Near Infrared Spectroscopy (NIRS) Calibrations for Moisture in High Moisture Corn Co-Products

Corn co-products from the ethanol industry have become a common feed ingredient in Midwest feedlot diets. However, nutrient composition, especially moisture, can vary by as much as 15% from load to load even from the same ethanol plant. Knowing the exact moisture content of the co-product is advantageous for proper ration formulation and cattle efficiency. This study demonstrates that near infrared spectroscopy (NIRS) technology shows promise for rapid, accurate assessment of moisture in wet corn co-products

Calibration of NIRS Whole Grain Analyzers for Amino Acid Measurement in Corn

In food and feed products, near infrared spectroscopy (NIRS) is 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

Sixth Year Performance of Honeycrisp Grafted on 31 Dwarfing Rootstocks of the NC-140Regional Apple Rootstock Trial

Introduction Dwarfing rootstocks have the potential to increase profitability of tree-fruit growers by providing smaller trees suitable for high density plantings. Although the initial installation cost can be 10 to 30 times more than lower-density plantings, the long-range returns can far exceed the traditional plantings. However, to be viable as a commercial rootstock, dwarfing rootstocks must be adapted to a range of agro-climatic conditions, moderately disease resistant, high yielding, and produce quality fruit.</p

Determination of Amino Acid Composition of Soybeans (Glycine max) by Near-Infrared Spectroscopy

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.Posted with permission from Journal of Agricultural and Food Chemistry 54 (2006): 3485–3491, doi:10.1021/jf052570u. Copyright 2006 American Chemical Society.</p

Calibration of NIRS Whole Grain Analyzers for Amino Acid Measurement in Corn

In food and feed products, near infrared spectroscopy (NIRS) is 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.</p