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.
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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 24th 2024
€1.5m Funding Success in CBE-JU
Celignis is a Partner in 3 Successful Proposals for EU Funding
We are pleased to announce that three of the proposals involving Celignis, submitted to the CBE-JU programme for funding collaborative biomass research in Europe, were successful. These projects will provide an additional funding of €1.5m to Celignis and build on our achievements in other CBE and EU projects. In particular, the projects are all at enhanced TRLs (6/7) and will use our existing Celignis Bioprocess infrastructure and will also fund further development of our bioprocessing capacities and the Bioprocess Development Services we offer our clients.
Details on the funded projects are provided below:
BIONEER - This project was funded under CBE-JU topic IA-06 and focuses on the TRL 6/7 production of biobased platform chemicals. Celignis's activities in the project focus on scaling up the work undertaken in our ongoing
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Jan 23rd 2024
Celignis to Exhibit and Present at Major Biochar Event
The 2024 North American Biochar Conference will take place in Sacramento, California, on Feb 12-15
On Feb 12-15 we'll be exhibiting at the 2024 North American Biochar Conference, taking place at the SAFE Credit Union Convention Centre in Sacramento, California.
We're looking forward to interacting with the 1000+ expected attendees, outlining our extensive range of analytical and application testing services for biochar.
Celignis CIO Lalitha Gottumukkala will also be a member of the expert panel focused on developing improved laboratory methods for biochar characterisation.
Click here to register for the event.
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Jan 22nd 2024
Celignis Attending BIC Event on Feb 8
This Networking Event Will Involve Discussions on Collaborations for Proposals to the 2024 CBE-JU Topics
The Circular Bioeconomy Europe Joint Undertaking (CBE-JU) is an organisation that funds biomass research in Europe at various Technology Readiness Levels (TRLs). Since 2016 Celignis has been an active participant in a number of projects funded by the CBE-JU.
The Biobased Industries Consortium (BIC) is the steering committee that helps to steer the focus of research for the CBE-JU programme. In 2023 Celignis joined the BIC as a Full Industry Member and participated in several proposals submitted for different research topics in the CBE-JU's 2023 Work Programme.
On Feb 8th Celignis's Dan Hayes, Lalitha Gottumukkala, and Oscar Bedzo will be attending a BIC networking event in Brussels where we will discuss potential collaborations in the research programme topics recently announced for 2024.
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Jan 19th 2024
We're Hiring - Business Administration & Client Relationship Manager
This position will involve working closely with senior management, fostering existing and new client relationships.
Situated in Limerick, Ireland, Celignis currently operates at two centres, Celignis Analytical and Celignis Bioprocess, actively engaging in a variety of private and public bioeconomy projects. As we continue to expand, we're looking to strengthen our team of 14 with a Business Administration and Client Relationship Manager who can bring a blend of enthusiasm and expertise.
This position will involve working closely with senior management, fostering existing and new client relationships, and ensuring successful delivery of our services, playing a key role in our ongoing growth and success.
Click here for more details about the position.
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Apr 30th 2023
Celignis to Sponsor and Present at Major Biochar Event
The event takes place on May 3rd at Carrick-on-Shannon
We are pleased to announce that, on May 3rd, Celignis will be presenting and exhibiting at the National Biochar and Carbon Products Conference 2023, which is taking place in Carrick-on-Shannon in County Leitrm, Ireland.
This conference is being organised under the auspices of the Interreg Northwest Europe-funded THREE C Project, entitled 'Creating and sustaining Charcoal value chains to promote a Circular Carbon economy in NWE Europe'.
The conference will highlight both Irish stakeholders who are currently working in the biochar and carbon products sector, but also partners from the THREE C project (covering Netherlands, Luxembourg, Germany, Belgium, France and Wales, as well as Ireland) who have interesting stories and products to share.
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