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Stretch Your Sampling Budget with NIRS


Stretch Your Sampling Budget with NIRS

By Bill Mahanna, Ph.D., Dipl. ACAN, DuPont Pioneer nutritional sciences manager

Brief History

NIRS (near infrared spectroscopy) has been around since 1939, but it was not until 1968 that Karl Norris and coworkers with the Instrumentation Research Lab of the U.S. Department of Agriculture first applied the technology to agricultural products.  They observed that cereal grains exhibited specific absorption bands in the NIR region and suggested that NIR instruments could be used to measure grain protein, oil and moisture. Research in 1976 demonstrated that absorption of other specific wavelengths was correlated with chemical analysis of forages. In 1978, the USDA NIRS Forage Network was founded to develop and test computer software to advance the science of NIRS grain and forage testing. By 1983, several commercial companies had begun marketing NIR instruments and software packages for forage and feed analysis.

How NIRS Works

NIRS is based on the interaction of physical matter with light in the near-infrared spectral region (700-2,500 nm). Sample preparation and presentation to the NIR instrument vary widely. Though dried, finely ground samples are often employed, whole grains or fresh, unground samples also can be scanned. Instruments can be stationary in a laboratory or mobile (e.g., on board a silage chopper).

Monochromatic light produced by an NIR instrument interacts with plant material in a number of ways, including as reflection, refraction, absorption and diffraction. Vibrations of the hydrogen bonded with carbon, nitrogen or oxygen cause molecular "excitement" responsible for absorption of specific amounts of radiation of specific wavelengths. This allows labs to relate specific chemical bond vibrations (generating specific spectra) to concentration of a specific feed component (e.g., starch). Spectroscopy is possible because molecules react the same way each time they are exposed to the same radiation. NIR instruments are much less sensitive in quantifying individual inorganic elements (e.g., calcium, phosphorus or magnesium) or mixtures (e.g., ash) because they are measuring the influence of these "contaminating materials" on the covalent bonds.

Building a “Calibration”

The individual laboratory or consortium that develops a prediction model uses software packages to perform the mathematical calculations necessary to associate the NIR spectra of reference samples with the reference chemistry of those reference samples. The mathematical equations developed are termed "prediction models," although they are also called "calibrations." The robustness of an NIR prediction model is, in part, determined by the size and representative nature of the calibration population samples that will be analyzed by reference methods. The sample population should represent the full diversity of plant materials to be scanned.

For instance, if the goal is to develop a prediction model for crude protein in corn grain, then samples of corn from diverse genetic and environmental backgrounds need to be included in the population to be analyzed by the chosen reference method in a lab with high National Forage Testing Association (NAFT) performance statistics. When a particular analytical methodology may not exist (e.g., for prediction of ethanol yield from corn fermentation), laboratories may develop an entirely new reference method. Numerous samples should be scanned by NIR and assayed by wet chemistry procedures to obtain good calibration statistics. A proof-of-concept model will utilize 50-60 samples; fully developed prediction models can be built from no fewer than 80-100 samples, but this number can be greater (1,000s) depending upon the error terms associated with each analyte. The final number of samples required is dependent upon the analytical and spectral diversity within the reference samples selected for developing the prediction model.

Questions for the Lab

As users of NIR-predicted values, we should all feel comfortable asking our chosen analytical partners questions about NIR prediction models and wet chemistry statistics. What is often forgotten about NIR-predicted values is that NIR is a secondary method based on a regression against a primary (or reference) method. Consequently, the NIR value can never be more accurate than the primary reference method. The limitations and laboratory errors associated with methods such as neutral detergent fiber (NDF) should not be blamed on NIRS prediction models for errors associated with the original reference chemistry.

Here are some calibration statistics that reputable NIR laboratories will be able to provide to give you more confidence in their NIRS results:

  1. Number of samples in the calibration set (N) is influenced by the natural variation in the trait of interest. The narrower the range, the more difficult it is to detect differences. Typically, 80-100 samples are required for developing an initial calibration, with up to multiple-hundreds of samples in a "mature" calibration.
  2. Standard error of calibration (SEC) defines how well the NIRS prediction model predicts the reference values (calibration sample set) that was used to build the model. Low SEC values are desired. For example, if the reference value is 30 and the SEC is three, this means 66.7% of the NIR predicted values should fall within the range of 27-33.
  3. Regression coefficient (R2 or RSQ) is the best-fit line when predicted values are plotted against the associated reference values. High R2 values are desired. An R2 of 1.0 means 100% of the analyte variance is explained by the prediction equation.

The Bottom Line

NIR analysis as an analytical technique has a long and credible history. NIR is a secondary method that never can be more accurate than the reference method upon which it is based. Statistically robust prediction models allow for a rapid and repeatable assay procedure for nutritional values that helps the livestock industry detect and manage variability in composition among and within feedstuffs.

The cost effectiveness of NIR analysis allows the total analytical error (sampling and laboratory) to be reduced because a larger number of subsamples or sequential samples can be assayed with a limited analytical budget than is possible using the more expensive wet chemistry approaches.  

To enhance trust, nutritionists, producers and laboratories are encouraged to communicate more fully and openly so that NIR prediction model and wet chemistry statistics are understood more clearly.

For more detailed information on NIRS, see Bill Mahanna's article from June 2008, Feedstuffs.



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