Location: Dairy Forage ResearchTitle: NIRS WHITE PAPER Near Infrared Spectroscopy for forage and feed testing.) Author
Submitted to: Popular Publication
Publication Type: Popular publication
Publication Acceptance Date: 7/22/2008
Publication Date: 7/23/2008
Citation: Sapienza, D., Berzaghi, P., Martin, N.P., Taysom, D., Owens, F., Mahanna, B., Sevenich, D., Allen, R. 2008. NIRS White Paper, Near infrared spectroscopy for forage and feed testing. Available: www.uwex.edu/ces/forage/NIRS/nirs_white_paper.pdf. Interpretive Summary:
Technical Abstract: 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. For developing reliable NIRS prediction models and valid results, laboratories must: 1) minimize sources of error in the entire process, 2) obtain values for reference samples using analytical methods that have high precision and accuracy, 3) Standardize sample preparation and analytical procedures, 4) standardize the NIRS instrument, 5) use appropriate software to obtain accurate, predictive spectral information, 6) perform routine instrument maintenance, 7) analyze only samples representative of the original population and 8) obtain routine diagnostics of all associated instruments and undergo yearly prediction model (calibration) updates. The cost-effectiveness of NIR analysis allows the total analytical error (sampling and laboratory) to be reduced because a larger number of sub-samples 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.