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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 Benoit Igne, Charles R. Hurburgh Jr. Agricultural and Biosystems Engineering Iowa State University Sunday, March 2nd 2008
Presentation Outline • The need for standardization • Calibration transfer methods • Performance of existing algorithms • Spectral processing for model transfer • Spatial and frequency domain
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
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?
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?
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?
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
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)
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)
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
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
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
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
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
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
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
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