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Spectral Pretreatment for Inter Brand Near Infrared Instrument Standardization

Spectral Pretreatment for Inter Brand Near Infrared Instrument Standardization. Benoit Igne, Charles R. Hurburgh Jr. Agricultural and Biosystems Engineering Iowa State University Sunday, March 2 nd 2008. Presentation Outline. The need for standardization Calibration transfer methods

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Spectral Pretreatment for Inter Brand Near Infrared Instrument Standardization

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  1. Spectral Pretreatment for Inter Brand Near Infrared Instrument Standardization Benoit Igne, Charles R. Hurburgh Jr. Agricultural and Biosystems Engineering Iowa State University Sunday, March 2nd 2008

  2. Presentation Outline • The need for standardization • Calibration transfer methods • Performance of existing algorithms • Spectral processing for model transfer • Spatial and frequency domain

  3. The need for standardization • Instrument changes • Lamp • Aging • Environmental conditions • Sample variability • New genetics • New sample presentation Shifts in absorbance and non-linear modifications of absorption intensities

  4. The need for standardization • Recalibrate? • Not always possible • Expensive • Time consuming • Most likely to provide the best fit • Standardize? • Cheaper, quicker • More complex…? • What about the final precision/accuracy?

  5. Standardization methods • Common standardization methods • Optical methods (spectral matching) • Post regression correction of predictions • Joined calibration set: Robust methods • Models harder to develop • Lower prediction accuracy?

  6. Standardization methods • Spectral pretreatment • Few have been published • Mainly used for intra brand calibration model transfer • Only correct for absorption differences… How can spectral pretreatment improve intra and inter brand standardization?

  7. Materials • 4 instruments - 2 different brands • Foss Infratec 1229 and 1241 • Bruins OmegAnalyzerG 106110 and 106118 • Calibration sets • ~600 whole soybean samples from 2002 to 2005 • All scanned on each instrument

  8. Material • Validation sets • Set 1: 20 samples of known variability • Set 2: 40 samples from 2006 • Prediction models • Protein (AOAC 990.03 ) • Oil (AOCS Ac 3-41)

  9. Instrumental differences

  10. Existing standardization algorithms • Optical methods • Piecewise direct standardization • Direct standardization • Post regression correction of predictions • Slope and bias correction • Robust regression • Joined calibration sets (include more variability)

  11. Spectral processing for model transfer • In the spatial domain: • Noise and baseline effects: Second derivative • Multiplicative effects: SNV, MSC, Normalization • Variable scaling: Autoscaling, Mean center • Orthogonal information: Orthogonal signal correction

  12. Spectral processing for model transfer • 4 combinations were kept: • Second derivative + Normalization • SNV + Second derivative + Normalization • MSC + Second derivative + Normalization • Second derivative + Normalization + OSC + Autoscaling • The simplest was used • Second derivative + Normalization

  13. Inter Brand standardization – Foss Master

  14. Inter Brand standardization –Bruins Master

  15. Performance of existing algorithms and spatial pretreatment methods • Except for optical methods, other standardization techniques gave statistically similar results to original calibrations • Network masters gave significantly higher precisions • Spatial pretreatment models gave better results in 75% of the cases • Bruins and Foss calibrations were inter-exchangeable for similar results on secondary units

  16. Filtering in the wavelet domain • Decompose spectral data to first level approximation and detail (Daubechies 4) • Apply a smoother (3-point window) to detail components • Recombine approximation and detail • Develop calibration on filtered spectra

  17. Filtering in the wavelet domain

  18. Filtering in the wavelet domain • For the set of known variability • Overall, no significant difference among all techniques • Some instrument precisions have been increased • For the set with new variability • Almost no instrument precision improvement • Infratecs performed better than Bruins

  19. Conclusions • Transfer of calibration from brand to brand is possible • Optical methods were not appropriate • Good results with post regression correction and joined calibration set methods

  20. Conclusions • Filtering in the spatial domain was successful • Filtering in the frequency domain was good when predicting known variability but not completely satisfactory for new variability • A specialization of the calibration set may occur

  21. Conclusions • Wavelength shift CAN be “erased” by signal processing methods • Signal filtering in the wavelet domain is a good option for model development and transfer of stable materials

  22. Thank you

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