1. 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

2. Introduction NIR instruments play an important role in predicting chemical composition and biological properties of food and agricultural material. 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. 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. 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: The advantages of NIR: a non-destructive procedure a non-destructive procedure minimal sample preparation minimal sample preparation fast analytical techniques (less than 1 minute) fast analytical techniques (less than 1 minute) Two important factors in NIR analysis: Two important factors in NIR analysis: a spectrum a spectrum reference values reference values

4. Introduction The development of calibration model on NIR instrument consists of two procedures: The development of calibration model on NIR instrument consists of two procedures: develop a base calibration develop a base calibration add the samples to the base calibration for instrument and temperature stabilization add the samples to the base calibration for instrument and temperature stabilization Temperature stabilization, collect at grain temperatures from -15 0 C to 45 0 C. Temperature stabilization, collect at grain temperatures from -15 0 C to 45 0 C. (Rippke et all., 1996) (Rippke et all., 1996)

5. Problem Statement The method to include some hot and cold samples does not work well and quite inconsistent. 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. 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: 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) 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) 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. 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) 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). 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. 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 slope from every prediction values of moisture, protein and oil using Excel™ function. Calculate the average and standard deviation of the slopes. Calculate the average and standard deviation of the slopes.

10. Materials and Methods One of the samples was discarded because of its extreme values. One of the samples was discarded because of its extreme values. Test the slopes on the original samples using this formula: Test the slopes on the original samples using this formula: Corrected value = Measured value +(m* (25 0 C – measured temperature)) Corrected value = Measured value +(m* (25 0 C – measured temperature)) Calculate the differences between the corrected values at non-room and room 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. 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 MoistureProteinOil Average (m) (%/ 0 C) 0.0164-0.0048 0.0063 Std Dev (%/ 0 C) 0.00830.01280.0042 m+sd (%/ 0 C) 0.02470.00790.0105 m-sd (%/ 0 C) 0.0081-0.01760.0021 Range (%/ 0 C) 0.01650.02550.0084

14. Correction Function M corrected = M measured + (0.0164 (25 0 C- T measured)) M corrected = M measured + (0.0164 (25 0 C- T measured)) P corrected = P measured + (- 0.0048 (25 0 C - T measured)) P corrected = P measured + (- 0.0048 (25 0 C - T measured)) O corrected = O measured + (0.0063 (25 0 C - T measured)) O corrected = O measured + (0.0063 (25 0 C - T measured)) M = Moisture M = Moisture P = Protein P = Protein O = Oil O = Oil

15. Results (19 Samples) Moisture Differences Non room and Room Temperature Without Temperature Using Correction With Temperature CompensationFunctionCompensation Average (%) 0.3840.2080.338 Std Dev (%) 0.2100.1370.177

16. Results

17. Results (19 Samples) Protein Differences Non room and Room Temperature Without Temperature Using Correction With Temperature CompensationFunctionCompensation Average (%) 0.479 0.4740.457 Std Dev (%) 0.309 0.3120.309

18. Results

19. Results (19 Samples) Oil Differences Non room and Room Temperature Without Temperature Using Correction With Temperature CompensationFunctionCompensation Average (%) 0.2000.1540.154 Std Dev (%) 0.1450.1130.115

20. Results

21. Moisture Correction Function (7 samples) M corrected = M measured + (0.0164 (25 0 C- T measured)) M corrected = M measured + (0.0164 (25 0 C- T measured)) M corrected = M measured + (0.0247 M corrected = M measured + (0.0247 (25 0 C - T measured)) (25 0 C - T measured)) M corrected = M measured + (0.0081 (25 0 C- T measured)) M corrected = M measured + (0.0081 (25 0 C- T measured))

22. Results (7 samples) Moisture Differences Non room and Room Temperature Without Temperature Using Correction With Temperature CompensationFunctionCompensation Average (%) 0.2970.1280.132 Std Dev (%) 0.1530.0890.103

23. Results

24. Results

25. Results

26. Protein Correction Function (7 Samples) P corrected = P measured + (- 0.0048 (25 0 C - T measured)) P corrected = P measured + (- 0.0048 (25 0 C - T measured)) P corrected = P measured + (0.0079 (25 0 C - T measured)) P corrected = P measured + (0.0079 (25 0 C - T measured)) P corrected = P measured + (- 0.0176 (25 0 C - T measured)) P corrected = P measured + (- 0.0176 (25 0 C - T measured))

27. Results ( 7 samples) Protein Differences Non room and Room Temperature Without Temperature Using Correction With Temperature CompensationFunctionCompensation Average (%) 0.4500.3880.261 Std Dev (%) 0.3430.3210.187

28. Results

29. Results

30. Results

31. Oil Correction Function O corrected = O measured + (0.0063 (25 0 C - T measured)) O corrected = O measured + (0.0063 (25 0 C - T measured)) O corrected = O measured + (0.0105 (25 0 C - T measured)) O corrected = O measured + (0.0105 (25 0 C - T measured)) O corrected = O measured + (0.0021 (25 0 C - T measured)) O corrected = O measured + (0.0021 (25 0 C - T measured))

32. Results (7 samples) Oil Differences Non room and Room Temperature Without Temperature Using Correction With Temperature CompensationFunctionCompensation Average (%) 0.1360.1050.118 Std Dev (%) 0.0900.0720.079

33. Results

34. Results

35. Results (7 samples)

36. Conclusion 1. A temperature adjustment function: M corrected = M measured + (0.0164 (250C- T measured)) M corrected = M measured + (0.0164 (250C- T measured)) P corrected = P measured + (- 0.0048 (250C- T measured)) P corrected = P measured + (- 0.0048 (250C- T measured)) O corrected = O measured + (0.0063 (250C- T measured)) O corrected = O measured + (0.0063 (250C- T measured)) M = Moisture M = Moisture P = Protein P = Protein O = Oil O = Oil can be used to improve the accuracy of NIR predicted values at conditions other than room temperature. can be used to improve the accuracy of NIR predicted values at conditions other than room temperature.

37. Conclusion 2. The correction function applied to soybean moisture and oil was more consistent than to protein. 3. 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. 4. The future work should implement the correction function into the soybean calibration development and test with other NIR instruments.