• Analytes Determined at Celignis
    Arabinose

Arabinose is a 5-carbon sugar (a pentose). In lignocellulosic biomass it is mostly present in hemicellulose, in particular it is present in many xylans and arabinogalactans.

Click here to place an order for determining Arabinose.

Request a QuoteArabinose Content

Analysis Packages for Arabinose

The Celignis Analysis Package(s) that determine this constituent are listed below:

Analytical Procedure for Arabinose


☑ Step 1: Acid Hydrolysis of the Sample

When this Method is the first step in the Analysis Package, the acid hydrolysis takes place on the biomass sample with no prior extraction carried out at Celignis. If previous Steps involving the extraction of the sample have take place then the extracted material is used for the acid hydrolysis.

In the case where three different types of extracted material exist (water-extracted, ethanol-extracted, and water- then ethanol-extracted) then the sample that has undergone both water and ethanol extraction is typically used for acid hydrolysis, unless otherwise requested by the customer.

The following steps are involved in the acid hydrolysis of a sample.

1.  The moisture content of the sample is determined, in duplicate.

2.  Approximately 300 mg (with the exact weight noted) of the sample is added to a pressure tube.

3.  3.00 mL of 72% H2SO4 is added by means of an automatic titrator, the weight of the acid added is noted.

4.  The sample is mixed thoroughly with the acid using a glass rod, care is taken that no sample stays adherent to the sides of the tube, but instead stays in contact with the acid.

5.  The tube is transferred to a water bath that is maintained at 30 degrees celcius.

6.  Steps 2-5 are repeated for the duplicate sample.

7.  Every 10 minutes the glass rod for each pressure tube is stirred so that the acid reaches all parts of the sample and complete hydrolysis occurs. This is a crucial step.

8.  Exactly one hour after it is placed in the water bath the pressure tube is removed and placed on a scales and 84 mL of water added (with the weight of the added water recorded). Any acid/sample on the rod is removed from the rod at this point using this water.

9.  A lid is screwed on the tube and the tube is inverted several times to ensure thorough mixing of the acid.

10. Two sugar recovery solution (SRS) pressure tubes are prepared in order to monitor the sugar-loss associated with the second-stage-hydrolysis. This involves the following steps:

(a) 348 microlitres of 72% H2SO4 is added to a test tube containing a solution containing a known weight (approximately 10 g) of a sugar standard. This standard should be of a similar sugar composition to that expected of the samples being analysed. The acid and sugar solution are thoroughly mixed.
(b) The sugar-acid mixture is transferred to a pressure tube which is then sealed.

11. All SRS and sample pressure tubes are placed in an autoclave which is run at 121 degrees celcius for 60 minutes.

12. After the temperature in the autoclave drops to under 80C the tubes are removed and are left (closed) in the lab until they reach room temperature.

13. The hydrolysates are then filtered (using vacuum suction) through filter crucibles of known weight and the resulting filtrate is stored.

14. Any residual solids are washed out from the tube using deionised water until all the Acid Insoluble Residue resides on the filter crucible.

☑ Step 2: Hydrolysate Dilution and Storage

Following the Acid Hydrolysis Step, the hydrolysate (the filtrate from the vacuum filtration) is diluted by a factor of 5 using a fucose-in-water solution. Fucose is the internal standard that is used in the chromatographic analysis of the hydrolysate. Following this dilution the samples are either immediately put on the chromatography system or stored in a freezer for future analysis.

☑ Step 3: Chromatographic Analysis of the Hydrolysate

Equipment Used for Arabinose Analysis



NIR Spectrophotometer

A FOSS XDS NIR device. It has a solid content module that can allow for samples of a heterogenous particle to be analysed.



Ion Chromatography

A Dionex ICS-3000 system that is equipmed with electrochemical, conductivity, and ultraviolet-visible detectors.



Autoclave

An autoclave is used in the protocols for the determination of the lignin and structural sugars (cellulosic and hemicellulosic) contents of samples.

NIR Model

Arabinose Global 1
Min. Value (%)0.04
Max. Value (%)6.21
Calibration Samples450
Validation Samples153
R2 (Validation)0.9027
RMSEP (%)0.3531
Bias (%)0.0139
SEP (%)0.3540
RPD3.2054
RER12.2310

Publications on Arabinose By The Celignis Team

Hayes, D. J. M., Hayes, M. H. B., Leahy, J. J. (2017) Use of Near Infrared Spectroscopy for the Rapid Low-Cost Analysis of Waste Papers and Cardboards, Faraday Discussions 202: 465-482

Link

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.

Hayes, D. J. M., Hayes, M. H. B., Leahy, J. J. (2015) Analysis of the lignocellulosic components of peat samples with development of near infrared spectroscopy models for rapid quantitative predictions, Fuel 150: 261-268

Link

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.

Hayes, D. J. M. (2013) Mass and Compositional Changes, Relevant to Biorefining, in Miscanthus x giganteus Plants over the Harvest Window, Bioresource Technology 142: 591-602

Link

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.

Hayes, D. J. M. (2012) Development of near infrared spectroscopy models for the quantitative prediction of the lignocellulosic components of wet Miscanthus samples, Bioresource Technology 119: 393-405

Link

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.

Hayes, D. J. M. (2011) Analysis of Lignocellulosic Feedstocks for Biorefineries with a Focus on The Development of Near Infrared Spectroscopy as a Primary Analytical Tool, PhD Thesis832 pages (over 2 volumes)

Download

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.

Miscanthus was focussed on as a major agricultural feedstock. A large number of plants have been sampled over the course of the harvest window (October to April) from several sites. These have been separated into their anatomical fractions and analysed. This has allowed observations to be made regarding the compositional trends observed within plants, between plants, and between harvest dates. Projections are made regarding the extents to which potential chemical yields may vary. For the DIBANET hydrolysis process that is being developed at the University of Limerick, per hectare yields of levulinic acid from Miscanthus could be 20% greater when harvested early compared with a late harvest.

The wet-chemical analysis of biomass is time-consuming. Near infrared spectroscopy (NIRS) has been developed as a rapid primary analytical tool with separate quantitative models developed for the important constituents of Miscanthus, peat, and (Australian) sugarcane bagasse. The work has demonstrated that accurate models are possible, not only for dry homogenous samples, but also for wet heterogeneous samples. For glucose (cellulose) the root mean square error of prediction (RMSEP) for wet samples is 1.24% and the R2 for the validation set ( ) is 0.931. High accuracies are even possible for minor analytes; e.g. for the rhamnose content of wet Miscanthus samples the RMSEP is 0.03% and the is 0.845. Accurate models have also been developed for pre-treated Miscanthus samples and are discussed. In addition, qualitative models have been developed. These allow for samples to be discriminated for on the basis of plant fraction, plant variety (giganteus/non-giganteus), harvest-period (early/late), and stand-age (one-year/older).

Quantitative NIRS models have also been developed for peat, although the heterogeneity of this feedstock means that the accuracies tend to be lower than for Miscanthus. The development of models for sugarcane bagasse has been hindered, in some cases, by the limited chemical variability between the samples in the calibration set. Good models are possible for the glucose and total sugars content, but the accuracy of other models is poorer. NIRS spectra of Brazilian bagasse samples have been projected onto these models, and onto those developed for Miscanthus, and the Miscanthus models appear to provide a better fit than the Australian bagasse models.

Additional Material

We can determine the Arabinose content of biomass, click here to learn more about our various biomass analysis methods.



...