Click here to place an order for determining Rhamnose.
Request a QuoteRhamnose Content
Total Sugars, Glucose, Xylose, Mannose, Arabinose, Galactose, Rhamnose, Lignin (Klason), Lignin (Acid Soluble), Acid Insoluble Residue, Extractives (Ethanol-Soluble), Extractives (Water-Soluble), Extractives (Exhaustive - Water then Ethanol), Extractives (Water-Insoluble, Ethanol Soluble) , Ash, Ash (Acid Insoluble)
Total Sugars, Glucose, Xylose, Mannose, Arabinose, Galactose, Rhamnose, Lignin (Klason), Lignin (Klason - Protein Corrected), Lignin (Acid Soluble), Acid Insoluble Residue, Extractives (Ethanol-Soluble), Extractives (Water-Soluble), Extractives (Exhaustive - Water then Ethanol), Extractives (Water-Insoluble, Ethanol Soluble) , Ash, Ash (Acid Insoluble), Glucuronic Acid, Galacturonic Acid, 4-O-Methyl-D-Glucuronic Acid, Protein Content of Acid Insoluble Residue, Carbon Content of Acid Insoluble Residue, Hydrogen Content of Acid Insoluble Residue, Nitrogen Content of Acid Insoluble Residue, Sulphur Content of Acid Insoluble Residue, Xylitol, Sucrose, Fructose, Sorbitol, Trehalose
Total Sugars, Glucose, Xylose, Mannose, Arabinose, Galactose, Rhamnose, Lignin (Klason), Lignin (Acid Soluble), Carbon, Extractives (Ethanol-Soluble), Extractives (Water-Soluble), Extractives (Exhaustive - Water then Ethanol), Extractives (Water-Insoluble, Ethanol Soluble) , Ash, Ash (Acid Insoluble), Starch, Pectin, Glucuronic Acid, Galacturonic Acid, 4-O-Methyl-D-Glucuronic Acid
We have several FOSS XDS NIR devices. These have solid content modules, that can allow for samples of a heterogenous particle to be analysed, and liquid modules that allow liquids to be analysed via transmittance spectroscopy.
A Dionex ICS-3000 system that is equipmed with electrochemical, conductivity, and ultraviolet-visible detectors.
An autoclave is used in the protocols for the determination of the lignin and structural sugars (cellulosic and hemicellulosic) contents of samples.
|Rhamnose Global 1|
|Min. Value (%)||0.02|
|Max. Value (%)||1.56|
Analytical data and quantitative near infrared (NIR) spectroscopy models for various lignocellulosic components (including Klason lignin and the constituent sugars glucose, xylose, mannose, arabinose, galactose, and rhamnose), ash, and ethanol-soluble extractives, were obtained for 53 samples of paper and cardboard. These samples were mostly the type of materials typically found in domestic wastes (e.g. newspapers, printing paper, glossy papers, food packaging). A number of the samples (48) were obtained by separating a sample, after milling, into two particle size fractions. It was found that the fractions containing the smaller particles typically had higher ash and Klason lignin contents and lower glucose and xylose contents that the larger particle size fractions. Nevertheless, all of the sample types had attractive total sugars contents (>50%) indicating that these could be suitable feedstocks for the production of biofuels and chemicals in hydrolysis-based biorefining technologies. NIR models of a high predictive accuracy (R2 of > 0.9 for the independent validation set) were obtained for total sugars, glucose, xylose, Klason lignin, and ash and with values for the Root Mean Square Error of Prediction (RMSEP) of 2.36%, 2.64%, 0.56%, 1.98%, and 4.87%, respectively. Good NIR models (R2 of > 0.8) were also obtained for mannose, arabinose, and galactose. These results suggest that NIR is a suitable method for the rapid, low-cost, analysis of the major lignocellulosic components of waste paper/cardboard samples.
Analytical data and quantitative near infrared (NIR) spectroscopy models for various lignocellulosic components (including Klason lignin and the constituent sugars glucose, xylose, mannose, arabinose, galactose, and rhamnose), moisture, and ash were obtained for 53 peat samples. These included samples with high, medium, and low degrees of humification. Klason lignin was the main constituent and was greatest in the samples classified as being highly humified, with structural sugars the lowest in this class. The total sugars contents of all samples were considered to be insufficient to allow for their use in biorefining hydrolysis processes for the production of chemicals and biofuels. NIR models were developed for spectral datasets obtained from the samples in their unprocessed (wet), dry and unground, and dry and ground states. Typically the most accurate models were based on the spectra of dry and ground samples. However the NIR models for the wet samples still offered reasonable predictive capabilities. All models were suitable at least for sample screening, with the models for total sugars, glucose, xylose, galactose, and moisture suitable for quantitative analyses.
Miscanthus plants were sampled from several plantations in Ireland over the harvest window (October-April). These were separated into their anatomical components and the loss of leaves monitored. Three distinct phases were apparent: there was minimal loss in the "Early" (October to early December) and "Late" (March and April) phases, and rapid leaf loss in the interim period. Samples were analysed for constituents relevant to biorefining. Changes in whole-plant composition included increases in glucose and Klason lignin contents and decreases in ash and arabinose contents. These changes arose mostly from the loss of leaves, but there were some changes over time within the harvestable plant components. Although leaves yield less biofuel than stems, the added biomass provided by an early harvest (31.9-38.4%) meant that per hectare biofuel yields were significantly greater (up to 29.3%) than in a late harvest. These yields greatly exceed those from first generation feedstocks.
Miscanthus samples were scanned over the visible and near infrared wavelengths at several stages of processing (wet-chopped, air-dried, dried and ground, and dried and sieved). Models were developed to predict lignocellulosic and elemental constituents based on these spectra. The dry and sieved scans gave the most accurate models; however the wet-chopped models for glucose, xylose, and Klason lignin provided excellent accuracies with root mean square error of predictions of 1.27%, 0.54%, and 0.93%, respectively. These models can be suitable for most applications. The wet models for arabinose, Klason lignin, acid soluble lignin, ash, extractives, rhamnose, acid insoluble residue, and nitrogen tended to have lower R(2) values (0.80+) for the validation sets and the wet models for galactose, mannose, and acid insoluble ash were less accurate, only having value for rough sample screening. This research shows the potential for online analysis at biorefineries for the major lignocellulosic constituents of interest.
The processing of lignocellulosic materials in modern biorefineries will allow for the
production of transport fuels and platform chemicals that could replace petroleum-derived
products. However, there is a critical lack of relevant detailed compositional information
regarding feedstocks relevant to Ireland and Irish conditions. This research has involved the
collection, preparation, and the analysis, with a high level of precision and accuracy, of a
large number of biomass samples from the waste and agricultural sectors. Not all of the
waste materials analysed are considered suitable for biorefining; for example the total sugar
contents of spent mushroom composts are too low. However, the waste paper/cardboard
that is currently exported from Ireland has a chemical composition that could result in high
biorefinery yields and so could make a significant contribution to Irelandís biofuel demands.