4.5 Review

High Throughput Screening Technologies in Biomass Characterization

期刊

FRONTIERS IN ENERGY RESEARCH
卷 6, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fenrg.2018.00120

关键词

biomass recalcitrance; biomass compositional analysis; high throughput analysis; neural networks; biomass conversion

资金

  1. Center for Bioenergy Innovation (CBI)
  2. Office of Biological and Environmental Research in the DOE Office of Science

向作者/读者索取更多资源

Biomass analysis is a slow and tedious process and not solely due to the long generation time for most plant species. Screening large numbers of plant variants for various geno-, pheno-, and chemo-types, whether naturally occurring or engineered in the lab, has multiple challenges. Plant cell walls are complex, heterogeneous networks that are difficult to deconstruct and analyze. Macroheterogeneity from tissue types, age, and environmental factors makes representative sampling a challenge and natural variability generates a significant range in data. Using high throughput (HTP) methodologies allows for large sample sets and replicates to be examined, narrowing in on more precise data for various analyses. This review provides a comprehensive survey of high throughput screening as applied to biomass characterization, from compositional analysis of cell walls by NIR, NMR, mass spectrometry, and wet chemistry to functional screening of changes in recalcitrance via HTP thermochemical pretreatment coupled to enzyme hydrolysis and microscale fermentation. The advancements and development of most high-throughput methods have been achieved through utilization of state-of-the art equipment and robotics, rapid detection methods, as well as reduction in sample size and preparation procedures. The computational analysis of the large amount of data generated using high throughput analytical techniques has recently become more sophisticated, faster and economically viable, enabling a more comprehensive understanding of biomass genomics, structure, composition, and properties. Therefore, methodology for analyzing large datasets generated by the various analytical techniques is also covered.

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