Research Article
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Effects of chromatographic conditions on retention behaviour of different psychoactive agents in high-performance liquid chromatography: A machine-learning-based approach

Year 2024, , 133 - 143, 26.08.2024

Abdullahi Garba Usman , Emine Erdağ , Selin Işık

https://doi.org/10.26650/IstanbulJPharm.2024.1225463

Abstract

Background and Aims: High-pressure liquid chromatography (HPLC) data on the effects of various chromatographic conditions on the retention behaviour of three different psychotropic drugs; clonazepam, diazepam, and oxazepam) were considered for simulation using a machine learning approach.

Methods: For the simulation of selected psychoactive compounds using HPLC, different machine learning techniques were used in this study: adaptive neuro-fuzzy inference system, multilayer perceptron, Hammerstein-Weiner model, and a traditional linear model in the form of stepwise linear regression. Four evaluation criteria were used to assess the effectiveness of the models: coefficient of determination, root mean squared error, mean squared error, and correlation coefficient.

Results: The results show that machine learning approaches, especially multilayer perceptions, are more reliable than classical linear models with an average coefficient of determination value of 0.98 in both calibration and validation phases.

Conclusion: The performance results also demonstrate that these models can be improved using additional approaches, such as hybrid models, ensemble machine learning, evolving algorithms, and optimisation techniques.

Keywords

Machine learning clonazepam diazepam oxazepam validation evaluation metrics

References

  • Abba, S.I., Hadi, S.J., & Abdullahi, J. (2017). River wa-ter modelling prediction using multi-linear regression, ar-tificial neural network, and adaptive neuro-fuzzy inference system techniques. Procedia Computer Science, 120, 75-82. https://doi.org/10.1016/j.procs.2017.11.212 google scholar
  • Abba, S. I., Hadi, S. J., Sammen, S. S., Salih, S. Q., Abdulkadir, R. A., Pham, Q. B., Yaseen, Z. M. (2020). Evolutionary computational intelligence algorithm coupled with a self-tuning predictive model for water quality index determination. Journal of Hydrology, 587, 124974. https://doi.org/10.1016/j.jhydrol.2020.124974 google scholar
  • Abba, S., Usman, A., & I, S. (2020). Simulation for response surface in the HPLC optimization method development us-ing artificial intelligence models: A data-driven approach. Chemometrics and Intelligent Laboratory Systems, 201, 104007. https://doi.org/10.1016/j.chemolab.2020.104007 google scholar
  • Abba, S. I., Linh, N. T. T., Abdullahi, J., Ali, S. I. A., Pham, Q. B., Abdulkadir, R. A., ... & Anh, D. T. (2020). Hy-brid machine learning ensemble techniques for modeling dis-solved oxygen concentration. IEEE Access, 8, 157218-157237. https://doi.org/10.1109/ACCESS.2020.3017743 google scholar
  • Chandwani, V., Vyas, S. K., Agrawal, V., & Sharma, G. (2015). Soft computing approach for rainfall-runoff modelling: a review. Aquatic Procedia, 4, 1054-1061. https://doi.org/10.1016/j.aqpro.2015.02.133 google scholar
  • Choubin, B., Khalighi-Sigaroodi, S., Malekian, A., & Kişi, Ö. (2016). Multiple linear regression, multi-layer percep-tron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrological Sciences Journal, 61(6), 1001-1009. https://doi.org/10.1080/02626667.2014.966721 google scholar
  • Cunha, D. L., Mendes, M. P., & Marques, M. (2019). Environmen-tal risk assessment of psychoactive drugs in the aquatic envi-ronment. Environmental Science and Pollution Research, 26(1), 78-90. https://doi.org/10.1007/s11356-018-3556-z google scholar
  • D’Archivio, A. A. (2019). Artificial neural network prediction of reten-tion of amino acids in reversed-phase HPLC under application of linear organic modifier gradients and/or pH gradients. Molecules, 24(3), 632. https://doi.org/10.3390/molecules24030632 google scholar
  • Elkiran, G., Nourani, V., & Abba, S. I. (2019). Multi-step ahead mod-elling of river water quality parameters using ensemble artificial intelligence-based approach. Journal of Hydrology, 577, 123962. https://doi.org/10.1016/j.jhydrol.2019.123962 google scholar
  • Erdag, E., Haskologlu, I. C., Mercan, M., Abacioglu, N., & Sehirli, A. O. (2023). An in silico investigation: Can melatonin serve as an adjuvant in NR1D1-linked chronotherapy for amyotrophic lat-eralsclerosis?. Chronobiology International, 40(10), 1395-1403. https://doi.org/10.1080/07420528.2023.2265476 google scholar
  • Fine, J., Mann, A. K. P., & Aggarwal, P. (2024). Structure Based Ma-chine Learning Prediction of Retention Times for LC Method De-velopment of Pharmaceuticals. Pharmaceutical Research, 41(2), 365-374. https://doi.org/10.1007/s11095-023-03646-2 google scholar
  • Ghali, U. M., Usman, A. G., Chellube, Z. M., Degm, M. A. A., Hoti, K., Umar, H., & Abba, S. I. (2020). Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach. SN Applied Sciences, 2(11), 1-12. https://doi.org/10.1007/s42452-020-03690-2 google scholar
  • Ghorbani, M.A., Deo, R.C., Yaseen, Z.M., H. Kashani, M., Mo-hammadi, B. (2017). Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran.Theoretical and Applied Climatology, 133 , 1119-1131. https://doi.org/10.1007/s00704-017-2244-0 google scholar
  • Hadi, S. J., Abba, S. I., Sammen, S. S., Salih, S. Q., Al-Ansari, N., & Yaseen, Z. M. (2019). Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation. IEEE Access, 7, 141533-141548. https://doi.org/10.1109/ACCESS.2019.2943515 google scholar
  • Işık, S., Usman, A., & Abba, S. I. (2023). A chemometrics-based approach for the determination of thymoquinone from Nigella sativa L. (Black Cumin) seeds of different geographical regions using the HPLC technique. İstanbul Journal of Pharmacy, 53(3), 320-328. https://doi.org/10.26650/IstanbulJPharm.2023.1181298 google scholar
  • Jouyban, A., Soltanpour, S., Acree Jr, W. E., Thomas, D., Agrafiotou, P., & Pappa-Louisi, A. (2009). Modeling the effects of different mobile phase compositions and temperatures on the retention of various analytes in HPLC. Journal of Separation Science, 32(22), 3898-3905. https://doi.org/10.1002/jssc.200900389 google scholar
  • Karimi, S., Kisi, O., Shiri, J., & Makarynskyy, O. (2013). Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia. Computers & Geosciences, 52, 50-59. https://doi.org/10.1016/j.cageo.2012.09.015 google scholar
  • Kim, S., & Singh, V. P. (2014). Modeling daily soil tem-perature using data-driven models and spatial distribu-tion. Theoretical and Applied Climatology, 118(3), 465-479. https://doi.org/10.1007/s00704-013-1065-z google scholar
  • Lee, J. K., Han, W. S., Lee, J. S., & Yoon, C. N. (2017). A Novel Com-putational Method for Biomedical Binary Data Analysis: Devel-opment of a Thyroid Disease Index Using a Brute-Force Search with MLR Analysis. Bulletin of the Korean Chemical Society, 38(12), 1392-1397. https://doi.org/10.1002/bkcs.11308 google scholar
  • Lee, S. Y., Lee, S. T., Suh, S., Ko, B. J., &Oh,H.B. (2022). Revealing unknown controlled substances and new psychoactive substances using high-resolution LC-MS-MS machine learning models and the hybrid similarity search algorithm. Journal of Analytical Tox-icology, 46(7), 732-742. https://doi.org/10.1093/jat/bkab098 google scholar
  • Noorizadeh, H., & Noorizadeh, M. (2012). QSRR-based estimation of the retention time of opiate and sedative drugs by comprehensive two-dimensional gas chromatography. Medicinal Chemistry Re-search, 21, 1997-2005. https://doi.org/10.1007/s00044-011-9727-9 google scholar
  • Osipenko, S., Bashkirova, I., Sosnin, S., Kovaleva, O., Fedorov, M., Nikolaev, E., & Kostyukevich, Y. (2020). Machine learn-ing to predict retention time of small molecules in nano-HPLC. Analytical and Bioanalytical Chemistry, 412(28), 7767-7776. https://doi.org/10.1007/s00216-020-02905-0 google scholar
  • Pasin, D., Mollerup, C. B., Rasmussen, B. S., Linnet, K., & Dalsgaard, P. W. (2021). Development of a single retention time prediction model integrating multiple liquid chromatography systems: appli-cation to new psychoactive substances. Analytica Chimica Acta, 1184, 339035. https://doi.org/10.1016/j.aca.2021.339035 google scholar
  • Pham, Q. B., Abba, S. I., Usman, A. G., Linh, N. T. T., Gupta, V., Malik, A., ... & Tri, D. Q. (2019). Potential of hy-brid data-intelligence algorithms for multi-station modelling of rainfall. Water Resources Management, 33(15), 5067-5087. https://doi.org/10.1007/s11269-019-02408-3 google scholar
  • Rodrıguez-Dıaz. J. M., & Sanchez-Leon, G. (2019). Optimal de-signs for multiresponse models with double covariance struc-ture. Chemometrics and Intelligent Laboratory Systems, 189, 1-7. https://doi.org/10.1016/j.chemolab.2019.03.009 google scholar
  • Sahu, P. K., Ramisetti, N. R., Cecchi, T., Swain, S., Patro, C. S., & Panda, J. (2018). An overview of experimental de-signs in HPLC method development and validation. Journal of Pharmaceutical and Biomedical Analysis, 147, 590-611. https://doi.org/10.1016/j.jpba.2017.05.006 google scholar
  • Samuelsson, J., Eiriksson, F. F., Âsberg, D., Thorsteinsdottir, M., & Fornstedt, T. (2019). Determining gradient conditions for pep-tide purification in RPLC with machine-learning-based retention time predictions. Journal of Chromatography A, 1598, 92-100. https://doi.org/10.1016/j.chroma.2019.03.043 google scholar
  • Shojaeimehr, T., & Rahimpour, F. (2018). Retention time mod-eling of short-chain aliphatic acids in aqueous ion-exclusion chromatography systems under several conditions using com-putational intelligence methods (artificial neural network and adaptive neuro-fuzzy inference system). Journal of Liquid Chromatography & Related Technologies, 41(12), 810-817. https://doi.org/10.1080/10826076.2018.1518846 google scholar
  • Silva, J. M., Azcarate, F. J., Knobel, G., Sosa, J. S., Carrizo, D. B., & Boschetti, C. E. (2020). Multiple response optimization of a QuEChERS extraction and HPLC analysis of diclazuril, nicar-bazin and lasalocid in chicken liver. Food Chemistry, 311, 126014. https://doi.org/10.1016/j.foodchem.2019.126014 google scholar
  • Sultanoglu, N., Erdag, E., & Ozverel, C. S. (2023). A single antiviral for a triple epidemic: is it possible?. Future Virology, 18(10), 633-642. https://doi.org/10.2217/fvl-2023-0048 google scholar
  • Tayyebi, S., Hajjar, Z., & Soltanali, S. (2019). A novel mod-ified training of radial basis network: Prediction of con-version and selectivity in 1-hexene dimerization process. Chemometrics and Intelligent Laboratory Systems, 190, 1-9. https://doi.org/10.1016/j.chemolab.2019.05.005 google scholar
  • Tomic, J., Ivkovic, B., Oljacic, S., Nikolic, K., Maljuric, N., Pro-tic, A., & Agbaba, D. (2020). Chemometrically assisted RP-HPLC method development for efficient separation of ivabradine and its eleven impurities. Acta Chromatographica, 32(1), 53-63. https://doi.org/10.1556/1326.2019.00659 google scholar
  • Usman, A. G., Işik, S., & Abba, S.I. (2020). A novel multi-model data-driven ensemble technique for the prediction of retention factor in HPLC method development. Chromatographia, 83(8), 933-945. https://doi.org/10.1007/s10337-020-03912-0 google scholar
  • Usman, A. G., Işik, S., & Abba, S. I. (2021). Hybrid data-intelligence algorithms for the simulation of thymoquinone in HPLC method development. (7)Journal of the Iranian Chemical Society, 18, 1537-1549. https://doi.org/10.1007/s13738-020-02124-5 google scholar
  • Usman, A. G., Işik, S., & Abba, S. I. (2022). Qualitative prediction of Thymoquinone in the high-performance liq-uid chromatography optimization method development us-ing artificial intelligence models coupled with ensemble ma-chine learning. Separation Science Plus, 5(10), 579-587. https://doi.org/10.1002/sscp.202200071 google scholar
  • Usman, A. G., Işik, S., Abba, S. I., & Meriçli, F. (2021). Chemometrics-based models hyphenated with ensemble ma-chine learning for retention time simulation of isoquercitrin in Coriander sativum L. using high-performance liquid chro-matography. Journal of Separation Science, 44(4), 843-849. https://doi.org/10.1002/jssc.202000890 google scholar
  • Zhang, Y., Liu, F., Li, X. Q., Gao, Y., Li, K. C., & Zhang, Q. H. (2024). Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration. Communications Chemistry, 7(1), 54. https://doi.org/10.1038/s42004-024-01135-0 google scholar

Year 2024, , 133 - 143, 26.08.2024

Abdullahi Garba Usman , Emine Erdağ , Selin Işık

https://doi.org/10.26650/IstanbulJPharm.2024.1225463

Abstract

References

  • Abba, S.I., Hadi, S.J., & Abdullahi, J. (2017). River wa-ter modelling prediction using multi-linear regression, ar-tificial neural network, and adaptive neuro-fuzzy inference system techniques. Procedia Computer Science, 120, 75-82. https://doi.org/10.1016/j.procs.2017.11.212 google scholar
  • Abba, S. I., Hadi, S. J., Sammen, S. S., Salih, S. Q., Abdulkadir, R. A., Pham, Q. B., Yaseen, Z. M. (2020). Evolutionary computational intelligence algorithm coupled with a self-tuning predictive model for water quality index determination. Journal of Hydrology, 587, 124974. https://doi.org/10.1016/j.jhydrol.2020.124974 google scholar
  • Abba, S., Usman, A., & I, S. (2020). Simulation for response surface in the HPLC optimization method development us-ing artificial intelligence models: A data-driven approach. Chemometrics and Intelligent Laboratory Systems, 201, 104007. https://doi.org/10.1016/j.chemolab.2020.104007 google scholar
  • Abba, S. I., Linh, N. T. T., Abdullahi, J., Ali, S. I. A., Pham, Q. B., Abdulkadir, R. A., ... & Anh, D. T. (2020). Hy-brid machine learning ensemble techniques for modeling dis-solved oxygen concentration. IEEE Access, 8, 157218-157237. https://doi.org/10.1109/ACCESS.2020.3017743 google scholar
  • Chandwani, V., Vyas, S. K., Agrawal, V., & Sharma, G. (2015). Soft computing approach for rainfall-runoff modelling: a review. Aquatic Procedia, 4, 1054-1061. https://doi.org/10.1016/j.aqpro.2015.02.133 google scholar
  • Choubin, B., Khalighi-Sigaroodi, S., Malekian, A., & Kişi, Ö. (2016). Multiple linear regression, multi-layer percep-tron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals. Hydrological Sciences Journal, 61(6), 1001-1009. https://doi.org/10.1080/02626667.2014.966721 google scholar
  • Cunha, D. L., Mendes, M. P., & Marques, M. (2019). Environmen-tal risk assessment of psychoactive drugs in the aquatic envi-ronment. Environmental Science and Pollution Research, 26(1), 78-90. https://doi.org/10.1007/s11356-018-3556-z google scholar
  • D’Archivio, A. A. (2019). Artificial neural network prediction of reten-tion of amino acids in reversed-phase HPLC under application of linear organic modifier gradients and/or pH gradients. Molecules, 24(3), 632. https://doi.org/10.3390/molecules24030632 google scholar
  • Elkiran, G., Nourani, V., & Abba, S. I. (2019). Multi-step ahead mod-elling of river water quality parameters using ensemble artificial intelligence-based approach. Journal of Hydrology, 577, 123962. https://doi.org/10.1016/j.jhydrol.2019.123962 google scholar
  • Erdag, E., Haskologlu, I. C., Mercan, M., Abacioglu, N., & Sehirli, A. O. (2023). An in silico investigation: Can melatonin serve as an adjuvant in NR1D1-linked chronotherapy for amyotrophic lat-eralsclerosis?. Chronobiology International, 40(10), 1395-1403. https://doi.org/10.1080/07420528.2023.2265476 google scholar
  • Fine, J., Mann, A. K. P., & Aggarwal, P. (2024). Structure Based Ma-chine Learning Prediction of Retention Times for LC Method De-velopment of Pharmaceuticals. Pharmaceutical Research, 41(2), 365-374. https://doi.org/10.1007/s11095-023-03646-2 google scholar
  • Ghali, U. M., Usman, A. G., Chellube, Z. M., Degm, M. A. A., Hoti, K., Umar, H., & Abba, S. I. (2020). Advanced chromatographic technique for performance simulation of anti-Alzheimer agent: an ensemble machine learning approach. SN Applied Sciences, 2(11), 1-12. https://doi.org/10.1007/s42452-020-03690-2 google scholar
  • Ghorbani, M.A., Deo, R.C., Yaseen, Z.M., H. Kashani, M., Mo-hammadi, B. (2017). Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran.Theoretical and Applied Climatology, 133 , 1119-1131. https://doi.org/10.1007/s00704-017-2244-0 google scholar
  • Hadi, S. J., Abba, S. I., Sammen, S. S., Salih, S. Q., Al-Ansari, N., & Yaseen, Z. M. (2019). Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation. IEEE Access, 7, 141533-141548. https://doi.org/10.1109/ACCESS.2019.2943515 google scholar
  • Işık, S., Usman, A., & Abba, S. I. (2023). A chemometrics-based approach for the determination of thymoquinone from Nigella sativa L. (Black Cumin) seeds of different geographical regions using the HPLC technique. İstanbul Journal of Pharmacy, 53(3), 320-328. https://doi.org/10.26650/IstanbulJPharm.2023.1181298 google scholar
  • Jouyban, A., Soltanpour, S., Acree Jr, W. E., Thomas, D., Agrafiotou, P., & Pappa-Louisi, A. (2009). Modeling the effects of different mobile phase compositions and temperatures on the retention of various analytes in HPLC. Journal of Separation Science, 32(22), 3898-3905. https://doi.org/10.1002/jssc.200900389 google scholar
  • Karimi, S., Kisi, O., Shiri, J., & Makarynskyy, O. (2013). Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia. Computers & Geosciences, 52, 50-59. https://doi.org/10.1016/j.cageo.2012.09.015 google scholar
  • Kim, S., & Singh, V. P. (2014). Modeling daily soil tem-perature using data-driven models and spatial distribu-tion. Theoretical and Applied Climatology, 118(3), 465-479. https://doi.org/10.1007/s00704-013-1065-z google scholar
  • Lee, J. K., Han, W. S., Lee, J. S., & Yoon, C. N. (2017). A Novel Com-putational Method for Biomedical Binary Data Analysis: Devel-opment of a Thyroid Disease Index Using a Brute-Force Search with MLR Analysis. Bulletin of the Korean Chemical Society, 38(12), 1392-1397. https://doi.org/10.1002/bkcs.11308 google scholar
  • Lee, S. Y., Lee, S. T., Suh, S., Ko, B. J., &Oh,H.B. (2022). Revealing unknown controlled substances and new psychoactive substances using high-resolution LC-MS-MS machine learning models and the hybrid similarity search algorithm. Journal of Analytical Tox-icology, 46(7), 732-742. https://doi.org/10.1093/jat/bkab098 google scholar
  • Noorizadeh, H., & Noorizadeh, M. (2012). QSRR-based estimation of the retention time of opiate and sedative drugs by comprehensive two-dimensional gas chromatography. Medicinal Chemistry Re-search, 21, 1997-2005. https://doi.org/10.1007/s00044-011-9727-9 google scholar
  • Osipenko, S., Bashkirova, I., Sosnin, S., Kovaleva, O., Fedorov, M., Nikolaev, E., & Kostyukevich, Y. (2020). Machine learn-ing to predict retention time of small molecules in nano-HPLC. Analytical and Bioanalytical Chemistry, 412(28), 7767-7776. https://doi.org/10.1007/s00216-020-02905-0 google scholar
  • Pasin, D., Mollerup, C. B., Rasmussen, B. S., Linnet, K., & Dalsgaard, P. W. (2021). Development of a single retention time prediction model integrating multiple liquid chromatography systems: appli-cation to new psychoactive substances. Analytica Chimica Acta, 1184, 339035. https://doi.org/10.1016/j.aca.2021.339035 google scholar
  • Pham, Q. B., Abba, S. I., Usman, A. G., Linh, N. T. T., Gupta, V., Malik, A., ... & Tri, D. Q. (2019). Potential of hy-brid data-intelligence algorithms for multi-station modelling of rainfall. Water Resources Management, 33(15), 5067-5087. https://doi.org/10.1007/s11269-019-02408-3 google scholar
  • Rodrıguez-Dıaz. J. M., & Sanchez-Leon, G. (2019). Optimal de-signs for multiresponse models with double covariance struc-ture. Chemometrics and Intelligent Laboratory Systems, 189, 1-7. https://doi.org/10.1016/j.chemolab.2019.03.009 google scholar
  • Sahu, P. K., Ramisetti, N. R., Cecchi, T., Swain, S., Patro, C. S., & Panda, J. (2018). An overview of experimental de-signs in HPLC method development and validation. Journal of Pharmaceutical and Biomedical Analysis, 147, 590-611. https://doi.org/10.1016/j.jpba.2017.05.006 google scholar
  • Samuelsson, J., Eiriksson, F. F., Âsberg, D., Thorsteinsdottir, M., & Fornstedt, T. (2019). Determining gradient conditions for pep-tide purification in RPLC with machine-learning-based retention time predictions. Journal of Chromatography A, 1598, 92-100. https://doi.org/10.1016/j.chroma.2019.03.043 google scholar
  • Shojaeimehr, T., & Rahimpour, F. (2018). Retention time mod-eling of short-chain aliphatic acids in aqueous ion-exclusion chromatography systems under several conditions using com-putational intelligence methods (artificial neural network and adaptive neuro-fuzzy inference system). Journal of Liquid Chromatography & Related Technologies, 41(12), 810-817. https://doi.org/10.1080/10826076.2018.1518846 google scholar
  • Silva, J. M., Azcarate, F. J., Knobel, G., Sosa, J. S., Carrizo, D. B., & Boschetti, C. E. (2020). Multiple response optimization of a QuEChERS extraction and HPLC analysis of diclazuril, nicar-bazin and lasalocid in chicken liver. Food Chemistry, 311, 126014. https://doi.org/10.1016/j.foodchem.2019.126014 google scholar
  • Sultanoglu, N., Erdag, E., & Ozverel, C. S. (2023). A single antiviral for a triple epidemic: is it possible?. Future Virology, 18(10), 633-642. https://doi.org/10.2217/fvl-2023-0048 google scholar
  • Tayyebi, S., Hajjar, Z., & Soltanali, S. (2019). A novel mod-ified training of radial basis network: Prediction of con-version and selectivity in 1-hexene dimerization process. Chemometrics and Intelligent Laboratory Systems, 190, 1-9. https://doi.org/10.1016/j.chemolab.2019.05.005 google scholar
  • Tomic, J., Ivkovic, B., Oljacic, S., Nikolic, K., Maljuric, N., Pro-tic, A., & Agbaba, D. (2020). Chemometrically assisted RP-HPLC method development for efficient separation of ivabradine and its eleven impurities. Acta Chromatographica, 32(1), 53-63. https://doi.org/10.1556/1326.2019.00659 google scholar
  • Usman, A. G., Işik, S., & Abba, S.I. (2020). A novel multi-model data-driven ensemble technique for the prediction of retention factor in HPLC method development. Chromatographia, 83(8), 933-945. https://doi.org/10.1007/s10337-020-03912-0 google scholar
  • Usman, A. G., Işik, S., & Abba, S. I. (2021). Hybrid data-intelligence algorithms for the simulation of thymoquinone in HPLC method development. (7)Journal of the Iranian Chemical Society, 18, 1537-1549. https://doi.org/10.1007/s13738-020-02124-5 google scholar
  • Usman, A. G., Işik, S., & Abba, S. I. (2022). Qualitative prediction of Thymoquinone in the high-performance liq-uid chromatography optimization method development us-ing artificial intelligence models coupled with ensemble ma-chine learning. Separation Science Plus, 5(10), 579-587. https://doi.org/10.1002/sscp.202200071 google scholar
  • Usman, A. G., Işik, S., Abba, S. I., & Meriçli, F. (2021). Chemometrics-based models hyphenated with ensemble ma-chine learning for retention time simulation of isoquercitrin in Coriander sativum L. using high-performance liquid chro-matography. Journal of Separation Science, 44(4), 843-849. https://doi.org/10.1002/jssc.202000890 google scholar
  • Zhang, Y., Liu, F., Li, X. Q., Gao, Y., Li, K. C., & Zhang, Q. H. (2024). Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration. Communications Chemistry, 7(1), 54. https://doi.org/10.1038/s42004-024-01135-0 google scholar
There are 37 citations in total.

Details

Primary Language English
Subjects Pharmacology and Pharmaceutical Sciences
Journal Section Original Article
Authors

Abdullahi Garba Usman 0000-0001-5660-4581

Emine Erdağ 0000-0002-1431-935X

Selin Işık 0000-0001-7601-3746

Publication Date August 26, 2024
Submission Date December 29, 2022
Published in Issue Year 2024

Cite

APA Usman, A. G., Erdağ, E., & Işık, S. (2024). Effects of chromatographic conditions on retention behaviour of different psychoactive agents in high-performance liquid chromatography: A machine-learning-based approach. İstanbul Journal of Pharmacy, 54(2), 133-143. https://doi.org/10.26650/IstanbulJPharm.2024.1225463
AMA Usman AG, Erdağ E, Işık S. Effects of chromatographic conditions on retention behaviour of different psychoactive agents in high-performance liquid chromatography: A machine-learning-based approach. iujp. August 2024;54(2):133-143. doi:10.26650/IstanbulJPharm.2024.1225463
Chicago Usman, Abdullahi Garba, Emine Erdağ, and Selin Işık. “Effects of Chromatographic Conditions on Retention Behaviour of Different Psychoactive Agents in High-Performance Liquid Chromatography: A Machine-Learning-Based Approach”. İstanbul Journal of Pharmacy 54, no. 2 (August 2024): 133-43. https://doi.org/10.26650/IstanbulJPharm.2024.1225463.
EndNote Usman AG, Erdağ E, Işık S (August 1, 2024) Effects of chromatographic conditions on retention behaviour of different psychoactive agents in high-performance liquid chromatography: A machine-learning-based approach. İstanbul Journal of Pharmacy 54 2 133–143.
IEEE A. G. Usman, E. Erdağ, and S. Işık, “Effects of chromatographic conditions on retention behaviour of different psychoactive agents in high-performance liquid chromatography: A machine-learning-based approach”, iujp, vol. 54, no. 2, pp. 133–143, 2024, doi: 10.26650/IstanbulJPharm.2024.1225463.
ISNAD Usman, Abdullahi Garba et al. “Effects of Chromatographic Conditions on Retention Behaviour of Different Psychoactive Agents in High-Performance Liquid Chromatography: A Machine-Learning-Based Approach”. İstanbul Journal of Pharmacy 54/2 (August 2024), 133-143. https://doi.org/10.26650/IstanbulJPharm.2024.1225463.
JAMA Usman AG, Erdağ E, Işık S. Effects of chromatographic conditions on retention behaviour of different psychoactive agents in high-performance liquid chromatography: A machine-learning-based approach. iujp. 2024;54:133–143.
MLA Usman, Abdullahi Garba et al. “Effects of Chromatographic Conditions on Retention Behaviour of Different Psychoactive Agents in High-Performance Liquid Chromatography: A Machine-Learning-Based Approach”. İstanbul Journal of Pharmacy, vol. 54, no. 2, 2024, pp. 133-4, doi:10.26650/IstanbulJPharm.2024.1225463.
Vancouver Usman AG, Erdağ E, Işık S. Effects of chromatographic conditions on retention behaviour of different psychoactive agents in high-performance liquid chromatography: A machine-learning-based approach. iujp. 2024;54(2):133-4.