Expectations from 2D NIRS

Transcription

1 Expectations from 2D NIRS Sven-Olof Lundqvist, Thomas Grahn Innventia Presentation at Trees4Future Final Conference Brussels, 4-6th April, 2016

2 Example of application of 2D NIRS in tree breeding Expansion of database on growth, wood and fibre properties with chemical traits A database on growth and properties of 6000 Norway spruce trees has been built by Innventia within the Swedish programme Bio4Energy Increment cores were sampled from trees in Skogforsk s experiments Sample strips for SilviScan analysis were prepared and analysed for radial variations from pith to bark of wood density, microfibril angle, wood stiffness fibre widths, wall thickness, coarseness, widths of annual rings, earlywood and latewood Trait averages calculated for all rings and their parts. All rings tagged with cambial age and year of formation Heritabilities, genetic correlations and gains have been evaluated, and tool for measurements on standing trees. Genetic markers are developed with association mapping The 6000 samples already prepared will be scanned with 2D NIRS for analogous data on and evaluations of chemical traits.

3 Flexible 2D NIRS environment for multiple uses in wood characterization Equipment possible to adapt for different spatial resolutions and types of objects Pith to bark 2D NIRS SilviScan Discs Wood chips Possibility to combine with analogous data from other instruments, i.e. SilviScan, for complementary traits and for development of NIRS models

4 Single spot NIRS and 2D scanning

5 NIRS with conventional spot measurements and 2D scanning Single spot measurements, most common today Spectrum recorded form one area at a time, small on a piece of wood, or larger to represent more material, from a more or less prepared surface or from a sample delicately prepared in the laboratory Representativity may be an issue. Solution are multiple spot measurements across the surface of the sample, or desintegration and mixing of material, such as production of wood powder with standardized character 2D NIRS scanning Large numbers of spectra are automatically collected with a hyperspectral camera from pixels covering the surface of the object. Such spectral imaging offers a number of benefits, which will be shown In common for both approaches NIRS is an indirect measurement method. Quantification of properties requires development of estimation models, based on spectra and reference data, and secured through additional reference analyses for samples of new origins and drift in procedures and instrumentation. These efforts shall not be under-estimated. The presentation illustrates Measurements with both methods of wood properties variations from pith to bark Other applications of imaging NIRS on wood materials

6 NIRS with conventional spot measurements and 2D scanning Examples of typical conventional measurements: Radial wood sample from pith to bark On wood surface for local data On wood powder for average of sample * ) Sample dissintegrated to wood powder with specific size distribution, etc. Spectrum acquired from spot on wood powder * ) Note that this average does not represent the stem cross-section, as juvenile wood is strongly over-represented

7 Multiple single spot measurements for data on radial variation Radial sequence of spectra from the wood surface Linear translation Weighted averages for the stem cross-section may be calculated, for spectra or properties Innventia s single spot NIRS scanner Probe (2008)

8 Principle of hyperspectral scanning for 2D NIRS Simultanious recording of multiple spectra across the sample with an array of 320 detector points Automatic stepwise measurement along the sample for information about two-dimesional spatial variations Pixel 1 Pixel 2 Pixel 3 Pixel 4 Linear translation Pixel 1 Pixel 2 Pixel 3 Pixel 4 Pixel 320 Pixel

9 Hyperspectral NIR imaging Three first principal components of all spectra from the sample Predicted variations in lignin concentration with a preliminary PLS model * PC1 * ) PC2 PC3 high value low * value Some data on capacity: Sample size: 10 mm x 130 mm Spatial resolution: 31 µm x 31 µm Moving sample measured with 100 Hz, resulting in 100 x 320 = spectra/sec Whole sample measured in 42 seconds, 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. Imaging NIR spectroscopy on wood MeMoWood, Nancy, 4 Oct 2013 Lundqvist, Grahn, Wallbäcks

10 Examples from work on 2D NIRS at Innventia

11 Development of models for estimation of statial variation in lignin content with 2D NIRS scanning The property variation among locations on a sample is larger than the variation among sample averages. Thus, models for 2D variation must be valid across a larger property span. On modelling, all this span must be represented in the calibration set, which has to be carefully built. We have done this stepwise: 1. Radial samples with large variability was compiled and scanned with 2D NIRS and SilviScan 2. A first calibration set was selected based on data and visual inspection: 25 small local wood samples distributed among the samples, assumed to represent large variability in lignin content and H/G ratio (representing juvenile/mature wood, earlywood/latewood, broad and thin annual rings, and compressionwood). The samples were analysed with with Py-GCMS at ULisboa/ISA/CEF (former IICT). Analytical pyrolysis is preferred as it allows very small samples representing mm-size features on the samples 3. Preliminary models were developed and used to identify another 25 locations, with wood expected to widen and fill gaps in the calibration set. Also these samples were analysed 4. The current models are developed from the widened set, and used to illustrate estimated spatial variations High spatial resolution may involve difficulties in matching local spectra with local samples

12 Current models for lignin content and H/G ratio Spectra recorded from and reference analysis performed on un-extracted wood material Lignin content H/G ratio Calibration Validation Calibration Validation R 2 =0,82 R 2 =0,86 Reference analyses performed by Ana Alves, ULisboa/ISA/CEF (former IICT) with Pyrolysis GCMS. Sample of ~80 µg wood powder required for the analysis, produced with very thin drill, no additional milling needed. The models are of same quality as previously achieved for un-extracted wood material with single spot laboratory NIRS

13 Illustration of spatial variations in Py-lignin estimated with 2D NIRS

14 Comparison between single spot NIRS and 2D scanning 50 samples of wood powder from Norway spruce were produced, extracted and analyzed for Klason lignin at BOKU. NIR spectra were recorded at BOKU, IICT, CNR-IVALSA, INRA and Innventia, and estimation models developed with the BOKU reference data. At Innventia, the samples were placed in pots and passed through the NIR scanner in a row. Spectra from thousands of pixels were recorded for each sample in ~ 1s and average spectra were calculated for all samples. At the other labs, spectra were recorded spot-wise, with longer integration time The 2D scanner measured extremely fast and gave estimates of similar, but slightly lower quality ) p g Range R 2 RMSEE BOKU ,9214 0,2757 IICT ,2747 CNR-IVALSA (SNV) 0,9020 0,3092 INRA ,8788 0,3457 INNVENTIA ,8710 0,8721 0,3614 0,

15 Other examples from use of imaging NIRS Wood density Stilbenes A larch sample, earlier used to model density from spotwise NIRS, was scanned with 2D NIRS, providing spectra from pith to bark. 1,4 1,2 1,0 0,8 A limited number of small cores from Scots pine, previuosly analysed for stilbenes at Metla (now Luke), were scanned with 2D NIRS in order to test if rapid imaging NIRS could be used for larger scale studies. 0,6 0,4 0,2 The radial density variation was scanned with SilviScan, red graph. A model was developed and density estimated, blue graphs. The estimates were of same quality as with the model from spot NIRS. The results were very promising. A larger effort was discussed, but was not materialised in this project

16 Classification Pre-studies based on spectral variation only (no model) Samples of different tree species A plank - three principal components NIR based classification Eucalypt Norway spruce Birch Scots pine The classification is performed for individual pixels across the samples, with no visible errors. Illustration from a batabase built to investigate if 2D NIRS may offer possibilities for better strength graing in sawmills

17 Summary and conclusions In most NIRS applications of today spectra are recorded spot-wise With hyperspectra NIRS cameras, hundreds of spectra along a line may be recorded simultaniously and spectral images of objects can be rapidly recorded by scanning Such spectral imaging offers a number of benefits: o Increased efficiency o Analysis of property variation across the sample o Analysis of special features on the sample o Better representativity through averaging spectra from all the sample o Rapid classification of samples The lignin estimates are of similar quality as obtained with spot-wise NIRS on un-extracted wood 2D NIRS is expected to be increasingly used. Applications for treebreeding are being developed