Biomass carbohydrate composition

The other analytical methods involving biomass composition that are currently in development (FT-IR, NIR, and pyMBMS) are able to resolve and quantify individual sugar composition. This is not possible with HR-TGA however, in conjunction with H nuclear magnetic resonance (NMR), sugar residues can be identified, and their abundance can be determined. Carbohydrate compositional profiles of lignocellulosic biomass can be accurately quantified based on the 600 MHz H-NMR spectram of unpurified acid hydrolyzates wherein the hemicellulose and cellulose ftactions of biomass have been reduced to a mixmre of sugars in acidic solution [23]. 

The carbohydrate composition and lignin were determined using the NREL laboratory analytical procedures (http //wwwl.energy.gov/biomass/analytical procedures.html). The moisture content of the bagasse samples was determined using a moisture analyzer (Computrac MAX 1000, Arizona Instrument Corporation, Tempe, AZ, USA). 

Process Design Strategies for Biomass Conversion Systems Table 6.5 Carbohydrate composition of selected microalgae... 

For anabolic reactions, which result in the production of new cells, it is important to know the approximate chemical composition of the biomass. The bacterial protoplasm comprises 75 to 80% water. The solid material is composed of several complex organic molecules, such as proteins, carbohydrates, and DNA. The mean composition of these molecules can be approximated by a relatively simple empirical formula, C60H87O23N12P, or in an even more simple form as C5H7O2N10.Numerous other elements such as sulfur, sodium, potassium, calcium, magnesium,... 

Macromolecular biomass composition is of obvious interest when the biomass itself is the product, such as algal biomass in [18], or for production of singlecell protein, for e.g. animal feedstock. Moreover, for a precise metabolic flux analysis, changes in biomass composition should be taken in account. For example, Henriksen et al. [19] observed with E. coli under different growth rates, that the levels of DNA and lipids were relatively constant, whereas the proteins and stable RNA levels increased with the specific growth rate and the total amount of carbohydrates decreased. 

An alternative, at least semi-quantitative method to follow changes in biomass composition is infrared (IR) spectroscopy [22]. From dried samples of microbial cells, IR spectra can be obtained which contain information on all major cell components. The spectra are analysed as a multi-component mixture Characteristic bands in the spectra are identified, the extinction coefficients for each component (protein, carbohydrate, lipid, and nucleic acids) at each band are determined, and the concentrations are calculated by a system of linear equations. The method gives results on all major cell components simultaneously, and is relatively quick and easy to perform, compared to the chemical analysis methods. For details see Sect. 8.4 below. 

Compositional variability can have a significant impact on biomass conversion process economics. The large effect (i.e., at least 0.30/gal ethanol) of observed compositional diversity on process economics is shown in Fig. 33.19 and is primarily due to the fact that the maximum theoretical product yield is proportional to feedstock carbohydrate content (Fig. 33.20).131 Yield is the major economic driver for the technoeconomic model used to assess the economic impact of composition on minimum product selling price,130 as can be seen from the data in Fig. 33.21. 

As said above, plant root chemistry may also influence deeply alpine soil microorganism s biomass. It turns out that the particular chemical composition of exudates is a strong selective force in favour of bacteria that can catabolize particular compounds. Plants support heterotrophic microorganisms by way of rhizodeposition of root exudates and litter from dead tissue that include phenolic acids, flavonoids, terpenoids, carbohydrates, hydroxamic acids, aminoacids, denatured protein from dying root cells, CO2, and ethylene (Wardle, 1992). In certain plants, as much as 20-30% of fixed carbon may be lost as rhizodeposition (Lynch and Whipps, 1990). Most of these compounds enter the soil nutrient cycle by way of the soil microbiota, giving rise to competition between the myriad species living there, from microarthropods and nematodes to mycorrhiza and bacteria, for these resources (e.g. Hoover and Crossley, 1995). There is evidence that root phenolic exudates are metabolized preferentially by some soil microbes, while the same compounds are toxic to others. Phenolic acids usually occur in small concentration in soil chiefly because of soil metabolism while adsorption in clay and other soil particles plays a minor role (Bliun et al., 1999). However, their phytotoxicity is compounded by synergism between particular mixtures (Blum, 1996). 

Microbial cell-wall-lytic enzymes are widely used in the laboratory for cell breakage, proto-plasting of yeasts and bacteria, and for studies of the structure and composition of microbial cell walls (J ). Recently lytic systems have come under consideration as a specific and chemically mild way to rupture microbial cells on an industrial scale (2 ). There appear to be attractive commercial applications of lytic systems for the recovery of enzymes, antigens and other recombinant products accumulated within cells, for upgrading of microbial biomass for food and feed uses (4 5) and for the manufacture of functional biopolymers from cell wall carbohydrates (6). 

Figure 7. The depletion of C in lipids relative to biomass as a function of cellular composition, where XcProt, XcLip, and Xcsacch are the mole fiactions of carbon in proteins, lipids, and carbohydrates, respectively (see Eqn. 5 and related discussion). The indicated relationships are based on isotopic mass-balance requirements and on concepts outlined by Laws (1991). The cross marked Redfield (Anderson) indicates the position of cells with C/NfP = 106/16/1 but with lower (and much more realistic) abundances of H and O than those specified by the conventional Redfield formula (Anderson 1995).

Com stover used for this study was harvested in 2003 at the Kramer Farm in Wray, Colorado. The stover was pretreated either in-house at the National Renewable Energy Laboratory or received via subcontract fiom the CAFI [12] pretreatment group members. The samples selected for this study were pietreated by alkaline peroxide (NREL), sulfite steam explosion (UBC), ammonia fiber explosion (MSU), and dilute sulfuric acid (NREL) methods. The composition of the pretreated stover was determined by a two-stage sulfuric acid hydrolysis treatment according to the NREL Laboratory Analytical Procedure titled Determination of Stmctural Carbohydrates and Lignin in Biomass [13]. The pretreatment conditions and compositional information for each substrate are listed in Table 2. 

Composition analysis of the treated/untreated biomass was done according to the NREL Laboratory Anal)dieal Procedures Preparation of samples for compositional analysis and determination of structural carbohydrates and lignin in biomass (draft version) [32]. The moisture content in biomass was measured by an inftared moisture balance (Denver Instrument, IR-30). Sugar content in compositional analysis and enzymatic digestibility was determined by HPLC using a Bio-Rad Aminex HPX-87P. 

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