| Title |
Elucidation of non-intentionally added substances migrating from polyester-polyurethane lacquers using automated LC-HRMS data processing
|
|---|---|
| Published in |
Analytical & Bioanalytical Chemistry, March 2018
|
| DOI | 10.1007/s00216-018-0968-z |
| Pubmed ID | |
| Authors |
Elsa Omer, Ronan Cariou, Gérald Remaud, Yann Guitton, Hélène Germon, Paul Hill, Gaud Dervilly-Pinel, Bruno Le Bizec |
| Abstract |
An untargeted strategy aiming at identifying non-intentionally added substances (NIAS) migrating from coatings was developed. This innovative approach was applied to two polyester-polyurethane lacquers, for which suppliers previously provided the identity of the monomers involved. Lacquers were extracted with acetonitrile and analyzed by liquid chromatography-high resolution mass spectrometry (LC-HRMS). Data, acquired in the full scan mode, were processed using an open-source R-environment (xcms and CAMERA packages) to list the detected features and deconvolute them in groups related to individual compounds. The most intense groups, accounting for more than 85% of cumulated feature intensities, were then investigated. A homemade database, populated with predicted polyester oligomer combinations from a relevant selection of diols and diacids, enabled highlighting the presence of 14 and 17 cyclic predicted polyester oligomers in the two lacquers, including three mutual combinations explained by common known monomers. Combination hypotheses were strengthened by chromatographic considerations and by the investigation of fragmentation patterns. Regarding unpredicted migrating substances, four monomers were hypothesised to explain several polyester or caprolactam oligomer series. Finally, considering both predicted and tentatively elucidated unpredicted oligomers, it was possible to assign hypotheses to features representing up to 82% and 90% of the cumulated intensities in the two lacquers, plus 9% and 3% (respectively) originating from the procedural blank. Graphical abstract Elucidation of non-intentionally added substances. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 37 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 19% |
| Researcher | 6 | 16% |
| Student > Master | 6 | 16% |
| Lecturer | 3 | 8% |
| Professor > Associate Professor | 2 | 5% |
| Other | 4 | 11% |
| Unknown | 9 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 10 | 27% |
| Agricultural and Biological Sciences | 5 | 14% |
| Environmental Science | 2 | 5% |
| Economics, Econometrics and Finance | 2 | 5% |
| Social Sciences | 2 | 5% |
| Other | 7 | 19% |
| Unknown | 9 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 August 2018.
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#20,233,045
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Outputs from Analytical & Bioanalytical Chemistry
#6,171
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#257,618
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Outputs of similar age from Analytical & Bioanalytical Chemistry
#103
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