Literature DB >> 26592648

Attenuated total reflectance-mid infrared spectroscopy (ATR-MIR) coupled with independent components analysis (ICA): A fast method to determine plasticizers in polylactide (PLA).

Amine Kassouf1, Alexandre Ruellan2, Delphine Jouan-Rimbaud Bouveresse3, Douglas N Rutledge4, Sandra Domenek5, Jacqueline Maalouly6, Hanna Chebib7, Violette Ducruet8.   

Abstract

Compliance of plastic food contact materials (FCMs) with regulatory specifications in force, requires a better knowledge of their interaction phenomena with food or food simulants in contact. However these migration tests could be very complex, expensive and time-consuming. Therefore, alternative procedures were introduced based on the determination of potential migrants in the initial material, allowing the use of mathematical modeling, worst case scenarios and other alternative approaches, for simple and fast compliance testing. In this work, polylactide (PLA), plasticized with four different plasticizers, was considered as a model plastic formulation. An innovative analytical approach was developed, based on the extraction of qualitative and quantitative information from attenuated total reflectance (ATR) mid-infrared (MIR) spectral fingerprints, using independent components analysis (ICA). Two novel chemometric methods, Random_ICA and ICA_corr_y, were used to determine the optimal number of independent components (ICs). Both qualitative and quantitative information, related to the identity and the quantity of plasticizers in PLA, were retrieved through a direct and fast analytical method, without any prior sample preparations. Through a single qualitative model with 11 ICs, a clear and clean classification of PLA samples was obtained, according to the identity of plasticizers incorporated in their formulations. Moreover, a quantitative model was established for each formulation, correlating proportions estimated by ICA and known concentrations of plasticizers in PLA. High coefficients of determination (higher than 0.96) and recoveries (higher than 95%) proved the good predictability of the proposed models.
Copyright © 2015 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  ICA_corr_y; Independent components analysis (ICA); Mid-infrared spectroscopy (MIR); Plasticizers; Polylactide (PLA); Random_ICA

Year:  2015        PMID: 26592648     DOI: 10.1016/j.talanta.2015.10.021

Source DB:  PubMed          Journal:  Talanta        ISSN: 0039-9140            Impact factor:   6.057


  1 in total

1.  Machine learning-assisted non-destructive plasticizer identification and quantification in historical PVC objects based on IR spectroscopy.

Authors:  Tjaša Rijavec; David Ribar; Jernej Markelj; Matija Strlič; Irena Kralj Cigić
Journal:  Sci Rep       Date:  2022-03-23       Impact factor: 4.379

  1 in total

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