A fast and convenient soil analytical technique is needed for soil quality assessment and precision soil management. The main objective of this study was to evaluate the ability of near-infrared reflectance spectroscopy (NIRS) to predict diverse soil properties. Near-infrared reflectance spectra, obtained from a Perstrop NIR Systems 6500 scanning monochromator (Foss NIRSystems, Silver Spring, MD), and 33 chemical, physical, and biochemical properties were studied for 802 soil samples collected from four Major Land Resource Areas (MLRAs). Calibrations were based on principal component regression (PCR) using the first derivatives of optical density [log(1/R)] for the 1300-to 2500-nm spectral range. Total C, total N, moisture, cation-exchange capacity (CEC), 1.5 MPa water, basal respiration rate, sand, silt, and Mehlich III extractable Ca were successfully predicted by NIRS (r 2 > 0.80). Some Mehlich III extractable metals (Fe, K, Mg, Mn) and exchangeable cations (Ca, Mg, and K), sum of exchangeable bases, exchangeable acidity, clay, potentially mineralizable N, total respiration rate, biomass C, and pH were also estimated by NIRS but with less accuracy (r 2 = 0.80∼0.50). The predicted results for aggregation (wt% > 2, 1, 0.5, 0.25 mm, and macroaggregation) were not reliable (r 2 = 0.46∼0.60). Mehlich III extractable Cu, P, and Zn, and exchangeable Na could not be predicted using the NIRS-PCR technique (r 2 < 0.50). The results indicate that NIRS can be used as a rapid analytical technique to simultaneously estimate several soil properties with acceptable accuracy in a very short time.

The soybean [Glycine max (L.) Merr.] is grown worldwide for its high protein and oil contents. Characterization of soybean seed components lends itself to understanding how soybean production can meet the needs of a growing world population. For this article, literature was reviewed and condensed to create a well‐rounded picture of the current understanding of structural, functional, and nutritional properties of soybean components. Natural variation in soybean protein, lipid, and carbohydrate components, as well as the minor constituents phytic acid and isoflavones, are mentioned. Environment‐ or genetic‐induced shifts in natural variation are described with respect to nutrition and functional improvements in soybean.

Mycotoxins, toxins produced by fungi that colonize food crops, can pose a heavy economic burden to the United States corn industry. In terms of economic burden, aflatoxins are the most problematic mycotoxins in US agriculture. Estimates of their market impacts are important in determining the benefits of implementing mitigation strategies within the US corn industry, and the value of strategies to mitigate mycotoxin problems. Additionally, climate change may cause increases in aflatoxin contamination in corn, greatly affecting the economy of the US Midwest and all sectors in the US and worldwide that rely upon its corn production. We propose two separate models for estimating the potential market loss to the corn industry from aflatoxin contamination, in the case of potential near-future climate scenarios (based on aflatoxin levels in Midwest corn in warm summers in the last decade). One model uses probability of acceptance based on operating characteristic (OC) curves for aflatoxin sampling and testing, while the other employs partial equilibrium economic analysis, assuming no Type 1 or Type 2 errors, to estimate losses due to proportions of lots above the US Food and Drug Administration (FDA) aflatoxin action levels. We estimate that aflatoxin contamination could cause losses to the corn industry ranging from $52.1 million to $1.68 billion annually in the United States, if climate change causes more regular aflatoxin contamination in the Corn Belt as was experienced in years such as 2012. The wide range represents the natural variability in aflatoxin contamination from year to year in US corn, with higher losses representative of warmer years.

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Calibration equations for the estimation of amino acid composition in whole soybeans were developed using partial least squares (PLS), artificial neural networks (ANN), and support vector machines (SVM) regression methods for five models of near-infrared (NIR) spectrometers. The effects of amino acid/protein correlation, calibration method, and type of spectrometer on predictive ability of the equations were analyzed. Validation of prediction models resulted in r 2 values from 0.04 (tryptophan) to 0.91 (leucine and lysine). Most of the models were usable for research purposes and sample screening. Concentrations of cysteine and tryptophan had no useful correlation with spectral information. Predictive ability of calibrations was dependent on the respective amino acid correlations to reference protein. Calibration samples with nontypical amino acid profiles relative to protein would be needed to overcome this limitation. The performance of PLS and SVM was significantly better than that of ANN. Choice of preferred modeling method was spectrometerdependent. Calibration equations for the estimation of amino acid composition in whole soybeans were developed using partial least squares (PLS), artificial neural networks (ANN), and support vector machines (SVM) regression methods for five models of near-infrared (NIR) spectrometers. The effects of amino acid/ protein correlation, calibration method, and type of spectrometer on predictive ability of the equations were analyzed. Validation of prediction models resulted in r 2 values from 0.04 (tryptophan) to 0.91 (leucine and lysine). Most of the models were usable for research purposes and sample screening.Concentrations of cysteine and tryptophan had no useful correlation with spectral information. Predictive ability of calibrations was dependent on the respective amino acid correlations to reference protein. Calibration samples with nontypical amino acid profiles relative to protein would be needed to overcome this limitation. The performance of PLS and SVM was significantly better than that of ANN. Choice of preferred modeling method was spectrometer-dependent.

The objectives of this research were: (1) to develop a technique for creating calibrations to predict the constituent concentrations of single maize kernels from near-infrared (NIR) hyperspectral image data, and (2) to evaluate the feasibility of an NIR hyperspectral imaging spectrometer as a tool for the quality analysis of single maize kernels. Single kernels of maize were analyzed by hyperspectral transmittance in the range of 750 to 1090 nm. The transmittance data were standardized using an opal glass transmission standard and converted to optical absorbance units. Partial least squares (PLS) regression and principal components regression (PCR) were used to develop predictive calibrations for moisture and oil content using the standardized absorbance spectra. Standard normal variate, detrending, multiplicative scatter correction, wavelength selection by genetic algorithm, and no preprocessing were compared for their effect on model predictive performance. The moisture calibration achieved a best standard error of cross-validation (SECV) of 1.20%, with relative performance determinant (RPD) of 2.74. The best oil calibration achieved an SECV of 1.38%, with an RPD of only 1.45. The performance and subsequent analysis of the oil calibration reveal the need for improved methods of single-seed reference analysis.

Near-infrared reflectance spectroscopy (NIRS), a nondestructive analytical technique, may someday be used to rapidly and simultaneously quantify several soil properties in agricultural fields. The objectives of this study were to examine the influence of moisture content on the accuracy of NIRS analysis of soil properties and to assess the robustness of a NIRS multivariate calibration technique. Four hundred agricultural soil samples (<2 >mm) from Iowa and Minnesota were studied at two moisture levels: moist and air-dried. The soil properties tested included total C, organic C, inorganic C, total N, CEC, pH, texture, moisture, and potentially mineralizable N. About 70% of the Iowa samples were selected for the calibration set, and the rest of the Iowa samples and all of the Minnesota samples were assigned to validation set I and validation set II, respectively. Calibrations were based on partial least-squares regression (PLSR), using the first differentials of log (1/R) for the 1100 to 2500-nm spectral range. The results for the calibration set and validation set I indicated that NIRS-PLSR was able to predict many soil properties (total C, organic C, inorganic C, total N, CEC, % clay, and moisture) with reasonable accuracy for both the air-dried (R 2 > 0.76) and moist (R 2 > 0.74) soils. The results for validation set II showed that NIRS-PLSR was able to predict some properties of soils (total C, organic C, total N, and moisture content) from a different geographic region, but other soil properties in validation set II were not accurately predicted. Although NIRS-PLSR predictions are slightly more accurate for air-dried soils than for moist soils, the results indicate that the NIRS-PLSR technique can be used for analysis of field moist samples with acceptable accuracy as long as diverse soil samples from the same region are included in the calibration database.