Bio-materials characterization with NIRcostfp1407.iam.upr.si/en/resources/files/... · 3 Goals...
Transcript of Bio-materials characterization with NIRcostfp1407.iam.upr.si/en/resources/files/... · 3 Goals...
Bio-materials characterization with NIRAnna Sandak & Jakub Sandak
2
Outline
Why spectroscopy?
Tendencies in biomaterials developmnet
NIR in bio-materials characterization
Trends in NIR applications
3
Goals
• to exploit the potential of near infrared spectroscopy
• to demonstrate its capabilities for bio-based materials characterization
• to highlight the potential of the NIR as a tool capable of providing complementary information to other techniques
• to present current trends in NIR developments
4
Bio-materials in construction sector
• In Italy 1 on 12 buildings is made of wood and growing tendency is observed nowadays
• Bio-based materials are often used for retrofitting of existing structures, upward construction or vertical gardens
• Buildings that use bio-materials are not just sustainable, strong and durable; they are also beautiful
5
Development priorities• Structural components
(need for developed wood products -Engineered Wood Products, high strength wood, moisture resistant sills, light-weight beams/joists/studs of bio-composites, sandwich panels for exterior walls)
• Insulation(need for compactable bats of cellulose insulation, environmentally friendly fire impregnation, high-performance insulation that provides thinner walls, insulation, optimized for soundproofing)
• Barrier Materials(need for bio-based wind and vapor barrier for moisture-proof exterior walls, waterproofing for wet areas, façade and roofing materials with improved durability/serviceability
Source: Per-Erik Eriksson: Future sustainable biobased buildings
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Durability and performance
7
Surface properties
roughness
color
brighntess
surface free energy
wetability
gloss
pattern
temperaturesoftiness
hardness
touch experience
facture
waviness
chemical activity
contamination
resistance to dirt
outlook
resistance to scratch
resistance to abrassion
adhesion
soft feel
aseptic
durability
leveling
water repellency
matting effect
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How to assess properties?
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Why NIR spectroscopy? No need special sample preparation Non-destructive testing Relatively fast measurement No residues/solvents to waste Determination of many components simultaneously High degree of precision and accuracy Direct measurement with very low cost
Not self standing technology Overlapping of spectral peaks Not straightforward interpretation Needs statistics methods for data analysis
10
chemical industry, microanalysis, pharmaceutical analysis, soil,
polymers, food and beverages, surface science, fuels, textile
industry, art conservation, forensics, PAT, remote sensing
and also wood and paper industry
NIR applications
11
…but what can we see in wood?
http://www.ifb.ethz.ch/research/sinergia
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Cellulose (40-50%)
• linear polymer• long chain
(DP 5000-10000)• Β-D-Glucopyranose
units• 1,4-glycosidic bonds• hydroxyl groups
bonds• different kind in
wood: • - amorphous,
- semi-crystalline, - crystalline
methylene groupshydroxyl groups
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Lignin (20-30%)
Softwood (guaiacyl units) Hardwood (guaiacyl + syringyl units)
methoxyl groups
phenolic hydroxyl groups
aldehyde groups carbonyl groups
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Hemicellulose (25-35%)
xylan
• hetero-polysaccharides• short chain
(DP 150-200)• amorphous• high reactivity• various composition
for hardwood and softwood
acetyl groups
hydroxyl groups
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NIR & biomaterials characterization
• Chemical compositions(cellulose, lignin, extractives)
• Physical and anatomical characterization(density, moisture, calorific value, microfibryl angle, defects detection, sapwood/heartwood detection)
• Mechanical properties(compression, tension, MOE, MOR)
• Identification and monitoring others properties of wood (decay monitoring, weathering, waterlogging, thermal treatment, chemical modifications)
• Paper industry(weight, thickness, moisture, Kappa number, mechanical resistance, type of pulp/paper)
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Virgin wood
• Species recognition• Wood provenance• Exotic wood evaluation• Biomass characterization• Clones recognition
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Recognition of wood species
1 2 1 3 1 94 1 151 61 7 1 81 91 10 11 121 13 1 9141 9151 161 171 181 191 20
softwood hardwood exotic wood
01- Picea abies, 02-Abies alba, 03-Pinus cembra, 04-Larix decidua, 05-Pinus sylvestris, 06-Pinus ponderosa, 07-Tsuga, 08- Thuja sp., 09-Quercus robur, 10-Robinia pseudoacacia, 11-Castanea sativa, 12-Iroko, 13-Afrormosia, 14-Okumè, 15-Sipo, 16-Meranti, 17-Wengè, 18-Azobè, 19-Ipè, 20-Itauba, 21-Pau Rosado, 22-Teck, 23-Bangkirai
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Use of CA for species classification
Picea abies Pinus cembra Larix sp. Thuja plicata Abies alba Pinus silvestris Tsuga sp.
inve
stig
ated
sam
ple
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Wood provenance & NIRS
2163 trees of Norway spruce from 75 location
in 14 European countries2163 samples measured
x 5 spectra/sample = 10815 spectra
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Differentation of origin
samples form 14 countries (75 locations); totally 10815 spectra was analyzed and evaluated
Austria ▲ Bulgaria ▼ Czech Republic ■ Croatia ■ Estonia Finland France Germany Hungary Italy Norway Poland Romania Sweden
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Italy
TRENTO
Moena
Cavalese
Garda lake
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Principal Component Analysis
PCA of spectra of wooden samples coming from six different locations (14 sites) in Trentino region
Note: second derivative, vector normalization, method: factorization (5 factors)region: 9590-5250cm-1, 5100-4160cm-1
Strada Alta Valcigolera Strada Orti Forestali, Juribello Paneveggio Cadino 06 Cadino 43 Cadino 49 Folgaria 04 Folgaria 22 Monte Sover 7 Monte Sover 11 Monte Sover 14 Monte Sover 69 Caoria Valsorda
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Cluster Analysis
CADINO CADINO CADINO
CA – wooden samples from 3 different districts of Cadino valley Note: second derivative, vector normalization, Ward’s algorithm, region: 7089-5072cm-1
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coconut shell tamarind grass
Identification of agricultural residues
Particleboards characterization
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A
B
C
Raw resources: Eastern redcedar (Juniperus virginiana L.) Single- and three-layers panelsmanufactured from raw material using 9% urea formaldehyde, combination of 15% modified corn starch and 2% urea formaldehyde adhesive
12345678910111213141516171819
-0,008
0
0,008
40004500500055006000650070007500wavenumber (cm-1)
PC
2, P
C3
PC2PC3
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Selection of biomass for optimal bio-conversion process
PCA of willow clones. Note: pre-processing: 2nd derivative + vector normalization,
17 smoothing points, region: 6869-5847 cm-1, method: factorization, 3 factors
duotursprint
turbo
wodtur
monoturstart
oltur
tur PC3
PC4
PC2
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Willows utilization
willows
thermo - chemicalconversion chemical
conversionmechanical-chemical
conversion
combustion pyrolysis gasification fermentationanaerobic digestion estrificationpulping
panel production
heat syngas pulp&paper fiber-panel ethanol biogas biodieselcharcoalsyngas bio-oil
high lignin content high carbohydrate contenthigh fiber content
extraction
high extractives content acumulation of heavy metalsfast resprouting
manufacturing environmental engineering
phytominigphyto- remediation biofiltrationfurniturebasketry
metalsclean soilclean waterchairscontainers
cosmetic foodpharmaceutic
cream anti- phlogistic anti-
pyretic preservativeanti-
oxidantshampoo
cultivation
ap
icu
ltu
re ho
ne
y p
olli
na
tio
n construction
gre
en
a
rch
itec
ture
fen
ce
sro
ad
ma
ts
fast growingearly flowering
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Suggested conversion paththermo-chemical
mechano-chemical chemical pharmaceut
ical phytoremediation
#1 0.93
0.07
0.13
0.96
0.19
-0.10
0.00
#2 0.98
0.04
-0.01
0.98
0.44
-0.03
0.02
#3 0.97
0.13
0.01
1.02
0.57
0.88
0.33
#4 1.05
-0.07
1.04
0.99
0.61
0.99
0.20
#5 0.89
0.13
0.19
0.80
0.57
-0.06
0.19
#6 0.05
-0.05
-0.13
1.14
0.68
0.15
0.42
#7 -0.08
0.78
0.02
0.03
0.33
-0.07
0.50
#8 0.09
1.10
-0.07
-0.11
0.06
0.00
-0.03
#9 -0.05
0.90
-0.06
0.02
0.28
0.15
0.30
#10 -0.03
0.04
0.07
0.03
0.09
0.14
-0.11
#11 -0.02
-0.05
1.01
-0.06
1.19
0.84
0.31
#12 -0.04
-0.07
1.05
-0.04
1.32
-0.15
0.22
#13 -0.02
-0.04
0.93
0.08
1.22
0.32
0.35
#14 -0.04
-0.04
0.92
0.10
1.00
0.86
0.66
#15 1.11
0.95
0.84
0.00
1.04
0.09
0.11
#16 0.16
1.12
-0.02
0.06
1.31
1.12
0.60
#17 0.06
0.06
1.07
0.01
1.10
-0.14
-0.05
high content of: high accumulation of: Discrimination rule high HHV
cellulose hemicelluose extractives Zn Pb Cu
Thresholdrule HHVexp>20.0 Xc>38% Xhe>34% Xe>11% XZn>55ppb XPb>8ppb XCu>8ppb
Average prediction
error 0.06 0.08 0.07 0.06 0.29 0.11 0.29 http://www.seco.cpa.state.tx.us/publications/renewenergy/biom
assenergy.php
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Chemical analysis
• cellulose (by Browning)
• holocellulose (by Browning)
• lignin (T 222 om-06 TAPPI)
• pentosans (T 223 cm-01 TAPPI)
• hot water extractives (T 207 cm-08 TAPPI)
• cold water extractives (T 207 cm-08 TAPPI)
• 1% NaOH extractives (T 212 om-07 TAPPI)
• organic solvent extractives (T 204 cm-07 TAPPI)
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NIR PLS calibration models #1of willows chemical composition (pilot study)
22
23
24
25
26
27
28
22 23 24 25 26 27 28
lignin measured (%)
ligni
n pr
edic
ted
(%)
42
43
44
45
46
47
48
49
50
51
52
42 43 44 45 46 47 48 49 50 51 52
cellulose measured (%)
cellu
lose
pre
dict
ed (%
)
17
18
19
20
21
17 18 19 20 21
pentosans measured (%)
pent
osan
spr
edic
ted
(%)
70
71
72
73
74
75
76
77
78
79
80
70 71 72 73 74 75 76 77 78 79 80
holocellulose measured (%)
holo
cellu
lose
pred
icte
d (%
)ligninr2 = 0.969RMSECV = 0.24RPD = 5.7
celluloser2 = 0.9 82RMSECV = 0. 39RPD = 7.4
pentosansr2 = 0.96 7RMSECV = 0.11RPD = 5.5
holocelluloser2 = 0.968RMSECV = 0.33RPD = 5.6
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NIR PLS calibration models #2of willows chemical composition (pilot study)
18
19
20
21
22
23
24
25
26
27
28
18 19 20 21 22 23 24 25 26 27 28soluble in 10% NaOH measured (%)
solu
ble
in 1
0% N
aOH
pred
icte
d (%
)
2
3
4
5
6
2 3 4 5 6soluble in organic measured (%)
solu
ble
in o
rgan
ic p
redi
cted
(%)
0
1
2
3
4
0 1 2 3 4soluble in cold water measured (%)
solu
ble
in c
old
wat
er p
redi
cted
(%)
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7soluble in hot water measured (%)
solu
ble
in h
ot w
ater
pre
dict
ed (%
)
soluble in cold waterr2 = 0.983RMSECV = 0.20RPD = 7.7
soluble in hot waterr2 = 0.983RMSECV = 0.12RPD = 7.6
soluble in organic solventsr2 = 0.981RMSECV = 0.12RPD = 7.2
soluble in 10% NaOHr2 = 0.965RMSECV = 0.37RPD = 5.4
18
19
20
21
22
23
24
25
26
27
28
18 19 20 21 22 23 24 25 26 27 28soluble in 10% NaOH measured (%)
solu
ble
in 1
0% N
aOH
pred
icte
d (%
)
18
19
20
21
22
23
24
25
26
27
28
18 19 20 21 22 23 24 25 26 27 28soluble in 10% NaOH measured (%)
solu
ble
in 1
0% N
aOH
pred
icte
d (%
)
2
3
4
5
6
2 3 4 5 6soluble in organic measured (%)
solu
ble
in o
rgan
ic p
redi
cted
(%)
2
3
4
5
6
2 3 4 5 6soluble in organic measured (%)
solu
ble
in o
rgan
ic p
redi
cted
(%)
0
1
2
3
4
0 1 2 3 4soluble in cold water measured (%)
solu
ble
in c
old
wat
er p
redi
cted
(%)
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7soluble in hot water measured (%)
solu
ble
in h
ot w
ater
pre
dict
ed (%
)
soluble in cold waterr2 = 0.983RMSECV = 0.20RPD = 7.7
soluble in hot waterr2 = 0.983RMSECV = 0.12RPD = 7.6
soluble in organic solventsr2 = 0.981RMSECV = 0.12RPD = 7.2
soluble in 10% NaOHr2 = 0.965RMSECV = 0.37RPD = 5.4
0
1
2
3
4
0 1 2 3 4soluble in cold water measured (%)
solu
ble
in c
old
wat
er p
redi
cted
(%)
0
1
2
3
4
0 1 2 3 4soluble in cold water measured (%)
solu
ble
in c
old
wat
er p
redi
cted
(%)
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7soluble in hot water measured (%)
solu
ble
in h
ot w
ater
pre
dict
ed (%
)
0
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7soluble in hot water measured (%)
solu
ble
in h
ot w
ater
pre
dict
ed (%
)
soluble in cold waterr2 = 0.983RMSECV = 0.20RPD = 7.7
soluble in hot waterr2 = 0.983RMSECV = 0.12RPD = 7.6
soluble in organic solventsr2 = 0.981RMSECV = 0.12RPD = 7.2
soluble in 10% NaOHr2 = 0.965RMSECV = 0.37RPD = 5.4
32
Wood modifications & NIRS
• Thermal treatment• Decay • ISPM-15 • Densification• Mechanical testing• Wood after wood machining• Coatings• Weathering• Service life prediction• …
33
Wood thermal treatment
fir spruce larchparameter ML EMC ML EMC ML EMC
range (cm-1) 7502-6098 5450-4246
6102-4246 10996-10098 9303-8501 5018-4435
8505-4435 9403-5446 9403-7498 6102-5446
pre-processing 1der +MSC 1 der + VN VN 1 der + VN 1 der + VN 1 der +MSC r2 98.67 97.58 98.48 98.83 97.88 97.69
RMSECV 0.26 0.25 0.25 0.17 0.39 0.19 RPD 8.66 6.42 8.11 9.42 6.87 6.58 rank 3 3 3 4 4 5
Termovuoto: open system: (volatile products of the process are continuously removed from the kiln by means of a vacuum pump)
The treatment time: 3 hoursPressure: 0.2 bartreatment temperatures of 160, 180, 200 and 220ºC.
+5
hard
woo
ds…
195 samples measured x 5 spectra/sample = 975 spectra
34
New spectra presentationWild cherry
(Prunus avium)
no treated 160ºC 180ºC 200ºC 220ºC
-1,25
-1
-0,75
-0,5
-0,25
0am. cell OH
cell CH CH2cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CHcell hemi lig CH
extr. CHhemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2OH2O
xylograms
35
Xylograms; different species
-1,25
-1
-0,75
-0,5
-0,25
0am. cell OH
cell CH CH2cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CHcell hemi lig CH
extr. CHhemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2OH2O
-1,25
-1
-0,75
-0,5
-0,25
0am. cell OH
cell CH CH2cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CHcell hemi lig CH
extr. CHhemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2OH2O
-1,25
-1
-0,75
-0,5
-0,25
0am. cell OH
cell CH CH2cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CHcell hemi lig CH
extr. CHhemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2OH2O
-1,25
-1
-0,75
-0,5
-0,25
0am. cell OH
cell CH CH2cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CHcell hemi lig CH
extr. CHhemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2OH2O
Black locust (Robinia pseudoacacia)
no treated 160ºC 180ºC 200ºC 220ºC
Norway spruce (Picea abies)
no treated 160ºC 180ºC 200ºC 220ºC
Silver fir (Abies alba)
no treated 160ºC 180ºC 200ºC 220ºC
European larch (Larix decidua)
no treated 160ºC 180ºC 200ºC 220ºC
Wild cherry (Prunus avium)
no treated 160ºC 180ºC 200ºC 220ºC
European beech (Fagus silvatica)
no treated 160ºC 180ºC 200ºC 220ºC
European ash (Fraxinus excelsior)
no treated 160ºC 180ºC 200ºC 220ºC
-1,25
-1
-0,75
-0,5
-0,25
0am. cell OH
cell CH CH2cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CHcell hemi lig CH
extr. CHhemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2OH2O
-1,25
-1
-0,75
-0,5
-0,25
0am. cell OH
cell CH CH2cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CHcell hemi lig CH
extr. CHhemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2OH2O
-1,25
-1
-0,75
-0,5
-0,25
0am. cell OH
cell CH CH2cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CHcell hemi lig CH
extr. CHhemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2OH2O
-1,25
-1
-0,75
-0,5
-0,25
0am. cell OH
cell CH CH2cell CH2
cell CO OH CH2
cell OH
cell OH CH CH2
cell OH CO
cryst. cell OH
cryst. cell CH
cryst. cell OH CH
cell hemi CH
cell hemi CH CH2 OH CO
cell hemi OH CHcell hemi lig CH
extr. CHhemi C=O
hemi CH
hemi CH
hemi OH
hemi CH
hemi lig extr. CH C=C C=O
lig CH
lig CH
lig CH
lig
lig OH
H2OH2O
Sessile oak (Quercus peraea)
no treated 160ºC 180ºC 200ºC 220ºC
36
PLS models for TM softwoods
y = 0.9522x - 0.1186
R2 = 0.944
-10
-8
-6
-4
-2
0
2
-10 -8 -6 -4 -2 0 2reference
estim
ated
y = 0.9782x + 0.218R2 = 0.9662
6
7
8
9
10
11
12
13
6 7 8 9 10 11 12 13reference
estim
ated
Mass loss Equilibrium Moisture Content
37
TM of poplar veneers
78910111213141516171819
-0,00001
0,000005
54505950645069507450wavenumber (cm-1)
2nd d
er o
f NIR
abs
orba
nce
NT239ºC, 1h, 250mb239ºC, 2h, 1000mb240ºC, 6.6h, 250mb
#26
#0
#37
#20
#18
#9
#15
#0 NT #26 155ºC, 1h, 250mb #9 174ºC, 22h, 250mb #20 210ºC, 1h, 250mb #18 239ºC, 1h, 250mb #15 239ºC, 2h, 1000mb #37 240ºC, 6.6h, 250mb
38
Wood sterilization
How to detect if the wood was appropriately thermally treated?
• standard ISPM No15;• Temperature: 56°C in the core of wood• Treatment time: 30minutes
39
ISPM-15Low temperature thermal treatment of wood
35
40
45
50
55
60
65
70
75
80
85
90
95
100
105
35 40 45 50 55 60 65 70 75 80 85 90 95 100 105
treatment temperature, degC
pred
icte
d te
mpe
ratu
re, d
egC
calibrationvalidation
• to investigate an effect of the temperature on wood samples of different wood species:
• Spruce (softwood with resin)• Fir (softwood without resin)• Poplar (hardwood)
• different treatment times:• 0.5 hours (30 minutes)• 1 hour• 3 hours• 6 hours
• different treatment temperatures: • 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95,
100°C• three samples for repetition/statistics• five-times spectra measurements for analysis
of weathering effect:• Conditioned after treatment• Measured after 1month• Measured after 3 months• Measured after 6 mounts• Measured after 12 mounts
468 samples measured x 5 spectra/sample x 5 repetition/sample = 9360 spectra
40
NIR-based method for verification of the ISPM-15 treatment*
chan
ges
to th
e sp
ectra
ch
ange
s to
the
spec
tra
NO TREATED
TREATED
Spectra +
model
41
891011121314
-0,000015
-0,000005
0,000005
55005600570058005900600061006200630064006500wavenumber (cm-1)
2nd d
er o
f abs
orba
nce
spruce referenceConiophora puteanaTrametes versicolor
Wood decay a c b
Trametes versicolor Postia placenta Coniophora puteana
y = 0.95x + 1.97R2 = 0.93
-10
0
10
20
30
40
50
60
-10 0 10 20 30 40 50 60reference mass loss, %
mas
s lo
ss p
redi
cted
with
NIR
, %
42
Decay type identification
Postia placenta
reference
Trametes versicolor
Coniphora puteana
Gloeophyllum trabeum
43
Archaeological wood
Q1 Q
2Q3Q4
Q5 Q
6
700-2700 years old
44
Short term waterloggingO
AK
band assessment pine oak nr wave number (cm-1) wood component functional group peat water peat water
1 4195 lignin not assigned ○ ○ ◦ 2 4268 cellulose CH, CH2 ○ ○ ○ ○ 3 4401 cellulose, hemicelluloses CH, CH2, OH, CO ○ ○ ○ 4 4546 lignin CH, C=O ◦ 5 4608 cellulose, hemicelluloses not assigned ◦ 6 4686 hemicelluloses, lignin, extractives CH, C=C, C=O 7 4739 cellulose OH ○ ○ ○ ○ 8 4808 cellulose semi-crystalline and crystalline OH, CH ○ ○ ○ 9 5051 water OH ○ ○ 10 5198 water OH center of the range ○ ○ ○ ○ 11 5245 hemicelluloses C=O 12 5495 cellulose OH, CO ◦ ◦ 13 5593 cellulose semi-crystalline and crystalline CH ◦ ○ ◦ 14 5666 not assigned CH, CH2 ◦ ○ 15 5692 not assigned CH2 16 5800 hemicelluloses (furanose / pyranose) CH ○ ○ 17 5865 hemicelluloses CH ○ ○ ◦ ○ 18 5935 lignin CH ◦ ○ ○ ◦ 19 5980 lignin CH ○ ○ 20 6126 cellulose OH ◦ ○ 21 6286 cellulose crystalline OH ◦ ◦ ◦ ◦ 22 6334 cellulose OH 23 6472 cellulose crystalline OH ◦ ◦ 24 6715 cellulose semi-crystalline OH ◦ ◦ ◦ ◦ 25 7003 amorphous cellulose, water OH ◦ ◦ ◦ ◦ 26 7092 lignin, extractives OH ○
360 samples measured x 3 spectra/sample = 1080
spectra
peat water years of exposition
0 2 4 6 8 2 4 6 8 arch
PIN
E
45
Paper & NIR
5% wheat
2nd day
7th day
14th day
5% rye 5% wheat 5% rye
Chaetomium globosum Aspergillus niger, Penicillium funiculosum, Trichoderma viride
5% wheat
2nd day
7th day
14th day
5% rye 5% wheat 5% rye
Chaetomium globosum Aspergillus niger, Penicillium funiculosum, Trichoderma viride
3240 samples measured x 3 spectra/sample =9720 spectra
46
Effect of soil type
-0,00004
-0,00003
-0,00002
-0,00001
0
0,00001
0,00002
0,00003
4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 5100 5200 5300 5400 5500
wavenumber, cm-1
2nd d
eriv
ativ
e of
abs
orba
nce
0 weeksforest soil 8 weeksagricultural soil 8 weekssand 8 weeks
biodegradation of the recycled paper with addition of 5% wheat bran
47
Effect of time
biodegradation of the recycled paper with addition of 5% wheat bran in the forest soil
0
4
8
PCA analysis of NIR spectra of paper before degradation tests and decomposed in forest soil for
4 and 8 weeks. Note: spectral range: 11000-4150cm-1,
pre-processing: 2nd derivative + VN
-0,00004
-0,00003
-0,00002
-0,00001
0
0,00001
0,00002
0,00003
4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 5100 5200 5300 5400 5500
wavenumber, cm-1
2nd d
eriv
ativ
e of
abs
orba
nce
0 week1 week8 week
48
PLS models for paper samples
samozerwalosc
y = 0.76x + 0.77R2 = 0.80
-1
0
1
2
3
4
5
6
7
-1 0 1 2 3 4 5 6 7
reference value (km)
estim
ated
val
ue (k
m)
porost
y = 0.75x + 0.80R2 = 0.80
0
1
2
3
0 1 2 3
reference value (3-0 scale)
estim
ated
val
ue (3
-0 s
cale
)
breaking length growth degree
49
Estimation of mechanical stressy = 0,90x + 68,91
R2 = 0,90
0
200
400
600
800
1000
1200
1400
1600
0 200 400 600 800 1000 1200 1400 1600reference force (N)
pred
icte
d fo
rce
(N)
stresses
50
why does it work?
wavenumber, cm-1
wavenumber, cm-1 wavenumbe
r, cm-1
wavenum
ber, cm-1
wavenumber, cm-1 wavenumber, cm-1
wav
enum
ber,
cm-1
wav
enum
ber,
cm-1
a) c)
b) d) in the plastic field in the elastic field
2D synchronous spectra during wood tension
40624193420042314266428142854362440145444605463246834737477947944806504951965234524254625493552055935616566256895774579357975813586358715898593259485959599460026125618762836333644964686519661966576700671167386754676967896796687369436974700170897213729873137321735274108158824786528748no
n tre
ated
norm
aliz
efir
st d
eriva
tive_
5fir
st d
eriva
tive_
15fir
st d
eriva
tive_
25fir
st d
eriva
tive
+ no
rmal
izat
ion_
5fir
st d
eriva
tive
+ no
rmal
izat
ion_
15fir
st d
eriva
tive
+ no
rmal
izat
ion_
25se
cond
der
ivativ
e_5
seco
nd d
eriva
tive_
15se
cond
der
ivativ
e_25
seco
nd d
eriva
tive
+ no
rmal
izat
ion_
5se
cond
der
ivativ
e +
norm
aliz
atio
n_15
seco
nd d
eriva
tive
+ no
rmal
izat
ion_
25
Determination coefficients (r2)
between NIR spectra amplitude and tension stresses in a relation
to the spectra preprocessing method
51
Identity test – coating recognition
Preprocessing: 2 derivative + vector normalization, 9 smoothing points
Method: factorization
Regions (cm-1): 4135-4350, 5365-5520, 5800-6000, 6290-6480, 7000-7200
Water solvent based products: #13, #26, #41
Organic solvent based products: #11, #12, #42
52
band: 4200-10000cm-1, 2 derivative, 5 smoothing points, vector normalization, 2 factors
Identification of coatings
■ Resotec ● Nordwal ■ Solas ▼ Adler aquawood ● Adler pullex ▲ Adler pullex oil
Product #1
Product #2
Product #3
Product #4
Product #5
Product #6
53
natural oak waxed oak varnished oak
Fale
jow
ka o
ak
Antique wooden floors
54
Monitoring of weathering
8 softwood, 3 hardwood, 12 exotic, 3 thermo modified wood 21 various water/organic based products with synthetic/natural/acrylic/alkyd resins/oils
55
Monitoring wood weathering
-0.000006
0
55005600570058005900600061006200630064006500wavenumber (cm-1)
2nd d
er o
f abs
orba
nce
reference1 year2 years3 years4 years
-OH
cr
ista
lline
cel
ulos
e
-CH
se
mi-c
rista
lline
cel
ulos
e
-OH
cr
ista
lline
cel
ulos
e
-CH
lig
nin
-CH
lig
nin
-CH
he
mic
ellu
lose
-CH
al
ipha
tic c
hain
s
56
Short term weathering of wood
891011121314
-0,000002
0
0,000002
55005600570058005900600061006200630064006500wavenumber (cm-1)
2nd d
er o
f abs
orba
nce
-CH S-cr cell-CH He-CH Al ch-OH Cr cell -CH L -CH S-cr cell-CH He-CH Al ch-OH Cr cell -CH L
28 days
Wind
57
Service life prediction
PCA of NIR spectra of non coated samples exposed to South (4 years of natural weathering); Reggio Emilia Roma Loninghens Macerata Udine Trento Lecce standard
EN
927-
6
EN
927-
6
EN
927-
6
1 ye
ar
2 ye
ars
2 ye
ars
2 ye
ars
1 ye
ar
1 ye
ar
Tim
e m
achi
ne
Tim
e m
achi
ne
Tim
e m
achi
ne
0 ye
ars
0 ye
ars
0 ye
ars
4 ye
ars
4 ye
ars
4 ye
ars
EN
115
07
EN
115
07
EN
115
07
3 ye
ars
3 ye
ars
3 ye
ars
EN
927-
6
EN
927-
6
EN
927-
6
1 ye
ar
2 ye
ars
2 ye
ars
2 ye
ars
1 ye
ar
1 ye
ar
Tim
e m
achi
ne
Tim
e m
achi
ne
Tim
e m
achi
ne
0 ye
ars
0 ye
ars
0 ye
ars
4 ye
ars
4 ye
ars
4 ye
ars
EN
115
07
EN
115
07
EN
115
07
3 ye
ars
3 ye
ars
3 ye
ars
a
58
Research directions
spatial measurementHyperspectral Imaging
in field measurementportable equipment
59
Weathered wood
a) b)
PCA analysis performed on not weathered (a) and sample after 28 days of weathering (b)
0 1 2 4 7 9 11 14 17 21 24 28
60
K-means classification
K‐means clustering on the mosaic of weathered wood after pre‐selecting 3 (a), 4 (b) and 5 classes (c)
b) c) A B C D
E F G H
I J K L I
A B C D
E F G H
J K L
A B C D
E F G H
I J K L
a)
61
NIR for log/biomass quality index in mountain forest
wood measurement at various harvesting moments
62
Wood defects detection
range 12500-5900cm-1
2nd derivative + VN
63
Calibration transfery = 0.93x + 1.86
R² = 0.93
25,0
27,0
29,0
31,0
33,0
25,0 27,0 29,0 31,0 33,0
Measured total lignin content (%)
NIR‐predicted
total lignin conten
t (%)
y = 0.93x + 1.86R² = 0.93
y = 0,99x + 0,31R2 = 0,86
25,0
27,0
29,0
31,0
33,0
25,0 27,0 29,0 31,0 33,0
Measured total lignin content (%)
NIR‐predicted
total lignin conten
t (%)
measurement of samples with equipment #1
measurement of selected samples with equipment #2equipment #2 model
development based on samples measured with instrument #1
model #1 development
calculation of transfer matrix
selection of calibration samples by Kennard Stone
algorithm
64
ConclussionsNIR technique has been successfully used for:• species and provenance recognition• estimation chemical composition, physical and mechanical
properties• monitoring chemical changes in wood during thermal modification,
weathering, waterlogging and decay• recognition and classification of coatings • characterization of archeological wood and cultural heritage objects• service life prediction of wooden elements and structures• paper characterization• in-field applications
Current developments in the fields of optics and electronics open new application for NIR. Its non-destructive character and simple
measurements allows assisting experts in the estimation of material properties in a fast and repeatable way
65Thank you very much