan yellow and Bromophenol blue dyes using polyaniline@SiO2 nanocomposite. Microchem J. 2020; 155: 104717. https://doi.org/10.1016/j.microc.2020.104717
16. Rastgordani M, Zolgharnein J. Simultaneous determination and optimization of Titan yellow and Reactive Blue 4 dyes removal using chitosan@hydroxyapatite nanocomposites. J Polym Environ. 2021; 29(6): 1789-1807. https://doi.org/10.1007/s10924-020-01982-7
17. Asfaram A, Ghaedi M, Ghezelbash GR, Pepe F. Application of experimental design and derivative spectrophotometry methods in optimization and analysis of biosorption of binary mixtures of basic dyes from aqueous solutions. Ecotoxicol Environ Saf. 2017; 139: 219-227. https://doi.org/10.1016/j.ecoenv.2017.01.043
18. Aazza M, Mounir C, Ahlafi H, Bouymajane A, Cacciola F. Performance of first derivative UV/Visible spectra for kinetic and isothermal study of simultaneous adsorption of o-nitrophenol and p-nitrophenol onto Al2O3 and HDTMA+/Al2O3 composite. J Mol Liq. 2023; 383: 122139. https://doi.org/10.1016/j.molliq.2023.122139
19. Toral MI, Richter P, Cavieres M, González W. Simultaneous determination of o- and p-nitrophenol by first derivative spectrophotometry. Environ Monit Assess. 1999; 54(2): 191-203. https://doi.org/10.1023/A:1005977421221
20. Ghaedi M, Hajati S, Barazesh B, Karimi F, Ghezelbash G. Saccharomyces cerevisiae for the biosorption of basic dyes from binary component systems and the high order derivative spectrophotometric method for simultaneous analysis of Brilliant green and Methylene blue. J Ind Eng Chem. 2013; 19(1): 227-233. https://doi.org/10.1016/j.jiec.2012.08.006
21. Asfaram A, Ghaedi M, Hajati S, Goudarzi A. Ternary dye adsorption onto MnO2 nanoparticle-loaded activated carbon: derivative spectrophotometry and modeling. RSC Adv. 2015; 5(88): 72300-72320. https://doi.org/10.1039/C5RA10815B
22. Bagheri AR, Ghaedi M, Asfaram A, Hajati S, Ghaedi AM, Bazrafshan A, et al. Modeling and optimization of simultaneous removal of ternary dyes onto copper sulfide nanoparticles loaded on activated carbon using second-derivative spectrophotometry. J Taiwan Inst Chem Eng. 2016; 65: 212-224. https://doi.org/10.1016/j.jtice.2016.05.004
23. Córdova BM, Santa Cruz JP, Ocampo M TV, Huamani-Palomino RG, Baena-Moncada AM. Simultaneous adsorption of a ternary mixture of brilliant green, rhodamine B and methyl orange as artificial wastewater onto biochar from cocoa pod husk waste. Quantification of dyes using the derivative spectrophotometry method. New J Chem. 2020; 44(19): 8303-8316. https://doi.org/10.1039/D0NJ00916D
24. Jabkhiro H, El Hassani K, Chems M, Anouar A. Simultaneous removal of anionic dyes onto Mg(Al)O mixed metal oxides from ternary aqueous mixture: derivative spectrophotometry and density functional theory study. Colloid Interface Sci Commun. 2021; 45: 100549. https://doi.org/10.1016/j.colcom.2021.100549
25. Dinç E. The spectrophotometric multicomponent analysis of a ternary mixture of ascorbic acid, acetylsalicylic acid and paracetamol by the double divisor-ratio spectra derivative and ratio spectra-zero crossing methods. Talanta. 1999; 48(5): 1145-1157. https://doi.org/10.1016/S0039-9140(98)00337-3
26. Mohamed EH, Lotfy HM, Hegazy MA, Mowaka S. Different applications of isosbestic points, normalized spectra and dual wavelength as powerful tools for resolution of multicomponent mixtures with severely overlapping spectra. Chem Cent J. 2017; 11(1): 43. https://doi.org/10.1186/s13065-017-0270-8
27. Mustafa MS, Mohammad NN, Radha FH, Kayani KF, Ghareeb HO, Mohammed SJ. Eco-friendly spectrophotometric methods for concurrent analysis of phenol, 2-aminophenol, and 4-aminophenol in ternary mixtures and water samples: assessment of environmental sustainability. RSC Adv. 2024; 14(23): 16045-16055. https://doi.org/10.1039/D4RA01094A
28. Bosch Reig F, Campins Falcó P. H-point standard additions method. Part 1. Fundamentals and application to analytical spectroscopy. Analyst. 1988; 113(7): 1011-1016. https://doi.org/10.1039/AN9881301011
29. Al-Sabha TN, Bunaciu AA, Aboul-Enein HY. H-point standard addition method (HPSAM) in simultaneous spectrophotometric determination of binary mixtures: an overview. Appl Spectrosc Rev. 2011; 46(8): 607-623. https://doi.org/10.1080/05704928.2011.570836
30. Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemom Intell Lab Syst. 2001; 58(2): 109-130. https://doi.org/10.1016/S0169-7439(01)00155-1
31. Al-Ghouti MA, Issa AA, Al-Saqarat BS, Al-Reyahi AY, Al-Degs YS. Multivariate analysis of competitive adsorption of food dyes by activated pine wood. Desalin Water Treat. 2016; 57(57): 27651-27662. https://doi.org/10.1080/19443994.2016.1174742
32. Issa AA, Al-Degs YS, El-Sheikh AH, Al-Reyahi AY, Al Bakain RZ, Abdelghani JI, et al. Application of partial least squares–kernel calibration in competitive adsorption studies using an effective chemically activated biochar. CLEAN – Soil Air Water. 2017; 45(5): 1600333. https://doi.org/10.1002/clen.201600333
33. Issa AA, Abdel-Halim HM, Al-Degs YS, Al-Masri HA. Application of multivariate calibration for studying competitive adsorption of two problematic colorants on acid-activated-kaolinitic clay. Res Chem Intermed. 2017; 43(1): 523-544. https://doi.org/10.1007/s11164-016-2638-0
34. Zolgharnein J, Bagtash M, Asanjarani N. Chemometrics approach for optimization of simultaneous adsorption of Alizarin red S and Congo red by cobalt hydroxide nanoparticles. J Chemom. 2017; 31(2): e2886. https://doi.org/10.1002/cem.2886
35. Bagtash M, Zolgharnein J. Removal of brilliant green and malachite green from aqueous solution by a viable magnetic polymeric nanocomposite: simultaneous spectrophotometric determination of 2 dyes by PLS using original and first derivative spectra. J Chemom. 2018; 32(3): e3014. https://doi.org/10.1002/cem.3014
36. Hassaninejad-Darzi SK, Torkamanzadeh M. Simultaneous UV-Vis spectrophotometric quantification of ternary basic dye mixtures by partial least squares and artificial neural networks. Water Sci Technol. 2016; 74(10): 2497-2504. https://doi.org/10.2166/wst.2016.440
37. Lima JP, Besegatto SV, Villanueva-Mejía F, García-Hernández E, Bonilla-Petriciolet A, Lopes TJ. Binary adsorption isotherms of methylene blue and crystal violet on mandarin peels: prediction via detailed multivariate calibration and density functional theory (DFT) calculations. Environ Sci Pollut Res. 2023; 30(39): 92436-92450. https://doi.org/10.1007/s11356-023-28873-3
38. Guo Y, Zhao H, Han Y, Liu X, Guan S, Zhang Q, et al. Simultaneous spectrophotometric determination of trace copper, nickel, and cobalt ions in water samples using solid phase extraction coupled with partial least squares approaches. Spectrochim Acta A Mol Biomol Spectrosc. 2017; 173: 532-536. https://doi.org/10.1016/j.saa.2016.10.003
39. Rinnan Å, van den Berg F, Engelsen SB. Review of the most common pre-processing techniques for near-infrared spectra. TrAC Trends Anal Chem. 2009; 28(10): 1201-1222. https://doi.org/10.1016/j.trac.2009.07.007
40. Niazi A, Yazdanipour A. Spectrophotometric simultaneous determination of nitrophenol isomers by orthogonal signal correction and partial least squares. J Hazard Mater. 2007; 146(1-2): 421-427. https://doi.org/10.1016/j.jhazmat.2007.03.063
41. Leardi R. Application of genetic algorithm-PLS for feature selection in spectral data sets. J Chemom. 2000; 14(5-6): 643-655. https://doi.org/10.1002/1099-128X(200009/12)14:5/6<643::AID-CEM621>3.0.CO;2-E
42. Araújo MCU, Saldanha TCB, Galvão RKH, Yoneyama T, Chame HC, Visani V. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemom Intell Lab Syst. 2001; 57(2): 65-73. https://doi.org/10.1016/S0169-7439(01)00119-8
43. Al-Degs YS, El-Sheikh AH, Saleh AI, Al-Reyahi AY. Interval wavelength selection and simultaneous quantification of spectrally overlapping food colorants by multivariate calibration. J Food Meas Charact. 2021; 15(3): 2562-2575. https://doi.org/10.1007/s11694-021-00848-3
44. Şahin S, Sarıburun E, Demir C. Net analyte signal-based simultaneous determination of dyes in environmental samples using moving window partial least squares regression with UV-vis spectroscopy. Anal Methods. 2009; 1(3): 208-214. https://doi.org/10.1039/b9ay00009g
45. Olivieri AC, Faber NM, Ferré J, Boqué R, Kalivas JH, Mark H. Uncertainty estimation and figures of merit for multivariate calibration (IUPAC Technical Report). Pure Appl Chem. 2006; 78(3): 633-661. https://doi.org/10.1351/pac200678030633
46. Olivieri AC. Analytical figures of merit: from univariate to multiway calibration. Chem Rev. 2014; 114(10): 5358-5378. https://doi.org/10.1021/cr400455s
47. Olivieri AC. Practical guidelines for reporting results in single- and multi-component analytical calibration: a tutorial. Anal Chim Acta. 2015; 868: 10-22. https://doi.org/10.1016/j.aca.2015.01.017
48. Abrantes G, Almeida V, Maia AJ, Nascimento R, Nascimento C, Silva Y, et al. Comparison between variable-selection algorithms in PLS regression with near-infrared spectroscopy to predict selected metals in soil. Molecules. 2023; 28(19): 6959. https://doi.org/10.3390/molecules28196959
49. Olivieri AC. Analytical advantages of multivariate data processing. One, two, three, infinity? Anal Chem. 2008; 80(15): 5713-5720. https://doi.org/10.1021/ac800692c
50. de Juan A, Tauler R. Multivariate curve resolution: 50 years addressing the mixture analysis problem – a review. Anal Chim Acta. 2021; 1145: 59-78. https://doi.org/10.1016/j.aca.2020.10.051
51. Mazivila SJ, Santos JLM. A review on multivariate curve resolution applied to spectroscopic and chromatographic data acquired during the real-time monitoring of evolving multi-component processes: from process analytical chemistry (PAC) to process analytical technology (PAT). TrAC Trends Anal Chem. 2022; 157: 116698. https://doi.org/10.1016/j.trac.2022.116698
52. Golshan A, Abdollahi H, Beyramysoltan S, Maeder M, Neymeyr K, Rajkó R, et al. A review of recent methods for the determination of ranges of feasible solutions resulting from soft modelling analyses of multivariate data. Anal Chim Acta. 2016; 911: 1-13. https://doi.org/10.1016/j.aca.2016.01.011
53. Ghaffari M, Olivieri AC, Abdollahi H. Strategy to obtain accurate analytical solutions in second-order multivariate calibration with curve resolution methods. Anal Chem. 2018; 90(16): 9725-9733. https://doi.org/10.1021/acs.analchem.8b00336
54. Chiappini FA, Pinto L, Alcaraz MR, Omidikia N, Goicoechea HC, Olivieri AC. Multivariate curve resolution-alternating least-squares and second-order advantage in first-order calibration. A systematic characterisation for three-component analytical systems. Anal Chim Acta. 2024; 1328: 343159. https://doi.org/10.1016/j.aca.2024.343159
55. Bro R. PARAFAC. Tutorial and applications. Chemom Intell Lab Syst. 1997; 38(2): 149-171. https://doi.org/10.1016/S0169-7439(97)00032-4
56. Bro R, Andersson CA, Kiers HAL. PARAFAC2 – Part II. Modeling chromatographic data with retention time shifts. J Chemom. 1999; 13(3-4): 295-309. https://doi.org/10.1002/(SICI)1099-128X(199905/08)13:3/4<295::AID-CEM547>3.0.CO;2-Y
57. Zhang X, Tauler R. Flexible implementation of the trilinearity constraint in multivariate curve resolution alternating least squares (MCR-ALS) of chromatographic and other type of data. Molecules. 2022; 27(7): 2338. https://doi.org/10.3390/molecules27072338
58. Pérez-López C, Oró-Nolla B, Lacorte S, Tauler R. Regions of interest multivariate curve resolution liquid chromatography with data-independent acquisition tandem mass spectrometry. Anal Chem. 2023; 95(19): 7519-7527. https://doi.org/10.1021/acs.analchem.2c05704
59. Benedetti B, Pérez-López C, MacKeown H, Magi E, Tauler R. Improving regions of interest multivariate curve resolution: development of an empirical metric system through the study of passive sampling extracts of wastewater in Antarctica. Anal Chem. 2025; 97(31): 13110-13119. https://doi.org/10.1021/acs.analchem.5c00777
60. Yang X, Muhammad T, Bakri M, Muhammad I, Yang J, Zhai H, et al. Simple and fast spectrophotometric method based on chemometrics for the measurement of multicomponent adsorption kinetics. J Chemom. 2020; 34(8): e3249. https://doi.org/10.1002/cem.3249
61. Murphy KR, Stedmon CA, Graeber D, Bro R. Fluorescence spectroscopy and multi-way techniques. PARAFAC. Anal Methods. 2013; 5(23): 6557-6566. https://doi.org/10.1039/c3ay41160e
62. Sgroi M, Anumol T, Roccaro P, Vagliasindi FGA, Snyder SA. Modeling emerging contaminants breakthrough in packed bed adsorption columns by UV absorbance and fluorescing components of dissolved organic matter. Water Res. 2018; 145: 667-677. https://doi.org/10.1016/j.watres.2018.09.018
63. Sciscenko I, Binetti R, Escudero-Oñate C, Oller I, Arques A. Dissolved organic matter behaviour by conventional treatments of a drinking water plant: controlling its changes with EEM-PARAFAC. Appl Sci. 2024; 14(6): 2462. https://doi.org/10.3390/app14062462
64. Yuan YY, Wang ST, Liu SY, Cheng Q, Wang ZF, Kong DM. Green approach for simultaneous determination of multi-pesticide residue in environmental water samples using excitation-emission matrix fluorescence and multivariate calibration. Spectrochim Acta A Mol Biomol Spectrosc. 2020; 228: 117801. https://doi.org/10.1016/j.saa.2019.117801
65. Osorio A, Toledo-Neira C, Bravo MA. Critical evaluation of third-order advantage with highly overlapped spectral signals. Determination of fluoroquinolones in fish-farming waters by fluorescence spectroscopy coupled to multivariate calibration. Talanta. 2019; 204: 438-445. https://doi.org/10.1016/j.talanta.2019.06.048
66. Aliasgharlou N, Bahram M, Zolfaghari P, Mohseni N. Modeling and optimization of simultaneous degradation of rhodamine B and acid red 14 binary solution by homogeneous Fenton reaction: a chemometrics approach. Turk J Chem. 2020; 44(4): 987-1001. https://doi.org/10.3906/kim-2002-59
67. Sciscenko I, Mora M, Micó P, Escudero-Oñate C, Oller I, Arques A. EEM-PARAFAC as a convenient methodology to study fluorescent emerging pollutants degradation: (fluoro)quinolones oxidation in different water matrices. Sci Total Environ. 2022; 852: 158338. https://doi.org/10.1016/j.scitotenv.2022.158338
68. Morales JMN, Alcaraz MR, Loto A, Parellada EA, Tulli F, Morán Vieyra FE, et al. Chemometric modeling of spectroscopic data for characterizing the visible-light-driven photocatalytic N-dealkylation of rhodamine B on a TiO2 film. Photochem Photobiol. 2025; 101(4): 1000-1012. https://doi.org/10.1111/php.14043
69. Yang XD, Gong B, Chen W, Chen JJ, Qian C, Lu R, et al. In situ quantitative monitoring of adsorption from aqueous phase by UV–vis spectroscopy: implication for understanding of heterogeneous processes. Adv Sci. 2024; 11(35): 2402732. https://doi.org/10.1002/advs.202402732
70. Estrada-Moreno JC, Rendon-Lara E, Jiménez-Núñez ML. Combination of artificial neural networks and principal component analysis for the simultaneous quantification of dyes in multi-component aqueous mixtures. Appl Sci. 2024; 14(2): 809. https://doi.org/10.3390/app1402809
71. Kelani KM, Fekry RA, Fayez YM, Hassan SA. Advanced chemometric methods for simultaneous quantitation of caffeine, codeine, paracetamol, and p-aminophenol in their quaternary mixture. Sci Rep. 2024; 14(1): 2085. https://doi.org/10.1038/s41598-024-52450-4
72. Li F, Chao S, Wang X, Hu Z, Wang D, Tang C, et al. AI-enhanced SERS with probe combinations for concurrent identification and quantification of coexisting metal ions in water. Environ Sci Technol. 2025; 59(35): 17322-17333. https://doi.org/10.1021/acs.est.5c07025
73. Pauletto PS, Gonçalves JO, Pinto LAA, Dotto GL, Salau NPG. Single and competitive dye adsorption onto chitosan-based hybrid hydrogels using artificial neural network modeling. J Colloid Interface Sci. 2020; 560: 722-729. https://doi.org/10.1016/j.jcis.2019.10.106
74. Duarte EDV, Ribeiro NFP, Silva MGC, Vieira MGA, Carvalho SML. Pirarucu hydroxyapatite applied to ternary competitive adsorption of synthetic basic dyes as contaminants of emerging concern: kinetic, equilibrium, and ANN studies. Environ Sci Pollut Res. 2024; 31(18): 26942-26960. https://doi.org/10.1007/s11356-024-32968-w
75. Monneyron P, Faur-Brasquet C, Sakoda A, Suzuki M, Le Cloirec P. Competitive adsorption of organic micropollutants in the aqueous phase onto activated carbon cloth: comparison of the IAS model and neural networks in modeling data. Langmuir. 2002; 18(13): 5163-5169. https://doi.org/10.1021/la020023m
76. Zhai M, Wu Z, Fu B, Sun J, Sleiman M, Meunier FC, et al. Interpretable-generative machine learning approaches for predicting simultaneous removal of organic pollutants and heavy metals from water by adsorbent materials. J Clean Prod. 2025; 532: 146980. https://doi.org/10.1016/j.jclepro.2025.146980
77. da Costa MFP, Araújo RS, Silva AR, Pereira L, Silva GMM. Predictive artificial neural networks as applied tools in the remediation of dyes by adsorption – a review. Appl Sci. 2025; 15(5): 2310. https://doi.org/10.3390/app15052310
78. Butler JAV, Ockrent C. Studies in electrocapillarity. III. J Phys Chem. 1930; 34(12): 2841-2859. https://doi.org/10.1021/j150318a015
79. Myers AL, Prausnitz JM. Thermodynamics of mixed-gas adsorption. AIChE J. 1965; 11(1): 121-127. https://doi.org/10.1002/aic.690110125
80. Walton KS, Sholl DS. Predicting multicomponent adsorption: 50 years of the ideal adsorbed solution theory. AIChE J. 2015; 61(9): 2757-2762. https://doi.org/10.1002/aic.14878
81. Acharya A, Jeppu G, Girish CR, Prabhu B, Murty VR, Martis AS, et al. Adsorption of arsenic and fluoride: modeling of single and competitive adsorption systems. Heliyon. 2024; 10(12): e31967. https://doi.org/10.1016/j.heliyon.2024.e31967
82. Duong VH, Phuong PX, Thuan PTD, Taisheva A, Dung DV, Phung LD, et al. A novel treatment of biogas digestate waste for biochar production and its adsorption of methylene blue and malachite green in a binary system. Biofuels Bioprod
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28/08/2026
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Nasr, M. S. “Chemometric Strategies for Simultaneous Quantification of Co-Existing Pollutants in Competitive and Multicomponent Adsorption Systems: A Critical Review”. International Journal of Pharmacognosy and Chemistry, Vol. 7, no. 2, Aug. 2026, pp. 72-83, doi:10.46796/ijpc.v7i2.949.
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