These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Excitation-emission fluorescence-kinetic third-order/four-way data: Determination of bisphenol A and nonylphenol in food-contact plastics.
    Author: Carabajal MD, Arancibia JA, Escandar GM.
    Journal: Talanta; 2019 May 15; 197():348-355. PubMed ID: 30771946.
    Abstract:
    The endocrine disrupting chemicals bisphenol A (BPA) and 4-nonylphenol (NP) were simultaneously quantified through third-order/four-way calibration. Excitation-emission fluorescence matrix-kinetic (EEFM-K) third-order data were generated by measuring the EEFMs of these priority xenoestrogens as a function of reaction time during their Fenton degradation. Third-order/four-way calibration notably improves the sensitivity of the method and provides the required selectivity for quantifying analytes with critically overlapped fluorescence signals. In fact, collinearity between BPA and NP emission spectra prevented their quantification using EEFM second-order data and three-way PARAFAC (parallel factor analysis); however, the addition of a third instrumental mode allowed the correct chemometric modeling with four-way PARAFAC. In this way, the compliance of Kruskal's theorem extended to higher-order data was verified. The method was applied for the determination of the analytes in samples of different plastic materials, which are in contact with food and/or beverages. In these cases, where unmodelled constituents are present, good results for BPA were achieved with four-way PARAFAC, but the predictions for NP using this model were deficient. A better predictive capability for NP in real samples was achieved when either U-PLS/RTL (unfolded partial least-squares combined with residual trilinearization) or MCR-ALS (multivariate curve resolution with alternating least-squares) was applied for data processing, demonstrating the power of these latter models for the resolution of more complex systems.
    [Abstract] [Full Text] [Related] [New Search]