A corn density calibration was developed for a near-infrared transmittance instrument (Infratec 1225). The calibration sample set included 96 corn samples grown in various locations of the United States. Samples were selected by principal component analysis (PCA) from a larger set of 410 samples, representing the 1986 through 1992 crop years. Samples for instrument and temperature stabilization were included in the calibration. The partial least squares calibration was then validated with 35 randomly selected samples not in the calibration sample set. With 14 PCA factors, the standard error of calibration was 0.0173 g/cm3, and the standard error of prediction was 0.0164 g/cm3. Fourteen factors were required because the first 12 reflected the physical correlations of density to protein and density to starch.
An equation was developed to predict corn breakage susceptibility based on the protein content, oil content, starch content, kernel density, and test weight. Reference values of breakage susceptibility were measured by Wisconsin Breakage Tester. Two statistical techniques were used to design the prediction equation, multiple linear regression (MLR) and principal factor method (PFM).
KeywordsBreakage susceptibility, Corn, Near infrared, Principal factor method, Grades and standards
In this report, two aspects of near infrared standardisation (calibration transfer) were studied: the selection of the master instrument and the standardisation algorithm. Their impact on the accuracy of barley protein measurements was evaluated using 285 fixed-filter Foss Grainspec instruments. Optical linear regression, Wiener filter and no standardisation were compared. The statistically-based master selection procedure had a significant impact on the spectral and calibration transfer when all standardisation techniques were applied. Spectral centralisation improved the accuracy of prediction, particularly when a poorer master was used.
Soybean physical and chemical properties changed by size (from 4.8 to 8.8 mm diameter), but soybean size and seed density did not affect the protein and oil determination accuracy of three near-infrared transmission analyzers. Corn samples were also separated by size and kernel density. Changes in corn kernel density and size introduced small errors in near-infrared transmission protein, oil, and starch measurements. In corn protein, the maximum error was about ±0.2% points. A robust calibration set is needed to eliminate the weak seed weight and density effects, as well as to support the corn density calibration for near-infrared analyzers.
Wiener and Second Order Volterra filters 3 Optical standardization compensating for wavelength and absorbance shift 4 Mantis standardization algorithm 4 Instrument design 5 Dissertation Organization 7 References 7
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