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Jacob Bolson Maureen Suryaatmadja Agricultural Engineering Iowa State University May 4, 2005

AE 469/569 TERM PROJECT DEVELOPING CORRECTION FUNCTION FOR TEMPERATURE EFFECTS ON NIR SOYBEAN ANALYSIS. Jacob Bolson Maureen Suryaatmadja Agricultural Engineering Iowa State University May 4, 2005. Introduction.

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Jacob Bolson Maureen Suryaatmadja Agricultural Engineering Iowa State University May 4, 2005

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  1. AE 469/569 TERM PROJECTDEVELOPING CORRECTION FUNCTION FOR TEMPERATURE EFFECTS ON NIR SOYBEAN ANALYSIS Jacob Bolson Maureen Suryaatmadja Agricultural Engineering Iowa State University May 4, 2005

  2. Introduction • NIR instruments play an important role in predicting chemical composition and biological properties of food and agricultural material. • NIR spectroscopy measures the wavelength and intensity of the absorption of near infrared light (800nm – 2.5μm) by a given sample.

  3. Introduction • NIR is primarily used for the detection of C-H, N-H and O-H bonds, which relate to concentration of oil, protein and moisture. • The advantages of NIR: • a non-destructive procedure • minimal sample preparation • fast analytical techniques (less than 1 minute) • Two important factors in NIR analysis: • a spectrum • reference values

  4. Introduction • The development of calibration model on NIR instrument consists of two procedures: • develop a base calibration • add the samples to the base calibration for instrument and temperature stabilization • Temperature stabilization, collect at grain temperatures from -150C to 450C. (Rippke et all., 1996)

  5. Problem Statement • The method to include some hot and cold samples does not work well and quite inconsistent. • NIR spectra of liquid component shift on wavelength axis as temperature changes, predicted results become less accurate.

  6. Problem Statement • Researchers have proven that NIR spectra of liquid components shift on the wavelength axis as temperature changes: • The bands corresponding to hydrogen-bonding groups (N-H, O-H bands) are expected to be highly influenced by temperature (Miller, 2001) • Temperature influences the spectra, the increase of temperature allows liberating a part of fixed water – meat measurement (Corbisier et all., 2004)

  7. Objective • To determine whether a temperature adjustment function could improve the accuracy of NIR analysis at conditions other than room temperature.

  8. Materials and Methods • Soybean sample temperature set from ISU Grain Quality Lab (20 samples) • Run in three conditions: cold, room, and hot using Omega G 6110 Analyzer with temperature compensation calibration (already exists).

  9. Materials and Methods • Recalculate the results using no temperature compensation calibration. • Calculate the slope from every prediction values of moisture, protein and oil using Excel™ function. • Calculate the average and standard deviation of the slopes.

  10. Materials and Methods • One of the samples was discarded because of its extreme values. • Test the slopes on the original samples using this formula: Corrected value = Measured value +(m* (250C – measured temperature)) • Calculate the differences between the corrected values at non-room and room temperature. • Test the slopes (m, m+sd, m-sd) on the new seven soybeans samples using the same previous procedure.

  11. Result

  12. Result

  13. Results

  14. Correction Function • M corrected = M measured + (0.0164 (250C- T measured)) • P corrected = P measured + (- 0.0048 (250C - T measured)) • O corrected = O measured + (0.0063 (250C - T measured)) M = Moisture P = Protein O = Oil

  15. Results (19 Samples)

  16. Results

  17. Results (19 Samples)

  18. Results

  19. Results (19 Samples)

  20. Results

  21. Moisture Correction Function(7 samples) • M corrected = M measured + (0.0164 (250C- T measured)) • M corrected = M measured + (0.0247 (250C - T measured)) • M corrected = M measured + (0.0081 (250C- T measured))

  22. Results (7 samples)

  23. Results

  24. Results

  25. Results

  26. Protein Correction Function(7 Samples) • P corrected = P measured + (- 0.0048 (250C - T measured)) • P corrected = P measured + (0.0079 (250C - T measured)) • P corrected = P measured + (- 0.0176 (250C - T measured))

  27. Results ( 7 samples)

  28. Results

  29. Results

  30. Results

  31. Oil Correction Function • O corrected = O measured + (0.0063 (250C - T measured)) • O corrected = O measured + (0.0105 (250C - T measured)) • O corrected = O measured + (0.0021 (250C - T measured))

  32. Results (7 samples)

  33. Results

  34. Results

  35. Results (7 samples)

  36. Conclusion • A temperature adjustment function: M corrected = M measured + (0.0164 (250C- T measured)) P corrected = P measured + (- 0.0048 (250C- T measured)) O corrected = O measured + (0.0063 (250C- T measured)) M = Moisture P = Protein O = Oil can be used to improve the accuracy of NIR predicted values at conditions other than room temperature.

  37. Conclusion • The correction function applied to soybean moisture and oil was more consistent than to protein. • The implementation of a correction function is less time consuming than developing temperature compensation calibration because a slope correction can be recalculated for new calibrations. • The future work should implement the correction function into the soybean calibration development and test with other NIR instruments.

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