Skip to main content
SpringerLink
Log in

Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning

  • Critical Review
  • Published:
Analytical and Bioanalytical Chemistry Aims and scope Submit manuscript

Abstract

Molecularly imprinted polymers (MIPs) rely on synthetic engineered materials able to selectively bind and intimately recognise a target molecule through its size and functionalities. The way in which MIPs interact with their targets, and the magnitude of this interaction, is closely linked to the chemical properties derived during the polymerisation stages, which tailor them to their specific target. Hence, MIPs are in-deep studied in terms of their sensitivity and cross-reactivity, further being used for monitoring purposes of analytes in complex analytical samples. As MIPs are involved in sensor development within different approaches, a systematic optimisation and rational data-driven sensing is fundamental to obtaining a best-performant MIP sensor. In addition, the closer integration of MIPs in sensor development requires that the inner properties of the materials in terms of sensitivity and selectivity are maintained in the presence of competitive molecules, which focus is currently opened. Identifying computational models capable of predicting and reporting the best-performant configuration of electrochemical sensors based on MIPs is of immense importance. The application of chemometrics using design of experiments (DoE) is nowadays increasingly adopted during optimisation problems, which largely reduce the number of experimental trials. These approaches, together with the emergent machine learning (ML) tool in sensor data processing, represent the future trend in design and management of point-of-care configurations based on MIP sensing. This review provides an overview on the recent application of chemometrics tools in optimisation problems during development and analytical assessment of electrochemical sensors based on MIP receptors. A comprehensive discussion is first presented to cover the recent advancements on response surface methodologies (RSM) in optimisation studies of MIPs design. Therefore, the recent advent of machine learning in sensor data processing will be focused on MIPs development and analytical detection in sensors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Adapted from MatLab website help

Fig. 6

Adapted from ref. [72, 75]

Similar content being viewed by others

References

  1. Bossi A, Bonini F, Turner A, Piletsky S. Molecularly imprinted polymers for the recognition of proteins: the state of the art. Biosens Bioelectron. 2007;22(6):1131–7.

    Article  CAS  PubMed  Google Scholar 

  2. Whitcombe MJ, Chianella I, Larcombe L, Piletsky SA, Noble J, Porter R, et al. The rational development of molecularly imprinted polymer-based sensors for protein detection. Chem Soc Rev. 2011;40(3):1547–71.

    Article  CAS  PubMed  Google Scholar 

  3. Malitesta C, Losito I, Zambonin PG. Molecularly imprinted electrosynthesized polymers: new materials for biomimetic sensors. Anal Chem. 1999;71(7):1366–70.

    Article  CAS  PubMed  Google Scholar 

  4. Canfarotta F, Poma A, Guerreiro A, Piletsky S. Solid-phase synthesis of molecularly imprinted nanoparticles. Nat Protoc. 2016;11(3):443–55.

    Article  CAS  PubMed  Google Scholar 

  5. Yan H, Row KH. Characteristic and synthetic approach of molecularly imprinted polymer. Int J Mol Sci. 2006;7(5):155–78.

    Article  CAS  Google Scholar 

  6. Lanza F, Sellergren B. Method for synthesis and screening of large groups of molecularly imprinted polymers. Anal Chem. 1999;71(11):2092–6.

    Article  CAS  PubMed  Google Scholar 

  7. Lamaoui A, García-Guzmán JJ, Amine A, Palacios-Santander JM, Cubillana-Aguilera L. Synthesis techniques of molecularly imprinted polymer composites. Molecularly Imprinted Polymer Composites: Elsevier; 2021. p. 49–91.

    Book  Google Scholar 

  8. Goncalves LM. Electropolymerized molecularly imprinted polymers: Perceptions based on recent literature for soon-to-be world-class scientists. Curr Opinion Electrochem. 2021;25:100640.

    Article  Google Scholar 

  9. Malitesta C, Mazzotta E, Picca RA, Poma A, Chianella I, Piletsky SA. MIP sensors–the electrochemical approach. Anal Bioanal Chem. 2012;402:1827–46.

    Article  CAS  PubMed  Google Scholar 

  10. Liu J, Xu Y, Liu S, Yu S, Yu Z, Low SS. Application and progress of chemometrics in voltammetric biosensing. Biosensors. 2022;12(7):494.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Singh A, Sharma A, Ahmed A, Sundramoorthy AK, Furukawa H, Arya S, et al. Recent advances in electrochemical biosensors: Applications, challenges, and future scope. Biosensors. 2021;11(9):336.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Nicholls IA, Andersson HS, Charlton C, Henschel H, Karlsson BC, Karlsson JG, et al. Theoretical and computational strategies for rational molecularly imprinted polymer design. Biosens Bioelectron. 2009;25(3):543–52.

    Article  CAS  PubMed  Google Scholar 

  13. Puthongkham P, Wirojsaengthong S, Suea-Ngam A. Machine learning and chemometrics for electrochemical sensors: moving forward to the future of analytical chemistry. Analyst. 2021;146(21):6351–64.

    Article  CAS  PubMed  Google Scholar 

  14. Desagani D, Ben-Yoav H. Chemometrics meets electrochemical sensors for intelligent in vivo bioanalysis. TrAC Trends in Anal Chem. 2023;164:117089.

    Article  CAS  Google Scholar 

  15. Namuduri S, Narayanan BN, Davuluru VSP, Burton L, Bhansali S. Deep learning methods for sensor based predictive maintenance and future perspectives for electrochemical sensors. J Electrochem Soc. 2020;167(3):037552.

    Article  CAS  Google Scholar 

  16. Hrichi H, Louhaichi MR, Monser L, Adhoum NJS, chemical AB. Gliclazide voltammetric sensor based on electropolymerized molecularly imprinted polypyrrole film onto glassy carbon electrode. Sens Actuat B: Chem. 2014;204:42-9.

  17. Tarley CRT, Silveira G, dos Santos WNL, Matos GD, da Silva EGP, Bezerra MA, et al. Chemometric tools in electroanalytical chemistry: methods for optimization based on factorial design and response surface methodology. Microchem J. 2009;92(1):58–67.

    Article  CAS  Google Scholar 

  18. Kunath S, Marchyk N, Haupt K, Feller K-H. Multi-objective optimization and design of experiments as tools to tailor molecularly imprinted polymers specific for glucuronic acid. Talanta. 2013;105:211–8.

    Article  CAS  PubMed  Google Scholar 

  19. George A, Cherian AR, Jacob B, Varghese A, Maiyalagan TJFC. Design optimisation and fabrication of amino acid based molecularly imprinted sensor for the selective determination of food additive tartrazine. Food Chem. 2023;404:134673.

    Article  CAS  PubMed  Google Scholar 

  20. Wang Y, Zhou W-Y, Yang Z-Q, Jiang T-M, Song J-L, Du Y-T, et al. An ultrasensitive bacterial imprinted electrochemical sensor for the determination of Lactobacillus rhamnosus GG. Food Chem. 2023;410:135380.

    Article  CAS  PubMed  Google Scholar 

  21. Motaharian A, Motaharian F, Abnous K, Hosseini MRM, Hassanzadeh-Khayyat M. Molecularly imprinted polymer nanoparticles-based electrochemical sensor for determination of diazinon pesticide in well water and apple fruit samples. Anal Bioanalytic Chem. 2016;408:6769–79.

    Article  CAS  Google Scholar 

  22. Candioti LV, De Zan MM, Cámara MS, Goicoechea HC. Experimental design and multiple response optimization. Using the desirability function in analytical methods development. Talanta. 2014;124:123–38.

    Article  PubMed  Google Scholar 

  23. Hanrahan G, Lu K. Application of factorial and response surface methodology in modern experimental design and optimization. Critic Rev Anal Chem. 2006;36(3–4):141–51.

    Article  CAS  Google Scholar 

  24. Zanoni C, Rovida R, Magnaghi LR, Biesuz R, Alberti GJC. Voltammetric Detection of Irbesartan by Molecularly Imprinted Polymer (MIP)-Modified Screen-Printed Electrodes. Chemosensors. 2022;10(12):517.

    Article  CAS  Google Scholar 

  25. Khadem M, Faridbod F, Norouzi P, Foroushani AR, Ganjali MR, Yarahmadi R, et al. Voltammetric determination of carbofuran pesticide in biological and environmental samples using a molecularly imprinted polymer sensor, a multivariate optimization. J Anal Chem. 2020;75:669–78.

    Article  Google Scholar 

  26. Di Masi S, Pennetta A, Guerreiro A, Canfarotta F, De Benedetto GE, Malitesta C. Sensor based on electrosynthesised imprinted polymeric film for rapid and trace detection of copper (II) ions. Sens Actuators B Chem. 2020;307:127648.

    Article  Google Scholar 

  27. Araujo PW, Brereton RG. Experimental design I screening. TrAC Trends in Anal Chem. 1996;15(1):26–31.

    Article  CAS  Google Scholar 

  28. Nezhadali A, Mojarrab M. Fabrication of an electrochemical molecularly imprinted polymer triamterene sensor based on multivariate optimization using multi-walled carbon nanotubes. J Electroanal Chem. 2015;744:85–94.

    Article  CAS  Google Scholar 

  29. Nezhadali A, Bonakdar G. Multivariate optimization of mebeverine analysis using molecularly imprinted polymer electrochemical sensor based on silver nanoparticles. J Food Drug Anal. 2019;27(1):305–14.

    Article  CAS  PubMed  Google Scholar 

  30. Nezhadali A, Senobari S, Mojarrab M. 1, 4-dihydroxyanthraquinone electrochemical sensor based on molecularly imprinted polymer using multi-walled carbon nanotubes and multivariate optimization method. Talanta. 2016;146:525–32.

    Article  CAS  PubMed  Google Scholar 

  31. Nezhadali A, Khalili Z. Computer-aided study and multivariate optimization of nanomolar metformin hydrochloride analysis using molecularly imprinted polymer electrochemical sensor based on silver nanoparticles. J Mater Sci Mater Electron. 2021;32:27171–83.

    Article  CAS  Google Scholar 

  32. Khalili Z, Nezhadali A. Nanomolar detection of lansoprazole: Computational–assisted to monomer–templet complex study based on molecularly imprinted polymer and electrochemical determination. Chem Papers. 2022:1-14.

  33. Nezhadali A, Mojarrab M. Computational design and multivariate optimization of an electrochemical metoprolol sensor based on molecular imprinting in combination with carbon nanotubes. Anal Chimica Acta. 2016;924:86–98.

    Article  CAS  Google Scholar 

  34. Biabani M, Nezhadali A, Nakhaei A, Nakhaei H. Melamine recognition: Molecularly imprinted polymer for selective and sensitive determination of melamine in food samples. Int J Anal Chem. 2020;2020:1–10.

    Article  Google Scholar 

  35. da Silva JL, Buffon E, Beluomini MA, Pradela-Filho LA, Araújo DAG, Santos AL, et al. Non-enzymatic lactose molecularly imprinted sensor based on disposable graphite paper electrode. Anal Chimica Acta. 2021;1143:53–64.

    Article  Google Scholar 

  36. Kalambate PK, Huang Z, Li Y, Shen Y, Xie M, Huang Y, et al. Core@ shell nanomaterials based sensing devices: A review. TrAC Trends Anal Chem. 2019;115:147–61.

    Article  CAS  Google Scholar 

  37. Nag A, Alahi MEE, Mukhopadhyay SC, Liu Z. Multi-walled carbon nanotubes-based sensors for strain sensing applications. Sensors. 2021;21(4):1261.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zaporotskova IV, Boroznina NP, Parkhomenko YN, Kozhitov LV. Carbon nanotubes: Sensor properties. A Rev Modern Electron Mater. 2016;2(4):95–105.

    Article  Google Scholar 

  39. Norizan MN, Moklis MH, Demon SZN, Halim NA, Samsuri A, Mohamad IS, et al. Carbon nanotubes: Functionalisation and their application in chemical sensors. RSC Advances. 2020;10(71):43704–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Zhao Q, Gan Z, Zhuang Q. Electrochemical sensors based on carbon nanotubes. Electroanalysis: An International Journal Devoted to Fundamental and Practical Aspects of Electroanalysis. 2002;14(23):1609-13.

  41. Li C, Thostenson ET. Chou T-WJCS, technology. Sensors and actuators based on carbon nanotubes and their composites: a review. Compos Sci Technol. 2008;68(6):1227–49.

    Article  CAS  Google Scholar 

  42. Trojanowicz M. Analytical applications of carbon nanotubes: a review. TrAC Trends Anal Chem. 2006;25(5):480–9.

    Article  CAS  Google Scholar 

  43. Białas K, Moschou D, Marken F, Estrela P. Electrochemical sensors based on metal nanoparticles with biocatalytic activity. Microchimica Acta. 2022;189(4):172.

    Article  PubMed  Google Scholar 

  44. Torres-Rivero K, Florido A, Bastos-Arrieta J. Recent trends in the improvement of the electrochemical response of screen-printed electrodes by their modification with shaped metal nanoparticles. Sensors. 2021;21(8):2596.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Montes-García V, Squillaci MA, Diez-Castellnou M, Ong QK, Stellacci F, Samori P. Chemical sensing with Au and Ag nanoparticles. Chem Soc Rev. 2021;50(2):1269–304.

    Article  PubMed  Google Scholar 

  46. Islam T, Hasan MM, Awal A, Nurunnabi M, Ahammad AS. Metal nanoparticles for electrochemical sensing: Progress and challenges in the clinical transition of point-of-care testing. Molecules. 2020;25(24):5787.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. de Oliveira GF, Hudari FF, Pereira FM, Zanoni MV, da Silva JL. Carbon Nanotube-Based Molecularly Imprinted Voltammetric Sensor for Selective Diuretic Analysis of Dialysate and Hemodialysis Wastewater. Chem Electro Chem. 2020;7(14):3006–16.

    Google Scholar 

  48. Ganjeizadeh Rohani F, Mohadesi A, Ansari M. Computational Design and Electropolymerization of Molecularly Imprinted Poly (p-Aminobenzoic-Acid-Co–Dapsone) Using Multivariate Optimization for Tetradifon Residue Analysis. Chem Sel. 2019;4(42):12236–44.

    CAS  Google Scholar 

  49. Costa M, Di Masi S, Garcia-Cruz A, Piletsky SA, Malitesta CJS, Chemical AB. Disposable electrochemical sensor based on ion imprinted polymeric receptor for Cd (II) ion monitoring in waters. Sens Actuators B Chem. 2023;383:133559.

    Article  CAS  Google Scholar 

  50. Ferreira SC, Bruns R, Ferreira HS, Matos GD, David J, Brandão G, et al. Box-Behnken design: An alternative for the optimization of analytical methods. Anal Chim Acta. 2007;597(2):179–86.

    Article  CAS  PubMed  Google Scholar 

  51. Zalpour N, Roushani M. A polydopamine imprinted array on a binder-free carbon cloth assembled by gold carbon quantum dots as a portable flexible 3D nano-electrochemical sensor for selective trace monitoring of orlistat (xenical). Microchem J. 2023;190:108750.

    Article  CAS  Google Scholar 

  52. Zalpour N, Roushani M, Hosseini H. Polydopamine imprinted polymer-based tunable electrochemical synthesis of copper benzene-1, 3, 5-tricarboxylate metal-organic framework film as a hybrid dual recognition element for ultra-trace sensing of pregabalin (lyrica). Sens Actuators B Chem. 2022;370:132418.

    Article  CAS  Google Scholar 

  53. Chen M, Challita U, Saad W, Yin C, Debbah M. Machine learning for wireless networks with artificial intelligence: A tutorial on neural networks. arXiv preprint arXiv02913. 2017;9.

  54. Mousavizadegan M, Firoozbakhtian A, Hosseini M, Ju H. Machine learning in analytical chemistry: From synthesis of nanostructures to their applications in luminescence sensing. TrAC Trends Anal Chem. 2023;167:117216.

    Article  CAS  Google Scholar 

  55. Herrera-Chacón A, Cetó X, Del Valle M. Molecularly imprinted polymers-towards electrochemical sensors and electronic tongues. Anal Bioanal Chem. 2021;413:6117–40.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Esteban M, Arino C, Díaz-Cruz J. Chemometrics for the analysis of voltammetric data. TrAC Trends Anal Chem. 2006;25(1):86–92.

    Article  CAS  Google Scholar 

  57. Cetó X, Pérez S. Voltammetric electronic tongue for vinegar fingerprinting. Talanta. 2020;219:121253.

    Article  PubMed  Google Scholar 

  58. Zhang X, Jin J, Zheng J, Gao H. Genetic algorithms based on wavelet transform for resolving simulated overlapped spectra. Anal Bioanal Chem. 2003;377:1153–8.

    Article  CAS  PubMed  Google Scholar 

  59. Zhao S, Liu W, Song D. Rapid detection and prediction model establishment of propachlor residues in food assisted by machine learning. J Food Measure Character. 2023:1-8.

  60. Yarahmadi B, Hashemianzadeh SM, Milani Hosseini HSM-R. Machine-learning-based predictions of imprinting quality using ensemble and non-linear regression algorithms. Sci Rep. 2023;13(1):12111.

  61. Nantasenamat C, Naenna T, Ayudhya CIN, Prachayasittikul V. Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network. J Comput-aided Mol Des. 2005;19:509–24.

    Article  CAS  PubMed  Google Scholar 

  62. Nezhadali A, Sadeghzadeh S. Experimental design-artificial neural network-genetic algorithm optimization and computer-assisted design of celecoxib molecularly imprinted polymer/carbon nanotube sensor. J Electroanal Chem. 2017;795:32–40.

    Article  CAS  Google Scholar 

  63. Nezhadali A, Shadmehri R. Neuro-genetic multi-objective optimization and computer-aided design of pantoprazole molecularly imprinted polypyrrole sensor. Sens Actuators B Chem. 2014;202:240–51.

    Article  CAS  Google Scholar 

  64. Meskher H, Belhaouari SB, Deshmukh K, Hussain CM, Sharifianjazi F. A Magnetite Composite of Molecularly Imprinted Polymer and Reduced Graphene Oxide for Sensitive and Selective Electrochemical Detection of Catechol in Water and Milk Samples: An Artificial Neural Network (ANN) Application. J Electrochem Soc. 2023;170(4):047502.

    Article  CAS  Google Scholar 

  65. Wang C, Hao T, Wang Z, Lin H, Wei W, Hu Y, et al. Machine learning-assisted cell-imprinted electrochemical impedance sensor for qualitative and quantitative analysis of three bacteria. Sens Actuators B Chem. 2023;384:133672.

    Article  CAS  Google Scholar 

  66. Yu H, Bro R, Gallagher NB. PARASIAS: A new method for analyzing higher-order tensors with shifting profiles. Anal Chim Acta. 2023;1238:339848.

    Article  CAS  PubMed  Google Scholar 

  67. Jalalvand AR, Goicoechea HC, Rutledge DN. Applications and challenges of multi-way calibration in electrochemical analysis. TrAC Trends Anal Chem. 2017;87:32–48.

    Article  CAS  Google Scholar 

  68. Jalalvand AR, Karami MM, Arkan E, Sadeghi E. A novel triple templates molecularly imprinted biosensor assisted by first derivatives of second-order hydrodynamic differential normal pulse voltametric data and multi-way calibration methods for simultaneous biosensing of penicillin, tetracycline and amoxicillin in dairy products: A novel multi-disciplinary study. Chemometr Intell Labor Syst. 2023;235:104765.

    Article  CAS  Google Scholar 

  69. Jalalvand AR, Pinto L. A novel triple templates molecularly imprinted biosensor assisted by second-order calibration methods based on generation of second-order hydrodynamic linear sweep voltammetric data for simultaneous biosensing of insulin, proinsulin and C-peptide: Application to comparing PARAFAC2 and PARASIAS. Chemometr Intell Labor Syst. 2023;233:104746.

    Article  CAS  Google Scholar 

  70. Lu N, Chen J, Rao Z, Guo B, Xu Y. Recent Advances of Biosensors for Detection of Multiple Antibiotics. Biosensors. 2023;13(9):850.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Huynh T-P, Kutner WJB. Bioelectronics. Molecularly imprinted polymers as recognition materials for electronic tongues. Biosens Bioelectron. 2015;74:856–64.

    Article  CAS  PubMed  Google Scholar 

  72. Bueno L, El-Sharif HF, Salles MO, Boehm RD, Narayan RJ, Paixão TR, et al. MIP-based electrochemical protein profiling. Sens Actuators B Chem. 2014;204:88–95.

    Article  CAS  Google Scholar 

  73. Wang M, Cetó X, Del Valle M. A Sensor Array Based on Molecularly Imprinted Polymers and Machine Learning for the Analysis of Fluoroquinolone Antibiotics. ACS Sensors. 2022;7(11):3318–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Herrera-Chacon A, González-Calabuig A, Campos I, del Valle M. Bioelectronic tongue using MIP sensors for the resolution of volatile phenolic compounds. Sens Actuators B Chem. 2018;258:665–71.

    Article  CAS  Google Scholar 

  75. Wang M, Cetó X, Del Valle M. A novel electronic tongue using electropolymerized molecularly imprinted polymers for the simultaneous determination of active pharmaceutical ingredients. Biosens Bioelectron. 2022;198:113807.

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

Sabrina Di Masi received financial support from “POC PUGLIA FESR-FSE 2014/2020 Fondo Sociale Europeo”, project “RIPARTI” (assegni di RIcerca per riPARTire con le Imprese) CUP: F87G22000260002.

Author information

Authors and Affiliations

  1. Laboratorio di Chimica Analitica, Dipartimento di Scienze e Tecnologie Biologiche ed Ambientali, Università del Salento, Lecce, Italy

    Sabrina Di Masi & Cosimino Malitesta

  2. Laboratorio di Spettrometria di Massa Analitica e Isotopica, Dipartimento di Beni Culturali, Università del Salento, Lecce, Italy

    Giuseppe Egidio De Benedetto

Authors
  1. Sabrina Di Masi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giuseppe Egidio De Benedetto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cosimino Malitesta
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Sabrina Di Masi: conceptualisation, formal investigation, writing—original draft preparation, writing—review and editing. Giuseppe E. De Benedetto: conceptualisation, writing—original draft preparation, writing—review and editing. Cosimino Malitesta: conceptualisation; writing—review and editing. resources, supervision.

Corresponding author

Correspondence to Cosimino Malitesta.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Published in the topical collection Advances in (Bio-)Analytical Chemistry: Reviews and Trends Collection 2024.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Di Masi, S., De Benedetto, G.E. & Malitesta, C. Optimisation of electrochemical sensors based on molecularly imprinted polymers: from OFAT to machine learning. Anal Bioanal Chem 416, 2261–2275 (2024). https://doi.org/10.1007/s00216-023-05085-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00216-023-05085-9

Keywords

Search

Navigation