1. Identification of western Canadian wheat classes at different moisture levels using near-infrared (NIR) hyperspectral imaging S. Mahesh, D.S. Jayas, J. Paliwal, and N.D.G. White CSBE Annual Meeting 2008

2. Outline  Introduction  Objectives  Materials and Methods  Results and Discussion  Conclusions and Future work  Acknowledgements

3. Introduction  Wheat production = 26.7 Mt and export = 14.0 Mt in Canada in 2005 (FAO statistics)  Eight major wheat classes in western Canada: Canada western red spring (CWRS) Canada western hard white spring (CWHWS) Canada western amber durum (CWAD) Canada western soft white spring (CWSWS) Canada western red winter (CWRW) Canada western extra strong (CWES) Canada prairie spring white (CPSW) Canada prairie spring red (CPSR)

4. Introduction  Wheat harvesting – 13 to 15% m.c. (normally  15% m.c.) – drying – storage  Wheat @ 12 to 13% m.c. - safe moisture for effective storage - prevention of spoilage by fungi - sprouting before processing can be prevented  Wheat class identification – Major task in grain handling facilities  Visual method (common method) - to identify different wheat classes - but not to identify their moisture levels  Machine vision, PAGE, and HPLC methods

5. Introduction  Near infrared (NIR) hyperspectral imaging - Machine vision + NIR spectroscopy - to develop a rapid and consistent method - Non destructive, non subjective method - Food science, Chemistry, Pharmaceuticals, Animal science - Grain storage: wheat class identification, moisture identification, protein and oil content determination in wheat

6. Objectives  To identify western Canadian wheat classes at different moisture levels by developing statistical classification models

7. Materials and Methods  Hyperspectral imaging system 1 2 3 4 5 66 7 1. Bulk wheat sample, 2. Liquid crystal tunable filter (LCTF), 3. Lens, 4. NIR camera, 5. Copy stand, 6. Illumination, and 7. Data processing system. 1 2 3 4 5 66 7

8. Methods and Materials  Wheat classes: CWRS, CWSWS, CWHWS, CWRW, and CWES  Moisture levels: 12, 14, 16, 18, and 20%  100 images/class/m.c. – 960 to 1700 nm – 10 nm interval

9. Methods and Materials  Relative reflectance intensity, R = ([S-D]/[W-D] where: R = relative reflectance intensity of each slice of the NIR hyperspectral image of wheat; S = reflectance intensity of each slice of the NIR hyperspectral image; D = reflectance intensity of the dark current; W = reflectance intensity of a 99% reflectance standard white panel  Linear and quadratic discriminant analyses: statistical classification models

10. Results  Linear discriminant analysis

11. Results  Quadratic discriminant analysis

12. Results  Top 10 wavelengths in wheat class identification No.Wavelength (nm)Partial R 2 ASCC 113100.660.03 214500.800.06 310600.760.09 417000.720.12 513300.550.13 612000.330.14 711600.330.15 810900.290.16 914900.28 0.16 1010700.260.18 ASCC = Average squared canonical correlation

13. Discussion  Identification of waxy wheat – 1 to 10 principal component scores as input – 42 to 71% (LDA) and 46 to 71% (QDA) (Delwiche and Graybosch 2002)  Classification of barley based on ergosterol levels -  86.6% (LDA and QDA) (Balasubramanian et al. 2006)  Mohan et al. 2005: Mean classification accuracies = 89.1% (LDA, Top 2 Ref. features), 99.1% (LDA, Top 5 Ref. features) – Cereal grains classification

14. Discussion  81 – 100% (LDA) and 60 – 89% (QDA) – relative reflectance intensities – Identification of wheat classes at different moisture levels

15. Conclusions and future work  NIR hyperspectral imaging was found useful to identify different moisture level wheat classes with the extracted relative reflectance intensities as input for classification  This technique could be used to develop an automatic grain assessment tool  Wheat samples from different crop years and locations could be included in the sample space to improve the robustness and classification efficiency of the models

16. Acknowledgements  Dr. Digvir S. Jayas  Dr. Jitendra Paliwal  Dr. Noel D.G. White