Imaging NIR spectroscopy
for investigation of wood and
applications on wood materials
Sven-Olof Lundqvist
Thomas Grahn
Lars Wallbäcks
MeMoWood, Nancy, October 4, 2013
Outline of presentation
 Technique
– Measurements with NIR spectroscopy
– Single and multiple spot NIR measurement
– Hyperspectral NIR imaging
 Examples from measurements on wood:
o Radial variations from pith to bark, high spatial resolution
o Wood discs, medium spacial resolution
o Identification of compression wood
o Classification of wood of different tree species
o Composition of paper
Illustrations from the projects Bio4Energy, Trees4Future, a Swedish - South
African collaboration on eucalypts, and various pre-studies at Innventia
 Summary and our next steps
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NIR spectroscopy
- an indirect measurement techique with many applications

Radiation of different wavelengths in the near infrared (NIR) range interacts
with different chemical bonds.
Therefore, NIR absorption spectra may provide information about the
chemical composition of materials and related features

The method is indirect and models are needed to estimate the contents of
chemical compounds or other properties from the NIR spectra

The models are developed using multivariate statistical methods applied
on sets of spectra and data from analyses with reference methods

The spectra can be used also without models to classify different types
of objects and to investigate spectral variations

NIR spectroscopy is today used for charcterisation of materials and objects
in a multitude of applications: On wood and materials from wood, in food,
medicin and chemical industries, in agriculture and mining, etc.
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Single and multiple spot NIR measurement
Single position NIR measurement
Stepwise single position measurement on translated sample
for information about linear variation, i.e. from pith to bark
Linear translation
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Example of stepwise singe spot measurement:
Innventia’s NIR Wood Scanner
- spectra from pith to bark on “SilviScan strips”
Probe
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Property estimation from NIR Wood Scanner data
Example: Content of cellulose
Radial sequence of NIR spectra
Prediction of cellulose content
from pith to bark on “SilviScan strip”
MeasuredYVar(Cellulose)
cellulose (mg/g)
450
y=1*x-1.702e-005
R2=0.8298
67
23 60
1 69
75
6663
70
64
359 45
4
2
62
88
46
6144 76
47
3335
41
74
87
42
4
3
7
28
82
12 37
848
5152
65
2457 68
2689 4939
20
71 50 53
73
27
81
32
9
787772
56
79
55 16
30 54
2983
25
11 91
40 58
86 6
85
17
3122 5 10
440
430
420
410
400
390
380
19
13 3634
84
370
Norway spruce
and Scots pine
14
360
18
15
80 90
38
21
350
350 360 370 380 390 400 410 420 430 440 450
YPred[2](Cellulose)
Predicted
cellulose (mg/g)
SIMCA-P+ 12 - 2008-12-11 14:23:07 (UTC+1)
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Hyperspectral NIR measurement
Single position hyperspectral NIR measurement
Array of detector points (320 points array)
Pixel 1
Pixel 2
…
Pixel 3
Pixel 4
…
Pixel 320
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Hyperspectral NIR imaging
Stepwise hyperspectral NIR measurement on translated sample
for information about two-dimesional spatial variations
Array of detector points (320 points array)
Linear translation
Pixel 1
Pixel 2
Pixel 3
Pixel 4
…
…
…
…
…
…
Pixel 320
Imaging NIR spectroscopy on wood MeMoWood, Nancy, 4 Oct 2013
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Hyperspectral NIR imaging:
Example: Wood from pith to bark, high resolution
Sample of larch
Photo
*
Images of the 3 first principal components of all spectra from the sample
high
value
PC1
PC2
PC3
Predicted variations in lignin concentration with a preliminary PLS model *
Sample size: 10 mm x 130 mm
Moving sample measured with 100 Hz,
Whole sample measured in 42 seconds,
low
value
Spatial resolution: 31 µm x 31 µm
resulting in 100 x 320 = 32000 spectra/sec
resulting in 1.34 million spectra from the sample
* The first principal component is the linear combination of absorptions for all wavelengths which describes most of their variation. The second is the linear combination
ortogonal to the first one which describes most of the remaining variation, etc. A PLS model combines a limited number of the principal components to estimate a property.
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Illustration of spatial resolution
pith
bark
Sample strip of Norway spruce, previously analysed on SilviScan
NIR PC 1
Spatial resolution/
/pixel size:
30 µm x 30 µm
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First large scale application:
Characterisation of samples from 6000 spruces trees
 Samples from 6000 spruce trees will be analysed within the
Swedish project Bio4Energy, aiming for genetic investigations
and development of genetic markers
 Our intention is to develop this into a platform for
high throughput phenotyping in tree breeding programs
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Illustration of spectral differencens
between and within 3 eucalypt species
Eucalypt
species A
Eucalypt
species B
Close to top of tree
Breast
height
Variation in the 1st principal component,
blue = low value, red = high value
Trees from South African stands of 3 eucalypt species were sampled at several heights.
Wood strips from pith to bark were imaged with NIR. A Principal Component Analysis was
performed on all the millions of spectra from all samples together.
Above, spectral differences between and vatiations within two of the trees representing
different species are illustrated with maps of the first principal component.
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Illustration of spectral differencens
between and within 3 eucalypt species
Maps of variation in the 1st principal component
blue = low value, red = high value
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Illustration: NIR imaging of wood discs
Wood used for illustration:
 Scots pine, fresh
 Infected on one side, bark lost,
deterioration
 Dot of syntetic chain oil applied
 Heartwood
 Sapwood
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Images of
Principal Components
high
value
low
value
PC 1
PC 2
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PC 3
Lundqvist, Grahn, Wallbäcks
Spatial resolution / pixel size: 1 mm x 1 mm
PC 3
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Example of tasks worked upon:
Identification of compressionwood
Norway spruce, preliminary results
Photo
NIR PC1
The aim is to develop a model for
classification of compression wood
Work in cooperation with
Urszula Zajaczkowska,
University of Life Sciences, Warsaw,
within the project Trees4Future
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Example of task worked upon:
Classification of tree species
Samples
NIR based classification
Eucalypt
Spruce
Birch
Pine
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Content of lignin in wood
A large number of containers with wood meal were scanned automatically within the EU
project Trees4Future. Average NIR spectra were calculated for each sample. Reference data
for ligning content were available from previous analyses by Manfred Schwanninger, BOKU.
A model for estimation of lignin content with R2 value 0,89 was developed.
Four of the containers and NIR spectra
Measured vs predicted lignin content
34
0,50
Analysed Klason Lignin, %
0,45
0,40
0,35
0,30
0,25
0,20
0,15
0,10
R2: 0.89
RMSEP: 0.38
32
30
28
26
Validation set
Calibration set
24
0,05
Linear ( )
0,00
22
800
1 300
1 800
2 300
Wavelength, nm
2 800
22
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26
28
30
Predicited Klason Lignin, %
Lundqvist, Grahn, Wallbäcks
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Example of task worked upon:
Variations in composition of paper
Interpretation from principal components:
Variations in gramage (g/m2) of
Fibres
Fillers
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Starch
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Summary and our next steps
 Hyperspectral imaging offers many posibilities
beyond conventional spot measurements with NIR
 It has many potential applications on wood and materials from wood
 Innventia has recently installed a new instrument for imaging NIR,
specially designed for large flexibility to work in different scales
and level of detail, as well as in different set-ups
 The instrument is now used in research on various types of materials
 We will also use it extensively as a development platform
for new measurement methods and applications
Imaging NIR spectroscopy on wood MeMoWood, Nancy, 4 Oct 2013
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