DE102007044606B4 - Determination of the state of pulp and paper - Google Patents

Abstract

Method for developing at least one correlation model for the nondestructive determination of a chemical property of pulp or paper by means of IR spectra, which comprises:
(a) chemical analytical determination of the chemical property of the pulp or paper reference sample to obtain reference values;
(b) measuring the absorption or reflection of the same pulp or paper reference sample from step (a) in the NIR and / or MIR range to obtain reference data;
(c) optionally mathematical pretreatment of the reference data;
(d) generating a reference characteristic for each reference sample by mathematically evaluating the reference data obtained from step (b) or (c);
(e) correlation of the reference parameters obtained from step (d) with the reference values obtained from step (a) by means of multivariate calibration to produce a correlation model,
characterized in that
for step (a), such a chemical analytical method is selected which allows quantitative determination of the reference value and the reference value in step (a) is selected from carbonyl group content, carboxyl group content, average molecular weight and surface pH of the pulp or paper.

Description

I. Field of application

The The present invention relates to a nondestructive process Determination of the chemical properties and thus of the condition of pulp and paper, in particular a method of determining the Carbonyl and Carboxylgruppengehaltes, the average molecular weight and of the surface pH of pulp and paper.

• a) Korrelation von Referenz-Kerngrößen aus IR-Spektren mit diesen chemischen Eigenschaften unter Verwendung von multivariater Datenanalyse und
• b) darauf basierend die Entwicklung von Modellen, um
• i) den aktuellen Zustand des Papiers zu bestimmen und
• ii) das zukünftige chemische und physikalische Verhalten von Cellulose oder daraus hergestellten Produkten vorhersagen zu können.

The present invention relates in particular to a method for

• a) Correlation of reference core sizes from IR spectra with these chemical properties using multivariate data analysis and
• b) based on this the development of models
• i) to determine the current state of the paper and
• ii) be able to predict the future chemical and physical behavior of cellulose or products made from it.

II. Technical background

In different areas of pulp analysis, it is of great interest To get insight especially in the oxidation state of cellulose.

detailed Information on the content of carbonyl and carboxyl groups and the middle Molecular weight, the surface pH and other properties be used on the one hand the current status and on the other hand the future chemical and physical behavior of the cellulose and / or it produced products, especially paper, to predict or quality assessments to substantiate.

Especially the carbonyl group content allows statements about the oxidative stress, the pulp or paper in the past z. B. during the Processing has been suspended (Lewin, M., Macromol., Symp. 1997 (118): 715-724).

carbonyl react more intensely on light and temperature, so the paper is exposed to light and climatic fluctuations sensitized. Consequently, increased photosensitivity can result of the pulp / paper examined.

something similar applies to Carboxyl groups (Chirat, C., De la Chapelle, V., Journal of Pulp and Paper Science 1999 (25): 201-205).

There due to optical properties not on the Carbonylgruppengehalt can be closed - one oxidatively bleached paper may, for. B. appear very bright and at the same time have a high carbonyl group content - it is necessary, about sound and meaningful to have analytical data.

Carboxyl groups are the last oxidation state, they are formed from carbonyl groups and are therefore also an indicator of oxidative stress. Furthermore, the H ⁺ ion of the carboxyl group may dissociate to give an internal acid. At the resulting COO group transition metal ions can bind to the cellulose, which adversely affect the stability of the molecule.

The mean molecular mass gives information about the average length of the Cellulose molecules, which are in a pulp / paper sample. Thus, these are Conclusions on the mechanical stability allowed.

Of the pH value gives an indication of the acids present in the paper. It It is considered certain that acid hydrolysis is a major factor in paper degradation is, a low pH thus indicates an already occurring Dismantling and deteriorating durability expectations. But also a higher one pH can lead to the oxidative destruction of cellulose (Knill, C.J., Kennedy, J.F. Carbohyd. Pole. 2003 (51): 281-300).

Other parameters, such as paper thickness or whiteness, support the description and assessment of paper quality. Under paper quality here is a term for the description of the chemical and mechanical stability of papers understood.

Of the whiteness and the paper thickness are individually rather ambivalent Parameter dar. They do not give a clear indication of the Paper state and can even be contradictory.

The trend However, it is to be expected that a thick paper of rather light color better durability properties than a thin, already heavily discolored paper will exhibit.

Of course, paper thickness can and whiteness also non-destructive by known, conventional methods However, their determination can also be determined by the method according to the invention carried out be, for. B. supplementary to other chemical properties.

All mentioned Parameters in common, carbonyl and carboxyl group content and average Molecular weight, surface pH, but also whiteness and paper thickness, provide information the current condition and aging tendency of paper (Whitmore, P.M., Bogaard, J., Restaurator 1995 (16): 10-30). They allow Statements about the future Behavior of the paper under climatic and mechanical stress Transport and storage.

Farther allow planning of appropriate stabilization measures and the consequent necessary treatment.

In Recently, a combination of IR spectroscopy has been successful and multivariate calibration models used to prepare chemical and physical properties of pulp, cellulose, etc. in paper and pulp production.

there become values that are usually expensive be obtained with wet chemical analysis methods, with Reference parameters Infrared spectra in both near-infrared (NIR) and mid-infrared (MIR), by means of a suitable multivariate calibration in one Model linked, which can then replace the wet-chemical analysis.

This Procedure has already been in the food industry, pharmaceutical industry as well as wood, pulp and paper analysis. Data that otherwise only with considerable effort by wet-chemical analysis methods accessible are, can determined in this way quickly and without sampling and thus non-destructive become.

In In pulp analysis, this combination became predictive from Z. B. Strength properties of pulp used.

The U.S. Patents 5,680,321 and 5,638,284 For example, methods for determining physical properties of pulp and / or paper, such as wet strength, tear resistance, hydrophilicity and the like, describe.

The U.S. Patent 6,476,915 describes methods for determining paper thickness, basis weight, paper moisture, optical properties, modulus of elasticity, tear propagation resistance, bursting strength, breaking length of paper and / or cardboard on a moving web.

• (I) die Entwicklung eines Kalibrationsmodells durch
• (Ia) Aufnahme von Absorptions-, Reflektions- oder Emissionsspektren von Referenzproben an Papier mit bekannten Eigenschaften,
• (Ib) Bearbeiten der Rohdaten aus den Spektren und
• (Ic) Analysieren der Daten mittels chemometrischer Methoden,
• (II) Bestimmen der unbekannten Eigenschaften von Papierbrei und/oder Papier durch
• (IIa) Aufnahme von Absorptions-, Reflektions- oder Emissionsspektren von Papierproben mit unbekannten Eigenschaften,
• (IIb) Bearbeiten der Rohdaten und
• (IIc) Anwendung des entwickelten Kalibrationsmodells aus (I).

These methods include

• (I) the development of a calibration model
• (Ia) recording absorption, reflection or emission spectra of reference samples on paper with known properties,
• (Ib) Processing the raw data from the spectra and
• (Ic) Analyzing the Data Using Chemometric Methods
• (II) determining the unknown properties of pulp and / or paper
• (IIa) recording absorption, reflection or emission spectra of paper samples with unknown properties,
• (IIb) edit the raw data and
• (IIc) Application of the developed calibration model from (I).

Thus, no spectra of the wet-chemically examined reference samples are recorded there men.

Farther NIR spectra are also for it used to determine the yield, the kappa number, the content of lignin, Glucose, xylose and uronic acids in pulps (Olsson, R.J.O., Tomani, P., Karlsson, M., Joseffson, T., Sjöberg, K., Björklund, C., Tappi Journal 1995 (78): 158-166).

Further Parameters that have been determined are intrinsic viscosity, degree of polymerization, Copper number and whiteness of various pulps and papers (Fardim, P., Ferreira, M.M., Durán, N., Journal of Wood Chemistry and Technology 2002 (22): 67-81).

MIR spectra in connection with multivariate calibration have been so far for the prediction of extractives, lignin, total carbohydrate content and density, but so far not for the determination of chemical properties, the correlate with the oxidative stress of the paper, in particular not the content of carbonyl and Carboxyl groups used.

The in the prior art described wet chemical processes for Determination of properties of pulp or strength properties Papers, such as intrinsic viscosity and copper number, were frequently predicted chemical properties, or the physical methods of determination For example, the tensile strength, the zero tensile strength and the Bursting strength have the disadvantage that for the determination of these parameters size Sample volumes and multiple repetitions are needed.

One further disadvantage of the determination of mechanical paper properties is that they oxidize the cellulose only at high degree of damage reflect high carbonyl and carboxyl groups or greatly reduced average molecular weights.

III. Presentation of the invention

a) Technical task

It It is therefore an object of the invention to provide a method for a non-destructive as possible accurate statements about to make the chemical properties of cellulose and paper and secondly, at a very early stage of the injury Cellulose and paper in which the mechanical properties of the paper are barely influenced.

b) solution the task

These Task is achieved by the characteristics of the independent claims 1, 11 and 12 solved, wherein advantageous developments specified in the dependent claims are.

The The invention provides a method for nondestructive determination of Carbonyl and Carboxylgruppengehaltes, the average molecular weight and the surface pH value ready. Other parameters, such. B. the whiteness of pulps and papers, as well as the thickness of papers can also be determined by this method.

1. inventive aspect

• (a) chemisch-analytische Bestimmung der chemischen Eigenschaften von Zellstoff- oder Papierreferenzproben, um Referenzwerte zu erhalten;
• (b) Messen der Absorption oder Reflexion derselben Zellstoff- oder Papierreferenzproben aus Stufe (a) im NIR- und/oder MIR-Bereich, um Referenzdaten zu erhalten;
• (c) gegebenenfalls mathematische Vorbehandlung der Referenzdaten;
• (d) Erzeugung von Referenz-Kenngrößen für jede Referenzprobe durch mathematische Auswertung der aus Stufe (b) oder (c) erhaltenen Referenzdaten;
• (e) Korrelation der aus Stufe (d) erhaltenen Referenz-Kenngrößen mit den aus Stufe (a) erhaltenen Referenzwerten mittels multivariater Kalibration zur Erstellung von Korrelationsmodellen,

dadurch gekennzeichnet, dass für Schritt (a) eine solche chemisch-analytische Methode ausgewählt wird, die eine quantitative Bestimmung der Referenzwerte ermöglicht, und der Referenzwert unter Carbonylgruppengehalt, Carboxylgruppengehalt, mittlerer Molmasse und Oberflächen-pH-Wert des Zellstoffs oder Papiers ausgewählt wird.The first aspect of the invention relates to a method for the development of correlation models for the nondestructive determination of chemical properties of pulp or paper by means of IR spectra, which comprises:

• (a) chemical analytical determination of the chemical properties of pulp or paper reference samples to obtain reference values;
• (b) measuring the absorption or reflection of the same pulp or paper reference samples from step (a) in the NIR and / or MIR range to obtain reference data;
• (c) optionally mathematical pretreatment of the reference data;
• (d) generating reference characteristics for each reference sample by mathematically evaluating the reference data obtained from step (b) or (c);
• (e) correlation of the reference parameters obtained from step (d) with the reference values obtained from step (a) by means of multivariate calibration to produce correlation models,

characterized in that for step (a) such a chemical analytical method is selected, the allows quantitative determination of the reference values and the reference value is selected from carbonyl group content, carboxyl group content, average molecular weight and surface pH of the pulp or paper.

The chemical properties of the reference samples are wet-chemical Methods determined. Under "chemical Properties " herein preferred is the carbonyl and carboxyl group content and the mean molecular weight, carbonyl and carboxyl group content are particularly preferred.

According to the first inventive aspect a method for the development of a correlation model is provided, with which it is possible is, very accurately the content of carbonyl and carboxyl groups, the middle Molecular weight and the surface pH of pulp and paper only from IR spectra.

in the The following are the stages of the method according to the first aspect closer to the invention explained.

Stage (a)

First of all the chemical-analytical determination of the sought-after chemical property of pulp and paper reference samples, by reference methods, a very accurate determination of this property, z. B. the content on carbonyl and carboxyl groups contained in the pulp or paper and the average molecular weight, allow.

Of the pH of the paper surface is determined according to TAPPI (T 529 om-88) as the reference method.

• – die CCOA-Methode, die beispielsweise in Biomacromolecules 2002 (3): 959–968; Biomacromolecules 2002 (3): 969–975; und Biomacromolecules 2003 (4): 743–747 beschrieben ist, und
• – die FDAM-Methode, die in Biomacromolecules 2006, 7(6), 1743–1750, beschrieben ist, zur Anwendung.

As a reference method for the determination of the content of carbonyl and carboxyl groups and the determination of the average molecular weight come

• The CCOA method described, for example, in Biomacromolecules 2002 (3): 959-968; Biomacromolecules 2002 (3): 969-975; and Biomacromolecules 2003 (4): 743-747, and
• The FDAM method described in Biomacromolecules 2006, 7 (6), 1743-1750.

As well can other methods, eg. Example by means of hydrazine (Analytica Chimica Acta 1962 (27): 434-440) or the copper number (TAPPI T 430 om-94) for determining the carbonyl group content be used, these much inaccurate values and thus result in less accurate correlation models.

Prefers are thus the CCOA and the FDAM method, since thereby the content on carbonyl and carboxyl groups present in pulp and paper as well as the average molecular weight very precisely in the form of reference values can be determined.

Of Furthermore, these methods have the advantage that they already deliver very accurate values with very low content of these groups.

In order to can be oxidative damage already in an early Stage, which is not possible with other methods, z. B. from a content of 3 .mu.mol / g at carbonyl groups and a content of 10 .mu.mol / g of carboxyl groups.

in the Compared to previous publications and patents that have the characteristics of pulp or the strength properties of papers, are the errors of the reference methods used here, in particular concerning carbonyl and Carboxyl groups, average lower, so that the obtained Correlation models are much more accurate.

So show the ones chosen here Determination methods even if the wet chemical provisions on a Period of at least one year a selected one Pulp has a standard deviation of still less than 5%, what kind of a very good reproducibility (within a laboratory) after speaks of these methods.

The intrinsic viscosity and the copper number, as common certain chemical properties are also average higher Error on (intrinsic viscosity according to TAPPI T 230 om-94, reproducibility within a laboratory 4%, reproducibility between two laboratories 19%, copper number loud TAPPI T 430 om-94, repeatability within a laboratory 10%).

common Methods for the strength determination, however, are the tensile strength (repeatability within a laboratory and reproducibility between two laboratories according to TAPPI 404 om-92 4-13%), the Zero tensile strength (repeatability within a laboratory and Reproducibility between two laboratories according to TAPPI 231 cm-96 3-10%) and the bursting strength (repeatability within a laboratory and Reproducibility between two laboratories according to TAPPI 403 om-97 22-28%), the at low damage cause but still inconspicuous for a long time Deliver values.

Stage (b)

In this stage of the process becomes the absorption or reflection of the same Pulp and / or paper reference samples in the NIR and / or MIR range from which in step (a) the sought chemical properties chemical analysis were determined. The wavelength range is to be determined so that the sought chemical property reflected in the spectral data.

For this purpose, spectra of these reference samples are recorded in the range of the near (NIR) and / or middle (MIR) infrared, thereby obtaining reference data. The NIR covers the wavelength range between 12,800 and 4,000 cm ^-1 and the MIR covers the wavelength range between 4,000 and 400 cm ^-1 .

It is recommended, inter alia, for the determination of meaningful reference data, that the measurement of the spectra at the reference samples the entire relevant wavelength range, ie at NIR of 12,800-4,000 cm ^-1 and MIR from 4,000 to 400 cm ^-1 , or both together, includes.

Further should be clarified that for Each chemical property to be determined, for example the content a specific chemical-functional group, separate reference data must be determined for what, though the same spectra, possibly only differentiated according to the wavelength range, can be used.

Out determined from the individual samples of a set of reference samples Spectra may also have an average range before further processing be generated, especially if several samples have a very similar Reference value.

Stage (c):

Preferably the reference data of the recorded spectra are pretreated, irrelevant, random or to reduce systematic sources of variation or completely to remove.

• – verschiedene Standardvorbehandlungen, wie Normierungen, Minimum/Maximum-Normierung und/oder besonders Vektornormierung (Standard Normal Variate Transformation, SNV) (Zeaiter, M., Roger, J.-M., Bellon-Maurel, V., TrAC – Trends in Analytical Chemistry, 24(5), 2005: 437–445, Robustness of models developed by multivariate calibration. Part II: The influence of pre-processing methods) und/oder Subtraktion einer Geraden (straight line substraction), und/oder mathematische Ableitungen (1. und 2.), und/oder
• – multiplikative Streuungskorrektur (Multiplicative Scatter Correction/MSC), beschrieben in Isaksson, T., Naes, T., Effect of multivariate scatter correction (MCS) and lineartity improvement in NIR spectroscopy, Applied Spectroscopy 42(7) 1988: 1273–1284, und/oder
• – erweiterte Multiplikative Streuungskorrektur (Extended Multiplicative Scatter Correction/EMSC,) beschrieben in Martens, H., Stark, E., Extended multiplicative signal correction and spectral interference subtraction: New pre processing methods for near infrared spectroscopy, Journal of Pharmaceutical and Biomedical Analysis, 9(8) 1991: 625–635 und/oder
• – orthogonale Signalkorrektur (Orthogonal Signal Correction/OSC, US-Patent 6,754,543 ), beschrieben in Wold, S., Antti, H., Lindgren, F., Öhman, J., Orthogonal signal correction of near-infrared spectra, Chemometrics and Intelligent Laborstory Systems, 44(1-2) 1998: 175–185 und in Fearn, T., On orthogonal signal correction, Chemometrics and Intelligent Laborstory Systems, 50(1) 2000: 47–52 und/oder
• – multivariate Curve Resolution (MCR), beschrieben in Schoonover, J. R., Marx, R., Zhang S. L., Multivariate curve resolution in the analysis of vibrational spectroscopy data files, Applied Spectroscopy, 57(5) 2003: 154A–170A
• – bzw. Kombinationen der genannten von Datenvorbehandlungsmethoden.

Various methods exist to overcome these problems. From these known methods, the following are selected according to the invention:

• Various standard pre-treatments, such as standardizations, minimum / maximum normalization and / or standard vector variegation (SNV) (Zeaiter, M., Roger, J.-M., Bellon-Maurel, V., TrAC Trends in Analytical Chemistry, 24 (5), 2005: 437-445, Robustness of models developed by multivariate calibration, and / or subtraction of a straight line (substraction), and / or mathematical derivatives (1st and 2nd), and / or
• Multiplicative Scatter Correction (MSC) described in Isaksson, T., Naes, T., Effect of Multivariate Scatter Correction (MCS) and linearity improvement in NIR spectroscopy, Applied Spectroscopy 42 (7) 1988: 1273-1284, and or
• Extended Multiplicative Scatter Correction (EMSC) described in Martens, H., Stark, E. Extended multiplicative signal correction and spectral interference subtraction: New Preprocessing Methods for Near Infrared Spectroscopy, Journal of Pharmaceutical and Biomedical Analysis, 9 (8) 1991: 625-635 and / or
• Orthogonal signal correction (Orthogonal Signal Correction / OSC, U.S. Patent 6,754,543 ) described in Wold, S., Antti, H., Lindgren, F., Öhman, J., Orthogonal signal correction of near-infrared spectra, Chemometrics and Intelligent Laboratory Systems, 44 (1-2) 1998: 175-185 and in Fearn, T., On Orthogonal Signal Correction, Chemometrics and Intelligent Laboratory Systems, 50 (1) 2000: 47-52 and / or
• Multivariate Curve Resolution (MCR) described in Schoonover, JR, Marx, R., Zhang SL, Multivari ate curve resolution in the analysis of vibrational spectroscopy data files, Applied Spectroscopy, 57 (5) 2003: 154A-170A
• - or combinations of said data pre-treatment methods.

at The mathematical pretreatment will be the reference data for each determining chemical property treated separately, in particular treated with different methods.

Stage (d):

In this level will be out of the for each reference sample in step b) or c) obtained reference data determined reference characteristics, for each chemical property to be determined separately. The goal is here the recognition of course occurring groupings or classes or possible outliers, d. H. untypical reference data or reference characteristics. Because only in this way is it possible in the next step for Each chemical property to be determined has its own correlation model to disposal to deliver.

Especially In this step, a sum parameter is calculated from the reference data of the spectra as a reference parameter determined with the content of a particular chemically functional Group or another chemical property correlates.

• – Principle Component Analysis (PCA, Hauptkomponentenanalyse)

The raw or pretreated measurement data from step b) or c) of the spectra are loaded into a corresponding computer program. There, multivariate methods, such as computational methods for orientation data analysis or structure recognition, are used to get an insight into the data structure:

• - Principle Component Analysis (PCA, Principal Component Analysis)

at Principal Component Analysis (PCA) is about getting a record on natural To investigate group formation without having a specific class affiliation is assumed. It is a mathematical one Conversion of a data matrix with the target contained in the data To represent variance by a smaller number of variables. These variables are called "factors" or "main components". A new coordinate system is calculated, in which the samples newly defined axes instead of the original measurement coordinates be assigned.

In order to can based on the reference parameters generated in this stage Reference samples determined for the total number of reference samples selected be that later for the Correlation in stage (e) should be used as it is a specific Belong to paper or other common parameters.

Stage (s):

In this step is correlated between those obtained from step a) wet-chemically determined reference values which are below carbonyl group content, Carboxyl group content, average molecular weight and surface pH to be selected, and those determined from the steps b) to d) by means of the spectra Reference parameters produced using multivariate calibration. The advantage of a multivariate Data calibration opposite The univariate data calibration is that not like in a univariate data calibration a measure of a Assign samples to create a calibration function, but multiple measures (typically Spectra with a large number of individual data points) with one property or more properties of a sample can. This will be for Each chemical property to be determined has its own correlation model achieved.

• – Soft Independent Modeling of Class Analogies (SIMCA).

First, the unknown sample to be determined must be classified, ie assigned to a specific class (pulp, rag paper, etc.). One possible method for this is:

• - Soft Independent Modeling of Class Analogies (SIMCA).

If a known class used for it is to develop a model to classify future samples, SIMCA is used. For SIMCA becomes a multidimensional box for each classifier membership created by PCA. The classification of future samples is place the samples in the best fitting box.

The data analysis performed in step (d) can be used to classify the reference samples using SIMCA. This classification is necessary insofar as that an unambiguous assignment different paper grades (ie pulp, rag paper, groundwood paper, hybrids, etc.) to a model specific to this class of paper is required to ensure reliable property determination based on the model.

• – Multiple Lineare Regression (MLR), und/oder
• – Partial Least Squares Regression (PLS-R), und/oder
• – Hauptkomponentenregression (Principle Component Regression, PCR).

By mathematical methods, namely multivariate methods for data analysis, applied, are obtained from the measured data of the spectra reference characteristics, such. By:

• - Multiple Linear Regression (MLR), and / or
• - Partial Least Squares Regression (PLS-R), and / or
• - Principal Component Regression (PCR).

The multiple linear regression (MLR) has the task of describing the relationship between more than two variables X ₁ , X ₂ ... and thus to arrive at prognostic statements for a variable Y considered to be dependent on which it is assumed depends not only on one but several variables X _i .

The Partial Least Squares Regression (PLS-R) is a statistical procedure for estimation of causal models. The PLS-R is divided into PLS-1 and PLS-2. While in PLS-1 a variable (paper property) for the correlation with the spectra PLS-2 can handle multiple variables at the same time. The Parameters obtained from the spectra are used for modeling the desired Quality parameters used.

The Principle component regression (PCR) is different from the PLS in it, as from collinear spectra an independent matrix is calculated. Although the mathematical algorithms are different are the two methods are often considered comparable. Your Application can also be done with the available computer program related.

One such created calibration model must be validated, for example through cross-validation or test-set validation, on the one hand the quality of the created calibration model and on the other hand, around for Each chemical property to be determined is a specific best suitable method for select the multivariate data analysis in step (e).

For example a cross validation is performed, taking care of the calibration according to e) and the subsequent validation one and the same set of samples and data set is used.

at the cross validation is done separately for each investigating chemical property - a sample used to create of the correlation model according to e) was omitted, and created a new calibration model without them. This new calibration model is used to test the chemical Property of the omitted sample only based on the reference characteristic, ie based on the recorded spectra.

the one multivariate reduction method, which in this way a Reference characteristic, the best the wet chemical reference value of these omitted Sample is thus considered to be the specific chemical Property selected the most suitable multivariate reduction method.

At the Example of over the concentration of a particular functional chemical group applied spectral sum parameter and from these values calibration line produced this means that one of the value pairs initially omitted and from the rest Value pairs a new calibration line is formed and as the best reduction method the one selected is at which the deviation of the new calibration line from the original Eichgerade, which was created from all value pairs, the lowest is.

The Correlation with cross validation is thus a summary the correlations of each sample through the model.

to Further optimization of this correlation model can now for further Reference samples that are not involved in the creation of the correlation model have participated according to the steps b) to d) reference parameters determined and with the other wet-chemically determined reference values These new reference samples are compared with the validation now consists solely of the reference parameters and the right model you want To determine values without any wet-chemical analysis.

2. Aspect according to the invention

After this a correlation model between wet chemical reference values and created by recording IR spectra determined reference parameters and possibly enhanced by validation and further optimization by applying this correlation model to a unknown sample a chemical property of interest, for example the content of carboxyl groups alone by the application of the correlation model according to step (e) and determining one according to steps (b) to at least (c), better (d) determined spectral characteristic can be determined.

For this Advantageously, NIR or MIR spectra will be directly on the intact paper or pulp without special sample preparation, preferably in reflection mode, measured. This may be due to of the correlation model are dispensed with sampling the method extensively for the investigation of cellulosic materials and very sensitive papers can be used as no sampling more is needed.

By the non-destructive Recording of the IR spectrum from the characteristic value of unknown Probe becomes immediately the desired chemical from the correlation model Property, in particular the content of a certain chemical-functional Group, received without any wet-chemical treatment and thus a partial destruction the paper sample according to step (a) becomes necessary.

This will ultimately also be through use of the above, respectively for individual Features optimal reduction methods (CCOA and FDAM), can be developed through the correlation models with which it is possible is already at a very early age Time the condition, in particular the oxidative damage, of cellulosic material or paper.

3. inventive aspect

• – unter Ermittlung einer Kenngröße für diese chemische Eigenschaft mittels Aufnahme von Infrarot-Spektren und daraus gewonnenen Daten; und
• – die Auswahl eines von mehreren zur Verfügung stehenden Korrelationsmodellen für diese chemische Eigenschaft gemäß dem ersten und zweiten Aspekt der Erfindung.

Another aspect of the invention is to determine an unknown chemical property of a new pulp or paper sample according to the invention

• - Determining a characteristic for this chemical property by recording infrared spectra and derived data; and
• The selection of one of several available correlation models for this chemical property according to the first and second aspects of the invention.

• (i) Messen der Absorption oder Reflexion des Zellstoffs oder Papiers im NIR- und/oder MIR-Bereich;
• (ii) gegebenenfalls mathematische Vorbehandlung der in Stufe (i) erhaltenen Messdaten;
• (iii) Erzeugung von Kenngrößen durch mathematische Auswertung der aus Stufe (ii) erhaltenen Referenzdaten; dadurch gekennzeichnet, dass
• (iv) Auswahl eines der gemäß den Ansprüchen 1 bis 20 erstellten Korrelationsmodells,
• (v) die Bestimmung der unbekannten chemischen Eigenschaften durch Anwendung des Korrelationsmodells der Stufe (iv) alleine anhand der in Stufe (iii) erhaltenen Kenngrößen ohne Probennahme direkt an der intakten Zellstoff- oder Papierprobe erfolgt.

Such a method comprises the following steps:

• (i) measuring the absorption or reflection of the pulp or paper in the NIR and / or MIR region;
• (ii) optionally mathematical pretreatment of the measurement data obtained in step (i);
• (iii) generating characteristics by mathematically evaluating the reference data obtained from stage (ii); characterized in that
• (iv) selecting a correlation model created according to claims 1 to 20,
• (v) determining the unknown chemical properties by applying the correlation model of step (iv) solely on the basis of the parameters obtained in step (iii) without sampling directly on the intact pulp or paper sample.

there Steps (i), (ii) and (iii) correspond to steps (b), (c) and (d) according to the first both aspects of the invention.

The in the steps (v) and (iv) to be selected and applied correlation model is one of those in step (e) of the first aspect of the invention resulting correlation models.

there are from one and the same reference data according to step (b) or (c) by Application of different multivariate reduction methods different Obtained correlation models.

Farther can or must be based on the parameters generated in step iii) also a classification of the one or more unknown samples take place, for example, according to the basic paper (cellulose paper or rag paper) or other sample-determining parameters, z. B. by means of SIMCA, since partially only then the selection of the calibration model can be done.

If this happens, then again, on the basis of the classification carried out the one or the selection of the appropriate correlation model for the determination of the sought chemical property can be selected for several unknown samples.

• – Restaurierung/Konservierung von Kunst- und Kulturgut (historische Papiermaterialien, besonders Hadernpapiere)
• – Zellstoffproduktion/Zellstoffverarbeitung/Papierherstellung (verringerte Analysezeiten, Vorhersagen von Vergilbungsneigung, Weißgradstabilität und Lagerfähigkeit).

The methods for determining the carbonyl and carboxyl group content and the average molecular weight, the surface pH, the paper thickness and the whiteness are particularly suitable for the non-destructive examination of cellulosic materials in two areas:

• - Restoration / conservation of artistic and cultural property (historical paper materials, especially rag paper)
• Pulp production / pulp processing / papermaking (reduced analysis times, prediction of yellowing tendency, whiteness stability and shelf life).

The advantage of the method according to the invention is clear from the following applications:
By non-destructive determination of parameters such as carbonyl and carboxyl group content and average molecular weight, recommendations can be made for the further treatment of pulps and papers. The following table lists three chemical properties to be determined, namely carbonyl group content, carboxyl group content and average molecular weight.

The influence of these parameters on pulp and paper is shown. These are chemo-physical properties that relate to interactions with environmental conditions and other treatment steps. These factors have a corresponding effect on further measures that are recommended to be carried out or even avoided with the pulp or paper concerned. It deals in particular with practical aspects of processing, use and storage. parameter influence effects carbonyl group • Light stability • Alkaline stability • Resistance to external influences • Interactions with carboxyl groups • Long-term stability • storage, transport • exhibition, loan • deacidification treatment • bleaching treatments • further processing carboxyl • Light stability • Water absorption capacity • Ion binding capacity • Resistance to external influences • Storage • Exhibition, loan • Aqueous treatments • Deacidification treatments • Further processing Mean molecular weight • Mechanical strength • Long-term stability • usability • exhibition, loan • all treatment, especially watery • further processing

Some these relationships will be exemplified below.

The Light stability, the alkali stability, the mechanical strength, the long-term stability, the stability against environmental influences and the water absorption capacity decrease when the number of carbonyl groups in the pulp or paper is increasing. In the case of transport, lending, storage, further processing, restorative treatment, usability this must be taken into account be minimized by exposure to light, acid and water and also mechanical loads.

takes the number of carboxyl groups decreases, the acid stability and the stability of the pulp or Paper opposite Environmental influences.

In contrast, the ion binding capacity and the water absorption capacity increase with increasing Number of carboxyl groups too.

The increase of the carboxyl group content to a deterioration of some pulp or paper properties while others increase. That hangs It depends on the further course of action, if this increase is positive or negatively.

The Decrease in the average molecular weight affects the mechanical strength, the long-term stability, the Acid stability and the stability across from environmental influences to the effect that these factors decrease in each case. The decrease of average molecular weight leads So to a deterioration of the pulp or paper.

from that can statements be made as to how the materials will be processed in the future, stored and transported, which conditions for the exhibition of exhibits are required so that the exhibits as possible take little harm, or how to treat restored pieces Need to become. Also can Statements about it are met, which conditions are met when lending Need to become.

In summary, the effects of these three chemical properties are:

The analytical method for the subsu-lation using the MIR and NIR spectra Cellulose in the form of pulp and paper sheets is also called a predictive model.

The Applicability of the predictive models can be on the one hand for new pulps (Papers, hand sheets), as widely used in the paper industry find, and for another historical rag paper, which is relevant for conservatory purposes are to be shown.

There the historical rag paper both in their color as well distinct in their composition from modern pulp sheets, be different models for Pulp paper on the one hand and rag paper on the other hand created.

c) embodiments

A embodiment according to the invention is explained in more detail below with reference to the figures by way of example. It demonstrate:

1 : a variety of recorded spectra,

2 : Scoreplot of spectral data,

3 : Correlation of wet-chemically determined values with the spectral characteristics,

4 : Validation of the correlation model 3a .

5 : tabular representation of different correlation models, and

6 : Graphical representations of some of the correlation models 5 ,

The Figures show the procedure for creating a correlation model, with its help later at new additional Samples chemical properties - such as about the carbonyl group content - non-destructive exclusively by means of spectral measurements and application of the correlation model to be determined. The correlation model is determined using available reference samples created, from which also material shares removed and wet-chemical may be edited.

1a shows the NIR spectra generated by these reference samples, in this case 100 spectra from 100 different pulp reference samples.

Since the frequency range of 12,000 cm ^-1 - at least 9,000 cm ^{-1 contains} no characteristic rashes, it is not included in the modeling. In 1b the selected location is displayed (step b) or i)).

Then - according to step c) or ii) - these Subjected spectral data to a mathematical pretreatment and furthermore from these spectral data one for each individual sample Reference characteristic determined according to step d) or iii).

To Fixing on a particular mathematical pretreatment is with The data pretreated in this way is the subject of a principal component analysis. These provides several main components.

2 FIG. 12 shows the exemplary graphical representation of samples in which the second versus the fourth major component is plotted and re-sorted for the largest occurring variance. FIG.

It In such a representation often also shows a classification the samples by dividing the value pairs into two or more individual areas are highly concentrated, indicating the existence of a common strongly formative Property, e.g. B. a main component, in each case those Samples specimens concentrated at one point in the chart are.

After selecting the most suitable mathematical reduction method, determining the principal components, and determining the obvious outliers among the reference samples and omitting to proceed, the remaining reference samples are determined according to 3a the correlation between the spectral data for each sample determined reference parameters of the sought chemical property and the previously wet-chemically determined from the same sample reference value of the same property.

in the In the present case, the correlation with the module PLS-1 was the Software Opus Quant created from the existing value pairs also immediately marks a resulting calibration line. The investigated chemical property here is the content of carbonyl groups.

3b shows an analogous representation for the same samples for the content of carboxyl groups.

Such a selected correlation model requires further optimization, first by validation, as in 4a for the carbonyl group content and in 4b shown for the carboxyl group content:
There is by means of a test set, ie a group of new reference samples, in accordance with the creation of the correlation model 3a was not used again, a comparison of the wet chemical values with the spectrally determined parameters, which are obtained with the same pretreatments and reduction methods and their location to that of the correlation model of 3a drawn Eichstraaden.

Provided the deviations of the test set from the calibration line on average is not bigger as the one in creating the correlation model with the first scope of reference samples, the correlation model as be classified successfully.

is If this is not the case, it is necessary to search for a new correlation model become.

at the determination of one and the same chemical property on the same Group of reference samples are used alone by using different mathematical pretreatment methods and mathematical reduction methods different reference parameters the reference samples can be obtained, and thus in the - Examination of unknown samples - different new spectral Parameters. The Confrontation these different characteristics with each of the same wet-chemically determined values in a correlation model thus gives different correlation models for the same chemical property.

5 shows in tabular representation for a series of samples, the wet-chemically determined "measured values" shown in the first column of values and compared the spectral parameters KG1 to KG7 in the last four columns determined by different pretreatment and reduction methods.

The correlation of the column KG1 with the "measured value" is in 6a graphically represented, that of the column KG3 in 6b ,

The comparison of these two 6a and 6b and the correlation coefficients given there on the top right each, clearly shows that the correlation model according to 6a better correlated than that of 6b , which is already visible graphically from the position of the pairs of values for the correlation line (calibration line) resulting from the individual value pairs, but also from the 6a closer to the ideal value of 1.0 correlation coefficients.

• – für die Bestimmung der mittleren Molmasse: die erste Ableitung (bevorzugter Spektralbereich: 4250–7500 cm^–1),
• – bei der Bestimmung des Gehalts an Carbonylgruppen: zweite Ableitung (bevorzugter Spektralbereich: 4250–7500 cm^–1),
• – bei der Bestimmung des Gehalts an Carboxylgruppen: und Multiplicative Scattering Correction (bevorzugter Spektralbereich: 4600–6100 cm^–1),

In summary, it can thus be said that the following mathematical pretreatments have proved to be advantageous:
For pulp papers:

• For the determination of the average molecular mass: the first derivative (preferred spectral range: 4250-7500 cm ^-1 ),
• In the determination of the content of carbonyl groups: second derivative (preferred spectral range: 4250-7500 cm ^-1 ),
• In the determination of the content of carboxyl groups: and Multiplicative Scattering Correction (preferred spectral range: 4600-6100 cm ^-1 ),

• – für die Bestimmung der mittleren Molmasse: Straight Line Substraction (bevorzugter Spektralbereich: 6100–7500 cm^–1,
• – bei der Bestimmung des Gehalts an Carbonylgruppen: erste Ableitung, Multiplicative Scattering Correction (bevorzugter Spektralbereich: 3800–4500 cm^–1 sowie 5000–5300 cm^–1 sowie 6000–7100 cm^–1),
• – bei der Bestimmung des Gehalts an Carboxylgruppen: erste Ableitung, Vektornormierung (bevorzugter Spektralbereich: 3800–7500 cm^–1),
• – Oberflächen-pH-Wert: Multiplicative Scattering Correction (bevorzugter Spektralbereich: 3800–5400 cm^–1 sowie 5800–7500 cm^–1).

For rag paper:

• For the determination of the average molecular mass: straight line subtraction (preferred spectral range: 6100-7500 cm ^-1 ,
• In the determination of the content of carbonyl groups: first derivative, multiplicative scattering correction (preferred spectral range: 3800-4500 cm ^-1 and 5000-5300 cm ^-1 and 6000-7100 cm ^-1 ),
• In the determination of the content of carboxyl groups: first derivative, vector normalization (preferred spectral range: 3800-7500 cm ^-1 ),
• Surface pH: Multiplicative Scattering Correction (preferred spectral range: 3800-5400 cm ^-1 and 5800-7500 cm ^-1 ).

Claims (14)

A method of developing at least one correlation model for nondestructive determination of a chemical property of pulp or paper by IR spectra, comprising: (a) chemical analytical determination of the chemical property of the pulp or paper reference sample to obtain reference values; (b) measuring the absorption or reflection of the same pulp or paper reference sample from step (a) in the NIR and / or MIR range to obtain reference data; (c) optionally mathematical pretreatment of the reference data; (d) generating a reference characteristic for each reference sample by mathematically evaluating the reference data obtained from step (b) or (c); (e) correlation of the reference parameters obtained from step (d) with the reference values obtained from step (a) by means of multivariate calibration to produce a correlation model, characterized in that for step (a) such a chemical analytical method is selected which allows a quantitative determination of the reference value, and the reference value in step (a) is selected from carbonyl group content, carboxyl group content, average molecular weight and surface pH of the pulp or paper. Method according to claim 1, characterized in that the reference value from step (a) is the carbonyl or carboxyl group content is. Method according to claim 2, characterized in that that the reference value is determined by the CCOA method or FDAM method becomes. Method according to claim 1, characterized in that that the reference value from step (a) is the pH of the pulp or paper surface is. Method according to claim 4, characterized in that that the reference value for the pH is determined according to TAPPI. Method according to Claim 5, characterized in that the measurement of the spectra in step (b) covers the entire relevant wavelength range, in particular for measurements in the mid-infrared range (MIR) of 4,000 to 6,000 cm ^-1 and in the near infrared range (NIR) of 12,800 to 4,000 cm ^{-1 is} performed. Method according to one of claims 1 to 6, characterized that by the reference parameters generated in step (d) a Classification of reference samples is made. Method according to one of claims 1 to 6, characterized that based on the reference parameters generated in step (d) specific Reference samples selected be that for the correlation in step (e) are used. Method according to one of the preceding claims, characterized characterized in that a validation of the obtained in step (e) Correlation models are made to one for each chemical property the pulp or paper sample suitable method for the multivariate Data analysis in step e). Method according to claim 9, characterized in that that validation by means of cross validation or test set validation carried out becomes. Use of the correlation model according to one of claims 1 to 10, for the non-destructive Determination of an unknown chemical property of pulp or paper by using steps (b) to (d) of the process according to one of the claims 1 to 9 to an unknown sample, characterized in that solely on the basis of the thus determined parameter of the sample with the unknown Property and with the help of the correlation model according to feature e) of the claims 1 to 9 the unknown chemical property of the pulp or Paper without application of step (a). Nondestructive method of determination unknown chemical property of a pulp or paper sample by means of IR spectra, which comprises: (i) measuring the absorption or reflection of the pulp or paper sample in the NIR and / or MIR range; (ii) optionally mathematical pretreatment the measurement data obtained in step (i); (iii) production of Characteristics by mathematical evaluation of the reference data obtained from step (ii); thereby marked that (iv) Selection of one of the claims 1 to 10 created correlation model, and (v) the determination of unknown chemical property by applying the correlation model alone of step (iv) to the characteristic obtained in step (iii), without Sampling takes place directly on the intact pulp or paper sample. Method according to claim 12, characterized in that that, based on the parameters generated in step (iii), a classification of the unknown pulp and paper samples. Method according to claim 13, characterized in that that based on the classification, a suitable correlation model is selected for determining the respective chemical property.