NIR Model Development at Celignis
Background to NIR
Near Infrared Spectroscopy (NIRS) provides a rapid means to analyse a wide variety of feedstocks. It requires minimal sample preparation, is non-destructive, and
can have a high throughput on a sample basis.
The procedure involves the focussing of radiation on a sample. While some of the radiation will be scattered, some will pass through the sample, interacting with it. When the radiation finally reaches the detector it will pass on this absorbance information. This, along with the scatter from the sample, forms the spectrum of the material.
The procedure involves the focussing of radiation on a sample. While some of the radiation will be scattered, some will pass through the sample, interacting with it. When the radiation finally reaches the detector it will pass on this absorbance information. This, along with the scatter from the sample, forms the spectrum of the material.
The FOSS XDS spectroscopy device used at Celignis Analytical detects radiation in the wavelength region of 400 to 2500nm. Visible light is
defined as 400 to 700nm with the longer wavelengths being in the near infrared region. The CH, OH and NH bonds of an organic substance (those of most interest in carbohydrate
chemistry) will absorb energy in this region. The infrared spectrum consists of overtones and combination bands of these and other fundamental absorptions.
NIR Model Development
NIRS is largely an indirect analytical technique requiring calibration using samples of known composition determined by using standard, wet-chemical,
methods. These calibrations are based on the correlations of spectra of samples with their wet chemistry data. Once calibrated,
quantitative predictions for the composition of a sample can be attempted from its NIR spectrum alone. The results and accuracy of
calibrations are then validated by presenting "unknown" samples to the NIR device and comparing the results with those obtained by the wet-chemistry analysis.
At Celignis spectra are collected using a FOSS XDS Spectrophotometer and the Vision software program. The spectra are then imported into our custom-built chemometric software program for subsequent treatment and model development. This software was developed by Celignis in the BIOrescue BBI project. Partial least squares regression using one Y variable (i.e. PLS1) is used for the development of Celignis models.
At Celignis spectra are collected using a FOSS XDS Spectrophotometer and the Vision software program. The spectra are then imported into our custom-built chemometric software program for subsequent treatment and model development. This software was developed by Celignis in the BIOrescue BBI project. Partial least squares regression using one Y variable (i.e. PLS1) is used for the development of Celignis models.
Spectral pre-treatment techniques are often applied prior to model development in order to simplify the models and reduce the effect that particle size variation may have on the scattering of light. At Celignis PLS models have been tested on the raw spectra and on spectra treated with various techniques including: multiplicative scatter correction (MSC), extended multiplicative scatter correction (EMSC), standard normal variate (SNV), standard normal variate and detrend (SNVDT), and Savitzky-Golay (SG) derivatives (1st to 4th order). Cross validation statistics have been used to determine the most appropriate model. The Haaland and Thomas (1988) criterion is used to select the number of PLS factors to use in the model.
A good model for the prediction of unknown samples should cover a wide variety of sample types and compositional values so that the unknown sample should not be a spectral/chemical/physical "outlier" but instead should be of a similar spectral and physico-chemical composition to some of the samples used to build the model.
A good model for the prediction of unknown samples should cover a wide variety of sample types and compositional values so that the unknown sample should not be a spectral/chemical/physical "outlier" but instead should be of a similar spectral and physico-chemical composition to some of the samples used to build the model.
Important Statistics
In order to get an idea of the predictive ability of a model a number of statistical measures are used. These can be applied to the
calibration set (the group of samples that are used to build the model parameters), the cross-validation set (samples temporarily
excluded from model development but still ultimately involved in the development of the model), and the independent set (samples
that have no input into the development of the model).
Statistics solely based on the calibration set can give an inaccurate representation of the predictive ability of the model for unknown samples since it is possible to "overfit" the model to the calibration set, particularly if a large number of PLS factors are used. Cross-validation statistics provide a better idea of the robustness of a model but, ideally, independent validation (a test set) should be used. When presenting our regression statistics Celignis will use the values for the test set, unless otherwise stated.
Some of the most important statistics, those that are used on this website, are described below:
Statistics solely based on the calibration set can give an inaccurate representation of the predictive ability of the model for unknown samples since it is possible to "overfit" the model to the calibration set, particularly if a large number of PLS factors are used. Cross-validation statistics provide a better idea of the robustness of a model but, ideally, independent validation (a test set) should be used. When presenting our regression statistics Celignis will use the values for the test set, unless otherwise stated.
Some of the most important statistics, those that are used on this website, are described below:
R-Squared, Coefficient of Multiple Determination - Describes how well the data points fit the statstical model (the line of regression). Values range from 0 to 1. A 100% accurate model would have an R-Squared of 1 with all samples lying on the regression line.
RMSEP, Root Mean Square Error of Prediction - This measures the average accuracy (i.e. the difference between the true and estimated compositional value) of the prediction. For the samples in the test set it can be considered that 2 times the RMSEP represents a 95% confidence interval for the real compositional value. For example, if the model predicts a glucan content of 40% and the RMSEP is 1%, then there is a 95% chance that the glucan content of that sample, as measured by wet-chemical means, lies between 38 and 42%.
Bias - This is defined as the average difference between the NIR-predicted value and the real value. A positive value means that, on average, the model is over-estimating the composition by this amount whilst a negative value represents an underestimation.
RMSEP, Root Mean Square Error of Prediction - This measures the average accuracy (i.e. the difference between the true and estimated compositional value) of the prediction. For the samples in the test set it can be considered that 2 times the RMSEP represents a 95% confidence interval for the real compositional value. For example, if the model predicts a glucan content of 40% and the RMSEP is 1%, then there is a 95% chance that the glucan content of that sample, as measured by wet-chemical means, lies between 38 and 42%.
Bias - This is defined as the average difference between the NIR-predicted value and the real value. A positive value means that, on average, the model is over-estimating the composition by this amount whilst a negative value represents an underestimation.
SEP, Standard Error of Prediction - Whilst the RMSEP measures the accuracy of prediction,
the SEP measures the precision of the prediction (i.e. the difference between repeated measurements). The SEP squared is approximately equal to the RMSEP squared minus
the Bias squared. Hence, if the bias is low the values for RMSEP and SEP will be similar. Since we at Celignis are focused on getting predictions to be as accurate as
possible, we consider the RMSEP to be more important.
RPD, Ratio of standard error of Performance to standard Deviation - This is equal to the SEP divided by the standard deviation of the compositional values (determined via wet-chemistry) of the samples in the test set. Whilst the RMSEP, SEP, and Bias use the same units of measurement as the constituent (e.g. percent for glucan content), the R-Squared, RPD, and RER (see below) values are dimensionless, meaning that they can be compared on the same basis between models for different constituents/properties. If the RPD is equal to one then the SEP is equal to the standard deviation of the reference data meaning that the model is not predicting the reference values. Higher values for the RPD suggest increasingly accurate models.
RPD, Ratio of standard error of Performance to standard Deviation - This is equal to the SEP divided by the standard deviation of the compositional values (determined via wet-chemistry) of the samples in the test set. Whilst the RMSEP, SEP, and Bias use the same units of measurement as the constituent (e.g. percent for glucan content), the R-Squared, RPD, and RER (see below) values are dimensionless, meaning that they can be compared on the same basis between models for different constituents/properties. If the RPD is equal to one then the SEP is equal to the standard deviation of the reference data meaning that the model is not predicting the reference values. Higher values for the RPD suggest increasingly accurate models.
RER, Range Error Ratio - This is equal to the range in the compositional values
(i.e. the maximum value minus the minimum value) divided by the SEP. The numbers obtained for the RER will typically be around four to five times larger than those for the RPD;
however, the exact relationship between the two will depend on the distribution of samples in the test set. AACC Method 39-00.01 (1999) provides quality thresholds for model
performance based on the RER values: For an RER > 4 - the calibration is acceptable for sample screening; for an RER > 10 - the calibration is acceptable for quality control;
and for an RER > 15 - the calibration is good for quantification. Thresholds are also provided for the RPD value but this value can be subject to manipulation according to
how the sample set is constructed. Celignis considers that the RER value is a better test for the quality of the model, providing that there are no concentration outliers to
inflate the value and that the concentration range of the constituent is well represented (as is the case in the Celignis NIR models).
When providing the results for NIR predictions of samples, Celignis provides the predicted compositional value and also a value for the "Deviation in Prediction". This Deviation value
can be considered to represent a form of a confidence interval around the predicted value. It is calculated based on how similar the spectrum of the
unknown sample is to the samples that constitute the calibration set.
If the type of sample to be predicted is already in the calibration set then the Deviation is likely to be low. Click here for a list of some of the sample types in the current Celignis models.
If the type of sample to be predicted is already in the calibration set then the Deviation is likely to be low. Click here for a list of some of the sample types in the current Celignis models.
A sample with a large value for its deviation will be quite different spectrally, and quite possibly phsically and chemically, from the calibration samples, hence the model
may not be appropriate for predicting that sample with a high degree of accuracy.
At Celignis, we pride ourselves on the accuracy and precision of our analysis. Customer satisfaction is of paramount importance. For our NIR Analysis Package, if we find that the deviation in prediction is relatively high (defined as a value over 3% for the total lignocellulosic sugars content) then we will undertake the chemical analysis of that sample at no extra charge and provide you with all of the data that we obtain in this analysis. This chemical analysis will cover the analysis packages that we used to develop our NIR models: P3 - Ash Content, P4 - Ethanol Extractives, and P9 - Lignocellulosic Sugars and Lignin.
Click here for more information on the NIR models that have been developed by Celignis and here for some of our peer-reviewed publications on them.
At Celignis, we pride ourselves on the accuracy and precision of our analysis. Customer satisfaction is of paramount importance. For our NIR Analysis Package, if we find that the deviation in prediction is relatively high (defined as a value over 3% for the total lignocellulosic sugars content) then we will undertake the chemical analysis of that sample at no extra charge and provide you with all of the data that we obtain in this analysis. This chemical analysis will cover the analysis packages that we used to develop our NIR models: P3 - Ash Content, P4 - Ethanol Extractives, and P9 - Lignocellulosic Sugars and Lignin.
Click here for more information on the NIR models that have been developed by Celignis and here for some of our peer-reviewed publications on them.
Analysis Packages
P8 : Lignin Content
Lignin (Klason), Lignin (Acid Soluble), Acid Insoluble Residue, Ash (Acid Insoluble),
Lignin (Klason), Lignin (Acid Soluble), Acid Insoluble Residue, Ash (Acid Insoluble),
P19 : Deluxe Lignocellulose Package
As P10 plus protein-corrected lignin, water-soluble sugars, uronic acids, acetyl content and starch.
As P10 plus protein-corrected lignin, water-soluble sugars, uronic acids, acetyl content and starch.
P15 : Uronic Acids
Glucuronic Acid, Galacturonic Acid, Mannuronic Acid, Guluronic Acid, 4-O-Methyl-D-Glucuronic Acid, Iduronic Acid,
Glucuronic Acid, Galacturonic Acid, Mannuronic Acid, Guluronic Acid, 4-O-Methyl-D-Glucuronic Acid, Iduronic Acid,
P121 : Evaluation of Biomass for Enzymatic Digestibility
Total Sugars in Enzyme Hydrolysate, Glucose in Enzyme Hydrolysate, Xylose in Enzyme Hydrolysate, Arabinose in Enzyme Hydrolysate, Mannose in Enzyme Hydrolysate, Galactose in Enzyme Hydrolysate, Rhamnose in Enzyme Hydrolysate, Cellobiose in Enzyme Hydrolysate, Enzymatic Hydrolysis Kinetics, Cellulose Conversion Yield, Xylan Conversion Yield, Combined Sugar Yield, Cellulose Conversion Rate, Xylan Conversion Rate,
Total Sugars in Enzyme Hydrolysate, Glucose in Enzyme Hydrolysate, Xylose in Enzyme Hydrolysate, Arabinose in Enzyme Hydrolysate, Mannose in Enzyme Hydrolysate, Galactose in Enzyme Hydrolysate, Rhamnose in Enzyme Hydrolysate, Cellobiose in Enzyme Hydrolysate, Enzymatic Hydrolysis Kinetics, Cellulose Conversion Yield, Xylan Conversion Yield, Combined Sugar Yield, Cellulose Conversion Rate, Xylan Conversion Rate,
P122 : Evaluation of Pre-Treatment on Enzymatic Digestibility
As P121 plus comparisons with data from the non-pretreated original sample, including: Increase in Cellulose Accessibility after Pre-Treatment, Percent Increase in Cellulose Conversion Efficiency, Percent Increase in Cellulose Conversion Rate.
As P121 plus comparisons with data from the non-pretreated original sample, including: Increase in Cellulose Accessibility after Pre-Treatment, Percent Increase in Cellulose Conversion Efficiency, Percent Increase in Cellulose Conversion Rate.
P123 : Composition of Residue from Enzymatic Hydrolysis
As P9 but on the solid residue after enzymatic hydrolysis.
As P9 but on the solid residue after enzymatic hydrolysis.
P124 : Fermentation Inhibitors in Enzymatic Hydrolysate
Formic Acid, Acetic Acid, Levulinic Acid, Furfural, Hydroxymethylfurfural,
Formic Acid, Acetic Acid, Levulinic Acid, Furfural, Hydroxymethylfurfural,
P125 : Cellulase Saccharification Efficiency
Includes all hydrolysate sugars and kinetics in P121 and: Cellulose Conversion Yield, Cellulose Conversion Rate
Includes all hydrolysate sugars and kinetics in P121 and: Cellulose Conversion Yield, Cellulose Conversion Rate
P126 : Xylanase Saccharification Efficiency
Includes all hydrolysate sugars and kinetics in P121 and: Xylan Conversion Yield, Xylan Conversion Rate
Includes all hydrolysate sugars and kinetics in P121 and: Xylan Conversion Yield, Xylan Conversion Rate
P127 : Amylase Saccharification Efficiency
Total Sugars in Enzyme Hydrolysate, Glucose in Enzyme Hydrolysate, Maltose in Enzyme Hydrolysate, a-Amylase Hydrolysis Kinetics, Glucoamylase Hydrolysis Kinetics,
Total Sugars in Enzyme Hydrolysate, Glucose in Enzyme Hydrolysate, Maltose in Enzyme Hydrolysate, a-Amylase Hydrolysis Kinetics, Glucoamylase Hydrolysis Kinetics,
P12 : Sugars in Solvent Extract
Glucose, Xylose, Fructose, Sucrose, Mannose, Arabinose, Galactose, Rhamnose, Xylitol, Sorbitol, Trehalose, Mannitol, Arabinitol, Glycerol, Raffinose,
Glucose, Xylose, Fructose, Sucrose, Mannose, Arabinose, Galactose, Rhamnose, Xylitol, Sorbitol, Trehalose, Mannitol, Arabinitol, Glycerol, Raffinose,
P22 : Organic Acids and Furans
Levulinic Acid, Formic Acid, Hydroxymethylfurfural, Furfural, Acetic Acid, gamma-Valerolactone,
Levulinic Acid, Formic Acid, Hydroxymethylfurfural, Furfural, Acetic Acid, gamma-Valerolactone,
P24 : Dimers and Trimers from Hemicellulose Hydrolysis
Xylobiose, Xylotriose, Arabinobiose, Arabinotriose,
Xylobiose, Xylotriose, Arabinobiose, Arabinotriose,
P29 : Oligomers from Starch Hydrolysis
Maltose, Maltotriose, Maltotetraose, Maltopentaose, Maltohexaose, Maltoheptaose, Maltooctaose,
Maltose, Maltotriose, Maltotetraose, Maltopentaose, Maltohexaose, Maltoheptaose, Maltooctaose,
P15 : Uronic Acids
Glucuronic Acid, Galacturonic Acid, Mannuronic Acid, Guluronic Acid, 4-O-Methyl-D-Glucuronic Acid, Iduronic Acid,
Glucuronic Acid, Galacturonic Acid, Mannuronic Acid, Guluronic Acid, 4-O-Methyl-D-Glucuronic Acid, Iduronic Acid,
P75 : Seaweed Phytohormones
Gibberellic Acid, Indole-3-acetic acid, Indole-2-acetic acid, Indole-3-propionic acid, Indole-3-butyric acid, 6-Benzylaminopurine, Kinetin riboside, Abscisic acid, Salicylic acid,
Gibberellic Acid, Indole-3-acetic acid, Indole-2-acetic acid, Indole-3-propionic acid, Indole-3-butyric acid, 6-Benzylaminopurine, Kinetin riboside, Abscisic acid, Salicylic acid,
P76 : Seaweed Vitamins (Fat-Soluble)
beta-Carotene, Ergocalciferol (Vitamin D2), Alpha-tocopherol (vitamin E), Phylloquinone (Vitamin K1),
beta-Carotene, Ergocalciferol (Vitamin D2), Alpha-tocopherol (vitamin E), Phylloquinone (Vitamin K1),
P77 : Seaweed Vitamins (Water-Soluble)
Thiamine (Vitamin B1), Riboflavin (Vitamin B2), Niacin (Vitamin B3), Niacinamide (vitamin B3), Pantothenic Acid (Vitamin B5), Pyridoxine (Vitamin B6), Folate (Vitamin B9), Cobalamin (Vitamin B12), Ascorbic Acid (Vitamin C),
Thiamine (Vitamin B1), Riboflavin (Vitamin B2), Niacin (Vitamin B3), Niacinamide (vitamin B3), Pantothenic Acid (Vitamin B5), Pyridoxine (Vitamin B6), Folate (Vitamin B9), Cobalamin (Vitamin B12), Ascorbic Acid (Vitamin C),
P71 : Seaweed Carbohydrates
Fucose, Mannitol, Glucose, Xylose, Mannose, Arabinose, Galactose, Rhamnose, Total Sugars, Glucuronic Acid, Galacturonic Acid, Mannuronic Acid, Guluronic Acid, Iduronic Acid,
Fucose, Mannitol, Glucose, Xylose, Mannose, Arabinose, Galactose, Rhamnose, Total Sugars, Glucuronic Acid, Galacturonic Acid, Mannuronic Acid, Guluronic Acid, Iduronic Acid,
P72 : Seaweed Amino Acids
Alanine, Arginine, Aspartic Acid, Asparagine, Cystine, Glutamic Acid, Glutamine, Glycine, Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Proline, Serine, Threonine, Tryptophan, Tyrosine, Valine,
Alanine, Arginine, Aspartic Acid, Asparagine, Cystine, Glutamic Acid, Glutamine, Glycine, Histidine, Isoleucine, Leucine, Lysine, Methionine, Phenylalanine, Proline, Serine, Threonine, Tryptophan, Tyrosine, Valine,
P36 : Major Elements
Aluminium, Calcium, Iron, Magnesium, Phosphorus, Potassium, Silicon, Sodium, Titanium,
Aluminium, Calcium, Iron, Magnesium, Phosphorus, Potassium, Silicon, Sodium, Titanium,
P73 : Seaweed Lipids as Fatty Acids
Arachidic Acid, Behenic Acid, Decanoic Acid, Erucic Acid, Lauric Acid, Linoleic Acid, Linolenic Acid, Myristic Acid, Caprylic Acid, Oleic Acid, Palmitic Acid, Palmitoleic Acid, Stearic Acid, Lignoceric Acid,
Arachidic Acid, Behenic Acid, Decanoic Acid, Erucic Acid, Lauric Acid, Linoleic Acid, Linolenic Acid, Myristic Acid, Caprylic Acid, Oleic Acid, Palmitic Acid, Palmitoleic Acid, Stearic Acid, Lignoceric Acid,
P74 : Pigments in Seaweed
Fucoxanthin, Astaxanthin, Chlorophyll-c, Chlorophyll-a, Chlorophyll-b, Lutein, beta-Carotene, Neoxanthin, Antheraxanthin, Violaxanthin,
Fucoxanthin, Astaxanthin, Chlorophyll-c, Chlorophyll-a, Chlorophyll-b, Lutein, beta-Carotene, Neoxanthin, Antheraxanthin, Violaxanthin,
P75 : Seaweed Phytohormones
Gibberellic Acid, Indole-3-acetic acid, Indole-2-acetic acid, Indole-3-propionic acid, Indole-3-butyric acid, 6-Benzylaminopurine, Kinetin riboside, Abscisic acid, Salicylic acid,
Gibberellic Acid, Indole-3-acetic acid, Indole-2-acetic acid, Indole-3-propionic acid, Indole-3-butyric acid, 6-Benzylaminopurine, Kinetin riboside, Abscisic acid, Salicylic acid,
P76 : Seaweed Vitamins (Fat-Soluble)
beta-Carotene, Ergocalciferol (Vitamin D2), Alpha-tocopherol (vitamin E), Phylloquinone (Vitamin K1),
beta-Carotene, Ergocalciferol (Vitamin D2), Alpha-tocopherol (vitamin E), Phylloquinone (Vitamin K1),
P77 : Seaweed Vitamins (Water-Soluble)
Thiamine (Vitamin B1), Riboflavin (Vitamin B2), Niacin (Vitamin B3), Niacinamide (vitamin B3), Pantothenic Acid (Vitamin B5), Pyridoxine (Vitamin B6), Folate (Vitamin B9), Cobalamin (Vitamin B12), Ascorbic Acid (Vitamin C),
Thiamine (Vitamin B1), Riboflavin (Vitamin B2), Niacin (Vitamin B3), Niacinamide (vitamin B3), Pantothenic Acid (Vitamin B5), Pyridoxine (Vitamin B6), Folate (Vitamin B9), Cobalamin (Vitamin B12), Ascorbic Acid (Vitamin C),
P150 : Plant Pigments
Chlorophyll-a, Chlorophyll-b, Lutein, beta-Carotene, Neoxanthin, Astaxanthin, Zeaxanthin, Antheraxanthin, Violaxanthin,
Chlorophyll-a, Chlorophyll-b, Lutein, beta-Carotene, Neoxanthin, Astaxanthin, Zeaxanthin, Antheraxanthin, Violaxanthin,
P81 : Biomethane Potential - 14 Days
Biomethane Potential (BMP), Total Biogas Volume, Total Solids, Volatile Solids, pH, Biogas Methane Content, Biogas Carbon Dioxide Content, Biogas Oxygen Content, Biogas Hydrogen Sulphide Content, Biogas Ammonia Content,
Biomethane Potential (BMP), Total Biogas Volume, Total Solids, Volatile Solids, pH, Biogas Methane Content, Biogas Carbon Dioxide Content, Biogas Oxygen Content, Biogas Hydrogen Sulphide Content, Biogas Ammonia Content,
P93 : Feedstock Chemical and Biological Analysis
Total Solids, Volatile Solids, pH, Chemical Oxygen Demand (COD),
Total Solids, Volatile Solids, pH, Chemical Oxygen Demand (COD),
P95 : Residual Biogas Potential - 14 Days
Residual Biogas Potential (RBP), Total Biogas Volume, Total Solids, Volatile Solids, pH, Biogas Methane Content, Biogas Carbon Dioxide Content, Biogas Oxygen Content, Biogas Hydrogen Sulphide Content, Biogas Ammonia Content,
Residual Biogas Potential (RBP), Total Biogas Volume, Total Solids, Volatile Solids, pH, Biogas Methane Content, Biogas Carbon Dioxide Content, Biogas Oxygen Content, Biogas Hydrogen Sulphide Content, Biogas Ammonia Content,
P222 : Volatile Fatty Acids (VFAs) Speciation
Acetic Acid, Lactic Acid, Propionic Acid, Butyric Acid, Isobutyric Acid, Valeric Acid, Isovaleric Acid,
Acetic Acid, Lactic Acid, Propionic Acid, Butyric Acid, Isobutyric Acid, Valeric Acid, Isovaleric Acid,
P61 : Sugars in Bio-Oil Water Extract
Levoglucosan, Cellobiosan, Mannosan, Galactosan, Glucose, Xylose, Mannose, Arabinose, Galactose, Rhamnose, Fucose, Sucrose, Cellobiose, Total Sugars,
Levoglucosan, Cellobiosan, Mannosan, Galactosan, Glucose, Xylose, Mannose, Arabinose, Galactose, Rhamnose, Fucose, Sucrose, Cellobiose, Total Sugars,
P63 : Semi-Volatile Oxygenated Components in Bio-Oil
31 constituents including Phenol, Furfural, Syringol, and Vanillin
31 constituents including Phenol, Furfural, Syringol, and Vanillin
P360 : Specific Surface Area (5-Point BET)
Specific Surface Area (Nitrogen Gas Adsorption), BET Isotherm (5 Point Using Nitrogen),
Specific Surface Area (Nitrogen Gas Adsorption), BET Isotherm (5 Point Using Nitrogen),
P364 : Pore-Size Distribution
Specific Surface Area (Nitrogen Gas Adsorption), BET Isotherm (20 Point Using Nitrogen), Pore Volume (Using Nitrogen), Pore Size Distribution (Using Nitrogen), Average Pore Width (Using Nitrogen),
Specific Surface Area (Nitrogen Gas Adsorption), BET Isotherm (20 Point Using Nitrogen), Pore Volume (Using Nitrogen), Pore Size Distribution (Using Nitrogen), Average Pore Width (Using Nitrogen),
P366 : Pore Size Distribution Deluxe
Specific Surface Area (Nitrogen Gas Adsorption), BET Isotherm (40 Point Using Nitrogen), Pore Volume (Using Nitrogen), Pore Size Distribution (Using Nitrogen), Average Pore Width (Using Nitrogen),
Specific Surface Area (Nitrogen Gas Adsorption), BET Isotherm (40 Point Using Nitrogen), Pore Volume (Using Nitrogen), Pore Size Distribution (Using Nitrogen), Average Pore Width (Using Nitrogen),
P34 : Calorific Value and Elements
Gross Calorific Value, Net Calorific Value, Ash, Carbon, Hydrogen, Nitrogen, Sulphur, Oxygen,
Gross Calorific Value, Net Calorific Value, Ash, Carbon, Hydrogen, Nitrogen, Sulphur, Oxygen,
P36 : Major Elements
Aluminium, Calcium, Iron, Magnesium, Phosphorus, Potassium, Silicon, Sodium, Titanium,
Aluminium, Calcium, Iron, Magnesium, Phosphorus, Potassium, Silicon, Sodium, Titanium,
P37 : Minor Elements
Antimony, Arsenic, Cadmium, Chromium, Cobalt, Copper, Lead, Manganese, Mercury, Molybdenum, Nickel, Vanadium, Zinc,
Antimony, Arsenic, Cadmium, Chromium, Cobalt, Copper, Lead, Manganese, Mercury, Molybdenum, Nickel, Vanadium, Zinc,
P42 : Ash Melting Behaviour (Reducing Conditions)
Ash Shrinkage Starting Temperature (Reducing), Ash Deformation Temperature (Reducing), Ash Hemisphere Temperature (Reducing), Ash Flow Temperature (Reducing),
Ash Shrinkage Starting Temperature (Reducing), Ash Deformation Temperature (Reducing), Ash Hemisphere Temperature (Reducing), Ash Flow Temperature (Reducing),
P393 : Biochar Thermal Properties Deluxe
Moisture, Ash Content (815C), Carbon, Hydrogen, Nitrogen, Sulphur, Oxygen, Chlorine, Volatile Matter, Fixed Carbon, Aluminium, Calcium, Iron, Magnesium, Phosphorus, Potassium, Silicon, Sodium, Titanium, Gross Calorific Value, Net Calorific Value, Ash Shrinkage Starting Temperature (Reducing), Ash Deformation Temperature (Reducing), Ash Hemisphere Temperature (Reducing), Ash Flow Temperature (Reducing),
Moisture, Ash Content (815C), Carbon, Hydrogen, Nitrogen, Sulphur, Oxygen, Chlorine, Volatile Matter, Fixed Carbon, Aluminium, Calcium, Iron, Magnesium, Phosphorus, Potassium, Silicon, Sodium, Titanium, Gross Calorific Value, Net Calorific Value, Ash Shrinkage Starting Temperature (Reducing), Ash Deformation Temperature (Reducing), Ash Hemisphere Temperature (Reducing), Ash Flow Temperature (Reducing),
P394 : Biochar Thermal Properties Ultimate
As P393 plus inorganic carbon, organic carbon, TGA (under nitrogen and air), and inherent moisture
As P393 plus inorganic carbon, organic carbon, TGA (under nitrogen and air), and inherent moisture
P36 : Major Elements
Aluminium, Calcium, Iron, Magnesium, Phosphorus, Potassium, Silicon, Sodium, Titanium,
Aluminium, Calcium, Iron, Magnesium, Phosphorus, Potassium, Silicon, Sodium, Titanium,
P37 : Minor Elements
Antimony, Arsenic, Cadmium, Chromium, Cobalt, Copper, Lead, Manganese, Mercury, Molybdenum, Nickel, Vanadium, Zinc,
Antimony, Arsenic, Cadmium, Chromium, Cobalt, Copper, Lead, Manganese, Mercury, Molybdenum, Nickel, Vanadium, Zinc,
P384 : Biochar Polycyclic Aromatic Hydrocarbons (PAH)
Acenaphthene, Acenaphthylene, Anthracene, Benz[a]anthracene, Benzo[b]fluoranthene, Benzo[k]fluoranthene, Benzo[ghi]perylene, Benzo[a]pyrene, Chrysene, Dibenz[a,h]anthracene, Fluoranthene, Fluorene, Indeno[1,2,3-cd]pyrene, 1-Methylnaphthalene, 2-Methylnaphthalene, Naphthalene, Phenanthrene, Pyrene,
Acenaphthene, Acenaphthylene, Anthracene, Benz[a]anthracene, Benzo[b]fluoranthene, Benzo[k]fluoranthene, Benzo[ghi]perylene, Benzo[a]pyrene, Chrysene, Dibenz[a,h]anthracene, Fluoranthene, Fluorene, Indeno[1,2,3-cd]pyrene, 1-Methylnaphthalene, 2-Methylnaphthalene, Naphthalene, Phenanthrene, Pyrene,
P388 : Biochar Plant Growth Trials
Time to Germination, Mean Shoot Length (Week 1), Mean Shoot Length (Week 2), Mean Shoot Length (Week 3), Mean Shoot Length (Week 4), Shoot Weight (Week 4), Mean Root Length (Week 4), Root Weight (Week 4),
Time to Germination, Mean Shoot Length (Week 1), Mean Shoot Length (Week 2), Mean Shoot Length (Week 3), Mean Shoot Length (Week 4), Shoot Weight (Week 4), Mean Root Length (Week 4), Root Weight (Week 4),
P397 : Biochar Soil Amendment Ultimate
As Deluxe package plus P383, SEM Imaging (P387) and Plant Growth Trials (P388)
As Deluxe package plus P383, SEM Imaging (P387) and Plant Growth Trials (P388)
P399 : Biochar Complete Evaluation Package
Includes everything from P391 (Physical Properties Ultimate), P394 (Thermal Properties Ultimate), and P397 (Soil Amendment Ultimate)
Includes everything from P391 (Physical Properties Ultimate), P394 (Thermal Properties Ultimate), and P397 (Soil Amendment Ultimate)
P34 : Calorific Value and Elements
Gross Calorific Value, Net Calorific Value, Ash, Carbon, Hydrogen, Nitrogen, Sulphur, Oxygen,
Gross Calorific Value, Net Calorific Value, Ash, Carbon, Hydrogen, Nitrogen, Sulphur, Oxygen,
P36 : Major Elements
Aluminium, Calcium, Iron, Magnesium, Phosphorus, Potassium, Silicon, Sodium, Titanium,
Aluminium, Calcium, Iron, Magnesium, Phosphorus, Potassium, Silicon, Sodium, Titanium,
P37 : Minor Elements
Antimony, Arsenic, Cadmium, Chromium, Cobalt, Copper, Lead, Manganese, Mercury, Molybdenum, Nickel, Vanadium, Zinc,
Antimony, Arsenic, Cadmium, Chromium, Cobalt, Copper, Lead, Manganese, Mercury, Molybdenum, Nickel, Vanadium, Zinc,
P40 : Combustion Package
Volatile Matter, Fixed Carbon, Moisture, Ash, Carbon, Hydrogen, Nitrogen, Sulphur, Oxygen, Gross Calorific Value, Net Calorific Value, Chlorine,
Volatile Matter, Fixed Carbon, Moisture, Ash, Carbon, Hydrogen, Nitrogen, Sulphur, Oxygen, Gross Calorific Value, Net Calorific Value, Chlorine,
P41 : Ash Melting Behaviour (Oxidising Conditions)
Ash Shrinkage Starting Temperature (Oxidising), Ash Deformation Temperature (Oxidising), Ash Hemisphere Temperature (Oxidising), Ash Flow Temperature (Oxidising),
Ash Shrinkage Starting Temperature (Oxidising), Ash Deformation Temperature (Oxidising), Ash Hemisphere Temperature (Oxidising), Ash Flow Temperature (Oxidising),
News
Jan 14th 2025
€1.6m Funding Success for Celignis in 2024 CBE-JU Calls
We have secured funding for involvement in 4 collaborative research projects
We are delighted to announce that Celignis has been successful in 4 project proposals submitted for funding to the Circular Bio-based Europe Joint Undertaking (CBE JU) programme.
These projects will provide funding of 1.6m EUR to Celignis over the next few years and build upon the 3 projects (worth 1.5m EUR) we secured last year and the 4 previous CBE/BBI projects that Celignis participated in.
Details on the projects are provided below:
WoodVALOR - This RIA project concerns the valorisation of contaminated/post-consumer wood waste (WW) via: (i) thermal conversion to biochar; and (ii) fractionation followed by conversions to paints & coatings ingredients. Celignis is involved in the chemo-enzymatic fractionation of decontaminated wood (DW) to sequentially extract/purify lignin and hemicellulose, and in developing hemicellulose-based emulsifiers/stabilizers and binder monomers for industrial formulations. Additionally, Celignis is involved in metals/mineral recovery from decontamination wastewater using
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Dec 2nd 2024
New Publication from a Celignis Bioprocess Development Project
The article, available in "Biomass Conversion and Biorefinery" is entitled "Process development for efficient pectin extraction from tobacco residues and its characterisation"
We are please to announce the publication of a peer-reviewed scientific article based on some of the research outputs of a Bioprocess Development Service (BDS) project undertaken by Celignis.
The article, entitled "Process development for efficient pectin extraction from tobacco residues and its characterisation" details the results of experiments targeting the optimised extraction of pectin from the laminae of a number of different varieties of tobacco plants. These tobacco-derived pectins were found to have a medium molecular weight and low methoxy content and our findings indicated that this feedstock could be suitable for the production of pectin with dietary applications.
Click here to view the publication.
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Sep 24th 2024
Kick-Off Meeting for CBE-JU MANUREFINERY Project
Meeting takes place at the coordinator's (ITA) headquarters in Zaragoza, SPAIN
Celignis personnel are today attending the kick-off meeting of the CBE-JU project MANUREFINERY at the facilities of the project's coordinator (ITA) in Zaragoza, Spain.
MANUREFINERY concerns the development of a small, decentralised, modular biorefinery concept for farms that converts manure and ammonia emissions into seven marketable bio-ingredients (animal-feed proteins, caproic acid, and fertiliser salts/ashes). The solution integrates fixed/mobile units across three valorisation lines (gas, liquid, solid) and a digital twin for optimisation and scale-up, targeting TRL6-7 validation on four EU demo farms.
Celignis has a number of key roles in the project, including:
- Comprehensive analysis of the feedstocks and products of the process.
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Jul 4th 2024
Kick-Off Meeting for CBE-JU PROMOFER Project
Meeting takes place at the coordinator's (AIMPLAS) headquarters in Valencia, Spain
PROMOFER, is an Innovation Action project funded by the CBE-JU, under topic HORIZON-JU-CBE-2023-IA-03 (Improve Fermentation Processes (Including Downstream Purification) To Final Bio-Based Products).
This project started in June 2024 with Celignis, an SME partner and full industry BIC member, playing a pivotal role in the project. Our core activities include undertaking the pre-treatment and hydrolysis of lignocellulosic biomass at scaled-up (TRL7, 1 m3) volumes. The resulting sugars are then provided to other partners for downstream fermentations.
Today Celignis's CIO Lalitha is attending the kick-off meeting of the project, at coordinator AIMPLAS's headquarters in Valencia, Spain.
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Jul 1st 2024
Kick-Off Meeting for CBE-JU BIONEER Project
We're attending the kick-off meeting for BIONEER in Trondheim, Norway (SINTEF)
Lalitha is attending the kick-off meeting of our CBE-JU project BIONEER, located at the coordinator's (SINTEF) premises in Trondheim, Norway.
BIONEER has the title "Scaled-up Production of Next-Generation Carbohydrate-Derived Building Blocks to Enhance the Competitiveness of a Sustainable European Chemicals Industry". It is a 4-year Innovation Action project with 7.5m EUR of funding provided by the CBE-JU.
Celignis plays a key role in BIONEER, being responsible for the scaled-up (TRL7) production of platform chemicals.
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