The Japan Oilseed Processors Association provided yearly data showing the average protein and oil content of imported soybeans from the U.S. (No. 2 Yellow and IOM grades), Brazil, Argentina, and the People's Republic of China. Throughout the years 1972-1988, U.S. No. 2 soybeans contained about 1-L5% less oil than Brazilian soybeans. Recently, the protein content of U.S. soybeans has also fallen behind that of Brazil. U.S. IOM soybeans, a designation based on seed size, contained about L5% more protein and about 0.5% less oil than U.S. No. 2 soybeans. Surveys of U.S. soybeans in the years 1986, 1987, and 1988 showed consistent state and regional differences in protein and oil content. Soybeans from northern and western soybean-growing states (North Dakota, South Dakota, Minnesota, Iowa, Wisconsin) contained 1.5-2% less protein and 0.2-0.5% more oil than soybeans from southern states (Texas, Arkansas, Louisiana, Mississippi, Tennessee, Kentucky, Alabama, Georgia, South Carolina, North Carolina). State and regional differences in composition represented differences of up to 25 cents per bushel in Estimated Processed Value for one set of soybean meal and oil prices.

Iowa State University coordinated an interlaboratory comparison study of Kjeldahl protein and ether oil extraction methods. Blind duplicates of 10 clean, single‐variety soybean samples were sent to 30 laboratories grouped in 3 categories of 10 each in public (government and university), commercial and processor facilities. Five of the commercial laboratories were AOCS‐certified. Standard deviations among laboratory means across all samples were 3.87 and 1.82 percentage points (dry basis) for protein and oil, respectively (0.48 and 0.27, respectively, for the AOCS‐certified laboratories). The average differences between blind duplicates of a sample were 0.71 percentage points for protein and 0.87 percentage points for oil (0.28 and 0.45, respectively, for the certified laboratories). Average standard deviations across laboratories on an individual sample were 2.37 and 1.71 percentage points for protein and oil, respectively (1.87 and 0.99, respectively, for the certified laboratories.

435Crambe moisture, protein and oil percentages were predicted by fixed-filter near-infrared reflectance with standard errors of prediction (SEP) of 0.26, 0.59 and 0.86 percentage points, respectively. Crambe had a large range of protein and oil percentages, 17.4%-25.0% and 17.7%-36.4% respectively. Calibration samples were selected on the basis of relative spectral data, with no advance knowledge of protein and oil content. This procedure selected samples representing the full range of constituent values, and resulted in calibrations that had lower SEP's than standard errors of calibration.is regarded as a promising new industrial oil seed. Crambe oil is about 55-60% erucic acid (cis-13docosenoic) (1). When treated with ozone, erucic acid gives brassylic and pelargonic acids. Brassylic acid can be used for polyesters, plasticizers, alkyd resins, lubricants, rubber additives and surface-active agents. Pelargonic acid is used for plasticizers, alkyd resins, vinyl stabilizers, hydrotropic salts, pharmaceuticals, synthetic flavors and odors, flotation agents and insect repellents (2).The oil-free, hull-free meal contains 46%-58% crude protein with a good amino-acid balance for feed. The meal will contain thioglucosides, which places some restrictions on its use as a feedstuff.Agronomic research in crambe breeding gave rise to a need for nutrient analysis of crambe seed. Previous success with soybeans (3) and other oilseeds (4) suggested that near-infrared reflectance (NIR) could be used in lieu of wet chemical methods. However, crambe seed is light and small, conditions certain to complicate grinding.The multiple-linear-regression calibration of fixedwavelength near-infrared reflectance analyzers has been reported extensively in the literature. Little has been written about the actual selection process of calibration and prediction samples. Typically, a number of samples are collected and randomly divided into two groups--one for calibration and one for prediction (5,6), or samples are compared against those of previously known chemical composition (4,7). Barton and Cavanagh {8) used two stepwise linear-regression analysis programs, which allowed every i th sample to be saved for prediction verification.Selection of calibration samples by spectra alone has been reported for near-infrared monochramotors (9) but not for fixed-filter units.The range of protein and oil values for crambe was unknown. Instead of running wet chemistry on all our available samples, we wanted to select a spectrally representative calibration set by using only reflectance *To whom correspondence should be addressed.

Three air‐oven moisture methods, AOCS Ac 2‐41, AACC 44‐18 and the official USDA method, were compared on 20 samples of 1987 crop soybeans. The AOCS method is a whole‐grain method, the other two are two‐stage, ground‐grain methods. The average difference between the AOCS and USDA methods was 0.04 percentage points with a standard deviation of 0.18 points. The AOCS method can be used interchangeably with the USDA method for calibration of moisture devices. The AACC method averaged 0.15 and 0.19 points higher than the AOCS and USDA methods, respectively.