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Multivariate identification of extruded PLA samples from the infrared spectrum

  • Polymers & biopolymers
  • Published:
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Abstract

Polylactic acid (PLA) is a biodegradable thermoplastic polymer that is presented as a good alternative to petroleum-derived plastics. Some of the major drawbacks of this material are its lack of thermal stability and rapid degradation in large-scale production; thus, special care must be taken during processing. To improve their properties, a reactive extrusion with a multi-epoxy chain extender (SAmfE) has been performed at pilot plant scale. The induced topological modifications produce a mixture of several types of non-uniform structures. Conventional chromatographic (SEC—static light scattering) or spectroscopic (nuclear magnetic resonance) techniques usually fail in characterizing non-uniform structures. A method for the classification of modified PLA samples based on a multivariate treatment of the spectral data obtained by Fourier-transform infrared spectroscopy, jointly with the application of feature extraction and classification algorithms, was applied in this study. The results of this work show the potential of the methodology proposed to improve quality control during manufacturing.

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References

  1. Cailloux J, Abt T, Garcia-Masabet V et al (2018) Effect of the viscosity ratio on the PLA/PA10.10 bioblends morphology and mechanical properties. Express Polym Lett 12:569–582. https://doi.org/10.3144/expresspolymlett.2018.47

    Article  CAS  Google Scholar 

  2. Lunt J (1998) Large-scale production, properties and commercial applications of polylactic acid polymers. Polym Degrad Stab 59:145–152. https://doi.org/10.1016/S0141-3910(97)00148-1

    Article  CAS  Google Scholar 

  3. Garlotta D (2001) A literature review of poly(lactic acid). J Polym Environ 9:63–84. https://doi.org/10.1023/A:1020200822435

    Article  CAS  Google Scholar 

  4. Nofar M, Sacligil D, Carreau PJ et al (2019) Poly(lactic acid) blends: processing, properties and applications. Int J Biol Macromol 125:307–360. https://doi.org/10.1016/J.IJBIOMAC.2018.12.002

    Article  CAS  Google Scholar 

  5. Chu C, Li X, Yu W et al (2019) Degradation behaviors of PLA-matrix composite with 20 vol% magnesium alloy wires under static loading conditions. J Mater Sci 54:4701–4709. https://doi.org/10.1007/s10853-018-03199-5

    Article  CAS  Google Scholar 

  6. Auras R, Harte B, Selke S (2004) An overview of polylactides as packaging materials. Macromol Biosci 4:835–864. https://doi.org/10.1002/mabi.200400043

    Article  CAS  Google Scholar 

  7. Anderson K, Schreck K, Hillmyer M (2008) Toughening polylactide. Polym Rev 48:85–108. https://doi.org/10.1080/15583720701834216

    Article  CAS  Google Scholar 

  8. Liu H, Zhang J (2011) Research progress in toughening modification of poly(lactic acid). J Polym Sci Part B Polym Phys 49:1051–1083. https://doi.org/10.1002/polb.22283

    Article  CAS  Google Scholar 

  9. Cailloux J, Santana OO, Franco-Urquiza E et al (2014) Sheets of branched poly(lactic acid) obtained by one-step reactive extrusion–calendering process: physical aging and fracture behavior. J Mater Sci 49:4093–4107. https://doi.org/10.1007/s10853-014-8101-y

    Article  CAS  Google Scholar 

  10. Cailloux J, Santana OO, Maspoch ML et al (2015) Using viscoelastic properties to quantitatively estimate the amount of modified poly(lactic acid) chains through reactive extrusion. J Rheol (N Y N Y) 59:1191–1227. https://doi.org/10.1122/1.4928071

    Article  CAS  Google Scholar 

  11. Dou T, Jing N, Zhou B, Zhang P (2018) In vitro mineralization kinetics of poly(l-lactic acid)/hydroxyapatite nanocomposite material by attenuated total reflection Fourier transform infrared mapping coupled with principal component analysis. J Mater Sci 53:8009–8019. https://doi.org/10.1007/s10853-018-2169-8

    Article  CAS  Google Scholar 

  12. Pilania G, Liu X-Y, Wang Z (2019) Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators. J Mater Sci 54:8361–8380. https://doi.org/10.1007/s10853-019-03434-7

    Article  CAS  Google Scholar 

  13. Jiang Y, Zhang SY, Zhang XL, Zhang T (2018) Improving the performance of UV-curable coatings with carbon nanomaterials. Express Polym Lett 12:628–639. https://doi.org/10.3144/expresspolymlett.2018.53

    Article  CAS  Google Scholar 

  14. Hernández-Guiteras J, Riba J-R, Romeral L (2014) Redesign process of a 765 kVRMS AC substation connector by means of 3D-FEM simulations. Simul Model Pract Theory 42:1–11. https://doi.org/10.1016/j.simpat.2013.12.001

    Article  Google Scholar 

  15. Riba JR, Cailloux J, Cantero R et al (2018) Multivariable methods applied to FTIR: a powerful technique to highlight architectural changes in poly(lactic acid). Polym Test 65:264–269. https://doi.org/10.1016/j.polymertesting.2017.12.003

    Article  CAS  Google Scholar 

  16. Jiang B, Zhu X, Huang D et al (2015) A combined canonical variate analysis and Fisher discriminant analysis (CVA–FDA) approach for fault diagnosis. Comput Chem Eng 77:1–9. https://doi.org/10.1016/J.COMPCHEMENG.2015.03.001

    Article  CAS  Google Scholar 

  17. Lu Q, Jiang B, Gopaluni RB et al (2018) Locality preserving discriminative canonical variate analysis for fault diagnosis. Comput Chem Eng 117:309–319. https://doi.org/10.1016/J.COMPCHEMENG.2018.06.017

    Article  CAS  Google Scholar 

  18. Zhang Y, Zhang N, You D et al (2019) High-power disk laser welding statuses monitoring based on analyses of multiple-sensor signals. J Manuf Process 41:221–230. https://doi.org/10.1016/J.JMAPRO.2019.03.028

    Article  Google Scholar 

  19. Liu J, Hu Y, Wu B, Wang Y (2018) An improved fault diagnosis approach for FDM process with acoustic emission. J Manuf Process 35:570–579. https://doi.org/10.1016/J.JMAPRO.2018.08.038

    Article  Google Scholar 

  20. Zhang Z, Zhang L, Wen G (2019) Study of inner porosity detection for Al–Mg alloy in arc welding through on-line optical spectroscopy: correlation and feature reduction. J Manuf Process 39:79–92. https://doi.org/10.1016/J.JMAPRO.2019.02.016

    Article  Google Scholar 

  21. Maurya D, Tangirala AK, Narasimhan S (2018) Identification of errors-in-variables models using dynamic iterative principal component analysis. Ind Eng Chem Res 57:11939–11954. https://doi.org/10.1021/acs.iecr.8b01374

    Article  CAS  Google Scholar 

  22. Luo L, Bao S, Tong C (2019) Sparse robust principal component analysis with applications to fault detection and diagnosis. Ind Eng Chem Res 58:1300–1309. https://doi.org/10.1021/acs.iecr.8b04655

    Article  CAS  Google Scholar 

  23. Johnson RA, Wichern DW (2007) Applied multivariate statistical analysis, 6th edn. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  24. Riba J-R, Canals T, Cantero R, Iturriaga H (2011) Potential of infrared spectroscopy in combination with extended canonical variate analysis for identifying different paper types. Meas Sci Technol 22:025601. https://doi.org/10.1088/0957-0233/22/2/025601

    Article  CAS  Google Scholar 

  25. Nørgaard L, Bro R, Westad F, Engelsen SB (2006) A modification of canonical variates analysis to handle highly collinear multivariate data. J Chemom 20:425–435. https://doi.org/10.1002/cem.1017

    Article  CAS  Google Scholar 

  26. Capelli F, Riba J-R, Rodriguez A, Lalaouna S (2017) Research towards energy-efficient substation connectors. Springer International Publishing, Cham, pp 295–301

    Google Scholar 

  27. Riba J-R, Canals T, Gómez R (2012) Comparative study of multivariate methods to identify paper finishes using infrared spectroscopy. IEEE Trans Instrum Meas 61:1029–1036. https://doi.org/10.1109/TIM.2011.2173048

    Article  Google Scholar 

  28. Taqvi SA, Tufa LD, Zabiri H et al (2018) Multiple fault diagnosis in distillation column using multikernel support vector machine. Ind Eng Chem Res 57:14689–14706. https://doi.org/10.1021/acs.iecr.8b03360

    Article  CAS  Google Scholar 

  29. Yuan S, Jiao Z, Quddus N et al (2019) Developing quantitative structure-property relationship models to predict the upper flammability limit using machine learning. Ind Eng Chem Res 58:3531–3537. https://doi.org/10.1021/acs.iecr.8b05938

    Article  CAS  Google Scholar 

  30. Gonçalves CMB, Coutinho JAP, Marrucho IM (2010) Optical properties. In: Auras RA, Lim LT, Selke SE, Tsuji H (eds) Poly(lactic acid): synthesis, structures, properties, processing, and applications. Wiley, Hoboken, pp 97–112

    Chapter  Google Scholar 

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Acknowledgements

The authors acknowledge the financial support from the Spanish Ministry of Economy and Competitiveness through the Project MAT2016-80045-R (AEI/FEDER, UE).

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Authors and Affiliations

  1. Electrical Engineering Department, Universitat Politècnica de Catalunya Tech, Rambla Sant Nebridi 22, 08222, Terrassa, Barcelona, Spain

    Jordi-Roger Riba

  2. Universitat de Lleida, Pla de la Massa 8, 08700, Igualada, Barcelona, Spain

    Rosa Cantero & Trini Canals

  3. Centre Català del Plàstic, Universitat Politècnica de Catalunya Barcelona Tech (UPC-EEBE), C/Colom 114, 08222, Terrassa, Spain

    Violeta García-Masabet, Jonathan Cailloux & Maria Lluïsa Maspoch

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  1. Jordi-Roger Riba
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  2. Rosa Cantero
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  3. Violeta García-Masabet
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  4. Jonathan Cailloux
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  5. Trini Canals
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  6. Maria Lluïsa Maspoch
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Correspondence to Jordi-Roger Riba.

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Riba, JR., Cantero, R., García-Masabet, V. et al. Multivariate identification of extruded PLA samples from the infrared spectrum. J Mater Sci 55, 1269–1279 (2020). https://doi.org/10.1007/s10853-019-04091-6

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  • DOI: https://doi.org/10.1007/s10853-019-04091-6

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