AUTHOR=Pennells Jordan , Martin Darren J. TITLE=Statistical genetics concepts in biomass-based materials engineering JOURNAL=Frontiers in Bioengineering and Biotechnology VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2022.1022948 DOI=10.3389/fbioe.2022.1022948 ISSN=2296-4185 ABSTRACT=

With the rise of biomass-based materials such as nanocellulose, there is a growing need to develop statistical methods capable of leveraging inter-dependent experimental data to improve material design, product development, and process optimisation. Statistical approaches are essential given the multifaceted nature of variability in lignocellulosic biomass, which includes a range of different biomass feedstock types, a combinative arrangement of different biomass processing routes, and an array of different product formats depending on the focal application. To account for this large degree of variability and to extract meaningful patterns from research studies, there is a requirement to generate larger datasets of biomass-derived material properties through well-designed experimental systems that enable statistical analysis. To drive this trend, this article proposes the cross-disciplinary utilisation of statistical modelling approaches commonly applied within the field of statistical genetics to evaluate data generated in the field of biomass-based material research and development. The concepts of variance partitioning, heritability, hierarchical clustering, and selection gradients have been explained in their native context of statistical genetics and subsequently applied across the disciplinary boundary to evaluate relationships within a model experimental study involving the production of sorghum-derived cellulose nanofibres and their subsequent fabrication into nanopaper material. Variance partitioning and heritability calculates the relative influence of biomass vs. processing factors on material performance, while hierarchical clustering highlights the obscured similarity between experimental samples or characterisation metrics, and selection gradients elucidate the relationships between characterisation metrics and material quality. Ultimately, these statistical modelling approaches provide more depth to the investigation of biomass-processing-structure-property-performance relationships through outlining a framework for product characterisation, quality evaluation, and data visualisation, not only applicable to nanocellulose production but for all biomass-based materials and products.