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One of the key challenges for the industrial translation of cellulose nanofiber (CNF) materials is appropriate characterization and evaluation of product quality. Characterization of CNF properties is difficult because direct nanofiber assessment is largely unreliable and unscalable, while indirect characterization is often inaccurate and unable to be generalized across different biomass sources, processing routes, and final product or component formats. In addition, quality is an ambiguous term that is difficult to define, encompassing material performance, processing sustainability, and any aspects impacting economic viability of industrial production, dependent on the application in question. Using existing data on CNF produced from sorghum biomass, we explored the development of versatile statistical methodologies as a framework to investigate quality and sustainability, including: (a) a novel visualization tool for the evaluationof biomass processing sustainability (Processing Sustainability Triangle); (b) correlation analysis of biomass chemical composition with metrics relating to processing sustainability and nanopaper performance; and (c) an application-tunable Quality Ranking methodology based on a user-defined definition, as built through structural equation modeling. Versatility of the framework allows researchers and technologists to map the statistical methodology onto their experimental system of interest, enhancing data analysis through visualization. Ultimately,more sophisticated techniques for evaluation of product quality and processing sustainability will assist researchers to elucidate relationships in biomass-derived materialperformance and advance the industrial translation of CNF products.