10 / 07bruins instruments1 training near infrared transmission whole grain analyzer

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10 / 07 BRUINS INSTRUMENTS 1 BRUINS INSTRUMENTS BRUINS INSTRUMENTS Training Near Infrared Transmission Whole Grain Analyzer

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Page 1: 10 / 07BRUINS INSTRUMENTS1 Training Near Infrared Transmission Whole Grain Analyzer

10 / 07 BRUINS INSTRUMENTS 1

BRUINS INSTRUMENTSBRUINS INSTRUMENTS

Training

Near Infrared Transmission

Whole Grain Analyzer

Page 2: 10 / 07BRUINS INSTRUMENTS1 Training Near Infrared Transmission Whole Grain Analyzer

10 / 07BRUINS INSTRUMENTS 2

Targets:Targets:

History and Basics of NIRCalibration developmentToubleshooting & Maintainance

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10 / 07BRUINS INSTRUMENTS 3

History of NIRHistory of NIR

First detected by William Herschel beginning of 19th century

First instrument built by W.W. Coblentz about 1900 First practical use in the 1930s First agriculture usage by Karl Norris 1968 First commercial scanning instruments 1978 First BRUINS NIR spectrophotometer 1979 First BRUINS NIT spectrophotometer 1982

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Basics of NIRBasics of NIR

NIR = NIR = NNear ear IInfranfraRRed Spectroscopyed Spectroscopy

Wavelength of Light

UV > VIS > NIR>(M)IR

nm 380 750 / 11002500

(NIT / NIR)

Energy of Light

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Basics of NIRBasics of NIR

Typical properties analysed by NIR are

organic compounds like

Oil

Protein

Starch

And

Water

Useful concentration are in % range,

ppm or ppb level are normally impossible.

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10 / 07BRUINS INSTRUMENTS 6

Basics of NIRBasics of NIR

NIR active groups

O-H Water, Alcohol

C-H Carbohydrates (Starch, Sugar, Cellulose)

Fat / Oil

N-H Protein

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10 / 07BRUINS INSTRUMENTS 7

Basics of NIR Basics of NIR

Molekular Vibrations

O

H H

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10 / 07BRUINS INSTRUMENTS 8

Basics of NIR Basics of NIR

Molekular Vibrations

Page 9: 10 / 07BRUINS INSTRUMENTS1 Training Near Infrared Transmission Whole Grain Analyzer

10 / 07BRUINS INSTRUMENTS 9

Basics of NIR Basics of NIR

Molekular Vibrations

Page 10: 10 / 07BRUINS INSTRUMENTS1 Training Near Infrared Transmission Whole Grain Analyzer

10 / 07BRUINS INSTRUMENTS 10

CalibrationCalibration

Reference Analysis Sample selection Sampling Sample Preparation / Homogenisation Sample Presentation

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Calibration: Reference AnalysisCalibration: Reference Analysis

NIR calibration will be dependend from reference analysis, so

Select best possible method Investigate „real“ method error Make sure that the same sample material will be

analysed Use at least double analysis Comment all specialities of actual sample

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10 / 07BRUINS INSTRUMENTS 12

Calibration: Lab Error InfluenceCalibration: Lab Error Influence

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Calibration: Lab Error InfluenceCalibration: Lab Error Influence

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Calibration: Sample SelectionCalibration: Sample Selection

The complete interesting concentration range must be covered by calibration samples.

The calibration set must also include samples with changes from other constituents, eg. low protein with high moisture, low protein with low moisture etc.

Further samples must be included which are representing influences from climate, soil, cultivars etc.

Due to the effect of temperature to NIR also cold and warm samples should be added.

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Calibration: SamplingCalibration: Sampling

The sample must be representative for the total amount.

Take subsamples from different locations. Thoroughly mix these to get a homogene sample.

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Calibration: Sample PreparationCalibration: Sample Preparation

No extra preparation needed for most grain and oil seeds.

Samples with thick shell must be ground (e.g. sunflower seeds).

The same sample preparation must be used for calibration and routine analysis.

By grinding a sample the moisture content will decrease.

Select the best grinder to reduce this moisture loss.

If a grinder with a sieve will be used, control the size of the mesh.

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Calibration: Sample PresentationCalibration: Sample Presentation

OmegAnalyzer and AgriCheck uses the NIR transmission, so for such meal the MultiCheck will be the better choice

As an alternative, but less efficient, a micro sample cup for meal or powders can be used

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Calibration: Sample PresentationCalibration: Sample Presentation

In all cases, if fine powder or ground sample: Thoroughly mix the sample before filling In cases of components with different density

make sure, that no material separate

If a micro sample cup is used: Make sure that the sample is filled without wholes Knock a bit, that the sample will slide together,

but not to often because different components may separate

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Calibration: StandardisationCalibration: Standardisation

Before using the instrument in routine, it must be compared against the reference method with a set of samples.

These samples should cover the range of concentrations and normal variations in moisture, area and genotypes.

In order to get a good adjustment, the sample must be well mixed that the same material will be analysed with both methods.

The data for the reference analysis need to be well controlled, at least a double analysis must be performed.

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Calibration: StandardisationCalibration: Standardisation

The easiest comparison of accuracy and necessary adjustment can be made with Excel.

Just two columns need to filled , one with reference analysis the second with predicted NIR data.

Using the Excel functions for mean, standard error xy and correlation will calculate all necessary information.

Additional the Excel graph can be used to show how good the predicted values fit to the reference analysis.

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Calibration: BiasCalibration: Bias

Actual Predicted8

9

10

11

12

13

14

15

16

17

18

7.5

8.5

9.7

10.6

11.7

12.4

13.7

14.4

15.5

16.6

17.7

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Calibration: BiasCalibration: Bias

Actual Predicted

8

9

10

11

12

13

14

15

16

17

18

7.5

8.5

9.7

10.6

11.7

12.4

13.7

14.4

15.5

16.6

17.7

Actual Predicted

Mean 13.00 12.57

SEP 0.121

R² 0.999

Bias - 0.43

Slope 1.001

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Calibration: BiasCalibration: Bias

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Calibration: Bias, 0.43 addedCalibration: Bias, 0.43 added

Actual Predicted8

9

10

11

12

13

14

15

16

17

18

7.93

8.93

10.13

11.03

12.13

12.83

14.13

14.83

15.93

17.03

18.08

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Calibration: BiasCalibration: Bias

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Calibration: SlopeCalibration: Slope

While the bias will move all data parallel, the slope will change the extreme values more than the values around the middle.

So adjusting the slope will much more influence the predicted result.

For the slope adjustment the sample set must really cover the full calibration range.

Normally this adjustment should not be used. If it seems to be necessary, look for the reason. It might be better to select or built another calibration.

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Calibration: SlopeCalibration: Slope

Actual Predicted8

9

10

11

12

13

14

15

16

17

18

7.5

8.3

9.8

10.5

12.1

12.9

14.5

15.0

16.6

17.2

18.5

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Calibration: SlopeCalibration: Slope

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Calibration: corrected SlopeCalibration: corrected Slope

Actual Predicted8

9

10

11

12

13

14

15

16

17

18

8.14

8.86

10.21

10.83

12.27

12.99

14.43

14.88

16.31

16.85

18.02

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Calibration: corrected SlopeCalibration: corrected Slope

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Calibration: SlopeCalibration: Slope

Actual Predicted11

12

13

14

10.5

12.1

12.9

14.5

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Calibration: SlopeCalibration: Slope

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Calibration: SlopeCalibration: Slope

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Calibration: corrected Slope?Calibration: corrected Slope?

Actual Predicted8

9

10

11

12

13

14

15

16

17

18

8.59

9.21

10.39

10.93

12.18

12.81

14.06

14.45

15.70

16.17

17.18

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Calibration: corrected Slope?Calibration: corrected Slope?