4.6 Article Proceedings Paper

Quantitative analysis of aqueous phases of bio-oils resulting from pyrolysis of different biomasses by two-dimensional comprehensive liquid chromatography

期刊

JOURNAL OF CHROMATOGRAPHY A
卷 1602, 期 -, 页码 359-367

出版社

ELSEVIER
DOI: 10.1016/j.chroma.2019.06.016

关键词

Bio-oil; By-products; Pyrolysis; Quantification; LCxLC

资金

  1. MINECO, Spain [AGL2017-89417-R]
  2. CSIC, Spain [iLINK+ 1096]
  3. CNPQ (Conselho Nacional de Desenvolvimento Cientifico e TecnolOgico)
  4. FINEP (Financiadora de Estudos e Projetos)
  5. Petrobras
  6. Prof. Antonio Imbesi Foundation of the University of Messina

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Agrifood by-products are perfect candidates to be further processed under the concept of circular economy, in order to produce their valorization. Although significant amounts of food-related wastes that are discarded are produced worldwide, these might still be rich in valuable compounds. A strategy to further valorize agrifood-related by-products is based on pyrolysis processes. The result of this process is a liquid product termed bio-oil which is composed of an organic phase and an aqueous phase. This bio-oil is rich on a variety of components and its analysis implies several challenges. In this work, quantitative on-line comprehensive two-dimensional liquid chromatography (LC x LC) is proposed for the first time to characterize several aqueous phases of different bio-oils. Rice husk, peanut shell, spent coffee grounds, peach core and Eucalyptus sawdust biomasses were analyzed. The developed quantitative LC x LC method presented very good linearity, precision, reproducibility, recovery and LODs and LOQs as low as 0.05 mu g mL(-1) and 0.16 mu g mL(-1), respectively. As much as 28 components were simultaneously separated and quantified in those samples. Our results found that the composition of these bio-oils was different but strongly related to the agrifood by-product submitted to pyrolysis. The developed methodology is foreseen as a valuable tool for the quantitative study of other bio-oils, considering the great complexity and high dimensionality of these samples. (C) 2019 Elsevier B.V. All rights reserved.

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