ABSTRACT
The composition and structural properties of lignocellulosic biomass have significant effects on its downstream conversion to fuels, biomaterials, and building-block chemicals. Specifically, the recalcitrance to modification and compositional variability of lignocellulose make it challenging to optimize and control the conditions under which the conversion takes place. Various characterization protocols have been developed over the past 150 years to elucidate the structural properties and compositional patterns that affect the processing of lignocellulose. Early characterization techniques were developed to estimate the relative digestibility and nutritional value of plant material after ingestion by ruminants and humans alike (e.g. dietary fiber). Over the years, these empirical techniques have evolved into statistical approaches that give a broader and more informative analysis of lignocellulose for conversion processes, to the point where an entire compositional and structural analysis of lignocellulosic biomass can be completed in minutes, rather than weeks. The use of modern spectroscopy and chemometric techniques has shown promise as a rapid and cost effective alternative to traditional empirical techniques. This review serves as an overview of the compositional analysis techniques that have been developed for lignocellulosic biomass in an effort to highlight the motivation and migration towards rapid, accurate, and cost-effective data-driven chemometric methods. These rapid analysis techniques can potentially be used to optimize future biorefinery unit operations, where large quantities of lignocellulose are continually processed into products of high value.
Subject(s)
Lignin/chemistry , Biomass , Chemistry Techniques, Analytical , Spectrum AnalysisABSTRACT
Pneumonia remains the single leading cause of childhood death worldwide. Despite the commercial availability of multiple pneumococcal conjugate vaccines (PCVs), high dosage cost and supply shortages prevent PCV delivery to much of the developing world. The current work presents high-yield pneumococcal conjugates that are immunogenic in animals and suitable for use in human vaccine development. The 13-valent pneumococcal conjugate vaccine (PCV-13) investigated in this research incorporated serotypes 1, 3, 4, 5, 6A, 6B, 7F, 9V, 14, 18C, 19A, 19F, and 23F. Pneumococcal polysaccharides (PnPSs) and CRM197 carrier protein were produced and purified in-house, and used to prepare PnPS-CRM conjugates using unique, cyanide-free, in vacuo glycation conjugation methods. In vitro characterization confirmed the generation of higher molecular weight PnPS-CRM conjugates low in free protein. In vivo animal studies were performed to compare PnuVax's PCV-13 to the commercially available PCV-13, Prevnar®13 (Pfizer, USA). A boost dose was provided to all groups post-dose 1 at t = 14 days. Post-dose 2 results at t = 28 days showed that all 13 serotypes in PnuVax's PCV-13 were boostable. Per serotype IgG GMCs demonstrated that PnuVax's PCV-13 is immunogenic for all 13 serotypes, with 10 of the 13 serotypes statistically the same or higher than Prevnar®13 post-dose 2. As a result, the novel polysaccharide-protein conjugates developed in this work are highly promising for use in human PCV development. The in vacuo conjugation technique applied in this work could also be readily adapted to develop many other conjugate vaccines.
ABSTRACT
PLS regression models were developed using mixtures of cellulose, xylan, and lignin in a ternary mixture experimental design for multivariate model calibration. Mid-infrared spectra of these representative samples were recorded using Attenuated Total Reflectance (ATR) Fourier Transform Infrared (FT-IR) spectroscopy and regressed against their known composition using Partial Least Squares (PLSs) multivariate techniques. The regression models were cross-validated and then used to predict the unknown compositions of two Arabidopsis cultivars, B10 and C10. The effect of various data preprocessing techniques on the final predictive ability of the PLS regression models was also evaluated. The predicted compositions of B10 and C10 by the PLS regression model after second derivative data preprocessing were similar to the results provided by a third-party analysis. This study suggests that mixture designs could be used as calibration standards in PLS regression for the compositional analysis of lignocellulosic materials if the infrared data is appropriately preprocessed.
