A new organic matter fractionation methodology for organic wastes: Bioaccessibility and complexity characterization for treatment optimization. (October 2015)

• Record Type: Journal Article
• Authors: Jimenez, Julie; Aemig, Quentin; Doussiet, Nicolas; Steyer, Jean-Philippe; Houot, Sabine; Patureau, Dominique
• Is Part Of: Bioresource technology. Volume 194(2015)
• Page Start: 344
• Page End: 353
• Publication Date: 2015-10
• Subjects: Biodegradability -- Bioaccessibility -- Fluorescence -- Organic matter -- Characterization; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2015.07.037
• Languages: English
• ISSNs: 0960-8524

Abstract

Highlights: Substrates bioaccessibility revealed by unifying two chemical fractionation protocols. Substrates classification by data from fluorescence spectroscopy and fractionation. Huge perspectives for statistical and dynamic treatment modeling. Abstract: Organic matter characterization is the starting point to describe organic waste treatments mechanisms and to propose their modeling. Two relevant characterization methodologies were frequently used in the literature based on chemical extractions and fluorescence spectroscopy. However, they could not be generalized to all the type of wastes because of the different molecules targeted. Consequently, a new fractionation methodology was proposed to unify the characterization of a wide range of organic wastes. This new method was built by merging the two previously mentioned protocols to simulate bioaccessibility combined with 3D fluorescence spectroscopy to highlight the complexity of the extracted organic fractions. Sixty samples including representative samples used to validate the method were characterized according to their bioaccessibility and their complexity. Thanks to a principal component analysis, organic wastes were classified according to their nature, their complexity and accessibility. The applicability of this method in statistical or dynamic models is very promising.