(Connie L. Hardy1, Glen R. Rippke2, Charles R. Hurburgh, Jr.3, Walter A. Goldstein4)
Connie Hardy, Program Specialist, Value Added Agriculture Program
In food products, near infrared spectroscopy (NIRS) is being used to measure an increasing number of chemical parameters that have traditionally been measured by analytical chemistry. The use of NIRS is driven by its speed and relative low cost, thereby allowing users to accurately measure nutrient components and other factors of interest in a matter of seconds versus several hours or days for traditional lab results. Calibration of NIRS analyzers relies on good representative sample sets and consistent reference chemistry; calibration is, in itself, a painstaking process but, once done, accurate calibrations provide rapid, reliable measurements in daily operations.
Rapid measurement of amino acids in raw and processed grain is becoming increasingly important in balancing livestock rations, and will be soon be critical for organic feeders. If the National Organic Program standards disallow synthetic amino acid supplements in organic feeds, sufficient amino acids must be supplied by the organically grown feed ingredients. Beyond the organic market, rapid measurement of amino acids in grains is desired by seed breeding programs and the feed industry to drastically cut costs for analytical chemistry and save time by efficiently screening inbound feed grain and breeder samples.
NIRS measurement of amino acids in corn and soybeans has been attempted by several scientists, but it has not succeeded because of the high correlation between the total protein content and the typical amino acid level. In typical corn, for example, methionine usually represents 0.20-0.25 percent of the total protein. When typical corn is then used to calibrate NIRS analyzers, the resulting calibration simply estimates the typical amount of methionine and cannot identify and measure samples where methionine is higher or lower than its typical level. Therefore, the calibration is not measuring the amino acid, but instead the amino acid level is implicitly calculated using the measurement of total protein.
The organic corn breeding program coordinated by Michael Fields Agricultural Institute is developing organic lines of corn with increased levels of methionine, lysine, and cysteine. This program offered a unique set of corn varieties in which amino acid levels were deliberately manipulated to break the correlation with total protein. These unique corn varieties, grown at four Midwestern locations (including Iowa State University, University of Minnesota, and Illinois and Wisconsin test plots) during two crop years, provided the set of samples necessary to calibrate NIRS to accurately measure methionine and lysine, two important limiting amino acids in poultry and swine rations.
To provide proof-of-concept calibrations that accurately measure methionine in corn.
Calibrations were developed for two whole-seed NIRS transmission analyzers (Bruins OmegAnalyzer G and Foss Infratec™ 1241 Grain Analyzer). Calibration data is shown below.
Coefficient of determination (R2) with:
Range (%db) Omega Infratec Total
Spectra Spectra Protein
Lysine 0.26-0.53 0.837 0.842 0.390
Methionine 0.14-0.39 0.746 0.730 0.542
Cysteine 0.14-0.37 0.783 0.787 0.797
Coefficient of determination (R2) is often used as a measure of accuracy by comparing NIR spectra to reference chemistry measurement of the selected amino acid. In this case, lysine and methionine have R2 of 0.73 – 0.84, significantly higher than the correlation of each amino acid with the total protein measured (039 – 0.54). With R2 values in this range for methionine and lysine, these calibrations are suitable for genetic screening in corn breeding programs (high vs. low genetic evaluations), according the criteria in AACC Method 39-00, Guidelines for Near Infrared Calibration Development (AACC, 2000). Cysteine is more reflective of protein only (R2= 0.797), but this amino acid is less important in poultry and swine ration formulation than lysine and methionine.
Validation with an independent set of samples (2008 crop) will show whether these calibrations are capable of measuring corn samples that were not previously included in the calibration set. With agricultural crops, such as corn, variation due to climate and new genetics affects the spectral interpretation of the grain; therefore, annual checks with new crop samples are necessary to maintain calibration accuracy and broaden the scope of the calibrations.
The project was presented as a poster at the Chemometrics Analytical Conference in Montpelier, France and at the International Diffuse Reflectance Conference in Chambersburg, PA in summer 2008. When a validation set is available from corn samples harvested in Fall 2008, calibrations will be tested with an independent sample set. If the results remain as encouraging as those of the “proof-of-concept” calibrations, then we will submit the project for publication in a refereed journal. For the agricultural sector using NIRS measurement systems, this is a significant step in developing efficiency and speed in the seed breeding industry and providing more accurate nutrient measurements for livestock feeders.
11111 NSRIC , Iowa State University, Ames, Iowa, USA; firstname.lastname@example.org
21547 Food Science Building, Iowa State University, Ames, Iowa, USA; email@example.com
31541 Food Science Building, Iowa State University, Ames, Iowa, USA; firstname.lastname@example.org
4Michael Fields Agricultural Institute, W2493 County Rd ES, PO Box 990, East Troy, WI 53120; email@example.com. This project was done with the ISU Grain Quality Lab and Michael Fields Agricultural Institute, as shown by the list of authors, and with sponsorship by the Agricultural Marketing Resource Center.
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Page last updated:
October 3, 2008
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