Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/34—Paper
  • G01N33/343—Paper pulp
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
  • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
  • G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
  • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
  • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light

Definitions

• the line can also be customers, sellers and their different product demands. Intermedi ⁇ ate process-steps in the line or parts thereof also have to be determined in order to obtain control feedback to the manufacturing process.
• Pulp is characterised by, for instance, the measuring of tensile and tear index, density and light scattering coefficient. It is thus evident that not only chemical parameters such as kappa number - a measure of remaining lignin in the pulp - cellulose and hemicellulose are crucial for the product characterisation.
• Tearindex and tensiieindex are examples of this.
• pulping and bleaching the pulp is changed as well in properties as in appearance. The same holds true for paper after addition of filler materials and treatment with different chemicals.
• Addi ⁇ tion of recycled fibre demands knowledge about the quality of virgin fibre as well as recycled.
• different parameters and quality properties are cru ⁇ cial for the final result.
• a future competitive manufacture will need improved measuring methods in order to be able to
• the invention is a method to determine properties, qualitative and quantita ⁇ tive, of cellulose fibre products such as trees, lumber, timber, chips, saw dust, pulp and paper.
• the method can be performed before, during and after processing or parts thereof to a from the charge to the final product.
• the method can be used for product classification, sorting and for monitor ⁇ ing of processes and additions during different process steps.
• the method is characterised by the mathematical treating data from spec- troscopy of named fibre products by multivariate methods so-called chemometrics, with the spectra preferably obtained in the wavenumber region of 200-15400 cm "1 .
• the invention is not limited to the cellulose fibre products given above.
• the method also can predict several parameters determined at the same time, chemical as well as physical as latent and collective parameters such as processability. Further, the status and changes in a process such as ageing or chemical conversion can be determined. Parameters and properties today tak ⁇ ing hours and days to determine now can be made in minutes, seconds or parts thereof.
• Mentioned manufacturing processes generally consists of several steps where the product of one step is the raw material for the next.
• the advantage of being able to use comparative methods of determination during and in- between several process steps is obvious.
• the figure below show determination of dependent variables in processes with several steps.
• latent information we mean results of several parameters together. Single parameters and correlation may however be meas- urable, but the demanded total result can also be grouped in a way where the effects of underlying chemical and physical parameters appear as one entity.
• Another example is the ability to predict quality as for instance flaws in me ⁇ chanical properties of lumber used for glued wood.
• NIR Near Infra Red spectroscopy
• Banerjee Lignin determination by FT-IR. Appl. Spect., 1992. 46(2): p. 246-248. Wright, J.A., M.D. Birkett, and M.J.T. Gambino, Prediction of pulp yield and cellu- lose content from wood samples using near infrared reflectance spectroscopy. Tappi J., 1990. 73(8): p. 164-166.
• Wallbacks, L., U. Edlund, and B. Norden Multivariate data analysis of in situ pulp kinetics using 13C CP/MAS NMR. J. of Wood Chemistry and Technology, 1989. 9(2): p. 235-249.
• Wallbacks, L. Pulp Characterization Using Spectroscopy and Multivariate Data Analysis. 1991, University of Umea, Dept. of Organic Chemis ⁇ try, S-901 87, Umea, Sweden: Wallbacks, L., et al, Multivariate characterization of pulp using solid-state C13 NMR, FT-IR and NIR. Tappi J., 1991. 74(10): p.
• NIR compared to NMR and FT-IR is the spectroscopic method gen ⁇ erating most information from a system of birch pulp. He shows that xylose, glucose, Klason lignin and galactose can be predicted by NIR, FT-IR, NMR in combination with multivariate methods. The best result is obtained by adding spectra from the three methods to a combination spectrum. Wallbacks tech ⁇ nique does not generate as good models as those in the present invention. This is caused by non linearities mentioned above that can not acceptably be mod- elled.
• Possibilities are also shown by transforming classic physical descriptors (18#) originating from 7 commercially available pulp samples to a latent vari ⁇ able plane (with PCA), to follow the influence of certain unit operations such as grinding and with some doubt predict those. Wallback points out that it is "extremely important to be able to characterise pulp (physically) in a fast and efficient manner in the future. One way would be using spectroscopic measures such as NIR or FT-IR". We show exactly this in the present invention.
• Wallback also show in: Characterisation of Chemical Pulps using Spectros ⁇ copy, Fibre Dimensions and Multivariate Data Analysis, in the 7th ISWPC. 1993. Beijing, PR of China. The possibility to predict physical descriptors by spectroscopy. It is however necessary that to independent data (spectra) add information about grinding which generally is not available in the real case. This makes Wallback's method to differ from the present invention.
• the absorbance data obtained are analysed by multi ⁇ variate statistical analysis e.g. chemometrics.
• multi ⁇ variate statistical analysis e.g. chemometrics.
• the spectral data first have been linearised.
• NIRR Near Infra Red Reflectance
• the measured absorbance is not line ⁇ arly correlated to the desired properties of the samples. This depends mostly on light scattering effects, in turn depending on the physical nature and form of the samples.
• Several methods of transforming for linearising have been devel- oped to overcome with this effect. Four of the most common are:
• K-M Kubelka-Munk transformation
• MSC Multiplicative Scatter Correction
• SNV Standard Normal Variate Transformation
• Multivariate analysis of absorbance or reflectance data identify spectral fea ⁇ tures, so-called principal components, which are then used to model qualitative and quantitative properties of the samples.
• chemometrics is used to construct a calibration set and to predict unknown samples.
• PCA Principal Component Analysis
• ASTM Standard E131-90, Def ofPCA, in Standard Definitions of Terms and Symbols Relating to Molecular Spectroscopy. De- vaux, M.F., et al., Application of multidimensional analyses to the extraction of dis ⁇ criminant spectral patterns from NIR spectra. Appl.
• chemometric algorithms preferred in this invention are PCA for qualita ⁇ tive results and PLS for quantitative results.
• PCA is a powerful transformation technique which projects a multidimen ⁇ sional data set with correlated variables to a smaller data set with non corre ⁇ lated variables.
• the purpose of this transformation is to rotate the coordinate system so that maximal amount of information is obtained on fewer axis than in the original arrangement.
• This smaller and non correlated amount of data which retains almost the same amount of information as the original data-set thereafter can be used to calculate predictive model.
• principal components explain up to 99% of the variance of the data.
• the principal com- ponents which can be related to desired properties of the samples are then used to determine these properties i.e. qualitative results.
• PLS is a development of PCA having a regression step between information from the principal components and a desired property of the samples, i.e. PLS gives quantitative results.
• the invention describes a method for the determination of quality of differ ⁇ ent material originating from wood, which we hereafter have given the collec ⁇ tive name of cellulose fibre products.
• the invention will now be described in detail.
• the procedure according to the invention is described for discrete wood, saw dust, chips and pulp samples which have been scanned by NIRR in a probe cell in laboratory environment, but the procedure is not limited to samples measured in this manner, measurements in situ can also be made.
• fibre optics can be used in any standard configuration i.e. the detector and/ or source of light can be placed remote from the measured object, the actual cellulose fibre product.
• the detector obtains its analyte-beam from the light source via one or more optical fibres by which in some way passes via the sample.
• absorbance data preferably in the wavelength region between 200 cm “1 and 15400 cm “1 , are collected for samples with known chemical concentrations and physical properties.
• the samples collected reflects the actual span of the parameters or properties of the product.
• the number of samples which in this way are gathered for the calibration should be more than 30 and they must also be representative for the parameters or properties to be measured.
• Standard methods e.g. TAPPI meth- ods which are common in wood, paper and pulp industry are used to obtain mentioned reference concentrations and properties.
• Absorbance data, in the above mentioned spectral region, of corresponding samples are then analysed with the mentioned multivariate statistics with the objective to build a predict ⁇ ing model.
• Figure 1 Qualitative analysis of wood chips show the possibilities to iden ⁇ tify wood chips from different tree species and trunk sampling location. The samples are from spruce, pine, maple, birch and are divided in bark and wood respectively. In the case of pine a separation of splint and core was made. Every quality are represented by 5 samples of each. Obtained spectral data were ana ⁇ lysed by PCA. Very good possibilities for identification are present as shown in figure 1.
• the charged saw dust must have passed through a certain storing/ageing.
• the storing process is af ⁇ fected by external factors, such as temperature, moisture, season, biological degradation, quality and origin of ingoing saw dust.
• external factors such as temperature, moisture, season, biological degradation, quality and origin of ingoing saw dust.
• Especially large problems are present in winter time.
• the extent of how far the storage procedure has pro- ceeded is today determined by sensoric human estimation.
• the present inven ⁇ tion correlates the qualitative judgement of the status of the storage procedure, so-called processability, by spectral data.
• n number of samples
• Chips were taken from birch, stored during 1.5 years, dried and sieved. The chips were boiled in an autoclave in laboratory scale and 46 pulp samples were taken out during different intervals to obtain a varying degree of delig- nification. The samples were dried and ground before analysis.
• Figure 3 a-f "characterisation of pulp I" show how yield, kappa number and concentrations of lignin, glucose, xylose and uronic acid were predicted by PLS.
• moisture was determined on 50 samples of spruce. The samples are sorted after area of growth, different sampling locations on the trunk. The samples were obtained during various periods from January through April. Samples from all these groups are used in the regression model. The samples were accuired as saw dust from above mentioned spruce logs which subsequently was submitted to NIRR analysis in the wavelength interval of 400-2500 nm. The spectra obtained have first been pretreated by the second derivative according to Savitzky et. al. Then a multivariate regression was made with all wavelengths using the PLS regression, for prediction results see figure 5.

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Cited By (18)

Citations (4)

• 1994
  • 1994-05-18 SE SE9401707A patent/SE9401707L/ not_active Application Discontinuation
• 1995
  • 1995-05-17 WO PCT/SE1995/000560 patent/WO1995031710A1/en not_active Application Discontinuation
  • 1995-05-17 EP EP95920348A patent/EP0723656A1/en not_active Withdrawn
  • 1995-05-17 AU AU25827/95A patent/AU2582795A/en not_active Abandoned

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