Standardization of NIR Instruments: How Useful Are the Existing Techniques

1
Standardization of NIR Instruments How Useful
Are the Existing Techniques? Benoit Igne
(igneb_at_iastate.edu), Glen R. Rippke
(rippke_at_iastate.edu), Charles R. Hurburgh, Jr.
(tatry_at_iastate.edu)
Department of Agricultural and Biosystems
Engineering, Iowa State University, Ames, Iowa.
Introduction

• Use of existing standardization techniques
  across brands
• Foss Infratec 1241 master of the network
• OmegAnalyzer G 6118 master of the network

• Standardization techniques
• Optical techniques Direct Standardization (DS)
  and Piecewise Direct Standardization (PDS).
• DS Reconstruct slave units spectra to match
  the master unit based on the whole slave units
  spectrum.
• PDS Reconstruct slave units spectra to match
  the master unit based on a small size window
  on the slave units spectrum.
• Slope and/or offset/bias post regression
  correction
• Correction to slave unit predictions based on
  comparison between a standardization set
  prediction on slave units and the reference.
• Robust calibration
• All available spectral data from various units
  (same of various brands) are combined to form the
  calibration set.
• Model comparison
• Relative Predictive Determinant (RPD) was used
  to determine model precision.
• RPD is a standardized parameter allowing an easy
  comparison between models validated on the same
  set.

The development of a near infrared (NIR)
prediction model is a long and costly process.
Often, a calibration is developed on a master
unit and transferred to secondary or slave units
using standardization techniques. Those
techniques have shown their usefulness when
employed within the same instrument
brand. Increasingly, network managers would like
to be able to add to their network instruments
from different manufacturers. This requires
chemometrics experts to develop techniques that
are either able to combine historical databases
from both brands or to transfer a model from one
brand to another.
Objectives
Evaluate the usefulness of existing
standardization techniques within and among
instrument brands for soybeans protein and oil
prediction models.
Materials and Methods

• Data collection
• Samples
• Calibration set 638 samples from 2002 to 2006
  crop years.
• Validation sets
• Set 1 20 samples representative of the
  variability of the calibration set.
• Set 2 40 very diverse samples from the 2006
  crop year.
• Spectral data
• 4 transmittance units, spectral range 850
  1048 nm with 2 nm increment.
• 2 Foss Infratec (Foss North America, Eden
  Prairie, MN) Foss Infratec 1229 and Foss
  Infratec 1241.
• 2 Dickey-john OmegAnalyzer G (Dickey-john
  corporation, Auburn, IL).
• Reference analysis
• Protein content by Combustion (AOAC 990.03),
  Eurofins, Des Moines.
• Oil content by ether extract (AOCS Ac 3-44),
  Eurofins, Des Moines.
• Calibration method
• Partial Least Squares Regression.
• Data Preprocessing
• Second derivative (5-point window) normalize
  (to unit area).

Results
Initial calibration performances Each instrument
was calibrated on its own database

• PDS and DS gave significantly lower RPD for among
  brand standardization.
• Other techniques were not significantly different
  from when each unit was calibrated on its own
  calibration set.
• Master units gave significantly higher results
  than slave units of the same brand, no matter the
  networks master.

Infratec 1241 and OmegAnalyzer G 6118 were
selected as masters.
Use of existing standardization techniques
within brands
Conclusions

• DS and PDS gave significantly lower RPDs.
• Standardization techniques gave either as good as
  or better RPDs than when slaves units were
  calibrated on their original calibration sets.
• Slope and Bias techniques appeared to give higher
  RPDs for both master and slave units.

• Intermediate parameters for optical
  standardization (DS, PDS) increased error
  significantly.
• Differences among standardization methods were
  proven consistent across standardization sets,
  across instrument brands and across
  standardization strategies.
• For within brand standardization, slope and bias
  correction appeared to give better results while
  for across brand, robust techniques were more
  suitable.