Multivariate identification of extruded PLA samples from the infrared spectrum


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.

This is a preview of subscription content, access via your institution.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10


  1. 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.

    CAS  Article  Google Scholar 

  2. 2

    Lunt J (1998) Large-scale production, properties and commercial applications of polylactic acid polymers. Polym Degrad Stab 59:145–152.

    CAS  Article  Google Scholar 

  3. 3

    Garlotta D (2001) A literature review of poly(lactic acid). J Polym Environ 9:63–84.

    CAS  Article  Google Scholar 

  4. 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.

    CAS  Article  Google Scholar 

  5. 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.

    CAS  Article  Google Scholar 

  6. 6

    Auras R, Harte B, Selke S (2004) An overview of polylactides as packaging materials. Macromol Biosci 4:835–864.

    CAS  Article  Google Scholar 

  7. 7

    Anderson K, Schreck K, Hillmyer M (2008) Toughening polylactide. Polym Rev 48:85–108.

    CAS  Article  Google Scholar 

  8. 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.

    CAS  Article  Google Scholar 

  9. 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.

    CAS  Article  Google Scholar 

  10. 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.

    CAS  Article  Google Scholar 

  11. 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.

    CAS  Article  Google Scholar 

  12. 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.

    CAS  Article  Google Scholar 

  13. 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.

    CAS  Article  Google Scholar 

  14. 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.

    Article  Google Scholar 

  15. 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.

    CAS  Article  Google Scholar 

  16. 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.

    CAS  Article  Google Scholar 

  17. 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.

    CAS  Article  Google Scholar 

  18. 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.

    Article  Google Scholar 

  19. 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.

    Article  Google Scholar 

  20. 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.

    Article  Google Scholar 

  21. 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.

    CAS  Article  Google Scholar 

  22. 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.

    CAS  Article  Google Scholar 

  23. 23

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

    Google Scholar 

  24. 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.

    CAS  Article  Google Scholar 

  25. 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.

    CAS  Article  Google Scholar 

  26. 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. 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.

    Article  Google Scholar 

  28. 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.

    CAS  Article  Google Scholar 

  29. 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.

    CAS  Article  Google Scholar 

  30. 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

    Google Scholar 

Download references


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

Author information



Corresponding author

Correspondence to Jordi-Roger Riba.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation