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How can NIR spectroscopy support the modern, efficient breeding of bio-organic cereals? - A case study 5 Authors: Nina Oesterle, M. Sc. Herbert C. Hoffmann, Ph. D. Malcolm Lee [email protected] herbert.c.hoff[email protected] [email protected] Cereal Breeding Research Darzau Hof Darzau 1 29490 Neu Darchau Germany HiperScan GmbH Weisseritzstr. 3 01067 Dresden Germany Swift Analytical Ltd. Baird Lane York, YO10 5GA UK Breeding of cereals is always connected with sample analysis (e.g. proteins, fat, ß-glucans) Classical methods of analysis are expensive, slow and ecologically harmful (due to solvents, aggressive digestion reagents) Using modern NIR spectroscopy has significant advantages: Direct measurement within several seconds No or minor sample treatment No need for solvents NIR offers the potential for representatively measuring many more samples, which is crucial for non-homogeneous natural products, because sampling is typically the most error-prone part of the measurement process. How can the general advantages of modern NIR spectroscopy be applied to the breeding process? The research team aims to answer this question with this project, which has the follo- wing main goals: Determination of the amount of • Protein in naked barley, spelt barley, oat, wheat and peas • Fat in naked oat ß-glucans in barley 1. Challenges in breeding Selecting representative samples for data acquisition is crucial It is necessary to provide samples over the whole measurement range The calibration model is just as good as the samples it has seen 2. The challenge of selecting representative samples for data acquisition 0.45 NIR absorbance spectra of oat 0.44 0.43 0.42 0.41 0.40 0.39 0.38 0.37 0.36 0.32 0.33 0.35 0.34 0.30 0.31 0.29 0.28 0.23 0.24 0.25 0.26 0.27 0.22 0.20 0.21 0.19 0.13 0.14 0.15 0.16 0.17 0.18 0.09 0.10 0.11 0.12 0.03 0.04 0.05 0.06 0.07 0.08 1,000 1,150 1,100 1,050 1,200 1,400 1,350 1,300 1,250 1,650 1,600 1,550 1,500 1,450 [nm] 1,850 1,800 1,750 1,700 1,900 Absorbance 2016 2015 The Finder SD combines the HiperScan SGS1900- NIR spectrometer in Czerny-Turner configuration with a stabilised light source, temperature control and in- tegrated wavelength standards by NIST for automatic calibration of the wavelength axis. It is a dustproof and robust instrument for quantification and identifica- tion. One of its key benefits is the stabilised data ac- quisition which guarantees very accurate quantitative analysis. 3. The instrument Spectral range 1,000 - 1,900 nm Spectral resolution 10 nm Measuring time < 5 s per scan (average 500 scans) Detector InGaAs single detector, uncooled Wavelength accuracy ± 0.5 nm Wavelength reproducibilty ± 0.2 nm Probe/optical input Diffuse reflection, 23 mm diam. Prior to the measurements: Practical aspects before starting NIR measurements: Setting measurement parameters (e.g. number of scans per spectrum) Setting number of spectra per sample to embrace sample non-homogeneity After the measurements – selection of the spectra and data processing: Careful selection of calibration samples is critical Scope of calibration samples vs. robustness of calibration An example: A calibration with barley and purple barley is a larger calibration set. But is the calibration really applicable for both types of barley sorts or is it neces- sary to perform two different calibrations? Another example for this trade-off is spelt barley and naked barley. How large should the modelled parameter range be? Is one generalized model better than several specialized models? Which ratio is the best for splitting measurements into sets (calibration and validation)? Is a cross validation useful? Selecting suitable data processing techniques and model parameters is a highly itera- tive and empirical process 4. Creating a multi-dimensional calibration for rapid determination of the protein content RMSEP: 0.67 % 16.0 15.5 13.0 13.5 14.0 14.5 15.0 10.5 11.0 11.5 12.0 12.5 9.5 10.0 7.5 8.0 8.5 9.0 7.0 Actual (%) 7.5 8.0 8.5 7.0 10.5 11.0 9.5 10.0 9.0 13.0 13.5 14.0 11.5 12.0 12.5 15.0 15.5 14.5 Predicted (%) Amount of protein in wheat There is great potential for optimisation of cereal breeding supported by NIR spectroscopy. However, there are some challenges to face: Sampling the right data set for creating a calibration Creating correct, precise and robust calibration needs some experience and endeavour Exchanging calibration models, know-how and test samples with other stakeholders (institutes, mill manufacturers, traders...) will have great synergetic effects. Therefore the research team welcomes you to contact us for collaboration. 5. Conclusion / Outlook

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How can NIR spectroscopy support the modern, efficient breeding of bio-organic cereals? - A case study

5

Authors:Nina Oesterle, M. Sc. Herbert C. Hoffmann, Ph. D. Malcolm [email protected] [email protected] [email protected] Breeding Research DarzauHof Darzau 129490 Neu DarchauGermany

HiperScan GmbHWeisseritzstr. 301067 DresdenGermany

Swift Analytical Ltd.Baird LaneYork, YO10 5GAUK

• Breeding of cereals is always connected with sample analysis (e.g. proteins, fat, ß-glucans)

• Classical methods of analysis are expensive, slow and ecologically harmful (due to solvents, aggressive digestion reagents)

• Using modern NIR spectroscopy has significant advantages:• Direct measurement within several seconds• No or minor sample treatment• No need for solvents

NIR offers the potential for representatively measuring many more samples, which is crucial for non-homogeneous natural products, because sampling is typically the most error-prone part of the measurement process.

How can the general advantages of modern NIR spectroscopy be applied to the breeding process?

The research team aims to answer this question with this project, which has the follo-wing main goals:

Determination of the amount of• Protein in naked barley, spelt barley, oat, wheat and peas• Fat in naked oat• ß-glucans in barley

1. Challenges in breeding

• Selecting representative samples for data acquisition is crucial• It is necessary to provide samples over the whole measurement range• The calibration model is just as good as the samples it has seen

2. The challenge of selecting representative samples for data acquisition

0.45

NIR absorbance spectra of oat0.44

0.43

0.42

0.41

0.40

0.39

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0.37

0.36

0.32

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0.35

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0.23

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0.09

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1,000 1,1501,1001,050 1,200 1,4001,3501,3001,250 1,6501,6001,5501,5001,450[nm]

1,8501,8001,7501,700 1,900

Abso

rban

ce

2016

2015

The Finder SD combines the HiperScan SGS1900-NIR spectrometer in Czerny-Turner configuration with a stabilised light source, temperature control and in-tegrated wavelength standards by NIST for automatic calibration of the wavelength axis. It is a dustproof and robust instrument for quantification and identifica-tion. One of its key benefits is the stabilised data ac-quisition which guarantees very accurate quantitative analysis.

3. The instrument

Spectral range 1,000 - 1,900 nmSpectral resolution 10 nmMeasuring time < 5 s per scan (average 500 scans)Detector InGaAs single detector, uncooledWavelength accuracy ± 0.5 nmWavelength reproducibilty ± 0.2 nmProbe/optical input Diffuse reflection, 23 mm diam.

Prior to the measurements:• Practical aspects before starting NIR measurements:

• Setting measurement parameters (e.g. number of scans per spectrum)• Setting number of spectra per sample to embrace sample non-homogeneity

After the measurements – selection of the spectra and data processing:• Careful selection of calibration samples is critical • Scope of calibration samples vs. robustness of calibration

• An example: A calibration with barley and purple barley is a larger calibration set. But is the calibration really applicable for both types of barley sorts or is it neces-sary to perform two different calibrations? Another example for this trade-off is spelt barley and naked barley.

• How large should the modelled parameter range be? Is one generalized model better than several specialized models?

• Which ratio is the best for splitting measurements into sets (calibration and validation)? Is a cross validation useful?

• Selecting suitable data processing techniques and model parameters is a highly itera-tive and empirical process

4. Creating a multi-dimensional calibration for rapid determination of the protein content

RMSEP: 0.67 %

16.0

15.5

13.0

13.5

14.0

14.5

15.0

10.5

11.0

11.5

12.0

12.5

9.5

10.0

7.5

8.0

8.5

9.0

7.0

Act

ual (

%)

7.5 8.0 8.57.0 10.5 11.09.5 10.09.0 13.0 13.5 14.011.5 12.0 12.5 15.0 15.514.5Predicted (%)

Amount of protein in wheat

There is great potential for optimisation of cereal breeding supported by NIR spectroscopy. However, there are some challenges to face:• Sampling the right data set for creating a calibration• Creating correct, precise and robust calibration needs some experience and endeavour

Exchanging calibration models, know-how and test samples with other stakeholders (institutes, mill manufacturers, traders...) will have great synergetic effects. Therefore the research team welcomes you to contact us for collaboration.

5. Conclusion / Outlook