Submitted to: Cereal Chemistry
Publication Type: Peer reviewed journal
Publication Acceptance Date: 9/19/2013
Publication Date: 1/1/2014
Publication URL: dx.doi.org/10.1094/CCHEM-07-13-0132-R
Citation: Armstrong, P.R. 2014. Development and evaluation of a near-infrared instrument for single-seed compositional measurement of wheat kernels. Cereal Chemistry. 91(1):23-28. Interpretive Summary: An instrument designed to measure single-seed composition was built and tested and its performance was evaluated using wheat kernels. The instrument uses near-infrared spectroscopy to measure seed composition on a continuous flow of singulated seeds. Single-seed protein, moisture content and mass of wheat kernels were used to evaluate the instrument. The instrument was shown to measure protein and moisture very well while seed mass was less accurate but still provided a useful measurement. Protein measurements were measured to within 0.7% of their true value, moisture content within 0.5% and kernel mass within 4 mg of true mass. The instrument measures seeds at a rate of at least 4 per second making it useful for small sample evaluation and can provide a useful tool for plant breeders wanting to evaluate compositional characteristics of breeding lines or selection of specific seeds and has potential for detecting disease symptoms or disease resistance.
Technical Abstract: A single kernel, near-infrared reflectance (NIR) instrument was designed, built and tested for its ability to measure composition and traits in wheat kernels. The major objective of the work was targeted at improving an existing design concept of an instrument used for larger seeds such as soybeans and corn but in this case designed for small seeds. Increases in throughput were sought by using vacuum to convey seeds without compromising measurement accuracy. Instrument performance was evaluated by examining measurement accuracy of wheat kernel moisture, protein, and kernel mass. Spectral measurements were obtained on individual wheat kernels as they were conveyed by air through an illuminated tube. Partial least squares (PLS) prediction models for these constituents were then developed and evaluated. PLS single kernel moisture predictions had a root mean square error of prediction (RMSEP) around 0.5% MC wet basis; protein prediction models had a RMSEP near 0.70%. Prediction of mass was not as good but still provided a reasonable estimate of single kernel mass, with RMSEP values between 2.8 to 4 mg. Data shows that kernel mass and protein were not correlated in contrast to previous research. Overall, results show the instrument performs comparably to other single seed instruments or methods based on accuracy, but with an increased throughput at a rate of at least four seeds per second.