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Monte Carlo Assisted FTIR Spectroscopy: A Python Tool for the Determination of the Constituents in Blended Biopolymer Samples
Author(s) -
Souza Fernando Gomes,
Cunha Cláudia Duarte,
Pereira Emiliane Daher,
Dias Diogo Simas Bernardes,
Pal Kaushik,
Pereira Michelle Colão,
Silva Rebecca Alves
Publication year - 2021
Publication title -
macromolecular symposia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 76
eISSN - 1521-3900
pISSN - 1022-1360
DOI - 10.1002/masy.202000174
Subject(s) - biopolymer , monte carlo method , fourier transform infrared spectroscopy , python (programming language) , mean squared error , composition (language) , materials science , polymer , spectroscopy , root mean square , biological system , computer science , mathematics , chemical engineering , physics , statistics , engineering , composite material , linguistics , philosophy , quantum mechanics , biology , operating system
Abstract A massive number of biopolymers and complex polymer blends are being developed every day. One of the significant challenges related to these materials is their characterization. In more specific terms, estimating the composition of polymeric mixtures is a substantial challenge for the technologist, who must understand the nuances of the material's composition for its proper application. Thus, this work develops a Python code capable of handling the composition analysis using the FTIR spectra of some biopolymers found in the literature. Through the Monte Carlo method associated with the Mixture Rule, these spectra are combined, generating a myriad of signals referring to different compositions. These signals are compared with the one from the FTIR spectrum of the mixture, allowing the root‐mean‐square error (RMSE) calculation. Finally, minimizing the RMSE value leads to the final composition of the material, which is presented and saved in a text file.