Quantitative Structure–Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order
Abstract
:1. Introduction
2. Results and Discussion
3. Methodology
3.1. Chromatographic Experiments
3.2. QSRR Model Development
3.3. QSRR Model Validation
3.3.1. External Validation
3.3.2. Applicability Domain
3.4. Elution Order Prediction
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Statistics | %RMSE(tR) MLR | %RMSE(tR) MLR-NLP |
---|---|---|
Mean | 26.635 | 36.848 |
Variance | 135.67 | 490.97 |
Observations | 19 | 19 |
Pearson Correlation | 0.961 | |
Df | 18 | |
t Stat | −3.897 | |
P(T<=t) one-tail | 0.00053 | |
t Critical one-tail | 1.734 | |
P(T<=t) two-tail | 0.00106 | |
t Critical two-tail | 2.100 |
CS a | Column | Analysis Parameters b | Model | %RMSE(tR) | %RMSE(order) |
---|---|---|---|---|---|
I | Supelcosil | tG = 10 min, T = 35 °C | MLR (control) | 8.57 | 59.07 |
MLR-NLP | 8.07 | 51.77 | |||
II | Xterra | tG = 20 min, T = 40 °C | MLR (control) | 11.50 | 25.01 |
MLR-NLP | 15.17 | 22.40 | |||
II | Licrospher | tG = 20 min, T = 40 °C | MLR (control) | 13.25 | 30.28 |
MLR-NLP | 12.42 | 39.59 | |||
II | Licrospher | tG = 60 min, T = 40 °C | MLR (control) | 25.60 | 34.11 |
MLR-NLP | 37.94 | 30.10 | |||
II | Licrospher | tG = 120 min, T = 40 °C | MLR (control) | 42.31 | 153.00 |
MLR-NLP | 85.62 | 25.17 | |||
II | Licrospher | tG = 20 min, T = 60 °C | MLR (control) | 18.45 | 36.12 |
MLR-NLP | 16.86 | 40.70 | |||
II | Licrospher | tG = 20 min, T = 80 °C | MLR (control) | 18.82 | 35.25 |
MLR-NLP | 21.06 | 34.65 | |||
II | Licrospher | tG = 20 min, T = 40 °C | MLR (control) | 39.28 | 195.82 |
MLR-NLP | 55.53 | 53.45 | |||
II | PRP | tG = 20 min, T = 40 °C | MLR (control) | 20.07 | 69.44 |
MLR-NLP | 20.72 | 58.09 | |||
II | PRP | tG = 60 min, T = 40 °C | MLR (control) | 37.92 | 107.94 |
MLR-NLP | 52.40 | 41.33 | |||
II | PRP | tG = 20 min, T = 60 °C | MLR (control) | 21.75 | 94.97 |
MLR-NLP | 24.06 | 82.54 | |||
II | PRP | tG = 60 min, T = 60 °C | MLR (control) | 40.11 | 321.65 |
MLR-NLP | 54.35 | 37.16 | |||
II | PRP | tG = 20 min, T = 80 °C | MLR (control) | 22.36 | 137.16 |
MLR-NLP | 26.19 | 53.30 | |||
II | PRP | tG = 60 min, T = 80 °C | MLR (control) | 42.60 | 194.56 |
MLR-NLP | 61.56 | 40.18 | |||
II | Discovery | tG = 20 min, T = 40 °C | MLR (control) | 36.73 | 261.22 |
MLR-NLP | 58.07 | 91.81 | |||
II | Discovery | tG = 20 min, T = 60 °C | MLR (control) | 36.37 | 219.01 |
MLR-NLP | 57.16 | 96.70 | |||
II | Discovery | tG = 20 min, T = 80 °C | MLR (control) | 36.74 | 241.63 |
MLR-NLP | 54.75 | 81.05 | |||
II | Discovery | tG = 20 min, T = 40 °C | MLR (control) | 12.81 | 34.00 |
MLR-NLP | 13.84 | 28.12 | |||
II | Chromolith | tG = 20 min, T = 40 °C | MLR (control) | 20.82 | 43.81 |
MLR-NLP | 24.36 | 28.55 |
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Liu, J.J.; Alipuly, A.; Bączek, T.; Wong, M.W.; Žuvela, P. Quantitative Structure–Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order. Int. J. Mol. Sci. 2019, 20, 3443. https://doi.org/10.3390/ijms20143443
Liu JJ, Alipuly A, Bączek T, Wong MW, Žuvela P. Quantitative Structure–Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order. International Journal of Molecular Sciences. 2019; 20(14):3443. https://doi.org/10.3390/ijms20143443
Chicago/Turabian StyleLiu, J. Jay, Alham Alipuly, Tomasz Bączek, Ming Wah Wong, and Petar Žuvela. 2019. "Quantitative Structure–Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order" International Journal of Molecular Sciences 20, no. 14: 3443. https://doi.org/10.3390/ijms20143443