Development of Single-Seed Near-Infrared Spectroscopic Predictions of Corn and Soybean Constituents using Bulk Reference Values and Mean Spectra

Rapid, non-destructive single-seed compositional analyses are useful for many areas of crop science, including breeding and genetics. Seeds are sometimes unique and require preservation due to small samples, which necessitates development of methods for total non-destructive measurement. Near-infrared reflectance spectroscopy (NIRS) can be used for non-destructive single-seed composition prediction, but the reference methods used to develop prediction models are usually destructive. Reference methods are costly, and extensive sets of seeds must be used to obtain prediction models for multiple constituents. In this research, single-seed NIRS prediction models were developed for common constituents of soybeans and corn using composition values from bulk reference measurement and respective averaged single-seed spectra as opposed to single-seed reference and spectra. The bulk reference model and a true single-seed model for soybean protein were also compared to determine how well the bulk model performs in predicting single-seed protein. This provided a basis for evaluating bulk model performance for other constituents. Bulk model statistics indicated that bulk models should perform well for soybean protein and oil, but not well for fiber; corn bulk models should perform well for protein, oil, starch, and seed density. Bulk model predictions of single-seed soybean reference protein show, at best, that bulk models work reasonably well, with a standard error of prediction (SEP) = 1.82%) compared to an SEP of 0.97% for a true single-seed protein model. Bias correction may be needed, though, depending how the bulk model is developed. Overall, the bulk models should be useful for selecting single seeds in breeding programs targeting specific composition traits and segregating individual samples based on composition

Visible-near-infrared absorbance spectroscopy for rapid estimation of leaf nitrogen contents of Philippine rice cultivars

Accurately estimating the leaf nitrogen contents of rice leads to more economical use of fertilizers, and reduces negative impacts of high fertilization rates on the environment. This study was aimed at developing visible-near-infrared absorbance spectroscopy prediction models for leaf nitrogen contents of rice. A field experiment was established comprising of four Philippines lowland rice cultivars receiving four levels of urea nitrogen fertilizer from 0 to 240 kg-N ha−1. Leaf samples were taken over a 5-week period around the panicle initiation stage, immediately dried and pulverized. Some 100 samples were scanned using a Foss NIRSystem 6500 from 400 to 2,498 nm, and analyzed for Kjeldahl-nitrogen in a laboratory. The full wavelength model with no spectral pretreatment gave the best model with a standard error of performance (SEP) and R2 of 0.142% and 0.958, respectively. Re-scanning of the samples suggested a better model from 700 to 2,498 nm with standard normal variate spectral pretreatment with a standard error of cross-validation (SECV) and R2 of 0.117% and 0.973, respectively

Differences between conventional and glyphosate tolerant soybeans and moisture effect in their discrimination by near infrared spectroscopy

Previous studies showed that Near Infrared Spectroscopy (NIRS) could distinguish between Roundup Ready® (RR) and conventional soybeans at the bulk and single seed sample level, but it was not clear which compounds drove the classification. In this research the varieties used did not show significant differences in major compounds between RR and conventional beans, but moisture content had a big impact on classification accuracies. Four of the five RR samples had slightly higher moistures and had a higher water uptake than their conventional counterparts. This could be linked with differences in their hulls, being either compositional or morphological. Because water absorption occurs in the same region as main compounds in hulls (mainly carbohydrates) and water causes physical changes from swelling, variations in moisture cause a complex interaction resulting in a large impact on discrimination accuracies.This article is from Food Chemistry 141 (2013): 1895–1901, doi:10.1016/j.foodchem.2013.04.087. </p