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Temperature Correction Function for Improved NIR Soybean Analysis

The project explores a temperature correction function to enhance the accuracy of NIR analysis on soybeans at various temperatures. Results show corrected values for moisture, protein, and oil content, benefiting agricultural practices for better analysis outcomes.

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Temperature Correction Function for Improved NIR Soybean Analysis

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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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