NIR spectroscopy for the rapid measurement

of wood properties

L. Schimleck, C.-L. So, D. Jones, L. Groom

R. Daniels, A. Clark, G. Peter and T. Shupe

Overview

• Description of NIR spectroscopy

• Application of NIR to whole-trees

• Application of NIR to lumber

• Application of NIR to short clears

• Application of NIR to cores

NIR spectrum of a typical wood sample

1100 1300 1500 1700 1900 2100 2300 2500

Log

(1/R

efle

ctan

ce)

0

0.1

0.2

0.3

0.4

0.5

0.6

Wavelength (nm)

Bands arise from rotations, vibrations of bonds in molecules

O-HN-H

S-H

N-H

O-H N-H

C-H vibration throughout spectrum

Methodology

• Estimation of a parameter involves the following steps:

• Collect spectra of calibration samples

• Develop a calibration (regression)(y = B0 + X1*B1 + X2*B2 + ………..+ XN*BN)

• Collect NIR spectra of test (or unknown) samples

• Estimate parameter of interest for test set samples using the calibration

Partial least squares regression

CALIBRATION MODEL

Measured CuO retention (pcf)-0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2

Pred

icte

d C

uO re

tent

ion

(pcf

)

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Calibration SetValidation Set

Wavelength (nm)1000 1200 1400 1600 1800 2000 2200 2400

Rel

ativ

e In

tens

ity

-300

-200

-100

0

100

200

300

400

MOE

REGRESSION COEFF.

DECOMPOSEMATRICES

TEST SET

Wavelength (nm)

Abs

orba

nce

PREDICTED PROPERTIES

MOEMOR MFASG

MC

n x pmatrix

CALIBRATION SET

Wavelength (nm)

Abs

orba

nce

nsa

mpl

es

p variables

n x qmatrix

MEASURED PROPERTIES

MOEMOR MFASG

MC

nsam

ples

q variables

Laboratory determined pulp yield (%)

NIR

fitte

d pu

lp y

ield

(%)

45

50

55

60

45 50 55 60

n = 1334 factorsR2 = 0.88SEC = 0.84

Pulp yield range= 45.6 to 57.1 %

Pulp yield calibration for Tasmania

Within-tree property variation

MicrofibrilAngle

Within-tree property variation

Extractives Lignin

Hemicellulose Cellulose

Application of NIR to whole-trees

• Many studies, since late 1980’s

• Pulp yield, cellulose, lignin, extractives

• Based on whole-tree composite chips

• Examination of within-tree variation of PY

• Studies have shown that breast height cores provide similar calibration statistics to composite chips for whole-tree properties

• = nondestructive estimation of whole-tree properties

Within-tree variation of pulp yield

• Little is known about the within-tree variation of

pulp yield. NIR predictions of pulp yield can be

used to obtain maps that show the variation

47 50 53 56 59 62

Whole-tree chip versus core calibrations

•Core and whole-tree calibrations were similar for basic density, pentosans, specific cons and total lignin

• Core calibrations could be used to rank trees

• 1.30 m identified as the most suitable sampling height

0

0.2

0.4

0.6

0.8

1.0

BasicDensity

SodaCharge

Pulpyield

Totallignin

SpecificCons.

Pento-sans

R2

Wood property calibrations Whole-tree

0.65 m core

1.30 m core

Schimleck et al. (2005). Estimation of whole-tree wood quality traits using near infrared spectra collected from increment cores. Appita J. (in press)

Application of NIR to lumber

• Meder et al. (2003)

• 185 P. radiata cant centers scanned by NIR in mill scale trial

• Aim to ID corewood stiff enough to be graded as MGP 8 (lowest structural grade)

• MGP 8 worth $80/m3 more than non-structural

• Data from 409 boards available for regression

• Calibration R2 = 0.54 (big logs)

• Calibration R2 = 0.57 (small logs)

• Sufficient for economic segregation of cants

Bruker Matrix-F scanning a cant

Scanning speed approx. 2 m/s

Picture courtesy A. Thumm and R. Meder, Forest Research, New Zealand

Application of NIR to lumber

• Meder et al. (2003) cont.

• Based on stiffness, 50% of central boards could be upgraded to MGP 8

• Further calibration expected to increase percentage of upgraded boards

• Many upgraded boards were unstable

• Both stiffness and stability (twist) must be predicted for NIR to be useful for segregating radiata pine structural timber

• Calibration for twist investigated (R2 = 0.26)

Application of NIR to short-clears

• Several studies reported, different species and approaches

• Hoffmeyer and Pedersen (1995) P. abies

• Gindl et al. (2001) L. decidua

• Thumm and Meder (2001) P. radiata

• Schimleck et al. (2001) E. delegatensis, Schimleck et al. (2001) P. radiata

• Via et al. (2003) P. palustris

• Kelley et al. (2004) 6 softwood species

• Density, MOE and MOR examined

Measuring Mechanical Properties

IncrementCore

(SilviScan)

BendingSpecimen(Instron)

NIRNIR

Stiffness (Bending Specimens)

Calibration (313 spectra)

Calibration (313 spectra)

Prediction (156 spectra)

Prediction (156 spectra)

NIR

-MO

EN

IR-M

OE

Instron-MOEInstron-MOE

0 1e+6 2e+6 3e+6 4e+60

1e+6

2e+6

3e+6

4e+6

0 1e+6 2e+6 3e+6 4e+6 5e+60

1e+6

2e+6

3e+6

4e+6

5e+6

8 factorsR2=0.82SEC=0.4 Mpsi

R2=0.79SEP=0.4 Mpsi

Strength (Bending Specimens)N

IR-M

OR

NIR

-MO

R

0 10000 20000 30000 400000

10000

20000

30000

40000

0 10000 20000 30000 400000

10000

20000

30000

40000

Instron-MORInstron-MOR

8 factorsR2=0.80SEC=4 Kpsi

R2=0.75SEP=4 Kpsi

Calibration (313 spectra)

Calibration (313 spectra)

Prediction (156 spectra)

Prediction (156 spectra)

Application of NIR to cores

Radial strips cut from cores used but core surface OK for

NIR spectroscopy

Properties examined :

Tracheid length (FQA)

Cellulose, sugars, lignin (wet chemistry)

Air-dry density, MFA, stiffness, tracheid properties (SilviScan)

Georgia-wide calibrations

• P. taeda grown in 3 regions in Georgia

• Three sites selected as being

representative of each region

• Selection based on site index

• Ten trees selected per site representing a

range of breast height diameters

• Pith-bark breast height samples obtained

Georgia-wide calibrations

• Strong calibrations for density, MFA, stiffness and tracheid coarseness, length and wall thickness

• These calibrations performed well on the separate test set

• Strong calibrations for cellulose, lignin, glucan, arabinan, mannan and xylan

• Moderate prediction accuracy - possibly due to the small number of samples

MFA – 729 MSC treated spectra

5

15

25

35

45

5 15 25 35 45

Fitted MFA (degrees)

Mea

sure

d M

FA (d

egre

es)

Factors = 8R2 = 0.90SEC = 2.33SECV = 2.38RPD = 3.11

Predicted MFA (225 spectra)

0

5

10

15

20

25

30

35

40

45

50

0 10 20 30 40 50

Predicted MFA (degrees)

Mea

sure

d M

FA (d

egre

es)

Factors=8R2= 0.84SEP= 3.12RPD= 2.34

What resolution??

• Increasing resolution = decrease in

calibration accuracy

• Management of spectra becomes

difficult owing to large number of

spectra

• Fiber optic probes with a small spot

size provides options

2 mm MFA calibration (4156 spectra)

8 FactorsR2 = 0.75

SEC = 4.05SECV = 4.10RPD = 1.48

0

10

20

30

40

50

60

0 10 20 30 40 50 60

Fitted MFA (degrees)

Mea

sure

d M

FA (d

egre

es)

2 mm MFA prediction

0

5

10

15

20

25

30

35

0 10 20 30 40 50 60 70 80 90

Distance from pith (mm)

MFA

(deg

)

2 mm MFA prediction

05

1015202530354045

0 10 20 30 40 50 60 70 80 90

Distance from pith (mm)

MFA

(deg

)

Wood property calibrations – green versus dry wood

0.10

0.15

0.20

0.25

0.30

0.35

1100 1300 1500 1700 1900 2100 2300 2500

T - greenRL - green T - dryRL - dry

Wavelength (nm)Wavelength (nm)

Log

(1/R

)Lo

g (1

/R)

Stiffness (green wood spectra)

0

5

10

15

20

25

30

0 5 10 15 20 25 300

5

10

15

20

25

30

0 5 10 15 20 25 30

CalibrationCalibration PredictionPrediction

SS2-

stiff

ness

SS2-

stiff

ness

NIR-stiffnessNIR-stiffness

5 factorsR2=0.88

SEC=1.8 GPaR2=0.81

SEP=3.0 GPa

Stiffness (dry wood spectra)

0

5

10

15

20

25

30

0 5 10 15 20 25 300

5

10

15

20

25

30

0 5 10 15 20 25 30

CalibrationCalibration PredictionPrediction

SS2-

stiff

ness

SS2-

stiff

ness

NIR-stiffnessNIR-stiffness

6 factorsR2=0.95

SEC=1.1 GPaR2=0.92

SEP=1.9 GPa

NIRVANA – Near Infrared Visual & Automated Numerical Analysis

• Automated spectra collection

• High resolution video camera

• Real time property predictions

• Ideal for process monitoring &

QC applications

MOE VariationM

OE

(GPa

)M

OE

(GPa

)

Core Length (mm)Core Length (mm)

0.E+00

1.E+06

2.E+06

3.E+06

4.E+06

5.E+06

6.E+06

7.E+06

8.E+06

9.E+06

-250 -200 -150 -100 -50 0 50 100 150 200 250

Conclusions – NIR spectroscopy

• Determination of within-tree variation

• Estimation of whole-tree properties

• Applicable to milled wood, short-clears, increment cores

• Automatic scanning of cores possible

• Important to improve resolution

• Green wood can be examined