Task 4.2

Evaluation of near infrared (NIR) spectroscopy as a tool for determination of log/biomass

quality index in mountain forests

Task 4.2: Partners involvement

Task Leader: CNRTask Partecipants: KESLA, BOKU, FLY, GRE

CNR: Project leader, •will coordinate all the partecipants of this task•will evaluate the usability of NIR spectroscopy for characterization of bio-resources along the harvesting chain•will provide guidelines for proper collection and analysis of NIR spectra •will develop the “NIR quality index”; to be involved in the overall log and biomass quality grading

Boku: will support CNR with laboratory measurement and calibration transfer

Kesla, Greifenberg and Flyby: will support CNR in order to collect NIR spectra at various stages of the harvesting chain

evaluating the usability of NIR spectroscopy for characterization of bio-resources along the harvesting chain

providing guidelines for proper collection and analysis of NIR spectra

The raw information provided here are near infrared spectra, to be later used for the determination of several properties (quality indicators) of the sample

4.2 Objectives

Electromagnetic spectrum

Kick-off Meeting 8-9/jan/2014

The study of the interactions between electromagnetic radiation (energy, light) and matter

1016 1018 1020 1022 1024 1014 1012 104 106 108 1010 100 102

10-16 10-14 10-12 10-10 10-8 10-6 10-4 10-2 100 102 104 106 108

frequency, ν (Hz)

wavelength, λ (m)

γ rays

x rays

UV mikrowave radio

waves

long radio waves

FM AM

400nm 500nm 600nm 700nm

near infrared

NIR

mid infrared

MIR

far infrared

FIR

1μm 10μm 100μm

visible light

IR

Source of spectra

TwistingWaggingRocking

Scissoringasimmetric stretching

simmetric stretching

Spectra represents molecular vibrations of chemical molecules exposed to infrared light.

http://en.wikipedia.org/wiki/Infrared_spectroscopy

NIR technique

No need special sample preparation Non-destructive testing Relatively fast measurement No residues/solvents to waste Possibility for determination of many components

simultaneously High degree of precision and accuracy Direct measurement with very low cost

Overlapping of spectral peaks Needs sophisticated statistics methods for data analysis Moisture sensitive Calibration transfer from lab equipment into field equipment

Spectrofotometers

How it works?

+

calibration (PLS)

0,3

0,4

0,5

0,6

0,7

0,3 0,4 0,5 0,6 0,7

gęstość referencja (g/cm3)gę

stoś

ć es

tym

acja

(g/

cm3)

r2 = 64,94RMSECV = 0,039RPD = 1,69

density

45

45,5

46

45 45,5 46

celuloza referencja (%)

celu

loza

est

yma

cja

(%

)

r2 = 84,98RMSECV = 0,0638RPD = 2,58

cellulose

26

27

28

29

30

26 27 28 29 30

lignina referencja (%)

lign

ina

est

yma

cja

(%

)

r2 = 98,67RMSECV = 0,102RPD = 8,86

lignin

R2 = 0.984

0

10

20

30

40

50

60

0 10 20 30 40 50 60reference stress (MPa)

pre

dict

ed s

tre

ss (

MP

a)

Tensile strength

spectra reference data

Identity test

Compare the unknown spectrum with all reference spectra, the result of comparison between two spectra is the spectral distance called hit quality. The better spectra match the

smaller is spectral distance; HQ for identical spectra is 0

Model sample1HQ1

> treshold1

Model sample3HQ3

> treshold3

Model samplenHQn

> tresholdn

Model sample2HQ2

< treshold2

???sample

NIR spectra will be collected at various stages of the harvesting chain

measurement procedures will be provided for each field test

In-field tests will be compared to laboratory results

4.2 Activities: Feasibility study and specification of the

measurement protocols for proper NIR data acquisition

• spectra pre-processing, wavelength selection, classification, calibration, validation, external validation (sampling – prediction – verification)

• prediction of the log/biomass intrinsic “quality indicators” (such as moisture content, density, chemical composition, calorific value) (CNR).

• classification models based on the quality indicators will be developed and compared to the classification based on the expert’s knowledge.

• calibrations transfer between laboratory instruments (already available) and portable ones used in the field measurements in order to enrich the reliability of the prediction (BOKU).

4.2 Activities: Development and validation of

chemometric models.

4.2 Deliverables

Kick-off Meeting 8-9/jan/2014

Deliverable D.4.03 Establishing NIR measurement protocol evaluating the usability of NIR spectroscopy for characterization of bio-resources along the harvesting chain, providing guidelines for proper collection and analysis of NIR spectra. Delivery Date M16 April 2015

Deliverable D.4.08 Estimation of log/biomass quality by NIR Set of chemometric models for characterization of different “quality indicators” by means of NIR and definition of “NIR quality index” Delivery Date M20 August 2015Estimated person Month= 3.45

Development of “provenance models”. The set of spectra collected from selected samples (of known provenance and silvicultural characteristics) along the supply chain will be also processed in order to verify applicability of NIR spectroscopy to traceability of wood (CNR).

4.2 Additional deliverable

Wood provenance & NIRS

2163 trees of Norway spruce from 75 location

in 14 European countries2163 samples measured

x 5 spectra/sample

= 10815 spectra

Wood provenance & NIRS

NIR workshop