ABSTRACT
USE OF NEAR INFRARED SPECTROSCOPY AND MULTIVARIATE
CALIBRATION IN PREDICTING THE PROPERTIES OF TISSUE PAPER
MADE OF RECYCLED FIBERS AND VIRGIN PULP
By Krishan Bhatia
Softness and tensile strength are two major tissue paper properties that govern
consumer acceptance. In this work an attempt was made to use Near Infrared
Spectroscopy combined with chemometric techniques to predict these properties.
For this study four variables were chosen; raw material, amount of debonder, amount of
wet strength resin and the level of refining. For each condition, handsheet spectra were
taken and then the softness and the tensile strength were measured in a conventional
manner. Data and the spectral absorbance values were then used with Quant + software to
generate a model which was used to predict the properties of the unknown samples.
Predictions obtained from this study show that it is possible to use NIR spectroscopy
combined with multivariate calibration and chemometric techniques to predict the
softness and tensile properties of tissue paper. Results show the model capability of
prediction is of same magnitude for each phase. The Root mean square error of prediction
(RMSEP) value obtained was approximately 2.0% for tensile strength and 0.15% for
softness in each phase. The technique can be used to replace the conventional procedures.
The results indicate the applicability of NIR and chemometric procedures for tissue. The
technique can be evaluated in actual mill conditions for maximum utilization. Although
there could be certain limitations of high instrumental cost but once installed the
procedure can be used to measure properties of paper very effectively and quickly. Also
it could reduce the amount of broke generated while maintaining a uniform product.
USE OF NEAR INFRARED
SPECTROSCOPY AND MULTIVARIATE
CALIBRATION IN PREDICTING THE
PROPERTIES OF TISSUE PAPER MADE
OF RECYCLED FIBERS AND VIRGIN
PULP
A thesis
Submitted to the faculty of
Miami University
in partial fulfillment of the requirements for the degree of
Master Of Science
Department Of Paper Science & Engineering
By
Krishan Bhatia
Miami University
Oxford. Ohio,
2004
Dr Robert C. Peterson
Dr. Andre J. Sommer
Prof. M. H. Waller
TABLE OF CONTENTS
1.0
2.0
3.0
Introduction………………………………………………………………………1
1.1
Background………………………………………………………………1
1.2
Literature Review………………………………………………………..2
1.3
Problem Statement………………………………………………………5
1.4
Recycled Fiber…………………………………………………………..6
1.5
Tissue Paper Properties………………………………………………….6
1.6
Tensile Strength………………………………………………………….6
1.7
Bulk………………………………………………………………………7
1.8
Absorbency………………………………………………………………7
1.9
Wet Tensile Strength…………………………………………………….7
1.10
Softness…………………………………………………………………..8
1.11
Sensory Testing…………………………………………………………..9
1.12
Tappi Method…………………………………………………………….9
1.13
Clark’s Softness Tester…………………………………………………..10
1.14
Kawabata Evaluation System……………………………………………11
1.15
Neural Network Models…………………………………………………11
Near Infrared Spectroscopy……………………………………………………...12
2.1
Chemometrics……………………………………………………………13
2.2
Multivariate Calibration.............................................................................13
2.3
Principal Component Regression………………………………………..14
2.4
PCA Theory………………………………………………………..……15
2.5
Partial Least Square………………………………………………..……16
2.6
Training Data Set Selection……………………………………………...17
2.7
Model Validation………………………………………………………...17
2.8
NIR equipment and software…………………………………………….18
Experimental Procedure………………………………………………………….19
3.1
Phase One………………………………………………………………...21
3.2
Phase Two………………………………………………………………..24
3.3
Phase Three………………………………………………………………25
3.4
Phase Four………………………………………………………………..27
3.5
Data Analysis…………………………………………………………….28
ii
4.0
3.5.1
Spectral Data Analysis…………………………………………...29
3.5.2
Model Review……………………………………………………31
3.5.3
Validation Review………………………………………………32
3.5.4
External Validation………………………………………………34
Results And Discussion………………………………………………………….35
4.1
Phase One………………………………………………………………..35
4.2
Phase Two……………………………………………………………….48
4.3
Phase Three………………………………………………………………54
4.4
Phase Three Repeat………………………………………………………61
4.5
Phase Four………………………………………………………….....….65
5.0
Final Conclusions..………………………………………………………………71
6.0
References……………………………………………………………………….73
7.0
Appendices..…….………………………………………………………………74
7.1
Appendix A..…………………………………………………………...77
7.2
Appendix B…………………………………………………………….78
7.3
Appendix C…………………………………………………………….82
iii
List of Figures:
Figure1: Clark stiffness/softness tester………………………………………………….10
Figure 2: Vacuum box used in the lab…………………………………………………..20
Figure 3: Electrically heated drum drier used in the lab…………………………………20
Figure 4: Structure of dialkyl dimethyl quaternary ammonium compound…………….24
Figure 5: Reaction of azetidinium group with carboxyl group of cellulose…………….26
Figure 6: Reactions of azetidinium group with secondary amine group……………….26
Figure 7: An example of a loading Plot………………………………………………....29
Figure 8: An example of the score vs. score plot………………………………………..30
Figure 9: An example of the Standard Leverage plot……………………………………31
Figure 10: Outlier Plot…………………………………………………………………..32
Figure 11: Average Spectra for first 10 conditions………………………………..……36
Figure 12: Spectra of extreme conditions 100%SW, 100%Re and 100% HW………….36
Figure 13: Score vs. Score plot of all the available samples……………………………37
Figure 14: Plot of PC2 vs. PC1………………………………………………………….38
Figure 15: PC1 vs. PC3 plot……………………………………………………………..38
Figure 16: Standard Leverage plot of all samples………………………………………39
Figure 17: Loading plot for the all available samples with PC1………………………..40
Figure 18: Loading plot for the all available samples with PC2………………………..40
Figure 19: Standard leverage plot for all the samples for phase 2……………………...51
Figure 20: Score vs. score plot for all the 46 samples…………………………………..51
Figure 21: Score vs. score plot PC1 vs. PC2……………………………………………52
Figure 22: Loading plot for samples in phase 2…………………………………………52
Figure 23: Standard leverage plot for all samples in phase 3 ………...………………..55
Figure 24: Score vs. score plot for index vs. PC1 for all samples………………………56
Figure 25: Score vs. score plot for PC1 vs. PC2…………………………………………57
Figure 26: Tensile index comparison of the two phases…………………………............63
Figure 27: Softness comparison of the two phases………………………………………63
Figure 28: Standard leverage plot for all the samples in phase 4…..……………………66
Figure 29: Score vs. score plot for samples in phase 4…….………………………….…67
Figure 30: Score vs. score plot PC1 vs. PC2…………………………………………….68
iv
List of Tables:
Table 1: Different variables for each phase……………………………………………..19
Table 2: Furnish ratio and additives used in making the handsheets……….…………..23
Table 3: Different Levels of debonder additions for phase 2…………………………..25
Table 4: Different Levels of WSR addition for phase 3………………………………...27
Table 5: Different levels of CSF for phase 4……………………………………………28
Table 6: Example of property validation report…………………………………………33
Table 7: Different values of M-distance and residual ratio and their inference…………34
Table 8: Calibration set for phase 1….………………………………………………….35
Table 9: Validation set for phase 1………………………………….…………………..35
Table 10: Prediction results for phase one………………………………………………41
Table 11: New Calibration set…….…………………………………………………….43
Table 12: New Validation set……………………………………………………………43
Table 13: Tensile strength prediction using PCR algorithm…………………………….44
Table 14: Softness prediction using PCR algorithm…………………………………….44
Table 15: Tensile strength prediction using PLS…………………………………..........45
Table 16: Softness prediction using PLS………………………………………………..45
Table 17: Calibration set for phase one model 3………………………………………..46
Table 18: Validation set for phase one model 3………………………………………..46
Table 19: Predicted tensile index values for phase one model 3………………………..47
Table 20: Predicted softness values for phase one model 3….…………………………47
Table 21: Different experimental conditions for calibration set………………………..49
Table 22: Different experimental conditions for validation set………………………...49
Table 23: New calibration set for phase 2………………………………………………50
Table 24: New validation set for phase 2……………………………………………….50
Table 25: Prediction results for tensile index……………………………………………53
Table 26: Prediction results for softness…………………………………………..…….53
Table 27: Prediction results for tensile index using PLS………………………………...54
Table 28: Prediction results for softness………………………………………………....54
Table 29: Calibration Set for phase three………………………………………………..55
Table 30: Validation Set for phase three…………………………………………………55
v
Table 31: New calibration set for phase three…………………………………………...58
Table 32: New validation set for phase three…………………………………………….58
Table 33: Prediction results for phase three tensile index using PCR…………………...59
Table 34: Prediction results for softness phase three using PCR………………………..59
Table 35: Results for tensile index for phase three using PLS………………………….60
Table 36: Results of softness for phase three using PLS………………………………...60
Table 37: Calibration set for phase three repeat model………………………………….61
Table 38: Validation set for phase three repeat model……………………………….….61
Table 39: Prediction of tensile index values for repeat phase three……………………..62
Table 40: Prediction of softness for repeat phase three………………………………….62
Table 41: Comparison of Prediction results…………………………………………..…64
Table 42: Calibration set for phase four…………………………………………………65
Table 43: Validation set for phase four…………………………………………………..66
Table 44: Prediction results for phase four for tensile index…………………………….69
Table 45: Prediction results for phase four for softness…………………………………69
Table 46: prediction results for phase two random samples…………………….……….78
Table 47: Prediction results for phase 3 random samples:…………………..….……….79
Table 48: Prediction results for phase 3 random samples:…………………..….……….80
Table 49: Prediction results for phase 4 random samples………………………………81
vi
ACKNOWLEDGEMENT
I would like to express my gratitude to Dr. R. C. Peterson for his patient guidance
and invaluable suggestions throughout the course of the study.
I would also like to thank Prof. M. H. Waller and Dr. Andre Sommer for their
valuable suggestions.
I sincerely appreciate the advice and assistance received from the entire staff of
the Paper Science Department at Miami University.
vii
List of abbreviations used:
•
NIR
Near Infrared
•
FTIR
Fourier Transformed Infra-Red
•
SW
Softwood
•
HW
Hardwood
•
NMR
Nuclear Magnetic Resonance
•
MD
Machine Direction
•
CD
Cross Direction
•
KES
Kawabata Evaluation System
•
MLR
Multiple Linear Regression
•
PC
Principal Component
•
SEC
Standard Error of Calibration
•
SEE
Standard Error of Estimate
•
SEP
Standard Error of Prediction
•
RMSEP
Root Mean Square Error of Prediction
•
PAE
Polyamine Epichlorohydrine
•
WSR
Wet Strength Resin
•
CSF
Canadian Standard Freeness
•
M-Distance
Mahalonabis Distance
•
PCA
Principal Component Analysis
•
PCR
Principal Component Regression
•
PLS
Partial Least Square
viii
1.0
1.1
Introduction
Background:
Tissue is a low basis weight paper. The basis weight is generally less than 35 gm/m2 for
tissue and less than 50 gm/m2 for towel grades. It is common practice, however, to use
the name mostly for lightly bonded, creped papers of low basis weight used mainly for
sanitary and household purposes. It is made from virgin pulp as well as blends containing
secondary fibers. The trend is to use more and more secondary fibers and to maintain a
quality tissue paper. There are some mills that use 100 % secondary fibers.
Practically all the tissue papers are creped. Creping imparts certain desired
characteristics to the paper. Creping increases the softness of the paper. Various kinds of
raw materials have different influences on the creping process and therefore on the
resulting paper properties. A certain kind of pulp may affect the adhesion between the
paper web and the Yankee dryer and therefore affect the crepe structure. The main
properties of the tissue paper that are of particular interest apart from softness are the
tensile strength and the machine directional stretch.
Near Infrared Spectroscopy (NIR) is a technique widely utilized in many different
industries. It is used to identify and quantify different components present in an unknown
sample. The technique is particularly useful when dealing with the complex samples such
as are present in the pulp and paper industry. Already many works have been completed
in the paper industry to utilize this technique. Some uses are estimating pulp yield (1),
identifying the wood components (1), estimation of lignin content (2), quantifying
hardwood and softwood content (2), online determination of major ionic species in Kraft
liquor (3), measurement of wood chip moisture (4), measurement of pulp moisture (4)
and estimation of mechanical properties of wood (4). The spectroscopy combined with
1
multivariate data analysis is considered feasible because it requires a small sample for the
testing and the regions of the spectra can be the characteristic of the chemical substances
present in the sample. There are methods available that relate the spectral wavelength
bands and intensities with the required properties of the samples. These methods are
called “chemometric methods”. The simple definition of the chemometric is “the use of
mathematics and statistics to find out the chemical information of samples by analyzing
the spectral data”. The multivariate calibration is the process of developing a model based
on the spectroscopic data.
1.2 Literature review:
Infrared spectroscopy has long contributed to the qualitative understanding of the
chemical structure of wood and pulp. Fourier Transformed Infra-Red (FTIR)
Spectroscopy has been widely used for this purpose. Mid and Near Infrared spectral
regions and multivariate analysis tools are being used to develop methods to correlate
physical and chemical properties with absorption of light.
Easty et al. (2) used reflectance NIR to quantify SW: HW ratios in paperboard
and to estimate lignin content (kappa number) in hardwoods and softwoods. Using
second derivative spectra, they identified wavelengths, which correlated with lignin
content at 1680 nm and in the 2100 to 2200 nm region.
Bob Meglen (4) reported the use of NIR in different applications of pulp and
paper and wood chemistry for National Renewable Energy Laboratory. He did
measurement of kappa number by NIR, pulp viscosity by NIR, pulp moisture by NIR and
the determination of recycled content by NIR.
2
Birkett and Gambino (5) used NIR to estimate kappa number for both Softwood
(SW) and Hardwood (HW). Standard errors of one were achieved over the range of 20 to
90 kappa numbers.
Schultz and Burns (6) compared NIR and FTIR techniques for analysis of HW
and SW for lignin, hemicellulose and cellulose content. The NIR was judged to be
superior for this analysis on the basis of simplicity of sample preparation, shorter
scanning time and smaller standard errors. The best results were for NIR analysis of
lignin, with standard errors of calibration less than 1% over the range of 10 to 30% of
lignin in pine and sweetgum.
In 1990 Wright et al. (7) used NIR to predict pulp yield and cellulose content in
grounded wood (finely divided wood particles) samples. The instrument used was Technicon Infra-Analyzer interfaced with a personal computer. The results assessed by the
Near Infrared Spectroscopy were found to be positively and significantly correlated with
common laboratory techniques for a total pulp yield (r2 = 0.876) and cellulose content
(r2=0.853).
In 1991, Wallbacks et al. (8) compared results for the analysis of birch pulp using
NMR, FTIR and NIR. The pulping process is essentially a delignification reaction in
which lignin’s phenyl propane units are cleaved at ether linkages with an alkaline
reagent. NIR bands from cellulose, hemicellulose and lignin overlap, but may be resolved
by using partial least squares (PLS) analysis. They developed PLS models for Klason
lignin, glucose and xylose. NIR gave the best prediction results, with standard errors of
1% or less for all three components. The range of the measurements was xylose: 21-26%,
glucose: 55-75%, and Klason lignin: 0-25%.
3
In 1994, Michell (9) used Mid-IR spectra as a rapid means of predicting pulpwood
quality using samples of wood drawn from three trees in ten provinces of Tasmanian E.
globulus. The measures of pulpwood quality were yield and soda charge at a fixed Kappa
number. Near infrared spectroscopy, usually with diffuse reflectance sampling, has been
used to estimate pulp kappa number, lignin content, cellulose content, pulp yield and the
hardwood content of HW: SW mixtures.
In 1999 Marklund et al. (10) explored the relationship between the softwood raw
material and the properties of the resulting Kraft pulps. The relationship was expressed in
terms of physical parameters for the pulps and strength properties for the corresponding
handsheets using NIR and Partial Least Square methods (PLS).
Hallsta, (11) a Swedish newsprint mill, is using on-line NIR technology to
optimize an integrated TMP line. Using data derived from the organic chemistry of the
wood chips, the mill is able to control tensile index, which changes in response to the
nature of the incoming wood chips. The result is a pulp with improved and more
consistent quality and significant energy savings.
In 2001, G. Chantre et al. (1) proposed a method for chemical wood composition
and paper physical properties based on the analysis of the sawdust by Near Infrared
Spectroscopy.
In 2001 Kester et al (3) developed an on-line method of determining the
concentration of major ionic species present in the Kraft liquors by short wavelength near
infrared spectroscopy. Liquor samples were analyzed for hydroxide, hydrosulfide,
carbonate and black liquor solid concentrations with the use of infrared absorbance
measurements.
4
In 2001, Lavalle Castellanos (12`) (Graduate student, Miami University) used
NIR and multivariate calibration to successfully characterize the pulp mixtures of ground
wood and clean office waste paper
1.3
Problem Statement:
The main objective of this thesis work is to focus the use of Near Infrared
Spectroscopy combined with chemometric techniques to predict the physical properties
of the tissue paper made from recycled fibers and virgin pulp. The physical properties
will be estimated based on the spectral data obtained from Near Infra-Red (NIR) analysis
of the samples.
In relation to the samples the raw material will be a mixture of virgin pulp
(Market SW and HW) and recycled fibers. The recycled fibers used will be the Scott
brand towel tissue rolls purchased from the grocery store. The selection of recycled fibers
was based upon the fact that any fibers that have been used once and are being used again
are called recycled fibers. The Scott brand tissue rolls chosen were unprinted white and
without any contaminants, or ink particles. This will reduce the effect of contaminants in
spectra. The hand sheets will be made in the laboratory. The physical properties of hand
sheets will be checked and at the same time the NIR spectra will be obtained. The
literature suggest the predictability of physical properties from the spectra. This work will
attempt to confirm this.
1.4
Recycled fibers:
Recycled fibers are those fibers that have been used once and will be reused as a
raw material in the manufacturing of a wide variety of papers and board. The tissue paper
5
can be made from a variety of recycled fibers depending upon the end use of the tissue
paper. For this study the Scott brand white towel tissue rolls purchased from the market
were used as a source of recycled fibers. The reason to choose this has been mentioned in
the problem statement.
1.5
Tissue paper properties:
The main properties of interest for the crepe tissue papers are the following:
•
Machine directional and cross directional tensile strengths.
•
Bulk.
•
Absorbency.
•
Wet tensile strength.
•
Softness.
Below is a description of each of these properties. Out of these properties the most
important for tissue and towel grades are tensile strength and softness. These two
properties will be predicted in this study.
1.6
Tensile strength:
The maximum tensile force developed in a test specimen before rupture is
measured. The tensile properties of low-density papers can be determined by using a
standard tensile tester. The line contact grips are used to hold the sample as directed by
TAPPI T 494 om-88. Samples of 25 + 1 mm (nominally 1 in) wide are cut using a die
cutter. Samples should not be stretched during preparation for testing. The standard gauge
length for a tensile test is 180 + 5 mm (nominally 7 in) leaving enough so that slack can
be removed from the strip before clamping. The testing rate is typically 25 + 5 mm/min
6
(nominally 1 in/min). The sample is stretched to failure and the maximum force to break
the sheet is recorded. (Full Procedure in Appendix A)
1.7
Bulk:
Compaction of the fiber network and inter-fiber bonding are undesirable in the
base tissue structure. The aim is to produce a sheet with the highest possible specific
volume, i.e., bulk (13).
1.8
Absorbency:
Absorbency is normally measured using water. The properties normally measured
are absorption capacity and absorption rate, which are important in products such as
towels and wipes. Absorption capacity reflects how much water the tissue can absorb (gm
water/gm paper). Absorption rate (seconds/cm) measures how quickly the product can
absorb the water.
1.9
Wet tensile strength:
There are certain grades of paper that must retain their strength while wet.
Examples are toweling, photographic base papers, map papers, blueprints, bags, and
wrappers. Wet strength is measured by running tensile tests on the wet specimens. A
specimen is soaked in water until completely saturated. Excess water is blotted off, and
the tensile strength of the strip is measured in the usual fashion. Papers that have not been
treated for wet strength will retain only 4-8 % of its original dry strength when tested wet.
Addition of a wet strength resin will produce retention of up to 40-50 % of the dry tensile
strength. Wet strength is more important in the toweling grades of tissue paper as these
7
papers are more used in such application involving excess of water. A paper towel that is
designed for wiping and drying would be useless if it fell apart as soon as it got wet.
Likewise a napkin or facial tissue will also be deficient if they didn’t retain their integrity
when moist.
1.10 Softness:
Softness is the most important property of a tissue grade that governs the
consumer acceptance of the paper, especially in sanitary applications. Softness can be
defined as a tactile sensory response to the texture that is pleasing to touch and handle or
a feeling of delicate texture lacking stiffness. The sensation of softness is better envisaged
as its two primary components, the surface softness and the bulk softness. The surface
softness is one that is perceived by the fingertips, which depends on the smoothness and
the surface finish of the paper. The bulk softness is the softness as perceived by gently
crumpling the paper, which depends on its stiffness and the ability of fibers to move
within the fiber networks. When we crumple the tissue by hand and stroke the surface
with a finger, the combined sensory response is the physical measurement of softness.
Traditionally, human panelists and the consumers are doing the determination of softness.
This is called subjective softness. Nowadays, attempts have been made to relate the
softness to some more measurable physical property. This type of measurement is termed
as objective softness (14).
Over the years several methods and instruments have been developed to measure
the softness. The methods that are most widely used presently and have been used earlier
are described below:
1.11 Sensory testing:
8
This testing is done by a sensory panel consisting of mill experts or a consumer
panel consisting of a selected group of consumers brought to a central location for
conducting the tests (15). This is the traditional approach and still the best in determining
softness as compared to other scientific methods. The method is based on the idea that a
panelist can translate the intensity of perception to a number. Each panelist judges the
softness or its components in relation to a standard and assigns a number on a scale. The
softest in the sample set can be chosen as the standard and assigned the number 100, and
the softness of the test samples can be estimated as a percentage of the softness of the
standard. For instance, if the test sample were perceived to be half as soft as the standard,
it would be given a rating of 50 %. Panelists are not allowed to see the sample during the
test to avoid the possibility of visual cues.
1.12 Tappi method:
This is done by a slot test. The instrument used for measurement is called HandleO-Meter. This instrument measures the force necessary to push the sample into a 6.35
mm slot. It can be used for both MD and CD test. The instrument is sensitive to surface
characteristics such as crepe undulations, embossing, and the friction between the sheet
and the metal surface as well as the bending characteristics of the sample. It gives an
indication of the handle, softness and drape. A 4.5 in x 4.5 in sample is cut for the test.
Both machine direction and cross direction tests can be performed. The sample is placed
across the slot and the force required to push the sample into the slot is measured. Results
are expressed in grams. Lower Handle-O-Meter values indicate less stiffness, smoother
samples and hence point toward higher softness. This method is not being used widely in
9
the industry because it does not offer any more information than that of a panel for the
process parameter changes.
1.13 Clark’s Softness Tester:
This method is used to measure the softness based on stiffness measurement of
the tissue paper (13). In this tester a strip of paper is gripped in a steel-to-steel nip at one
end and is free at the other end. The two rollers forming the nip are mounted on a frame
that can be rotated about a horizontal axis, parallel to the length in the nip. The
instrument is shown in Figure 1.
Figure 1: Clark stiffness/softness tester.
In a given position where the nip is pointing upward, the paper sample falls over
in one direction due to its mass and flexibility. The nip is rotated through 900, and the
sample is observed. If the sample still continues to fall over in the same direction as
before, the length for the overhang of the sample is reduced until; when the nip is rotated
through 900 the sample would fall over equally on either side of the nip to the tip of the
sample. In addition to this length L, the caliper and the basis weight are used to calculate
the softness as given below:
Softness = 107 log (t+1)
L3W
10
Where W is the basis weight (g/m2), t is the caliper (mm.), and L is the overhang length
(mm.) from the tester. The test does not consider the surface effects on softness and is
purely a stiffness-based parameter (Full procedure in Appendix A).
1.14 Kawabata Evaluation System (KES):
This method measures mechanical properties that correspond to the fundamental
deformations of the paper towels during hand manipulation. It includes a set of four
instruments KES-FB1 to KES-FB4 (indicates Kawabata Evaluation System for Fabric)
on which five different test types are performed: tensile, bending, shear, compression and
surface generating different physical property measurements. KES-FB1 was used to
measure the tensile and the shear properties and the KES-FB2 was used for bending,
KES-FB3 for compression and KES-FB4 for surface (smoothness or roughness). The
KES instruments were originally developed for fabrics for suits. The fabrics of different
physical/mechanical properties require modified settings. These settings were modified
later for paper towels. More extensive discussion of KES methods is provided in the
literature (16).
1.15 Neural Networks Models:
This work has been done to use the neural network approach for developing
models that infer softness from the other process variables (17). This methodology can be
used online and can save time as well as provide the operators with the real time
prediction of the quality of the produced tissue. The method was based on the process
variables that include Yankee dryer speed, fan pump speed, Yankee temperature,
headbox pressure, refiner power, and basis weight valve position. Neural networks
models are developed using all these variables.
11
Since softness in industry is still measured using subjective testing, no objective
method was found which has been used commercially so far. The method, which will be
used in this study, will be the Clark’s softness tester. This method is designed to give the
measure of the softness based on stiffness of the sample of tissue.
2.0
Near Infrared Spectroscopy:
The Near Infrared (NIR) spectrum extends from 780nm (12,800cm–1) to
2,500nm (4000cm–1) and can be divided into two main regions (18, 19). In the
wavelength range from 780nm to 1200 nm the silicon detectors are used, and in range
from 1100 nm and 2500 nm lead sulfide is used as a material in the detectors. There are
several advantages of working in the NIR wavelength region. First is that the rapidity of
NIR measurements. This helps in the collection of the descriptive data, and is a
prerequisite for creating a control system working in real time. Secondly there is reduced
sample preparation so the technique is applicable to various types of materials. This is an
advantage compared to other spectroscopic techniques, in which the sample preparation
may require quite complex steps. This is a good reason for choosing NIR as a preferred
characterization technique in the process application (20).
When molecules are exposed to NIR radiation, specific wavelengths will be
absorbed by the specific chemical bonds within the molecules while other wavelengths
will be transmitted. The amount of absorbed radiation is proportional to the concentration
of the constituent in the sample, which absorbs at a particular wavelength. The drawbacks
of NIR are related to the fact that NIR is sensitive to the variation in texture and scatter
(20).
The characterization of the complicated samples like wood and pulp and paper
has been associated with difficulties. The signals originating from the three main polymer
12
constituents (cellulose, hemicellulose and lignin) are partly overlapping, but combining
NIR spectroscopy with multivariate data analysis and chemometric reduces the problem.
These processes reduce the large numbers of wavelengths to be studied into a few
selective wavelengths. The details about these methods are given below:
2.1
Chemometrics:
Chemometrics can be defined as the chemical discipline that uses mathematical
and statistical methods to design or select optimal procedures and experiments, and to
provide maximum chemical information by analyzing the chemical data. It includes the
multivariate calibration theory and the two main statistical tools for the multivariate
calibration that is PCR (principal component regression) and the PLS (partial least
square).
2.2
Multivariate Calibration:
The basic principle of chemometric methods is Beer’s law, which states that
absorbance, is directly proportional to the concentration of the analyte. According to this
if A is the absorbance of light, b is the path length of light beam, ε is the molar
absorbtivity and c is the concentration of the analyte then
A = εbc
Now to correlate these it is necessary to locate an isolated spectral band that changes
when analyte concentration is changing. Calibration using one wavelength at a time is
called univariate calibration. The calibration using several of these wavelengths at the
same time is called multivariate calibration. It is particularly more useful when dealing
13
with complex samples as in pulp and paper. These methods relate properties to spectra.
The basic principle is to find out a relation of the type Y=f (X) using special regression
software and then use that formula to predict the properties of the unknown samples.
Since there are thousands of wavelengths to be analyzed, multiple linear regression
(MLR) is used. The common problems while predicting Y from X are lack of selectivity
and the collinearity. The entire wavelength in the spectra doesn’t contain the useful
information regarding the components. Only certain wavelengths are to be selected out of
the spectra and that is to be used in the calibration step. This selection of wavelengths is
done by principle component analysis (PCA). The process is used to select the “variation
spectra” which is the subset of the whole spectrum and represents the properties of the
whole spectrum. The method can model the systems that are very large and very
complex.
2.3
Principal Component Regression (PCR):
PCR (principal component regression) is a combination of PCA (principal
component analysis) and MLR (multiple linear regression). The principal components
(also called latent variables) are chosen by the PCA and only the relevant ones, chosen by
MLR, are kept for the calibration. There are many different variations that can contribute
to the spectrum, for example constituents in the sample, instrument variation, and noise
and changes in the environmental conditions, etc. The largest variation in the spectral
data is due to the change in the component concentration. A set of variation spectra could
be found by the PCR that will represent the properties of the whole spectra and will be
used in building the calibration model instead of using the whole spectra. This will
reduce the total number of calculations in the calibration equation. Each variation spectra,
14
which is also the principal component (PC), is multiplied by a constant scaling factor,
which is known as scores, to get a new spectrum that matches the unknown spectrum.
The only difference between the spectra of unknown samples would be the fraction of the
principal components (scores) added. To calculate the PCR model the spectra should
change in some way. The best way to change the spectra is to change the concentration of
the components.
2.4
Principal Component Analysis (PCA) theory:
An N x K data matrix X, consisting of N samples or objects upon which K
variables have been measured or calculated contains a description of the N samples in the
K dimensional variable space. Hence each sample in the X can be described as a point in
this variable space, obtained by plotting all K variables against each other. Together, all
samples will constitute a swarm of N points in the variable space. Applying PCA means,
essentially, that we are projecting the swarm of points down on a few dimensional hyperplane in the K dimensional space. This hyper plane can then be lifted out, to be viewed in
2-dimensional space. PCA creates this hyper-plane in such a manner that it explains as
much of the object variation as possible. This is done by applying a set of A score vectors
or principal components each describing the maximum variation left to explain. Hence
the first score vector, t1, describes the largest variation, the second score vector, t2, the
second largest variation orthogonal to t1 and so on until A score vectors have been
calculated. The final N x A score matrix, T, will then constitute a new latent variable
space, within the K dimensional variable space, describing the position of each object.
The corresponding A loading vectors, forming the K x A loading matrix, P, contain the
contribution of each variable to the principal components, described by the angle between
15
the K variable axes and the principal component. The loading gives a measure of
similarity between the original variables and the principal components in terms of
direction. PCA can be described as a bilinear decomposition of the absorbance matrix, X
giving a set of scores matrix T (describing the object variation) and a set of loadings
matrix P (describing the variable influence) on the principle components. The nonsystematic part not described by the model forms the residuals, E. The following equation
shows the relation.
X = TP + E
2.5
Partial Least Squares (PLS):
Another method that can be used for the purpose of calibration is the PLS method
which is also called Partial Least Square Projection to Latent Structure or PLS. This
method is different from the PCR in the way that it uses concentration information at the
same time while decomposing the spectral matrix and so the more prominent spectra that
contain the higher concentrations are given the priority in the calibration. PLS considers
the errors in both X and Y data (calibration spectra and the concentration data). This way
it reduces the impact of large but irrelevant X variations in the calibration modeling. The
only disadvantage with PLS is that it tends to include more latent variables than
necessary, thus including noise in the model. This is called overfitting the model and it
damages the model’s prediction ability (21).
2.6
Training Data set Selection
A data set must be obtained in order to train the model. This data set must have all
the required information for the generation of the model. The samples must have all the
16
possible combination of concentrations and the sampling points must be uniformly
spaced. These are required since it is expected that the model is going to be used for the
identification of the unknown samples. The number of samples required for the
calibration set will be a minimum of 10 to 15.
2.7
Model validation:
Validation of the model is done to verify the accuracy of the model to predict the
properties of the unknown samples. They are of two different types:
internal validation and external validation. The validation is internal when the samples
present in the calibration set are used to validate the model. The cross validation is an
example of internal validation. One sample at a time is kept out of the model and is
predicted by the model. There are partial cross validation (when done dimension wise)
and full cross validation (when done model wise). Cross validation is very efficient in
determining the complexity of the system. After the cross validation the standard
deviation between the true and the calculated value is calculated. This is Standard Error
of Calibration (SEC). This SEC gives an idea of the quality of the fit of the regression.
In full cross validation, a standard is removed from the data one at a time and the
model is constructed without using the excluded standard. Thus estimated standard error
of prediction SEPest can be calculated. This SEPest then gives the error magnitude when
independent samples are to be predicted. This decides how many PC’s are to be kept in
the model. If prediction results are not comparable that is SEPest is not comparable to
SEC, then the number of factors in the regression model might be overfitted.
The external validation on the other hand is the process of validating the model
using samples that are not the part of the original model. This is based on the principle
17
that a number of samples are kept out of the model calculations to use solely for the
validation of the predictive ability of the model. The root mean square error of prediction
(RMSEP) is used to determine the magnitude of the error of prediction for the
independent sample used in the external validation. The RMSEP is calculated using the
following formula:
i =n
RMSEP =
∑ ( x − xi)2 / ni
i =1
2.8
NIR equipment and software:
The instrument used for this work was diffuse reflectance Perkin-Elmer Paragon
Identicheck FT-NIR instrument. The instrument is capable of looking at a large area of
the sample as well as a wide range of spectra. The spectral wavelength range was kept at
15000 cm-1 to 2700 cm-1 using this instrument. The spectral conditions were the
following: resolution: 8 cm-1, gain 1 and a 32 signal averaged spectra. The software used
for this study was QUANT +TM software. This is chemometric software provided by
Perkin-Elmer. This software is able to determine the sample characteristics by analyzing
the spectral data. The two algorithms PCR and PLS are provided by the QUANT +TM
software. The software also provides many tools to review the model after calibration.
The NIR spectrometer is located in the Molecular Microspectroscopy Laboratory in the
Department of Chemistry & Biochemistry at Miami University
3.0 Experimental Procedures:
18
The whole work was divided in four different phases. For each phase handsheets
of tissue paper were made according to the different conditions for that phase as shown in
Table 1.
Phases
Phase1
Phase2
Phase3
Phase4
Variables
Furnish Ratio
Amount of debonder
Amount of wet strength resin added
Level of refining
Table 1: Different variables for each phase
The different conditions were generated based on the different values of the
variables for that phase. There are total four variables. They are the amount of furnish
ratio, the amount of debonder added, the amount of wet strength resin added and the level
of refining done. The details of each phase have been provided in the descriptions below
in the next section. For each phase one of these factors was varied keeping other three
constant. For phase one the variable was amount of furnish ratio, for phase two the
variable was amount of debonder added, for phase three the variable was amount of wet
strength resin added and for phase four variable was level of refining. For each phase
three handsheets were made for each condition. The tissue handsheets were made using
the Noble & Wood handsheet machine. The procedure is the standard procedure used by
most tissue paper mills for making handsheets in their research laboratories. The sheets
were not pressed during hand sheet making and were directly transferred to the felt from
the wire using a vacuum box as shown in Figure 2. The sheets were then dried in the
drum drier. This drum drier is electrically heated. The Figure 3 shows the drum drier
available in the laboratory. This process is the simulation of the manufacturing of tissue
on a Yankee paper machine.
19
Figure 2: Vacuum box used in the lab.
Figure 3: Electrically heated Drum Drier used in the lab.
In the actual tissue machine Yankee dryer has several crucial functions. First it
supports the sheet during the bulk of drying process, which takes less than one second to
remove about 60% of water from the sheet. The hood removes the remaining water. The
Yankee also serves as part of the pressure roll nip, and so must be crowned to maintain
even pressing of the sheet. Using steam the Yankee dryer provides roughly half of the
energy used to dry the sheet after the press roll nip. Finally the Yankee dryer must
20
provide a surface on which creping process occurs. During the drying process, the
Yankee is exposed to high temperatures, and coating chemicals used in adhering the sheet
to the Yankee and releasing it. Although the Yankee dryer serves so many critical roles in
the tissue making process, in making the tissue paper handsheets in the lab all these
phenomenon are not possible to achieve. In this work the dryer available in the laboratory
was used. Since the creping can not be provided in the laboratory, the handsheets were
not as soft as the actual paper made on the machine. The handsheets were made
randomly. After making the handsheets the NIR spectra were taken on each of the
handsheets. Next the properties of the sheets were checked in the lab using standard
procedures already mentioned. The spectra with all the properties data were fed in the
PCR and PLS software provided with the instrument. Once the model was built it will be
validated to see the accuracy of the model to predict the properties of the unknown
samples.
Below is a description of each phase.
3.1
Phase one: Effect of change in furnish ratio
To predict the properties of tissue paper with change in furnish ratio.
The main objective of this work was to test the use of NIR spectroscopy to
estimate the properties of towel tissue paper made of recycled fibers. Phase one is the
preliminary stage where handsheets were made using virgin pulp and the recycled fiber
pulp. The virgin pulp is a combination of hardwood (HW) and softwood (SW) mixed in
different amounts. The HW used was northern hardwood (from IP’s mill in Quinnsec MI)
and the SW used was northern softwood (from IP’s mill in Hinton AL.). Both the market
pulps were taken from the Miami University paper lab. The hardwood and softwood
21
were pulped separately. The raw material to be used as recycled fiber was the towel tissue
paper rolls purchased from the market. Recycled fiber in general contains contaminants,
fillers, long fibers, short fibers, ink particles and other additives. Each of these
components can contribute to the overall spectra. The presence of too many different
components in unknown amounts can create problems during the modeling stage. Using
only one type of recycled fiber permitted attain of the change in spectra with a change in
the amount of recycle content only. The additives used are de-bonder (Softness
Additives) to provide Softness and Wet Strength Resin to provide wet tensile strength.
The additives for this study were supplied by the courtesy of Procter and Gamble
research lab in Cincinnati Ohio.
The softness additive used was a dialkyl dimethyl quaternary amine. The cationic
quaternary compound portion attaches to the negative sites on the fiber. The long chain
alkyl portion of the cellulose fiber is hydrophobic and increases the surface tension on the
residual water molecules between the fibers. The net bonded area is decreased as the
water evaporates. The alkyl group also interferes with hydrogen bonding. The resulting
sheet is therefore bulkier and weaker giving a sensation of softness.
The wet strength resin added would be polyamide polyamine epichlorohydrine
(PAE) resin. The resin is a thermosetting resin because it will chemically cross-link with
itself (and possibly fiber) in the dryer section and paper after it is retained on the fiber. It
affords a semipermanent wet strength because the product will maintain strength for
extended periods of time after it is wetted. The wet strength resin is retained on the fibers
and fines because of its cationic charge. The fibers, including potential bonding areas, are
coated with a layer of polymer. The resin cross-links itself forming a hydrophobic surface
22
at the bond site. This cross-linked coating protects the hydrophilic hydrogen bonds. When
the fibers are wet again, the hydration of the fibers is impaired because of the
hydrophobic surface.
In the first part the handsheets of tissue towel paper will be made in the lab using
the standard Noble and Wood Handsheet machine. The ratio of the amount of virgin
fibers and the recycled fibers taken is shown in Table 2.
Recycled
Fiber (%)
0
10
20
30
40
50
60
70
80
90
100
Softwood Hardwood Debonder
(%)
(%)
(lbs/ton)
30
27
24
21
18
15
12
9
6
3
0
70
63
56
49
42
35
28
63
14
7
0
25
25
25
25
25
25
25
25
25
25
25
WSR
(PAE)
(lbs/ton)
20
20
20
20
20
20
20
20
20
20
20
Table 2. Furnish ratio and additives used in making
the handsheets for Phase 1
The furnish ratio was decided on the basis of changing from 0 % recycled and 100
% virgin to 100 % recycled and 0 % virgin. The virgin pulp was taken in the ratio of 30
% SW and 70 % HW which is the commonly used ratio in the mill when using combined
SW and HW in making tissue paper. The amounts of additives (debonder and wet
strength resin) used were kept constant for the first phase. The addition levels of additives
are based on the actual practice in the mills. The only factor that was changed in the first
phase is the furnish ratio. The grammage was kept constant at 20 gsm. Ten handsheets
were made for each of the pulp composition.
23
3.2
Phase Two: Effect of change in amount of debonder
In phase two the effect of variation of the amount of debonder was examined.
Debonder addition contributes to the softness. Generally speaking, debonding agents or
softeners applied in the paper industry refer to the use of cationic surfactants. Traditional
cationic debonders usually are quaternary ammonium compounds. The long fatty alkyl
groups in the debonding agents disrupt the fiber-fiber bonding, which weakens the tissue
sheet strength and increases the sheet bulk. Debonder used in this study was dialkyl
dimethyl quaternary ammonium compound. The cationic quat attaches to the fiber and
the long alkyl chain debonds fiber and provides a slippery, soft feel. The molecular
structure of a debonder is shown in Figure 4.
CH3
+
R− N −R
CH3
Figure 4: Structure of dialkyl dimethyl quaternary ammonium compound
With different levels of addition of debonder there will be a change in the softness. The
handsheets were made using different amounts of debonder and keeping all other factors
constant. The furnish ratio selected was the one having a small amount (10%) of recycled
fibers in it. This was done to minimize the effects of any contaminants on the final
spectra and to see the effect of changing debonder addition level on the spectra. The
different level of debonder addition is given in Table 3. The furnish ratio for this phase
was selected as the one having the minimum amount of recycled fiber. The remainder of
the furnish was kept constant at 27% SW and 63% HW. This was done to reduce the
24
effect of any possible contaminants in the spectra. The debonder amount was changed
from 20lbs/T to 70lbs/T in an increment of 5 lbs/T.
Recycled
Fiber (%)
10
10
10
10
10
10
10
10
10
10
10
Softwood Hardwood Debonder
(%)
(%)
(lbs/ton)
27
27
27
27
27
27
27
27
27
27
27
63
63
63
63
63
63
63
63
63
63
63
WSR
(lbs/ton)
20
25
30
35
40
45
50
55
60
65
70
20
20
20
20
20
20
20
20
20
20
20
Table 3: Different Levels of debonder additions
used for phase 2
The purpose of this phase is to see if NIR would be able to predict the change in softness
and tensile strength affected by different addition level of debonder. The NIR spectra
were obtained on the handsheets and the properties were checked. The data was fed to the
computer using the software to generate the model. Once the model was generated it was
validated to verify the model accuracy.
3.3
Phase Three: Effect of change of wet strength resin
The objective of phase three was to see if NIR could predict the effect of
changing wet strength resin on the softness and tensile strength. For most tissue products,
proper strength must be retained when they are wet. Wet strength resin is added to the
tissue sheet for this purpose. The polyamide-epichlorohydrine (PAE) type wet strength
resin is the most common type that is used in the paper industry. The 3hydroxyazetidinium groups in the PAE type wet strength resin undergo two types of
chemical reactions to enhance wet strength.
25
(a) The 3-hydroxyazetidinium group at the end of the polymer chain reacts with the
carboxyl group at the cellulose fiber surface as shown in Figure 5.
(b) It reacts with the secondary amine group of another wet strength molecule so that the
polymer chain can grow in length as shown in Figure 6.
Figure 5: Reaction of azetidinium group with carboxyl group of cellulose
Figure 6: Reactions of azetidinium group with secondary amine group within another
PAE molecule
The amount of Wet Strength Resin (WSR) was changed keeping all other factors
constant. The furnish ratio was chosen the one with minimum amount of recycled fibers
so as to avoid the interference from the recycled fibers in the spectrum. The amount of
debonder was kept constant at 40lbs/T. This was chosen as the average amount of
26
debonder used in phase two. The amount used in the industry for debonder is
approximately 30lbs/T. In this study the total amount needed for making all the
handsheets is approximately 1 gram. Since the debonder dialkyl dimethyl quaternary
amine is very viscous in nature some measurement errors are expected in measuring such
a small amount. To minimize these errors the amount of debonder was kept a little bit
higher at 40lbs/T. Variation in amount of WSR addition changed the strength properties
of paper. The amount of wet strength resin added is shown in Table 4. The level was
chosen based upon the standard practice in the mill.
Recycled Softwood Hardwood Debonder
WSR
Fiber (%)
(%)
(%)
(lbs/ton) (lbs/ton)
10
10
10
10
10
10
10
10
10
10
10
27
27
27
27
27
27
27
27
27
27
27
63
63
63
63
63
63
63
63
63
63
63
40
40
40
40
40
40
40
40
40
40
40
0
10
15
20
25
30
35
40
45
50
55
Table 4: Different Levels of WSR addition used for phase 3.
The NIR spectra will be obtained at the same time Softness and Tensile Strength
will be measured. The data will be used to generate the model using QUANT + software.
The generated model will further be verified to test the accuracy.
3.4
Phase Four: Effect of change of level of refining
The objective of phase 4 was to see if NIR spectroscopy can detect a change in
paper properties with different levels of CSF (Canadian Standard Freeness) values in
27
furnish. CSF is a way of determining freeness of pulp after refining. The tissue paper is
usually made at a CSF level of 300 to 350 in the mill. Handsheets were made with
different levels of CSF values keeping all other factors constant as shown in Table 5. The
constant parameters were furnish ratio, amount of de-bonder added and amount of wet
strength resin added. The different levels of CSF were obtained in the lab beater by
refining the pulps separately for different amounts of time and then mixing them in the
required proportions. NIR spectrum were taken and the properties of the handsheets were
checked. The data was analyzed with the software to generate a model. Once the model
is obtained if it doesn’t provide the satisfactory results more standards will be added in
the calibration set.
CSF level
450
375
300
225
Recycled
(%)
Softwood Hardwood Debonder WSR
(%)
(%)
(lbs/T)
(lbs/T)
10
10
10
27
27
27
63
63
63
40
40
40
20
20
20
10
27
63
40
20
Table 5: Different levels of CSF for phase 4
3.5
Data analysis:
In the process of making the model, the samples for the calibration set have been
selected first. Once the samples have been selected the spectra is taken and then the
properties of these samples are measured using the conventional techniques. The spectra
and the properties are then fed to the QUANT + software and the software calculates a
multi linear regression for each of a very large number of wavelength combinations.
Every regression generates an equation that can determine the properties from the
spectral data. There are two main methods to calculate these wavelength combinations.
28
These methods are principal component regression (PCR) and partial least square (PLS),
which were explained previously.
After the model building, the software provides tools to review the spectral data,
model and the validation to check for the possible outliers.
3.5.1 Spectral Data Analysis:
1) Loading Plot:
This is the plot between the chosen principal component (PC, also called variation
spectra as explained in section 2.3) vs. wavelength or wavenumber. An example is shown
below in Figure 7.
A blank region not contributing to spectra
0.150
Spectral bands provides information
0.10
0.05
P C 1 (89.25% )
0.00
-0.05
-0.10
-0.15
-0.20
-0.25
-0.30
-0.371
15000.0
14000
12000
10000
8000
6000
4000
2700.0
cm-1
wavenumber
Figure 7: An example of a Loading Plot.
From the loading plot it is clear that between the wavenumber 15000 cm-1 to
about 7500 cm-1 there is no visible change in PC1 so these wavelengths are not
contributing in the calculation of PC1. We can apply blanks in these regions while
29
calibrating the model. In the remaining part of wavenumber there are some bands
observed which correspond to some information regarding the change in variables.
2) Score vs. Score Plot:
This is the plot between two principal components. After doing the PCA, all the
samples group themselves based on the intensities (scores) of the principal component
used. This plot shows the clusters. If all the samples lie in the clusters there is no outlier.
If there are some outliers they show up outside the clusters. In that case we can analyze
PC1 vs. PC2 or PC1 vs. PC3 and can detect the outliers. An example is shown in Figure
8. In this figure there are two clusters in which most of the samples lie. Also there are
some data points outside the clusters. These are outliers.
0.39
x avg28
x avg5
0.3
Cluster1
x avg29
x avg30
0.2
x avg2
x avg15
avg14x avg32
xxavg1
x avg23
avg3 x avg24
x avg18 x xavg31
x avg6
0.1
PC 1 0.0
x avval4
-0.1
x avval10
x avg27
x avg16
x avg19
x avg12
avg13
x avg9
x avg17
x avg20
x avg34
x avg26 x avg36
x avg22
x avg21
avg33
x avg8
x avg25
x avg10
x avg7
x
avg4
x avg11
x avval6
x avval7
x avval3
x avval2 x avval5
x avval8
x avval9
-0.2
Cluster2
avg38
x xavg39
x avg35
Outlier
x avg37
-0.3
-0.4
x avval1
-0.46
-0.28
-0.2
-0.1
0.0
0.1
PC 2 (5.96%)
0.2
0.3
0.4
0.47
Figure 8: An example of the score vs. score plot.
3) Standard Leverage Plot:
Standard leverage plot is the plot between the number of standards and the
leverage calculated for each standard. The leverage measures how large an effect a
30
standard has compared to other standards. The cutoff line is set by the software at twice
the average leverage value. High vales that are above the cutoff lines are considered to be
the outliers. Figure 9 shows plot of 39 samples with the leverage values. In the figure the
samples that are above the cutoff line are avg5, avg28 and avval1. The notation avgX was
used to represent the average of the spectra taken at three different points for the
respective handsheets for the calibration set. Similarly avvalX was used for the validation
set samples. These three samples are different from others and considered as outliers.
0.412
x avval1
Outliers
0.35
x avg28
0.30
x avg5
Le verag e
0.25
x avg35
x avg34
x avg33x avg36
0.20
0.15
x avg29
x avg25
x avg24x avg27
x avg2
x avg3
0.10 x avg1
x avg10
0.05
x avg17
x avg19x avg22
x avg26
x avval5 x avval9
x avval3
x avval8
x avval4
x avval2
x avg39
x avg4
x avg37
x avg38
x avg30
x avg32
x avg31
x avval10
x avg8
x avg6
x avg23
x avg16 x avg20
x avg21
x avg15
x avg13
x avg11
x
avg18
x avg12
x avg14
x avval7
x avval6
x avg9
x avg7
0.009
1.0
5
10
15
20
25
30
35
40
45
50
53.8
Index
Figure 9: An example of the Standard Leverage plot.
3.5.2 Model Review:
To review the model an outlier plot is considered. In this plot the Y-axis is the residuals
between the real values and predicted values and the X-axis is the leverage values. An
example for this plot is shown in Figure 10. The plot can be divided into four regions.
The samples that lie in the lower left corner are considered to be the best samples as they
have both spectra and the specified property values that fit well with each other.
Standards in the upper left part have good spectral values but poor properties values.
31
Standards in the lower right part have good properties values but whose spectrums are
possibly not good. These standards are good extremes. And lastly standards in the upper
right part have suspicious spectrum as well as property value. It is desirable that all
standards should lie in the lower left part only, but certain extremes can lie in the lower
right part also.
HIGH RESIDUAL HIGH LEVERAGE
OUTLIER
HIGH RESIDUAL OUTLIER
3.68
3.5
3.0
GOOD SAMPLES
HIGH LEVERAGE GOOD EXTREMES
2.5
Res idua l
x 3rd12
2.0
x 3rd7
x 3rd10 x 3rd19
x 3rd1
x 3rd3
x 3rd15
1.5
x 3rd2 x 3rd5
x 3rd22
x 3rd25
x 3rd27
x 3rd29
x 3rd34
1.0
0.5
x 3rd24
3rd8
x x3rd11
x 3rd9
3rd13
x 3rd20
0.000
0.02
0.04
0.06
x 3rd36
x 3rd26
x 3rd16
x 3rd30
0.00
x 3rd32
x 3rd33
x 3r d23
x 3rd28
x 3rd14 x 3rd31
x 3rd21
x 3rd18
0.08
0.10
x 3rd35
0.12
0.14
Leverage
x 3rd6
x 3rd17
0.16
x 3rd4
0.18
0.20
0.22
0.24
0.264
Figure 10: Outlier Plot
3.5.3 Validation Review:
The validation review is done to assess the quality of the validation samples. This
is important to ensure that the validation results are not adversely affected by
abnormalities in the validation data. The validation results are summarized as shown in
the Table 6 below. This is an example of the validation results for the property tensile. A
table like this is created every time the model is calibrated.
32
Property Validation Report: Tensile.md - (tensile)
* -------------------------------------------------------------------- *
Spectrum QUANT+ V 4.10 PCR+ VALIDATION REPORT FOR PROPERTY
tensile
Date: Fri Jan 24 2003
Time: 15:18:46
* -------------------------------------------------------------------- *
Method name: Tensile
METHOD NOT SECURED
Current version: 1 Method ID: 456
Title: Softness and tensile
Analyst: Krishan Bhatia
Validation type:
Full Cross
No. of Calibrations: 39
No. of Standards:
39
SUMMARY OF ALL CALIBRATIONS
* -------------------------------------------------------------------- *
No. Of
SEP
Bias
% S.L. to % S.L. of
Factors
SEP min.
Extra term
* -------------------------------------------------------------------- *
1
0.6223
0.02877
5.0
----2
0.5707
-0.02605
12.6
1.3
3
0.5102
0.007153 31.0
0.5
4
0.468
0.01546
50.1
1.6
5
0.4949
0.02176 --------6
0.5057
0.01964 --------7
0.5055
0.01907 ----88.4
* -------------------------------------------------------------------- *
Min. SEP (No. Factors) = 0.4680 (4)
Min. Significant SEP (No. Factors) = 0.5102 (3)
Table 6: Example of property (tensile) validation report
The term minimum significant Standard Error of Prediction (SEP = 0.5102 with 3
PC’s) and the minimum Standard Error of Prediction (SEP = 0.4680 with 4 PC’s) shown
at the bottom in Table 6 are most important. The minimum significant Standard Error of
Prediction corresponds to the number of factors before which a significant change in the
prediction errors from the minimum Standard Error of Prediction is detected and leads to
a more robust model. The numbers of factors that lead to this minimum significant are
kept.
33
3.5.4 External Validation:
Once the model is generated, the prediction of unknown samples is done to verify
the accuracy of the model. The prediction is achieved by determining the scores for the
sample spectra and substituting these values in the regression equation.
There are two parameters that are of importance while applying the chemometric
techniques. These are “Residual Ratio” and Total M-distance Ratio (Mahalonabis
Distance). The residual ratio is the amount of unknown sample’s spectra that cannot be
accounted for by the model. If its value is greater than 3, it shows that the unknown
sample’s spectra have features that cannot be modeled. The second parameter, MDistance is a way of determining the similarity of a set of values from an unknown
sample to a set of values measured from a collection of known samples. The M-distance
value should be always less than 1. A value greater than 1 shows that the unknown
sample has features that are not reflected in the calibration model. Table 7 shows the
different values of residual ratio and M-Distance and their meaning in spectroscopic
terms.
Total MDistance ratio
High (>1)
Residual Ratio
Inference
High (>3)
High (>1)
Low (<3)
Low (<1)
High (>3)
Low (<1)
Low (<3)
Additional overlapping features present in the
unknown spectra
Some modeled features are more intense than in
the calibration set
Additional unmodeled features present in the
unknown spectra
Unknown spectra representative of the calibration
set
Table 7: Effect of different values of M-distance ratio and residual ratio and their
inference
34
4.0
Results and discussion
4.1
Phase One: Effect of change in furnish ratio
For the phase one (Effect of change in furnish ratio) calibration set, 13 different
conditions were generated. For each condition three handsheets were made. This way a
total of 39 handsheets were made for the calibration set. For validation set 10 different
conditions were generated. For each condition 1 handsheet was made. This way a total of
10 different handsheets were made for the validation set. The calibration set and the
validation set for phase one is shown in Table 8 and 9. For each handsheet spectra was
taken at three different points. The average of these three spectra was considered in
making the model. The notation Avg1 corresponds to the average of the spectra taken at
three different points on first handsheet of first condition with a furnish ratio of 0%
recycled, 30% softwood and 70% hardwood. Similarly Avg2 is the second handsheet of
the first condition. Avval1-10 are the average spectra for the samples in the validation set
with the corresponding furnish ratio as shown.
Sample Recycled SW HW
(#)
(%)
(%) (%)
Avg1-3
0
30 70
Avg4-6
10
27 63
Avg7-9
20
24 56
Avg10-12
30
21 49
Avg13-15
40
18 42
Avg16-18
50
15 35
Avg19-21
60
12 28
Avg22-24
70
9
21
Avg25-27
80
6
14
Avg28-30
90
3
7
Avg31-33
100
0
0
Avg34-36
0
100
0
Avg37-39
0
0
100
Sample Recycled SW HW
(#)
(%)
(%) (%)
Avval1
5
28.5 66.5
Avval2
15
25.5 59.5
Avval3
25
22.5 52.5
Avval4
35
19.5 45.5
Avval5
45
16.5 38.5
Avval6
55
13.5 31.5
Avval7
65
10.5 24.5
Avval8
75
7.5 17.5
Avval9
85
4.5 10.5
Avval10
95
1.5 3.5
Table 9: Validation set for the phase 1
Table 8: Calibration set for phase 1
35
Figure 11 shows the average spectra for the standards in the first 10 conditions of the
calibration set (from Avg1 to Avg30). The X-axis shows the frequency expressed in wave
numbers and the Y-axis is the % reflectance. The wavenumbers range from 15000 cm-1
to 2700 cm-1.
82.5
70
60
50
40
30
20
7.9
15000.0
14000
12000
10000
8000
6000
4000
2700.0
-1
Wavenumber cm
Figure 11: Average spectra for first 10 conditions for phase one
Figure 12 shows the average spectra for the standards in the last three conditions (from
Avg31 to Avg39). These standards show how intensity changes by using only softwood,
only hardwood and only recycled fibers. These standards are the extremes having furnish
of 100 % SW, 100% HW and 100% Recycled.
76.2
70
65
60
55
50
45
40
35
30
25
20
15
7.7
15000.0
14000
12000
Wavenumber cm-1
10000
8000
6000
4000
2700.0
Figure 12: Spectra of extreme conditions 100%SW, 100%Re and 100% HW.
36
The two axes are % light reflectance and the wavenumbers. PCA was done on all the
available 49 standards for calibration set and the data cluster was analyzed. The first plot
to analyze was the score vs. score plot (index vs. PC1) of the samples as shown in Figure
12. 0.39
0.3
x avg5
Cluster1
x avg29
x avg30
0.2
PC 1 (89.25%)
0.1
Possible outlier
x avg2 8
x avg2
x avg1
x avg3
0.0
x avg15
x avg14
x avg18
x avg23
x avg24
x avg32
x avg31
x avg27
x avg19
x avg12
x avg13 x avg16
x avg17 x avg20
x avg26
x avg22
x avg21
x avg25
x avg10
x avg11
x avg6
x avg34
x avg33
x avg36
x avg35
-0.1
x avg4
x avg9
x avg8
x avg7
Cluster2
x avval4
x avval10
x avval6
x avval7
x avg38
x avg39
x avg37
x avval3
x avval2 x avval5
x avval8
x avval9
-0.2
-0.3
Possible outlier
-0.4
x avval1
-0.46
1.0
5
10
15
20
25
30
35
40
45
50
53.8
Index
Figure 13: Score vs. Score plot of all the available samples.
The plot shows two different clusters of the samples. The first cluster on the left side in
the plot shows all the calibration set from avg1 to avg 39 and the second cluster on the
right side of the plot show all the validation set samples from avval1 to avval 10. It was
found that all the samples lie in the clusters except avg28 and avval1 as shown in the
Figure 13. These two samples are shown slightly far away from the cluster. After that
PC1 vs. PC2 plot were analyzed as shown in Figure 14. This plot also shows that avg28
and avval1 are showing different behavior than other samples. Although this time avg5
and avg35 are also showing different behavior but these samples are not outliers in Figure
13 and Figure 15.
Figure 14 shows the PC1 vs. PC2 plot.
37
Possible outlier
0.39
x avg28
x avg5
0.3
x avg29
x avg30
0.2
x avg2
x avg15xx avg1
avg14x avg32
x avg23
avg3 x avg24
x xavg31
x avg18
x avg6
PC 1 (89.25%)
0.1
0.0
x avval4
x avval10
-0.1
x avg27
x avg19
x avg16
avg13
x avg9
x avg17x avg20 x avg12
x avg26 x avg36
x avg34
x avg22
x avg21
avg33
x avg8
x avg25
x avg7
x avg10
x avg4
x avg11
x avval6
x avval7
-0.2
avg38
x xavg39
x avg35
x avg37
x avval3
x avval5
x avval8
x avval9
x avval2
-0.3
-0.4
Possible outlier
x avval1
-0.46
-0.28
-0.2
-0.1
0.0
0.1
PC 2 (5.96%)
0.2
0.3
0.4
0.47
Figure 14: Plot of PC1 vs. PC2.
Finally PC1 vs. PC3 plot was analyzed since it is common to analyze first two or three
PC’s to detect the outliers. It was found that samples avg28 and avval1 exhibit same
behavior. The two samples are found to be not a part of the samples cluster in all the
three observations. This analysis clearly indicates that these two samples are outliers. The
PC1 vs. PC3 plot is shown in Figure 15.
Definite outlier
0.39
x avg28
x avg5
0.3
x avg29
x avg30
0.2
PC 1 (89.25%)
0.1
x avg2
x avg15
x avg32
x avg14
x avg1
x avg23
x avg24
x avg3
xxavg18
avg6
x avg27
avg16
x avg12
x avg13
x avg9
x avg17
x avg26 x avg22 x avg20
avg21
x xavg8
x avg10
x avg7
x avg4
x avg11
x avval4
x avg35
x avg31
x avg19
x avg34
x avg36
avg33
x avg25
0.0
x avval10
-0.1
x avval6
x avval7
-0.2
x avg38
x avg39
x avg37
x avval3
x avval5
x avval2
x avval8
x avval9
-0.3
-0.4
Definite outlier
x avval1
-0.46
-0.286 -0.25
-0.20
-0.15
-0.10
-0.05
-0.00
0.05
PC 3 (2.01%)
Figure 15: PC1 vs. PC3 plot.
38
0.10
0.15
0.20
0.25
0.302
After analyzing score vs. score plot the next plot to analyze was the standard leverage
plot for all the 49 standards. Plot is shown in Figure 16.
0.412
x avval1
Possible outliers
Cutoff line
0.35
x avg28
0.30
x avg5
Leverage
0.25
x avg35
x avg34
x avg33x avg36
0.20
0.15
x avg29
x avg25
x avg24x avg27
x avg2
x avg3
0.10 x avg1
x avg10
0.05
x avg17
x avg19x avg22
x avg26
x avval5 x avval9
x avval3
x avval8
x avval4
x avval2
x avg39
x avg4
x avg37
x avg38
x avg30
x avg32
x avg31
x avval10
x avg8
x avg6
x avg23
x avg16 x avg20
x avg21
x avg15
x avg13
x avg11
x
avg18
x avg12
x avg14
x avval7
x avval6
x avg9
x avg7
0.009
1.0
5
10
15
20
25
30
35
40
45
50
53.8
Index
Figure 16: Standard leverage plot for all samples.
The plot shows that most of the samples were below cutoff line except for the two
suspect samples, identified as avg28 and avval1. A third sample avg5 was also found
above the cutoff line but it was close to cutoff line and also it was not an outlier in the
score vs. score plots. It was concluded that all the samples were good except avg28 and
avval1.
Next the loading plots were analyzed for the first few PC`s to see whether the
whole spectral range should be used in our calibration or not. It was found that the
spectral region from 15000 cm-1 to about 7000 cm-1 apparently has no information in it.
The same spectral range was checked for PC2 also.
Figure 17 and 18 shows the loading plot for PC1 and PC2 for all the 49 samples.
39
0.150
A blank region not contributing to spectra
0.10
Spectral bands provides information
0.05
0.00
PC 1 (89.25%)
-0.05
-0.10
-0.15
-0.20
-0.25
-0.30
-0.371
15000.0
14000
12000
10000
8000
6000
4000
2700.0
cm-1
Figure 17: Loading plot for the all the samples with PC1.
0.133
0.12
0.10
Effect of noise
PC 2 (5.96%)
0.08
0.06
0.04
0.02
0.00
-0.02
-0.034
15000.0
14000
12000
10000
8000
6000
4000
2700.0
cm-1
Figure 18: Loading plot for the all the samples with PC2.
In Figure 18 the amount of noise is more. Finally we decided to apply blanks from 15000
to 7500 cm-1 during the calibration process. A blank is an option on the Method WizardCalculations dialog. It enables to blank out the regions of the spectra so that those
regions are not used in the calculation. A blank enables one to eliminate unwanted
features from a spectrum, such as regions affected by CO2 or H2O bands, sample matrix
or regions containing purely noise. After eliminating the two outliers it was decided to
40
use the calibration set as the 38 regular standards (after eliminating avg28) and the
validation set as 9 random samples (after eliminating avval1). Also the region from
15000 cm-1 to 7500 cm-1 was not used in the calibration.
After getting the spectra for all the available samples (including calibration set
and validation set as well) the tensile and softness properties of those samples were
checked. Due to the destructive nature of the testing that we have to use for the tensile
strength and softness (measured by stiffness tester) we have to take the spectra first and
then do the physical testing for the properties of tensile strength and softness. The model
was built using all the samples. The model was calibrated and validated using full cross
validation. After that we analyzed the model by predicting the properties of the validation
set samples. It was found that model gave some good mathematical values for all the
samples. These values are shown in the Table 10.
Prediction results for phase one using PCR (Tensile & Softness)
Tensile (kN/m)
Sample
avval2
avval3
avval4
avval5
avval6
avval7
avval8
avval9
avval10
Softness (/gm.m)
Real Value Predicted Real Value Predicted
Value
Value
1.33
1.29
0.88
1.27
1.12
1.24
1.84
2.01
1.09
1.39
1.11
0.81
1.23
1.26
1.77
1.14
1.69
1.51
0.0026
0.0025
0.0029
0.0026
0.0026
0.0023
0.0021
0.0019
0.0026
0.0026
0.0027
0.0040
0.0025
0.0034
0.0032
0.0022
0.0023
0.0039
Residual M-Distance
Ratio
9.53
6.67
3.71
7.76
5.98
6.75
12.10
13.50
5.86
6.42
3.90
2.04
4.92
4.04
6.55
8.81
9.82
4.32
Table 10: Prediction results for phase one
The values obtained from prediction were close to the real values. After that
M-distance ratio and the residual ratio were analyzed and it was found that M-distance
was more than 1 in all of the cases and residual ratio was more than 3 in all of the cases
as needed to get the best correlation. Although a few exceptions were observed where
41
these values were good. M-distance, which is also called total Mahalonabis distance, is a
useful way of determining the similarity of a set of values from an unknown sample to a
set of values measured from a collection of known samples. The M-distance ratio should
not be greater than 1. A ratio greater than 1 indicates that the unknown sample spectra
contain features that are not reflected in the calibration model. The ratio will exceed 1 if
there are additional overlapping features present in the spectrum of the unknown sample.
Another important factor to consider is the residual ratio. The residual ratio reflects the
amount of the unknown’s spectral information that can not be explained by the model. A
value greater than 3 indicates that the spectrum contains the features that can not be
explained by the model.
The model was reliable for the values that were obtained. When using
spectroscopy the model reliability can not be defined just by the values obtained. The two
parameters the total M-distance and the residual ratio values should also be checked. In
this case when these values were checked it was found that the values are not in the
required range. This shows that the spectra have features that can not be explained by the
model. One possible reason for this poor prediction was that no random samples were
included in the calibration set. In a further attempt to modify the model the calibration set
was changed somewhat. All random samples were introduced in the calibration set and
one sample was taken out from each set of the original calibration set. The new
calibration and validation sets are shown in Table 11 and Table 12.
42
Sample (#)
Avg1, 3
Avg4, 5
Avg8, 9
Avg10, 12
Avg13, 15
Avg17, 18
Avg19, 20
Avg22, 24
Avg25, 26
Avg30
Avg31-33
Avg34-36
Avg37-39
Avval2
Avval3
Avval4
Avval5
Avval6
Avval7
Avval8
Avval9
Recycled
(%)
0
10
20
30
40
50
60
70
80
90
100
0
0
15
25
35
45
55
65
75
85
SW (%)
30
27
24
21
18
15
12
9
6
3
0
100
0
25.5
22.5
19.5
16.5
13.5
10.5
7.5
4.5
HW (%)
70
63
56
49
42
35
28
21
14
7
0
0
100
59.5
52.5
45.5
38.5
31.5
24.5
17.5
10.5
Avval10
95
1.5
3.5
Serial (#)
Avg2
Avg6
Avg7
Avg11
Avg14
Avg16
Avg21
Avg23
Avg27
Avg29
Recycled
(%)
0
10
20
30
40
50
60
70
80
SW (%)
30
27
24
21
18
15
12
9
6
HW (%)
70
63
56
49
42
35
28
21
14
90
3
7
Table 12: New Validation set.
Table 11: New Calibration set.
Calibration was done using the set of values in Table 9. Model was validated
using full cross validation. Blanks were applied in the range from 15000 cm-1 to 7500 cm1
during the calibration. The results are shown below. The standard error of estimate
(SEE) for tensile strength was 0.3944 and standard error of prediction (SEP) was 0.4799
whereas SEE for softness was 0.0019 and SEP was 0.0022. (As shown in the Appendix C)
The predicted values for the validation set are shown below in Table 13 and Table 14 for
tensile strength and softness.
43
Standard
Avg2
Avg6
Avg7
Avg11
Avg14
Avg16
Avg21
Avg23
Avg27
Avg29
Total MActual Predicted Distance Residual
tensile Tensile
Ratio
ratio
0.98
0.98
0.648
1.19
1.21
1.49
0.231
1.29
1.57
1.61
0.204
1.05
1.31
1.52
0.261
1.28
1.18
1.25
0.274
1.4
1.11
1.5
0.143
1.15
1.59
1.56
0.132
0.989
1.57
1.37
0.465
1.77
1.63
1.9
0.313
1.67
1.36
1.25
0.69
1.5
RMSEP 0.0203
Table 13: Tensile strength prediction using
PCR algorithm for phase one
Standard
Avg2
Avg6
Avg7
Avg11
Avg14
Avg16
Avg21
Avg23
Avg27
Avg29
Actual
softness
0.0072
0.0048
0.0035
0.0039
0.0044
0.004
0.0036
0.0033
0.0033
0.0036
RMSEP
Total MPredicted Distance Residual
softness
ratio
Ratio
0.0053
0.543
1.19
0.0047
0.351
1.29
0.0037
0.261
1.05
0.0035
0.288
1.28
0.0051
0.513
1.4
0.0043
0.27
1.15
0.004
0.25
0.989
0.0047
0.359
1.77
0.0042
0.26
1.67
0.0063
1.33
1.5
0.0013
Table 14: Softness prediction using PCR
algorithm for phase
.
The predicted values for the tensile strength and softness are close to the real
values. Although the values are not exactly the same but their magnitude is the same with
a few exceptions. The results are more reliable this time. This is based on the fact that
RMSEP (Root Mean Square Error of Prediction) values are low and the M-distance and
residual ratio are in the correct range of values. (M-distance is always less than 1 and
residual ratio is always less than 3).
In order to verify the prediction capabilities a second model was built applying a
different algorithm. The same calibration using the PLS algorithm was done and the
results are shown in Table 15 and 16 below. This time also the M-distance was less than
1 and the residual ratio was less than 3 so they were in the right range. The SEE for
tensile was 0.3964 and SEP was 0.4481 whereas for softness SEE was 0.001970 and SEP
was 0.002141. (As shown in the Appendix C).
44
Total MActual Predicted distance Residual
ratio
Standard tensile Tensile
ratio
Avg2
0.98
1.17
0.325
1.87
Avg6
1.21
1.56
0.14
0.704
Avg7
1.57
1.68
0.034
0.548
Avg11
1.31
1.61
0.039
0.769
Avg14
1.18
1.28
0.265
0.328
Avg16
1.11
1.46
0.02
0.437
Avg21
1.59
1.57
0.004
0.337
Avg23
1.57
1.53
0.136
1.85
Avg27
1.63
1.95
0.351
1.07
Avg29
1.36
1.22
1.09
0.476
RMSEP 0.0229
Total MActual Predicted distance Residual
Standard softness softness ratio
ratio
Avg2
0.0072 0.0053
0.633
0.564
Avg6
0.0048 0.0047
0.217
0.29
Avg7
0.0035 0.0037
0.018
0.36
Avg11 0.0039 0.0035
0.066
0.495
Avg14 0.0044 0.0052
0.532
0.174
Avg16 0.0040 0.0043
0.038
0.24
Avg21 0.0036
0.004 0.00058 0.17
Avg23 0.0033 0.0048
0.246
0.68
Avg27 0.0033 0.0042
0.025
1.08
Avg29 0.0036 0.0064
2.13
0.84
RMSEP 0.0013
Table 15: Tensile strength prediction using
PLS for phase one
Table 16: Softness prediction using PLS
for phase one
The two error values are close to each other. Also the RMSEP for tensile and
softness are almost equal to the values obtained in PCR model. The above two tables
show that the model is good this time as the M-distance ratio and the Residual ratio are in
the right range as per rule.
In conclusion it was judged that it is possible to predict the tissue’s tensile
strength and the softness values with different furnish ratios, using the NIR spectroscopy
and the Chemometrics techniques.
With increase in the amount of recycle content in the furnish, the tensile strength
should decrease. The data in Table 15 show that the values do not follow the trend. When
the actual tensile values were plotted against the predicted tensile values the plot shows a
R2 value of 0.35, which is not a good correlation. This discrepancy in the value of tensile
strength and softness observed was due to some constraints in the lab handsheet making
of tissue paper. The tissue paper handsheets could not be made exactly the same way as
on the actual machine. The one reason for this is that creping could not be provided in the
45
lab. A second reason for this problem was that the two chemicals (WSR and debonder)
were required in very small quantity. Due to highly viscous nature of these chemicals
they should be controlled with high accuracy. In actual mill continuous operation these
are controlled carefully than in a lab batch process.
In an attempt to check the effect of not using the validation set completely in the
model and predicting some of the samples from the original calibration set, another
model was made. This time only calibration set was used. 10 samples were taken out of
the calibration set and were used for prediction purpose. The calibration set and
validation set are shown in Table 17 and 18.
Sample (#)
Avg1, 3
Avg4, 5
Avg8, 9
Avg10, 11
Avg13, 14
Avg16, 18
Avg19, 20
Avg23, 24
Avg25, 26
Avg29
Avg31, 32
Avg34,35,36
Avg37,38,39
Recycled
(%)
0
10
20
30
40
50
60
70
80
90
100
0
0
SW HW
(%) (%)
30 70
27 63
24 56
21 49
18 42
15 35
12 28
9
21
6
14
3
7
0
0
100 0
0 100
Sample Recycled SW HW
(#)
(%)
(%) (%)
Avg6
0
30 70
Avg7
10
27 63
Avg12
20
24 56
Avg15
30
21 49
Avg15
40
18 42
Avg17
50
15 35
Avg21
60
12 28
Avg22
70
9
21
Avg27
80
6
14
Avg30
90
3
7
Avg33
100
0
0
Table 18: Validation set for model 3
Table 17: Calibration set for model 3.
.
After taking the outliers out and feeding the values in the model, the values of the
samples of the validation set were predicted. Table 19 and 20 show the values of
predicted tensile and softness.
46
Standard
Avg6
Avg7
Avg12
Avg15
Avg17
Avg21
Avg22
Avg27
Avg30
Avg33
Total MActual Predicted distance Residual
tensile Tensile
Ratio
ratio
0.058 0.083
0.256
1.20
0.073 0.065
0.082
0.96
0.066 0.067
0.101
1.25
0.064 0.067
0.164
1.10
0.06
0.051
0.438
1.24
0.075 0.059
0.116
1.01
0.09
0.072
0.116
1.51
0.072 0.089
0.594
1.66
0.073 0.091
0.607
1.52
0.069 0.120
0.843
0.86
RMSEP 0.0213
Standard
Avg6
Avg7
Avg12
Avg15
Avg17
Avg21
Avg22
Avg27
Avg30
Avg33
Table 19: Predicted tensile index values
for phase one model 3
Actual
softness
0.0048
0.0035
0.0036
0.0039
0.0042
0.0036
0.0036
0.0033
0.0036
0.0071
Predicted
softness
0.0051
0.0038
0.0045
0.0058
0.0044
0.0041
0.0039
0.0043
0.0066
0.0037
RMSEP
Total Mdistance Residual
ratio
ratio
0.256
1.20
0.082
0.96
0.101
1.25
0.164
1.10
0.438
1.24
0.116
1.01
0.116
1.51
0.594
1.66
0.607
1.52
0.843
0.86
0.0016
Table 20: Predicted softness values for
phase one model 3
The above values were good as the M-distance ratios and the residual ratios are in the
proper range. The SEE value for the tensile index was 0.01854 and SEP was 0.02075
whereas for softness the SEE value was 0.002053 and SEP was 0.002252. (As shown in
the appendix C.)
4.1.1 Conclusions from phase one results: Effect of change in furnish
ratio (Recycled fiber, SW and HW)
After completing phase one it was concluded that NIR spectroscopy combined
with multivariate analysis could be used to predict the tensile and softness properties of
tissue paper made with variable furnishes. The results obtained would have been much
better if the actual values of tensile strength and softness were consistent. The
discrepancy in the actual values was due to several constraints that have already been
mentioned. The results obtained using PCR and PLS algorithms show an average
RMSEP value for tensile of 2.1 % and RMSEP value for softness as approximately of
47
0.15 %. The low values of root mean square error of prediction (RMSEP) indicate good
prediction capabilities of the model.
4.2
Phase Two: Effect of change in debonder addition
Phase two of the work was to see if the technique could be used to predict the
properties while changing the amount of debonder (dialkyldimethyl quaternary amine) in
the furnish. This time the other three parameters of furnish ratio, amount of wet strength
resin and the level of refining were kept constant (10 % recycled, 27 % SW, 63 % HW
and 20 lbs/T WSR) throughout and the amount of debonder addition in the furnish was
varied.
Different conditions were generated by varying the amount of debonder starting
from 20 lbs/T to 70 lbs/T in the increment of 5 lbs/T. To create an extreme condition one
0 lbs/T debonder addition was also considered. The amount of wet strength resin was
kept constant at a level of 20 lbs/T. Five handsheets were made for each condition. Out
of five handsheets made under each condition 3 were chosen at random for the calibration
set. Finally there were total 12 different conditions for the calibration set and 36 total
samples. For the validation set a total 10 samples were available. Table 21 and 22 shows
the calibration set and validation set respectively.
48
Sample (#)
Sec01-03
Sec1-3
Sec4-6
Sec7-9
Sec10-12
Sec13-15
Sec16-18
Sec19-21
Sec22-24
Sec25-27
Sec28-30
Sec31-33
Recycled SW HW Debonder WSR
(%)
(%) (%) (lbs/Ton) (lbs/Ton)
10
10
10
10
10
10
10
10
10
10
10
27
27
27
27
27
27
27
27
27
27
27
63
63
63
63
63
63
63
63
63
63
63
0
20
25
30
35
40
45
50
55
60
65
20
20
20
20
20
20
20
20
20
20
20
10
27
63
70
20
Sample Recycled SW HW Debonder WSR
(#)
(%)
(%) (%) (lbs/ton) (lbs/ton)
Valsa1
10
27 63
22
20
Valsa2
10
27 63
27
20
Valsa3
10
27 63
32
20
Valsa4
10
27 63
37
20
Valsa5
10
27 63
42
20
Valsa6
10
27 63
47
20
Valsa7
10
27 63
52
20
Valsa8
10
27 63
57
20
Valsa9
10
27 63
62
20
Valsa10
10
27 63
67
20
Table 22: Different experimental
conditions for validation set for phase 2
Table 21: Different experimental conditions
for calibration set for phase 2.
In the first phase it was observed that the model gave good prediction results when the
random samples were introduced in the calibration set and the samples that were taken
out from the original calibration set were predicted. In phase two the same approach was
applied. The results from the first model using samples in Table 19 as calibration set and
Table 20 as validation set were not reliable. The M-distance value and the residual ratio
for these results were more than required. So these results were kept in the Appendix B
for reference. The calibration and validation sets were changed accordingly. The new
calibration set and validation set are shown in Table 23 and 24.
49
Sample
Sec1, 2
Sec4, 6
Sec7, 9
Recycled
10
10
10
SW
(%)
27
27
27
HW Debonder WSR
(%) (lbs/ton) (lbs/ton)
63
20
20
63
25
20
63
30
20
Sec11,12
Sec13,14
Sec16,18
Sec19,21
Sec23,24
Sec26,27
Sec29,30
Sec31-33
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
10
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
63
35
40
45
50
55
60
65
70
22
27
32
37
42
47
52
57
62
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
20
10
27
63
67
20
Valsa1
Valsa2
Valsa3
Valsa4
Valsa5
Valsa6
Valsa7
Valsa8
Valsa9
Valsa10
SW HW Debonder
Sample Recycled (%) (%) (lbs/ton)
Sec3
10
27 63
22
Sec5
10
27 63
27
Sec8
10
27 63
32
Sec10
10
27 63
37
Sec15
10
27 63
42
Sec17
10
27 63
47
Sec20
10
27 63
52
Sec22
10
27 63
57
Sec25
10
27 63
62
Sec28
10
27 63
67
WSR
(lbs/ton)
20
20
20
20
20
20
20
20
20
20
Table 24: New validation set for phase 2.
Table 23: New calibration set for phase 2.
Since this work deals with the property predictions very slight change in the final
properties measurements can give a different result during the prediction. It was realized
in phase one that sheets are varying slightly in basis weights. Although the basis weight
was intended to be kept constant at 20 gsm throughout the work, still there is some
variation in basis weight. So for the phase two instead of using tensile strength it was
decided to use tensile index values to adjust for the variation in basis weight. PCA was
done on all the available 46 samples and the plots were analyzed. The first plot to
analyze was standard leverage plot as shown in Figure 19.
50
Possible outliers
0.280
x sec27
x sec16
0.25
Cutoff line
x valsa1
0.20
x valsa8
Leverage
x sec14
x sec13
x sec1
x valsa3
x valsa2
x sec10
0.15
x sec2
x sec29
x sec30
x sec19
x sec21
x sec7
x sec12
x sec3
x sec5
x sec8
x sec31
x sec32
x sec23
x sec15
x sec9
x sec4
0.05
x valsa10
x valsa4
x valsa5
x valsa7
x sec28
x sec6
0.10
x sec11
x sec25
x sec18
x sec20
x valsa6
x sec02
x sec01
x sec22
x valsa9
x sec03
x sec24
x sec26
x sec33
x sec17
0.015
1.0
5
10
15
20
25
Index
30
35
40
45
50.5
Figure 19: Standard leverage plot for all the samples for phase 2.
It was found that standards labeled as sec16 (which refers to the second phase
sample number 16) and sec27 are outliers since they are above the cutoff line. After this
the score vs. score plot as shown in Figure 20 was checked and it was found that sec27 is
far away from the cluster. This time sec16 was found inside the cluster and was not an
outlier.
0.48
x sec27
Only outlier
0.4
0.3
x sec8
PC 1 (74.47%)
0.2
x sec2
x sec3
0.1
x sec21
x sec12
x sec20
x sec18
x sec19
x sec4x sec6
x sec1
x sec23
x sec9
x sec24
x sec5x sec7
0.0
x sec22
x sec15
x sec10
x sec11 x sec14
x sec17
x sec16
x sec25
x sec13
-0.1
x valsa3
x sec01
x sec26
x sec33
x sec03
x sec02
x sec30
x sec32
x valsa8
x valsa9
x valsa1
x valsa7
x valsa10
x valsa4
x sec31
-0.2
x valsa6
x valsa5
x sec28
x sec29
x valsa2
-0.32
1.0
5
10
15
20
25
Index
30
35
40
45
Figure 20: Score vs. score plot for all the samples for phase 2.
51
50.5
Definite outlier
0.48
x sec27
0.4
0.3
PC 1 (74.47%)
0.2
x sec23
x sec21
x sec12
x sec8
x sec20
x sec3 x sec2
x sec9
x sec18x sec19
x sec24
0.1
x sec22
0.0
x sec32
x sec25
-0.1
x sec26
xxxsec14
sec33
sec15
sec11
xxx
sec17
sec16
x sec30
x sec13
x sec4
x sec6
x valsa3
x sec01
x sec7 x sec5
x sec1
x sec10
x valsa8
x sec03
x sec02
x valsa9
x valsa7
x valsa10
x sec31
-0.2
x sec29
x valsa1
x valsa4
x valsa6
x valsa5
x valsa2
x sec28
-0.32
-0.26
-0.2
-0.1
0.0
0.1
PC 2 (9.88%)
0.2
0.3
0.40
Figure 21: score vs. score plot PC1 vs. PC2.
After this the PC1 vs. PC2 plot was analyzed and this time the standard sec27
seems to be a definite outlier as shown in Figure 21. So this standard was removed from
the calibration set. The next plot to analyze was loading plot for the samples as shown in
Figure 22. The region from 7000 cm-1 to 2700-1 contains some bands that correspond to
some information regarding change in variables. The region between 15000 cm-1 and
7000 cm-1 does not contain any relevant information other than noise. So while making
the model a blank was applied in this region.
No bands observed except noise
0.058
Visible bands
PC 1 (74.47%)
0.00
-0.05
-0.10
-0.15
-0.20
-0.218
15000.0
14000
12000
10000
8000
cm-1
Figure 22: Loading plot for phase 2
52
6000
4000
2700.0
Calibration was done using the samples in the calibration set as shown in Table
21. After the calibration the model was validated using full cross validation. The samples
in Table 22 were predicted using this model. The prediction results are shown in Table 25
and 26.
Actual Predicted Total MTensile Tensile Distance Residual
Standard Index
Index
Ratio
Ratio
sec3
0.032
0.034
0.285
1.050
sec5
0.032
0.033
0.266
1.100
sec8
0.033
0.035
0.351
1.370
sec10
0.026
0.031
0.502
1.330
sec15
sec17
sec20
sec22
sec25
sec28
0.031
0.026
0.030
0.027
0.036
0.031
RMSEP
0.031
0.032
0.034
0.031
0.031
0.029
0.433
0.377
0.259
0.443
0.426
0.010
1.090
1.170
0.967
1.360
1.640
1.810
0.0037
Table 25: Prediction results for tensile
Actual
Standard softness
sec3
0.0058
sec5
0.0052
sec8
0.0060
sec10
0.0066
sec15
0.0074
sec17
0.0092
sec20
0.0087
sec22
0.0082
sec25
0.0079
sec28
0.0060
RMSEP
Total MPredicted Distance Residual
softness
Ratio
ratio
0.0063
0.425
1.050
0.0062
0.465
1.100
0.0063
0.348
1.370
0.0062
0.432
1.330
0.0066
0.251
1.090
0.0067
0.286
1.170
0.0067
0.272
0.967
0.0070
0.513
1.360
0.0069
0.403
1.640
0.0062
0.002
1.810
0.0012
Table 26: Prediction results for softness.
From the prediction results obtained for tensile index and softness it is clear that
the values of M-distance ratio and residual ratio are in the acceptable range. The Mdistance is less than 1 in each case and residual ratio is always less than 3. The RMSEP
value for tensile index is 0.37 % and for softness is 0.12%. Accordingly the results can be
trusted based on PCR algorithm. A second model was generated using the PLS algorithm
on the same data set to see whether the prediction results were reliable with a different
algorithm or not. It was found that the PLS algorithm also gave reliable results. The Mdistance values and residual ratio values were as per rule and the RMSEP values were
0.39% and 0.13% for tensile and softness respectively. Results of PLS algorithm are
shown in Table 27 and Table 28 for tensile index and softness respectively.
53
Actual Predicted Total MTensile Tensile Distance Residual
Ratio
Standard Index
Index
Ratio
sec3
0.032
0.032
0.341
0.912
sec5
0.032
0.033
0.099
0.306
sec8
0.032
0.031
0.950
2.150
sec10
0.025
0.032
0.196
0.497
sec15
0.031
0.032
0.238
0.173
sec17
0.026
0.033
0.066
0.251
sec20
0.030
0.031
0.870
1.430
sec22
0.026
0.032
0.195
0.359
sec25
0.035
0.033
0.001
0.405
sec28
0.031
0.035
0.367
2.610
RMSEP
Total MActual Predicted Distance Residual
Ratio
Standard softness softness
ratio
sec3
0.0058
0.0068
0.352
0.99
sec5
0.0052
0.0067
0.05
0.552
sec8
0.006
0.0071
1.14
1.54
sec10
sec15
sec17
sec20
sec22
sec25
sec28
0.0039
Table 27: Prediction results for tensile
index using PLS
RMSEP
0.0066
0.0074
0.0092
0.0087
0.0082
0.0079
0.006
0.0066
0.0067
0.0067
0.007
0.0068
0.0066
0.0061
0.015
0.076
0.036
0.99
0.141
0.003
0.836
1.25
0.642
0.426
0.803
0.552
0.606
1.77
0.0013
Table 28: Prediction results for softness.
using PLS
From Table 25 and 26 it is evident that as the amount of debonder in increasing from
sample sec3 to sec28 the softness should increase and the tensile index should decrease.
The actual trend in the tables is different. The two values are changing in a random
fashion. The reasons for this discrepancy in the values obtained was due to lab handsheet
making and have already been explained in phase one.
4.2.2 Conclusions for phase two: Effect of change in amount of
debonder
After phase two it was concluded that it could be possible to predict the tensile
and softness properties of tissue paper made with different amounts of debonder in the
furnish. The results show that the predicted values of tensile and softness are close to real
values with satisfying the rules of M-distance ratio and residual ratio.
4.3
Phase Three: Effect of change in amount of wet strength resin
Phase three involved change of wet strength resin (WSR) amount while keeping
all other parameters (recycled fiber at 10 %, SW at 27 % and HW at 63 % and debonder
54
at 40 lbs/T) same. The amount of wet strength resin was changed from 0lbs/T to 60lbs/T.
The debonder level was kept at 40 lbs/ton while wet strength resin was changed starting
from 0 lbs/ton to 60 lbs/ton in the increment of 5 lbs/ton. For the calibration set 12
different conditions and for validation set 10 random conditions were generated. These
conditions are shown in the Table 29 and Table 30 below.
Sample
DB
WSR
Recycled SW HW (lbs/ton) (lbs/ton)
3rd 1-3
3rd 4-6
10
27
63
10
27
3rd 7-9
10
27
3rd 10-12
10
27
63
40
40
0
10
63
40
15
63
40
20
25
3rd 13-15
10
27
63
40
3rd 16-18
10
27
63
40
30
35
3rd 19-21
10
27
63
40
3rd 22-24
10
27
63
40
40
45
3rd 25-27
10
27
63
40
3rd 28-30
10
27
63
40
50
63
40
55
63
40
60
3rd 31-33
10
3rd 34-36
27
10
27
DB
WSR
Sample Recycled SW HW (lbs/ton) (lbs/ton)
Rand1
10
27 63
40
5
40
Rand2
10
27 63
12
40
Rand3
10
27 63
18
40
Rand4
10
27 63
24
40
Rand5
10
27 63
28
40
Rand6
10
27 63
32
40
Rand7
10
27 63
38
40
Rand8
10
27 63
42
40
Rand9
10
27 63
48
40
Rand10
10
27 63
52
Table 30: Validation Set for phase three.
Table 29: Calibration Set for phase three.
After taking the spectra for all the 46 samples (36 calibrations and 10 validations)
PCA was done on all the available samples to detect any outliers. Figure 23 shows the
standard leverage plot of the PCA done on the 46 samples.
0.402
Possible outliers
x rand10
0.35
x rand5
0.30
x rand2
Leverage
0.25
x 3rd36
x 3rd7
0.20
0.15
x rand1
x 3rd1
x 3rd3
x 3rd2
x 3rd6
x 3rd12
x 3rd8
0.10
0.05
x rand6
x 3rd4
x 3rd5
x 3rd23
x 3rd28
x 3rd26
x 3rd25
x 3rd31
x 3rd33
x 3rd32
x 3rd30
x 3rd35
x 3rd24
x 3rd22
x 3rd21
x 3rd29
x 3rd34
x 3rd19
x 3rd20
x 3rd13 x 3rd16
x 3rd17
x 3rd18
x 3rd11
x 3rd15
x 3rd9
x 3rd14
x 3rd10
x rand3
x rand9
x rand7
x rand8
x rand4
x 3rd27
0.005
1.0
10
15
20
25
30
35
Figure
23:5 Standard
leverage
plot
for samples
in phase
3
Index
55
40
45
50.5
The standard leverage plot shows how large an effect a standard has compared to all
other standards. The X-axis shows the number of standards and Y-axis shows the
corresponding leverage values. The graph shows the two samples rand5 (used for random
sample # 5) and rand10 are above the cutoff line. These two samples could be suspected
outlier. To confirm this it is required to further analyze the score vs. score plots. Score vs.
score plots for the available samples were checked. These plots for two different
principal components are shown in Figure 24 and 25 for PC1 vs. index and PC1 vs. PC2
respectively.
0.348
x rand5
x rand7
x rand9
x rand6
x rand2
x rand8
x rand3
x rand4
x rand10
0.30
0.25
Cluster 2
0.20
PC 1 (93.06%)
0.15
0.05
x 3rd4
0.00 x 3rd1
-0.05
-0.10
-0.15
x rand1
Cluster 1
0.10
x 3rd5x 3rd7
x 3rd8
x 3rd6
x 3rd10
x 3rd9
x 3rd12
x 3rd23
x 3rd11
x 3rd15
x 3rd14
x 3rd18 x 3rd21 x 3rd24
x 3rd16
x 3rd17
x 3rd19 x 3rd22
x 3rd27
x 3rd25
x 3rd20
x 3rd31
x 3rd29
x 3rd13
x 3rd34
x 3rd30
x 3rd28
x 3rd35
x 3rd26
x 3rd32
x 3rd33
x 3rd2
x 3rd3
-0.20
x 3rd36
-0.235
1.0
5
10
15
20
25
Index
30
35
40
Figure 24: Score vs. score plot for PC1 vs. index for samples in phase 3.
56
45
50.5
0.348
0.30
x rand6
x rand5
x rand7
x rand9
x rand2
x rand8
0.25
x rand4
x rand10
0.20
PC 1 (93.06%)
0.15
x rand3
Cluster 2
x rand1
0.10
0.05
Cluster 1
0.00
-0.05
x 3rd3
x 3rd17 x 3rd16
x 3rd27
x 3rd25
x 3rd29
x 3rd13
x 3rd34
-0.10
-0.15
x 3rd28
3rd26
x x3rd33
x 3rd4
x 3rd7
x 3rd30
x 3rd5 x 3rd8
x 3rd1
x 3rd12
x 3rd23
x 3rd11
x 3rd15
x
3rd14
x
3rd21
x 3rd18
x 3rd24
x 3rd19
x 3rd22
x 3rd31
x 3rd20
x 3rd2
x 3rd6
x 3rd9 x 3rd10
x 3rd35
x 3rd32
-0.20 x 3rd36
-0.235
-0.327
-0.25
-0.20
-0.15
-0.10
-0.05
-0.00
PC 2 (3.72%)
0.05
0.10
0.15
0.20
0.25
0.287
Figure 25: Score vs. score plot for PC1 vs. PC2.
From the above Score vs. score plots in Figure 24 & 25 it was concluded that
rand5 and rand10, which were outliers in standard leverage plot, are actually a part of a
different cluster. So the two samples were not outliers.
After checking for the possible outliers and removing them the model was
constructed with calibration set and the 10 random samples were predicted from the
model. The results were not good as the M-distance values and the residual ratio values
were not in the range as per the rule. These results were kept for reference in the
Appendix B. The 10 validation set samples were added in the calibration set and ten
samples from the original calibration set were taken out. For the prediction purpose 10
samples were taken out from the calibration set at the same time. The new calibration and
validation sets are shown in Table 31 and 32 respectively.
57
Sample Recycled SW HW
DB
WSR
(#)
(%)
(%) (%) (lbs/ton) (lbs/ton)
3rd 1,3
3rd 4,5
10
27
63
10
27
3rd 8,9
10
27
rd
3 10,12
10
27
3rd 13,14
10
27
63
40
40
63
40
63
40
63
40
Sample Recycled SW HW
DB
WSR
(#)
(%)
(%) (%) (lbs/ton) (lbs/ton)
10
27
63
10
3rd 2
3rd 6
10
27
63
40
40
10
15
3rd 7
10
27
63
40
15
20
0
rd
20
3 11
10
27
63
40
25
3rd 15
10
27
63
40
25
30
3rd 16
10
27
63
40
30
35
3rd 20
10
27
63
40
35
10
27
63
40
40
10
27
63
40
45
63
40
50
3rd 17,18
10
27
63
40
3rd 19,21
10
27
63
40
40
3rd 24
45
3rd 26
3rd 30
3rd 22,23
10
27
63
40
3rd 25,27
10
27
63
40
63
40
50
55
3rd 28,29
10
27
3rd 31-33
10
27
63
40
3rd 34-36
10
27
63
40
60
10
10
10
10
10
10
10
10
10
27
27
27
27
27
27
27
27
27
63
63
63
63
63
63
63
63
63
5
12
18
24
28
32
38
42
48
10
27
63
40
40
40
40
40
40
40
40
40
40
Rand1
Rand2
Rand3
Rand4
Rand5
Rand6
Rand7
Rand8
Rand9
Rand10
0
10
27
Table 32: New validation set for phase 3
52
Table 31: New calibration set for phase 3
Again the procedure of calibration followed by validation was done for the model.
The samples taken out from the original calibration set were predicted using this model.
The results are shown in Table 33 and 34 for tensile index and softness respectively.
58
Actual Predicted Total MTensile Tensile Distance Residual
Ratio
Standard Index
Index
Ratio
3rd2
0.026
0.035
0.549
1.030
3rd6
0.029
0.037
0.273
0.969
3rd7
0.036
0.036
0.345
1.480
3rd11
0.040
0.035
0.439
2.210
3rd15
0.056
0.040
0.401
0.747
3rd16
0.050
0.043
0.181
0.980
3rd20
0.051
0.044
0.211
0.679
3rd24
0.055
0.046
0.424
0.889
3rd26
0.046
0.058
0.759
0.027
3rd30
0.049
0.056
0.646
0.968
RMSEP
Total MActual Predicted Distance Residual
Ratio
Standard softness softness
ratio
3rd2
0.0081
0.0064
0.714
1.030
3rd6
0.0071
0.0062
0.360
0.969
3rd7
3rd11
3rd15
3rd16
3rd20
3rd24
3rd26
3rd30
0.009
Table 33: Prediction results for phase 3
tensile index using PCR
0.0056
0.0054
0.0052
0.0056
0.0054
0.0043
0.0585
0.0056
0.0061
0.0064
0.006
0.0057
0.0058
0.0056
0.0046
0.0047
RMSEP
0.0171
0.460
0.567
0.515
0.191
0.189
0.522
0.798
0.706
1.480
2.210
0.747
0.980
0.679
0.889
0.027
0.968
Table 34: Prediction results for phase 3
softness using PCR
Tables 31 and 32 shows that the prediction results are reliable this time. The
values obtained are close to the original values and the M-distance and the residual ratio
are in the required range as per the rules. Also RMSEP is 1.71 % and 0.9 %, which is
low. So the model can be accepted as valid.
To check the model validity a different model was generated using the PLS
algorithm. These results are shown in Table 35 and 36 for tensile index and softness
respectively.
59
Standard
3rd2
3rd6
3rd7
3rd11
3rd15
3rd16
3rd20
3rd24
3rd26
3rd30
Total MActual Predicted Distance Residual
Ratio
Standard softness softness
ratio
3rd2
0.0081
0.0060
0.157
1.070
3rd6
0.0071
0.0061
0.280
0.651
Actual Predicted Total MTensile Tensile Distance Residual
Ratio
Index
Index
Ratio
0.026
0.043
0.045
0.783
0.029
0.042
0.007
0.750
0.034
0.042
0.0008
0.656
0.040
0.043
0.041
1.360
0.056
0.043
0.048
1.190
0.051
0.045
0.154
0.288
0.052
0.045
0.270
0.506
0.055
0.044
0.122
0.836
0.046
0.048
0.693
0.452
0.049
0.047
0.504
0.310
RMSEP
3rd7
3rd11
3rd15
3rd16
3rd20
3rd24
3rd26
3rd30
0.0097
0.0056
0.0054
0.0052
0.0056
0.0054
0.0043
0.0058
0.0056
0.0059
0.0064
0.0062
0.0056
0.0058
0.0059
0.0048
0.0049
RMSEP
0.017
0.056
0.582
0.378
0.077
0.161
0.147
1.040
0.734
1.210
0.728
0.877
0.541
0.352
0.792
1.200
0.745
Table 35: Results for tensile index for phase Table 36: Results of softness for phase 3
3 using PLS
using PLS.
The above two tables show that the results are good and meet all the validity
requirements so they can be trusted. Increasing the WSR in the furnish should increase
the tensile strength while decrease the softness. Tables 35 and 36 show that the actual
value of tensile index is increasing but with a few exceptions also the value of softness is
decreasing but not consistently. The reason for this has already been mentioned in phase
one and two previously.
4.3.1 Conclusions for phase three: Effect of varying the amount of wet
strength resin
After completing phase three it was concluded that NIR can predict the softness
and tensile properties of tissue paper made with different amount of wet strength resin.
The predicted values of the two properties were approximately close to the real values
with RMSEP values of 0.97 % and 1.70 % for tensile index and softness respectively.
The two parameters of importance, the M-distance ratio and the residual ratio that are
used to trust the prediction results were as per rule.
60
4.4
Phase Three Repeat:
After completing the three phases it was decided to do one experiment for the
repeatability of the data. To do this it was decided to repeat phase three. The whole phase
three was done again to see whether there is a change in the results and to what extent the
result would change. All the same conditions were maintained as were used in original
phase three and the same numbers of new handsheets were made for the calibration set
samples. It was decided to use the same random samples that were used for original phase
three. First the random samples were used for prediction but the prediction results were
not good as the M-distance values and the residual ratio values were not in the required
range. These results are shown in the Appendix B. The new calibration and validation
sets are shown in Table 37 and 38 below.
Sample Recycled SW
Rep 1,3
10
27
Rep 4,6
10
27
Rep 7,9
10
27
Rep 10,11
10
27
Rep 13,14
10
27
Rep 16,18
10
27
Rep 19,21
10
27
Rep 22,23
10
27
Rep 25,26
10
27
Rep 29,30
10
27
Rep 31-33
10
27
Rep 34-36
10
27
Rand1
10
27
Rand2
10
27
Rand3
10
27
Rand4
10
27
Rand5
10
27
Rand6
10
27
Rand7
10
27
Rand8
10
27
Rand9
10
27
Rand10
10
27
DB
WSR
HW (lbs/ton) (lbs/ton)
63
40
0
40
63
10
40
63
15
40
63
20
40
63
25
40
63
30
40
63
35
40
63
40
40
63
45
40
63
50
40
63
55
40
63
60
63
40
5
40
63
12
40
63
18
40
63
24
40
63
28
40
63
32
40
63
38
40
63
42
40
63
48
40
63
52
Sample Recycled
Rep2
10
Rep5
10
Rep8
10
Rep12
10
Rep15
10
Rep17
10
Rep20
10
Rep24
10
Rep27
10
Rep28
10
DB
WSR
HW (lbs/ton) (lbs/ton)
63
40
5
40
63
12
40
63
18
40
63
24
40
63
28
40
63
32
40
63
38
40
63
42
40
63
48
40
27 63
52
SW
27
27
27
27
27
27
27
27
27
Table 38: Validation set for phase 3 repeat
model
Table 37: Calibration set for phase 3 repeat model
61
The model was calibrated and validated using the full cross validation. The
samples shown in Table 36 were predicted. The results are shown in Table 39 and 40
below. It can be observed that the M-distance and the Residual ratio values were in the
range as per rule. (M-distance less than 1 and residual ratio less than 3)
Actual Predicted Total MTensile Tensile Distance Residual
Ratio
Standard Index
Index
Ratio
Rep2
0.026
0.029
0.514
0.981
Rep5
0.028
0.03
0.851
1.86
Rep8
0.028
0.035
0.151
1.32
Rep12 0.034
0.035
0.18
0.924
Rep15 0.033
0.033
0.31
1.25
Rep17 0.033
0.043
0.338
1.23
Rep20
0.04
0.039
0.224
1.22
Rep24 0.037
0.038
0.407
1.66
Rep27 0.036
0.036
0.247
1
Rep28 0.052
0.036
0.346
1.22
RMSEP
Total MActual Predicted Distance Residual
Ratio
Standard softness softness
ratio
Rep2
0.0104
0.0101
0.514
0.989
Rep5
Rep8
Rep12
Rep15
Rep17
Rep20
Rep24
Rep27
Rep28
0.0068
0.0093
0.0107
0.859
1.86
0.0088
0.0075
0.151
1.32
0.0066
0.0079
0.18
0.924
0.0061
0.0082
0.31
1.25
0.0057
0.0056
0.338
1.23
0.0057
0.0062
0.224
1.22
0.0055
0.0055
0.407
1.66
0.005
0.0049
0.247
1
0.0049
0.0051
0.346
1.22
RMSEP
0.001
Table 39: Prediction of tensile index values Table 40: Prediction of softness for repeat
for repeat phase 3
phase 3
Finally it was decided to analyze the two results obtained from the original phase
three and the repeated phase three. From the two experiments, the values obtained for
softness and tensile index were different. The handsheets were made under the same
conditions in both the phases. The results of softness and tensile index for phase 3 and
repeat phase 3 were plotted. The results are shown in the following graphs in Figure 26
and 27.
62
Repeat phase 3 tensile
index values
Phase3 tensile index comparison for repeatability
test
0.060
y = 0.8061x
R2 = 0.6905
0.050
0.040
0.030
0.020
0.010
0.000
0.000
0.010
0.020
0.030
0.040
0.050
0.060
Original phase 3 tensile index values
Figure 26: Tensile index comparison of the two phases.
Phase three softness comparison for
repeatability test
Repeat phase 3
0.0120
0.0100
0.0080
y = 1.1862x
R2 = 0.5938
0.0060
0.0040
0.0020
0.0000
0.0000
0.0020
0.0040
0.0060
0.0080
0.0100
Original phase 3
Figure 27: Softness comparison of the two phases.
From the two graphs it is evident that the two results are not the same even when
the sheets were made under the same conditions. The R2 values of 0.69 and 0.59 from the
graphs are not a good correlation. This is expected due to the fact that there are many
conditions that can not be achieved in lab handsheets making of tissue paper. Some of
these factors are the amount of wet strength resin and debonder added. The average
quantity of these chemicals when added in the mills ranges from 20 lbs/T to 30 lbs/T of
paper. The amounts used in this study were from 0 lbs/T to 60lbs/T for WSR and 20 lbs/T
63
to 70 lbs/T debonder. The reason for this higher dosage of chemicals was due to the fact
that these chemicals were highly viscous in nature. And for the lab purpose the amounts
of these chemicals required were unexpectedly low (approximately 0.1-0.2 g for one
whole phase). Although proper measurement techniques were used, some of the
measurement errors were still expected.
The prediction results for the two phases were also analyzed to see the similarity
of results. The following Table 41 shows the comparison of two prediction results. The
descriptions of these results were already mentioned in the respective phases.
Phase 3 predictions
Standard
Rep2
Rep5
Rep8
Rep12
Rep15
Rep17
Rep20
Rep24
Rep27
Rep28
Phase 3 repeat predictions
Actual
Tensile
Index
0.026
0.029
0.034
0.04
0.056
0.051
0.052
0.055
0.046
Predicted
Tensile
Index
0.036
0.037
0.037
0.036
0.041
0.044
0.044
0.046
0.058
Actual
softness
0.0081
0.0071
0.0057
0.0054
0.0053
0.0056
0.0055
0.0043
0.0585
Predicted
softness
0.0064
0.0062
0.0062
0.0065
0.0061
0.0058
0.0058
0.0056
0.0046
Actual
Tensile
Index
0.026
0.022
0.028
0.034
0.033
0.034
0.041
0.038
0.037
Predicted
Tensile
Index
0.029
0.03
0.035
0.035
0.033
0.043
0.04
0.039
0.036
Actual
softness
0.0104
0.0093
0.0089
0.0066
0.0061
0.0057
0.0057
0.0055
0.005
Predicted
softness
0.0101
0.0108
0.0076
0.008
0.0083
0.0057
0.0063
0.0056
0.005
0.049
0.057
0.0057
0.0047
0.052
0.037
0.0049
0.0051
Table 41: Comparison of prediction results for the phase 3 and repeat phase 3
The two results show that the predictions are good for repeat phase three. The values of
the tensile index for original phase three are slightly higher than the repeat phase. The
reason for this variability in data was some of the experimental constraints in making
handsheets of the tissue paper in the lab that have already been discussed.
64
4.4.1 Conclusions for phase 3 repeat: Effect of change in wet strength
resin concentration
After completing phase three repeat it was found that the predicted values of
tensile index and softness for the repeat phase are good with RMSEP values of 0.68 %
and 0.1 % as shown in the Table 37 and 38. It was concluded from phase 3 that it is
possible that NIR spectroscopy combined with multivariate calibration can detect the
change in addition of wet strength resin in the furnish and can predict the softness and
tensile properties of tissue paper.
4.5
Phase four: Effect of change in CSF
Phase four of this thesis work was based on change in refining levels. In this
phase the level of refining was changed keeping the other three factors of furnish ratio,
amount of debonder and amount of wet strength resin constant (furnish ratio was 10%
recycled, 27% SW and 63% HW, amount of debonder 40 lbs/T, amount of WSR 20
lbs/T). Four different levels of refining at 450 CSF, 375 CSF, 300 CSF and 225 CSF
were generated. Ten handsheets were made for each of these levels. The amount of
debonder was kept at 40 lb/ton and the amount of wet strength resin was kept at 20 lb/ton.
The calibration and validation sets are shown in Table 42 and 43 below.
CSF level
450
375
300
Sample
nw1-nw10
nw11-nw20
nw21-nw30
225
nw31-nw40
Recycle
(%)
10
10
10
SW (%)
27
27
27
HW (%)
63
63
63
10
27
63
Table 42: Calibration set for phase 4
65
Debonder
(lbs/T)
WSR
(lbs/T)
40
40
40
20
20
20
40
20
The notation nw1 to nw40 refer to the sample numbers of the handsheets for phase four
calibration set made under different conditions.
CSF level
Sample
550
avefor1-avefor2
400
avefor3-avefor4
350
avefor5-avefor6
250
Recycle
(%)
avefor7-avefor8
10
10
10
SW (%)
27
27
27
HW (%)
63
63
63
10
27
63
Debonder
(lbs/T)
WSR
(lbs/T)
40
40
40
20
20
20
40
20
Table 43: Validation set for phase 4
Where avefor1 to avefor8 refer to the average spectra of the phase four validation set
samples. For each sheet two spectra were collected at two different points. Average of
those two spectra was taken and this way there were 40 different spectra for the
calibration set. After taking the spectra, PCA was done on the samples to see their spatial
distribution and to remove any outliers. The plots were analyzed as was done in other
previous phases. The first plot to analyze was the standard leverage plot as shown in
Figure 28.
Two possible outliers
0.225
x nw10
x nw7
0.20
0.18
x nw6
x nw1
x nw2
0.16
x nw5
x nw37
Leverage
0.14
0.12
0.10
x nw28
x nw17x nw19
x nw8
x nw34
x nw15
x nw4
x nw26
x nw27
x nw16
0.08
x nw9
x nw3
x nw11
x nw13
x nw18
0.06
x nw39
x nw33
x nw22
x nw23
x nw21
x nw14
x nw12
0.04
x nw40
x nw29
x nw20
x nw25
x nw32
x nw36x nw38
x nw35
x nw30
x nw31
x nw24
0.014
1.0
5
10
15
20
25
30
Index
Figure 28: Standard leverage plot for the samples in phase 4.
66
35
40
43.9
It was noticed that the samples nw7 (refers to the sample number 7 of the calibration set)
and nw10 are above the cutoff line. As mentioned earlier the leverage is a measure of
how large an effect a standard has compared to other standards. The samples that have
extreme leverages i.e. samples above the cutoff line indicate samples that are different
from the others and thus should be considered possible outliers. All the other samples
except nw7 and nw10 were below the cutoff line. Next the score vs. score plot shown in
Figure 29 was analyzed
0.41
x nw37
x nw28
0.3
x nw26
x nw38
0.2
x nw15x nw17
x nw22
PC 1 (37.37%)
x nw32
x nw31
x nw24
0.1
x nw1
x nw14
x nw6
x nw5
0.0
x nw3
x nw4
-0.1
x nw13
x nw11
x nw12
x nw8
x nw7
x nw20
x nw16
x nw18 x nw21
x nw33
x nw36
x nw39
x nw30
x nw23
x nw9
-0.2
x nw35
x nw27
x nw25
x nw29
x nw34
30
35
x nw40
x nw19
-0.3
x nw2
-0.4
x nw10
-0.45
1.0
5
10
15
20
25
40
43.9
Index
Figure 29: Score vs. score plot for samples in phase 4
It was found that nw10 is far away from the cluster but nw7 appears to be accepted. It is
apparent that two more samples nw1 and nw2 are slightly outside the cluster and are
possible outliers. To confirm this PC1 vs. PC2 plot was analyzed as shown in Figure 30.
67
0.41
x nw37
x nw28
0.3
x nw26
x nw38
0.2
x nw32
x nw31
PC 1 (37.37%)
0.1
x nw1
x nw14
0.0 x nw6
x nw7
x nw24
x nw35
x nw25
x nw16 x nw33
x nw13
x nw12
x nw11
x nw22
x nw27
x nw39
x nw36
x nw20
x nw5
x nw3
x nw8 x nw4
-0.1
x nw15
x nw17
xxnw18
nw21x nw30
x nw23
x nw40
x nw34
x nw29
x nw9
-0.2
x nw19
-0.3
x nw2
-0.4
x nw10
-0.45
-0.33
-0.3
-0.2
-0.1
-0.0
PC 2 (28.16%)
0.1
0.2
0.32
Figure 30: Score vs. score plot PC1 vs. PC2.
This time it was found that nw10 and nw2 are outside the cluster. To reconfirm this PC1
vs. PC3 plot was analyzed and it was found again that nw2 and nw10 are the definite
outliers. These two samples were removed from the calibration set. The calibration and
validation was done for the samples in Table 41 and 42. the results were not reliable due
the fact that M-distance value and the residual ratio values were not in the range as per
rule. These results are shown in the Appendix B. The calibration set was again changed
as was done for the previous phases but this time instead of using the random samples in
the calibration set the model was made using the calibration samples but taking 10
samples out of the set. These 10 samples were used for the prediction purpose.
The results of this prediction are shown in the Table 44 for tensile index and Table 45 for
softness respectively.
68
Actual Predicted Total MTensile Tensile Distance Residual
Ratio
Standard Index
Index
Ratio
nw5
nw6
nw15
nw16
nw25
nw26
nw35
nw36
0.037
0.034
0.036
0.033
0.045
0.044
0.041
0.039
0.035
0.035
0.039
0.036
0.041
0.044
0.042
0.041
RMSEP
0.0026
0.655
0.703
0.400
0.116
0.564
0.872
0.226
0.499
1.730
1.800
1.500
1.490
1.110
1.410
1.230
1.330
Table 44: Prediction results for phase 4
for tensile index
Total MActual Predicted Distance Residual
Ratio
Standard softness softness
ratio
nw5
0.0080
0.0076
0.655
1.730
nw6
0.0062
0.0075
0.703
1.800
nw15
0.0084
0.0082
0.400
1.500
nw16
0.0088
0.0076
0.116
1.490
nw25
0.0084
0.0084
0.564
1.110
nw26
0.0085
0.0089
0.872
1.410
nw35
0.0082
0.0087
0.226
1.230
nw36
0.0081
0.0085
0.499
1.330
RMSEP
0.0007
Table 45: Prediction results for phase 4 for
softness
The results were good as the M-distance values are less than 1 and the residual ratio
values are less than 3 for tensile index as well as the softness. The root mean square error
of prediction (RMSEP) was low (0.26 % for tensile and 0.07 % for softness). The
prediction results can be trusted based on these values.
From the tables 44 and 45 it can be seen that the actual values of tensile index and
the softness did not follow the usual trend. By decreasing the CSF the value of tensile
index should go up as well as the softness should go down. The tensile index values are
increasing but almost getting constant. The softness values are not decreasing at all. This
is due the reason that actual tissue paper machine conditions can not be maintained in lab.
4.5.1 Conclusions for phase four: Effect of change in CSF
After completing phase four it was concluded that it is possible to predict the
tensile and softness properties of tissue paper made with the same furnish but different
refining level. Predicted values were good and close to the actual values. The properties
69
changes with change in CSF values and so was the change in sheet spectra. This shows
that NIR was able to detect the CSF variation in the furnish. The prediction results are
good based upon the M-distance ratio and residual ratio rule and the low values of
RMSMP obtained for tensile 0.26 % and softness 0.07%.
70
5.0
Final Conclusions and recommendations:
The following are the final conclusions of this thesis work:
1> Tissue paper properties were changed by varying the different components of
furnish in each phase. The results obtained in different phases show that NIR
combined with chemometric was able to predict the tensile and softness properties
of lab made paper by correlating the spectra with the measured properties.
2> It is expected that the actual value of tensile strength and softness should vary
with change in different components. Values obtained in this study did not follow
the trend of increase or decrease in a consistent manner. The probable reason was
that it is not possible to provide all the commercial manufacturing conditions in
the lab. For example tissue creping is not possible in the lab. Also the
concentration of the additives required for this lab work was extremely small and
was difficult to measure accurately. The highly viscous nature of these chemicals
makes measurement error more likely than one would expect. The overall
objective of this study was to see if NIR spectroscopy combined with
chemometric methods could be used to predict the tensile and softness of tissue
paper. It was concluded that the predictions can be made provided the above
mentioned constraints are carefully controlled.
3> As the different components in the furnish changed there was a change in spectra.
Therefore from the work it is clear that NIR can detect changes in the furnish ratio
(SW: HW: Recycled fiber), and in the amount of debonder and wet strength resin
added as well as a change in refining as indicated by CSF values.
71
4> The results of prediction for unknown samples were not consistently good. In
each phase when the random samples were added to the calibration set and some
of the calibration set samples were predicted from the model, the results were
acceptable. One possible reason for these results was that for making the
calibration set samples three handsheets were made for each condition with a total
of 9 spectra. But for making validation set only one handsheet was made with
three spectra per handsheet for each condition. Samples generated for validation
set were at different conditions than the calibration set. More handsheets in the
calibration set gave more data variability to the samples. This was a possible
reason that random samples predictions were not good. Additional studies of this
effect are warranted.
5> Predicted values of properties differ too much from the original values in some
cases. The results are acceptable only based upon the two parameters the Mdistance ratio and the residual ratio. This study suggested that it could be possible
to construct models that can be useful on a mill scale. Obviously more work must
be done under the actual mill conditions. Based upon actual mill conditions, one
could incorporate machine/mill variables not used in the lab study, for example
machine speed, Yankee speed, fan pump speed and many other process variables.
6> Applying the NIR technique combined with chemometric in the mill could yield a
very rapid indication of paper properties compared to conventional measurement
techniques. It is recommended that more work needs to be done to provide data
that would indicate validity on a mill scale basis. Applying these techniques
72
would improve the paper quality and mill performance which should enhance
profitability and quality.
6.0
REFERENCES
1. G. Chantre, J. M. Thieule, J. C. Rodrigues, A. Guillemain “ Application of Near
Infrared Spectroscopy to the prediction and the analysis of the variability of the
wood papermaking potential within the maritime pine, pulping yield, chemical
wood components and the paper properties “ Tappi Pulping Conference 2001.
2. Dwight B. Easty and Sally A. Berben, Frank A. DeThomas and Paul J. Brimmer
“Near Infrared Spectroscopy for the analysis of the wood pulp: quantifying
hardwood-softwood mixtures and estimating lignin content” Tappi J. Vol. 73 Oct
1990.
3. Michael Kester, Thanh Trung, Denya Leclere and Jim Carver “Online
determination of major ionic species in the kraft liquors by short-wavelength Near
Infrared Spectroscopy” Tappi pulping conference. 2001.
4. Bob Meglen “ Measurement of wood chemistry by NIR spectrometry” National
Renewable Energy Laboratory. Golden Colorado Undated.
5. Birkett M. D., Gambino M. J. T. “ Estimation of pulp and kappa number with
NIR”. Tappi J. p 193-197 Sep. 1989.
6. Schultz, T. P., Burns D. A. “Rapid secondary analysis of Lignocellulose:
comparison of NIR and FTIR” Tappi J. P 209-212 May 1990.
7. J. A. Wright, Birkett and Gambino. “ Prediction of pulp yield and cellulose from
wood samples using Near Infrared reflectance spectroscopy” Tappi J. Aug 1990.
73
8. Lars Wallbacks, Ulf Edlund and Torbjorn Lindgren “ Multivariate characteization
of Pulp” Nordic Pulp and Paper Reaserach Journal no. 2. 1995.
9. Anthony J. Michell “ Vibrational Spectroscopy – A rapid means of estimating
plantation pulpwood quality.” Appita Vol 47. Jan 1994.
10. Ann Marklund, Jon B. Hauksson and Michael Sjostrom “Multivariate data
analysis based on orthogonal signal correction and Near Infrared Spectroscopy”
Nordic Pulp and Paper Reaserach Journal Vol 14 no. 2. 1999.
11. Mikael Karlsson And Charlotte Wancke Stahl “ Hallsta Install NIR technology to
optimize TMP quality” Paper Technology Feb 2001.
12. Lavalle-Castellanos. “Evaluation of the use of Infrared Spectroscopy and
Multivariate Analysis as the tools for pulp characterization.” Thesis. Miami
University. 2001.
13. Richard E Mark, “Handbook of physical and mechanical testing of paper and
paperboard” Vol. 1. Second edition. pp 661-695. 2001.
14. Richard E Mark, “Handbook of physical and mechanical testing of paper and
paperboard” Vol. 1. First edition. pp 497-521. 1983.
15. Jin Liu and Jeffery Hsieh “ A novel method of understanding the softness of
tissue paper” Tappi Proceedings pp 77-88 March 1-4 Atlanta, Georgia 1999.
16. Jong J. Kim, Itzhak Shalev, and Roger L. Barker “ Softness properties of paper
towels” Tappi J. Vol 77, No. 10 Oct 1994.
17. H. Sarimveis, T. Retsina “Tissue Softness Prediction using neural network
methodologies” Pulp & Paper p. 42-45 Canada May 2001.
74
18. Siesler, Ozaki, Kawata, Heise. “Near Infrared Spectroscopy Principles,
Instruments, Applications.” Weinheim: Wiley-VCH, 2002
19. Burns, Ciurczak. “Handbook of Near-Infrared Analysis.” New York: Marcel
Dekker, 2nd ed., rev. and expanded. c2001.
20. Antti Henrik. “ Multivariate characterization of Wood related materials” PhD.
Thesis. Umea University. Umea. Sweden. 1999.
21. PLS theory Algorithm From www.galactic.com.
22. Holger Hollmark “ Evaluation of tissue paper softness” Tappi J. Vol. 66 p 97-99
February 1983.
23. J. B. Hauksson, G. Bergqvist, U. Bergsten, M. Sjostrom, U. Edlund “ Prediction
of basic wood properties for Norway spruce. Interpretation of Near Infrared
Spectroscopy data using partial least square regression” Wood Science and
Technology 35. 2001.
24. Ralf J. O. Olsson, Per Tomani, Mikael Karlsson, Tomas Joseffson, Kjell Sjoberg
and Christer Bjorklund “Multivariate characterization of the chemical and
physical descriptors in pulp using NIR.” Tappi J. Oct 1995.
25. Nelson L. Sefara, Denise Conradie and Philip Turner “ Progress in the use Near
Infrared spectroscopy as tool for the rapid determination of the pulp yield in the
plantation eucalyptus” TAPPSA J. pp 15-17 Nov 2000.
26. Clark P. Woitkovich et al. “ NIR based yield estimation in the semichemical
pulping mill experiences” Tappi Pulping Conference 1997.
27. Spiro S. Grivas “ Impact of recycled fiber on sheet forming” Tappi J. April 1992.
75
28. Schimleck et al. “ Application of NIR spectroscopy to forest research” Appita
J.vol 53 no. 5 Sep 2000.
29. W. E. Scott. “Properties Of Paper: An Introduction.” Tappi Press Publication
1989.
30. Harald Martens, Tormod Naes. “ Multivariate Calibration.” 1991.
31. PCR theory Algorithm From www.galactic.com.
32. Paper making science and technology, “Paper and Board Grades” Book 18.
Published in cooperation with Finnish paper engineers association and Tappi. pp
80-85. 2000.
76
7.0
APPENDIX A
STANDARD LAB MEASUREMENT METHODS OF
TENSILE STRENGTH ANF SOFTNESS
77
7.1
APPPENDIX B
Prediction results of phase 2 random samples:
Calibration was done using 32 samples after excluding the sample identified as
sec27 and model was validated using full cross validation. After validation the model was
used to predict the values of random samples. The predicted values are shown in Table
46.
Tensile index
Softness
Sample
valsa1
valsa2
valsa3
valsa4
valsa5
valsa6
valsa7
valsa8
valsa9
Real Predicted
Value
Value
0.032
0.047
0.043
0.050
0.037
0.046
0.036
0.049
0.030
0.049
0.030
0.045
0.034
0.049
0.032
0.042
0.032
0.043
MReal Predicted Residual distance
Value
Value
Ratio
ratio
0.0073
0.0029
4.51
2.04
0.0066
0.0040
3.30
2.11
0.0121
0.0048
3.04
1.20
0.0062
0.0033
3.36
1.68
0.0071
0.0038
2.54
1.82
0.0063
0.0043
2.74
1.18
0.0056
0.0038
2.69
1.49
0.0055
0.0043
3.51
1.09
0.0055
0.0045
2.90
0.76
valsa10
0.033
0.0056
0.042
0.0047
3.29
0.75
Table 46: Prediction results for phase 2 random samples using PCR.
From the prediction results it is clear that the residual ratio and M-distance ratio
are not as per the rule (M-distance is less than 1 and residual ratio less than 3). Based on
their values some additional and overlapping features are present in the model also some
modeled features are more intense than in the calibration set. To avoid this the calibration
set was changed. The 10 random samples were included in the calibration and one
original sample was taken out of the calibration set.
78
Prediction results for phase 3 random samples:
After analyzing the plots the prediction of the random samples from the model
was done. The prediction reports are shown in Table 47 for tensile index and softness.
Actual
Tensile
Index
0.037
Predicted
Tensile
Index
0.015
Total MDistance
Ratio
1.9
Residual
Ratio
0.036
0.003
10.5
Rand3
Rand4
Rand5
0.039
0.047
0.046
0.0008
0.002
0.005
4.62
6.07
8.13
Rand6
Rand7
Rand8
Rand9
Rand10
0.044
0.039
0.035
0.018
0.005
0.002
0.002
0
0.033
0.002
Standard
Rand1
Rand2
Residual
Ratio
0.0063
Total MDistance
ratio
3.68
0.0078
11
47.6
0.0058
0.0055
0.0043
0.0071
0.0068
0.0077
7.36
9.57
9.97
25.8
33.1
49.3
45.5
36.3
28.8
36.1
0.0045
0.0041
0.0057
0.0073
0.0077
0.0076
0.0075
0.0074
13
9.99
7.8
9.96
45.5
36.3
28.8
36.1
34.6
0.0054
0.006
9.93
34.6
Actual
softness
Predicted
softness
8.1
0.0066
47.6
0.0056
25.8
33.1
49.3
11.5
8.8
7.36
8.75
3.39
8.1
Table 47: Prediction results for phase 2 random samples using PCR for tensile index and
softness
The two tables show that the M-distance values are more than 1 in each case and the
residual ratio are much more than 3, clearly there are many additional overlapping
features present in the spectra. The results can not be trusted based on this data.
79
Prediction results for phase 3 repeat random samples:
The sheets were made under the same conditions as in original phase 3. The NIR
spectra were taken on all the standards. PCA was done on all the samples and the loading
plot and score vs. score plots were analyzed for the possible outliers. After carefully
analyzing the plots and removing the possible outliers, all the properties data was fed in
the software to make the model. The model was calibrated and validated in the same way
as earlier and was used for the prediction of the random samples. The prediction was not
good as the M-distance ratio and the Residual Ratio are not in the range as per rules. The
values obtained are shown in the Table 48 below.
Standard
Rand1
Rand2
Rand3
Rand4
Rand5
Rand6
Rand7
Rand8
Rand9
Rand10
Actual
Tensile
Index
0.037
Predicted
Tensile
Index
0.003
Total MDistance
Ratio
1.6
Residual
Ratio
3.62
Actual
softness
0.0066
Predicted
softness
0.0052
Total MDistance
ratio
1.6
Residual
Ratio
3.62
0.036
0.039
0.047
0.046
0.044
0.039
0.035
0.018
0.011
0.019
0.013
0.008
0.008
0.013
0.018
0.014
10.1
4.18
7.2
6.62
10.3
6.63
5.45
7.06
11
6.07
7.23
14.2
11.8
7.63
6.29
6.74
0.0056
0.0058
0.0055
0.0043
0.0045
0.0041
0.0057
0.0073
0.007
0.0071
0.0078
0.0091
0.0084
0.0079
0.0069
0.0072
10.1
4.18
7.2
6.62
10.3
6.63
5.45
7.06
11
6.07
7.23
14.2
11.8
7.63
6.29
6.74
0.033
0.011
6.56
8.1
0.0054
0.0087
6.56
8.1
Table 48: Tensile index and softness prediction for random for phase 3 repeat
80
Prediction results for phase 4 random samples:
Samples were checked for the properties and then the model was generated. After
making the model the random samples were used for the prediction.
The final prediction results for the random samples are shown in the following Table 49.
Standard
Avefor1
Avefor2
Actual
Tensile
Index
0.03
0.033
Predicted
Tensile
Index
0.047
0.045
Total MDistance
Ratio
0.889
0.736
Avefor3
Avefor4
Avefor5
0.037
0.032
0.042
0.048
0.044
0.046
Avefor6
Avefor7
0.053
0.039
Avefor8
0.042
Residual
Ratio
Actual
softness
Predicted
softness
4.81
7.03
0.0064
0.0065
0.0094
0.0092
Total MDistance
ratio
0.889
0.736
1.94
0.396
0.993
5.65
4.48
4.93
0.008
0.0077
0.0085
0.0097
0.0089
0.0093
1.94
0.396
0.993
5.65
4.48
4.93
0.041
0.039
0.806
1.54
6.04
6.59
0.0101
0.0081
0.0085
0.0082
0.806
1.54
6.04
6.59
0.04
1
4.99
0.0096
0.0084
1
4.99
Residual
Ratio
4.81
7.03
Table 49: Prediction results of tensile index and softness for phase 4 random samples
The results can not be trusted because of the high values of M-distance and the residual
ratios. The model was regenerated using the random samples as part of the calibration set
samples.
82
7.2
APPENDIX C
SUMMARY OF FINAL REGRESSION DATA FOR ALL THE FOUR PHASES
Final regression model for property tensile for phase one PCR model
Number of PCs used: 3
* -------------------------------------------------------------------- *
PC Correl. of Prop. Regression Std. error t-value Sig.
Number
vs. PC
Coefficient
of R.C.
Lev. %
* -------------------------------------------------------------------- *
1
0.3180
1.146
0.3944
2.91 0.64
2
-0.5842
-2.105
0.3944
-5.34 0.00
3
0.3876
1.397
0.3944
3.54 0.12
Intercept 0.0684
1.519
0.0640
23.75 0.00
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.4481
Multiple Correlation
= 0.7699
Mean Property Value
= 1.519
% Variance (R squared)
= 59.2692
Std Error of Estimate (SEE)
= 0.3944
F-value
= 16.49
Actual = 0.4799
Final regression model for property softness for phase one PCR model
Number of PCs used: 1
* -------------------------------------------------------------------- *
PC Correl. of Prop. Regression Std. error t-value Sig.
Number
vs. PC
Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
1
-0.5128
-0.007104
0.0020
-3.58 0.10
Intercept 0.0469
0.004003
0.0003
12.45 0.00
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.002179
Multiple Correlation
= 0.5128
Mean Property Value
= 0.004003
% Variance (R squared)
= 26.2966
Std Error of Estimate (SEE)
= 0.001982
F-value
= 12.84
83
Actual = 0.002148
Final regression model for property tensile for phase one PLS model
Number of LVs used: 2
* -------------------------------------------------------------------- *
LV Correl. of LV Regression Std. error t-value Sig.
Number with property Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
1
0.4455
1.605
0.3964
4.05 0.03
2
0.6148
2.215
0.3964
5.59 0.00
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.4283
Multiple Correlation
= 0.7592
Mean Property Value
= 1.519
% Variance (R squared)
= 57.6378
Std Error of Estimate (SEE)
= 0.3964
F-value
= 23.81
Actual = 0.4481
Final regression model for property softness for phase one PLS model
Number of LVs used: 1
* -------------------------------------------------------------------- *
LV Correl. of LV Regression Std. error t-value Sig.
Number with property Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
1
0.1228
0.2754
0.3711
0.74 46.27
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.3926
Multiple Correlation
= 0.1228
Mean Property Value
= 1.766
% Variance (R squared)
= 1.5075
Std Error of Estimate (SEE)
= 0.3711
F-value
= 0.551
84
Actual = 0.4014
Final regression model for property tensile Index for phase 2 using PCR
Number of PCs used: 4
* -------------------------------------------------------------------- *
PC Correl. of Prop. Regression Std. error t-value Sig.
Number
vs. PC
Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
1
0.2786
0.006302
0.0032
1.97 5.97
2
0.3698
0.008365
0.0032
2.61 1.47
4
-0.2950
-0.006673
0.0032
-2.08 4.71
5
-0.4215
-0.009534
0.0032
-2.98 0.62
Intercept 0.2574
0.03241
0.0006
56.38 0.00
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.003642
Multiple Correlation
= 0.6922
Mean Property Value
= 0.03241
% Variance (R squared)
= 47.9164
Std Error of Estimate (SEE)
= 0.003201
F-value
= 5.98
Actual = 0.003586
Final regression model for property softness for phase 2 using PCR
Number of PCs used: 1
* -------------------------------------------------------------------- *
PC Correl. of Prop. Regression Std. error t-value Sig.
Number
vs. PC
Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
2
-0.3881
-0.976
0.4304
-2.27 3.10
Intercept 0.1586
2.221
0.0773
28.73 0.00
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.4887
Multiple Correlation
= 0.3881
Mean Property Value
= 2.221
% Variance (R squared)
= 15.0623
Std Error of Estimate (SEE)
= 0.4304
F-value
= 5.143
85
Actual = 0.5309
Final regression model for property tensile Index for phase 2 using PLS
Number of LVs used: 1
* -------------------------------------------------------------------- *
LV Correl. of LV Regression Std. error t-value Sig.
Number with property Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
1
0.3417
0.01229
0.0060
2.06 4.80
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.005995
Multiple Correlation
= 0.3417
Mean Property Value
= 0.0339
% Variance (R squared)
= 11.6744
Std Error of Estimate (SEE)
= 0.005976
F-value
= 4.23
Actual = 0.006144
Final regression model for property softness for phase 2 using PLS
Number of LVs used: 22
* -------------------------------------------------------------------- *
LV Correl. of LV Regression Std. error t-value Sig.
Number with property Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
1
0.2268
0.001914
0.0000
1198317.03 0.00
22
0.0000
1.297e-008 0.0000
8.12 0.00
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 2.851e-009
Multiple Correlation
= 1.0000
Mean Property Value
= 0.006624
% Variance (R squared)
= 100.0000
Std Error of Estimate (SEE)
= 1.597e-009
F-value
= 1.269e+012
86
Final regression model for property tensile Index for phase 3 using PCR
Number of PCs used: 3
* -------------------------------------------------------------------- *
PC Correl. of Prop. Regression Std. error t-value Sig.
Number
vs. PC
Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
1
-0.6500
-0.0403
0.0052
-7.77 0.00
2
-0.5130
-0.03181
0.0052
-6.13 0.00
4
0.3006
0.01864
0.0052
3.59 0.11
Intercept 0.1183
0.04399
0.0009
50.89 0.00
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.005619
Multiple Correlation
= 0.8809
Mean Property Value
= 0.04399
% Variance (R squared)
= 77.6032
Std Error of Estimate (SEE)
= 0.005187
F-value
= 36.96
Actual = 0.005457
Final regression model for property softness for phase 3 using PCR
Number of PCs used: 3
* -------------------------------------------------------------------- *
PC Correl. of Prop. Regression Std. error t-value Sig.
Number
vs. PC
Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
1
0.4639
0.002444
0.0007
3.69 0.08
2
0.3706
0.001952
0.0007
2.95 0.59
3
-0.3776
-0.001989
0.0007
-3.01 0.51
Intercept 0.1826
0.005772
0.0001
52.33 0.00
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.0007273
Multiple Correlation
= 0.7037
Mean Property Value
= 0.005772
% Variance (R squared)
= 49.5132
Std Error of Estimate (SEE)
= 0.0006618
F-value
= 10.46
87
Actual = 0.0007169
Final regression model for property tensile Index for phase 3 using PLS
Number of LVs used: 1
* -------------------------------------------------------------------- *
LV Correl. of LV Regression Std. error t-value Sig.
Number with property Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
1
0.4843
0.02935
0.0091
3.23 0.28
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.00921
Multiple Correlation
= 0.4843
Mean Property Value
= 0.04235
% Variance (R squared)
= 23.4505
Std Error of Estimate (SEE)
= 0.009094
F-value
= 10.42
Actual = 0.00936
Final regression model for property softness for phase 3 using PLS
Number of LVs used: 2
* -------------------------------------------------------------------- *
LV Correl. of LV Regression Std. error t-value Sig.
Number with property Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
1
0.4437
0.002319
0.0007
3.11 0.39
2
0.3610
0.001887
0.0007
2.53 1.64
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.0007763
Multiple Correlation
= 0.5721
Mean Property Value
= 0.005669
% Variance (R squared)
= 32.7264
Std Error of Estimate (SEE)
= 0.0007463
F-value
= 8.027
88
Actual = 0.0008105
Final regression model for property tensile Index for repeat phase 3 using PCR
Number of PCs used : 1
* -------------------------------------------------------------------- *
PC Correl. of Prop. Regression Std. error t-value Sig.
Number
vs PC
Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
4
-0.5675
-0.0286
0.0072
-3.96 0.04
Intercept 0.1241
0.037
0.0012
30.30 0.00
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.007563
Multiple Correlation
= 0.5675
Mean Property Value
= 0.037
% Variance (R squared)
= 32.2085
Std Error of Estimate (SEE)
= 0.007223
F-value
= 15.68
Actual = 0.007528
Final regression model for property softness for repeat phase 3 using PCR
Number of PCs used: 4
* -------------------------------------------------------------------- *
PC Correl. of Prop. Regression Std. error t-value Sig.
Number
vs. PC
Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
2
0.3797
0.004585
0.0010
4.61 0.01
3
-0.3486
-0.004209
0.0010
-4.23 0.02
4
0.6821
0.008236
0.0010
8.28 0.00
5
0.2557
0.003087
0.0010
3.10 0.42
Intercept 0.0887
0.006337
0.0002
37.68 0.00
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.001096
Multiple Correlation
= 0.8924
Mean Property Value
= 0.006337
% Variance (R squared)
= 79.6340
Std Error of Estimate (SEE)
= 0.0009949
F-value
= 29.33
89
Actual = 0.001108
Final regression model for property tensile index for phase 4
The sum of the leverages = 2.0, cutoff point = 0.0526
Number of PCs used: 1
* -------------------------------------------------------------------- *
PC Correl. of Prop. Regression Std. error t-value Sig.
Number
vs. PC
Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
1
-0.7608
-0.02537
0.0036
-7.03 0.00
Intercept 0.1850
0.03804
0.0006
65.01 0.00
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.003738
Multiple Correlation
= 0.7608
Mean Property Value
= 0.03804
% Variance (R squared)
= 57.8874
Std Error of Estimate (SEE)
= 0.003607
F-value
= 49.49
Actual = 0.003806
Final regression model for property softness for phase 4
Number of PCs used: 1
* -------------------------------------------------------------------- *
PC Correl. of Prop. Regression Std. error t-value Sig.
Number
vs. PC
Coefficient
of R.C.
Lev.%
* -------------------------------------------------------------------- *
1
-0.7286
-0.004253
0.0007
-6.38 0.00
Intercept 0.2223
0.007998
0.0001
73.98 0.00
* -------------------------------------------------------------------- *
Std Error of Prediction: Estimate = 0.0006961
Multiple Correlation
= 0.7286
Mean Property Value
= 0.007998
% Variance (R squared)
= 53.0813
Std Error of Estimate (SEE)
= 0.0006664
F-value
= 40.73
90
Actual = 0.0007357