Abstract
Statistical 1H nuclear magnetic resonance (NMR) analyses were conducted with ternary copolymer blends. Two out of the three monomers, acrylonitrile, styrene, and α-methylstyrene, were subjected to radical copolymerization to synthesize three kinds of copolymers that were mixed to prepare binary and ternary copolymer blends. The 1H NMR spectral matrix for the copolymers and their blends (explanatory variables) was combined with the blending parameter matrix (objective variables). Cross-validation with the least absolute shrinkage and selection operator regression confirmed that excellent regression models were constructed with a dataset composed of data for eight copolymers and forty-five binary blends; these were used to predict the blending parameters for the binary blends, such as the chemical compositions and mole fractions of the component copolymers. Accordingly, the models were then used to predict the blending parameters for the ternary blends, which resulted in successful and highly accurate predictions. Other regularized regression models, such as Ridge regression and Elastic Net, were also examined.
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References
Kowalski BR, Bender CF. Pattern recognition. Powerful approach to interpreting chemical data. J Am Chem Soc. 1972;94:5632–9. https://doi.org/10.1021/ja00771a016
Kowalski BR, Bender CF. Pattern recognition. II. Linear and nonlinear methods for displaying chemical data. J Am Chem Soc. 1973;95:686–93. https://doi.org/10.1021/ja00784a007
Wold S. Spline functions in data analysis. Technometrics. 1974;16:1–11. https://doi.org/10.1080/00401706.1974.10489142
Wold S. Nonlinear partial least squares modelling II. Spline inner relation. Chemom Intell Lab Syst. 1992;14:71–84. https://doi.org/10.1016/0169-7439(92)80093-J
Wold S, Kettaneh-Wold N, Skagerberg B. Nonlinear PLS modeling. Chemom Intell Lab Syst. 1989;7:53–65. https://doi.org/10.1016/0169-7439(89)80111-X
Lavine BK. Chemometrics. Anal Chem. 1998;70:209–28. https://doi.org/10.1021/a19800085
Lavine BK, Brown SD, Booksh KS. 40 years of chemometrics—from Bruce Kowalski to the future. Washington, DC: Oxford University Press; 2015.
Johnels D, Edlund U, Grahn H, Hellberg S, Sjöström M, Wold S, et al. Clustering of aryl carbon-13 nuclear magnetic resonance substituent chemical shifts. A multivariate data analysis using principal components. J Chem Soc, Parkin Trans. 1983;2:863–71. https://doi.org/10.1039/P29830000863
Gartland KP, Beddell CR, Lindon JC, Nicholson JK. Application of pattern recognition methods to the analysis and classification of toxicological data derived from proton nuclear magnetic resonance spectroscopy of urine. Mol Pharm. 1991;39:629–42.
Lindon JC, Nicholson JK, Holmes E, Everett JR. Metabonomics: Metabolic processes studied by NMR spectroscopy of biofluids. Concepts Magn Reson. 2000;12:289–320. https://doi.org/10.1002/1099-0534
Bovey FA. High-resolution NMR of macromolecules. New York: Academic Press; 1972.
Hatada K, Kitayama T. NMR spectroscopy of polymers. Berlin: Springer; 2004.
Sardashti M, Gislason JJ, Lai X, Stewart CA, O’Donnell DJ. Determination of total styrene in styrene/butadiene block copolymers by process NMR and chemometrics modeling. Appl Spectrosc. 2001;55:467–71. https://doi.org/10.1366/0003702011951984
Momose H, Hattori K, Hirano T, Ute K. Multivariate analysis of 13C NMR spectra of methacrylate copolymers and homopolymer blends. Polymer. 2009;50:3819–21. https://doi.org/10.1016/j.polymer.2009.05.048
Momose H, Maeda T, Hattori K, Hirano T, Ute K. Statistical determination of chemical composition and monomer sequence distribution of poly(methyl methacrylate-co-tert-butyl methacrylate)s by multivariate analysis of 13C NMR spectra. Polym J. 2012;44:808–14. https://doi.org/10.1038/pj.2012.110
Hirano T, Anmoto T, Umezawa N, Momose H, Katsumoto Y, Oshimura M, et al. Application of multivariate analysis of NMR spectra of poly(n-isopropylacrylamide) to assignment of stereostructures and prediction of tacticity distribution. Polym J. 2012;44:815–20. https://doi.org/10.1038/pj.2012.61
Mayer BP, Lewicki JP, Chinn SC, Overturf GE, Maxwell RS.Nuclear magnetic resonance and principal component analysis for investigating the degradation of poly[chlorotrifluoroethylene-co-(vinylidene fluoride)] by ionizing radiation.Polym Degrad Stab.2012;97:1151–7. https://doi.org/10.1016/j.polymdegradstab.2012.04.001.
Hirano T, Kamiike R, Hsu Y, Momose H, Ute K. Multivariate analysis of 13C NMR spectra of branched copolymers prepared by initiator-fragment incorporation radical copolymerization of ethylene glycol dimethacrylate and tert-butyl methacrylate. Polym J. 2016;48:793–800. https://doi.org/10.1038/pj.2016.16
Kukackova O, Dung NV, Abbrent S, Urbanova M, Kotek J, Brus J. A novel insight into the origin of toughness in polypropylene–calcium carbonate microcomposites: Multivariate analysis of ss-NMR spectra. Polymer. 2017;132:106–13. https://doi.org/10.1016/j.polymer.2017.10.071
Hsu Y, Chuang M, Hirano T, Ute K. Multivariate analysis of 13C NMR spectra to extract information about monomer sequences in poly(methyl methacrylate-co-benzyl methacrylate)s prepared by various polymer reactions. Polym J. 2018;50:355–63. https://doi.org/10.1038/s41428-018-0027-9
Kusaka Y, Hasegawa T, Kaji H. Noise reduction in solid-state NMR spectra using principal component analysis. J Phys Chem A. 2019;123:10333–8. https://doi.org/10.1021/acs.jpca.9b04437
Fernando LD, Dickwella Widanage MC, Penfield J, Lipton AS, Washton N, Latgé J-P, et al. Structural polymorphism of chitin and chitosan in fungal cell walls from solid-state NMR and principal component analysis. Front Mol Biosci. 2021;8. https://doi.org/10.3389/fmolb.2021.727053.
Hirano T, Kamiike R, Yuki T, Matsumoto D, Ute K.Determination of monomer reactivity ratios from a single sample using multivariate analysis of the 1H NMR spectra of poly[(methyl methacrylate)-co-(benzyl methacrylate)].Polym J.2022;54:623–31. https://doi.org/10.1038/s41428-022-00618-y.
Kamiike R, Hirano T, Ute K. Statistical determination of chemical composition and blending fraction of copolymers by multivariate analysis of 1H NMR spectra of binary blends of the copolymers. Polymer. 2022;256:125207. https://doi.org/10.1016/j.polymer.2022.125207
Novotny EH, Garcia RHS, deAzevedo ER. Pulse sequence-induced variability combined with multivariate analysis as a potential tool for 13C solid-state NMR signals separation, quantification, and classification. J Magn Reson Open. 2023;14-15:100089. https://doi.org/10.1016/j.jmro.2022.100089
Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc B. 1996;58:267–88. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 1970;12:55–67. https://doi.org/10.1080/00401706.1970.10488634
Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc B. 2005;67:301–20. https://doi.org/10.1111/j.1467-9868.2005.00503.x
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Kamiike, R., Hirano, T. & Ute, K. Multivariate statistical analyses of 1H NMR data for binary and ternary copolymer blends to determine the chemical compositions and blending fractions of the components. Polym J 55, 967–974 (2023). https://doi.org/10.1038/s41428-023-00794-5
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DOI: https://doi.org/10.1038/s41428-023-00794-5