Particle classification by image analysis improves understanding of corn stover degradation mechanisms during deconstructionShow others and affiliations
2023 (English)In: Industrial crops and products (Print), ISSN 0926-6690, E-ISSN 1872-633X, Vol. 193, article id 116153Article in journal (Refereed) Published
Abstract [en]
Biomass feedstock heterogeneity is a principal roadblock to implementation of the biorefinery concept. Even within an identical cultivar of corn stover, different bales contain not only varying abundance moisture, ash, glucan, and other chemical compounds, but also varying abundance of tissue anatomies (e.g., leaf, husk, cob, or stalk). These different anatomical components not only differ in their response to pretreatment and enzymatic hydrolysis to glucose, but also vary in their mechanical and conveyance properties. Although this heterogeneous nature of corn stover feedstock has been identified as a challenge, a fundamental knowledge gap of how these tissues behave during biorefining processing remains. In this work, we demonstrate the use of a commercial fiber image analyzer typically used for wood fiber characterization to monitor the particle size and shapes of non-woody feedstock during milling, pretreatment, and hydrolysis. Additionally, we present novel use of Gaussian process classification to distinguish bundle, parenchyma, and fiber particles to an accuracy of 96.4%. Quantitative probability distribution plots for characteristics such as length and roundness allow elucidation of particle morphology as pretreatment and enzymatic hydrolysis progress. In both stalk pith and stalk rind, particles peel into individual cells whose walls are subsequently fragmented during enzymatic hydrolysis.
Place, publisher, year, edition, pages
Elsevier B.V. , 2023. Vol. 193, article id 116153
Keywords [en]
Biomass conversion, Feedstock enhancement, Gaussian process classification, Particle image analysis
National Category
Bioprocess Technology Biochemistry and Molecular Biology
Research subject
Biochemical Process Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-95302DOI: 10.1016/j.indcrop.2022.116153ISI: 001145015300012Scopus ID: 2-s2.0-85145251321OAI: oai:DiVA.org:ltu-95302DiVA, id: diva2:1732538
Note
Validerad;2023;Nivå 2;2023-01-31 (sofila);
Funder: U.S. Department of Energy (EERE), Bioenergy Technologies Office (BETO); FOA-0002029 (grant no. DE-EE000890)
2023-01-312023-01-312024-03-07Bibliographically approved