Location: Food Quality LaboratoryTitle: Near-infrared hyperspectral imaging of blends of conventional and waxy hard wheats Author
Submitted to: Journal of Spectral Imaging
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/24/2017
Publication Date: 2/9/2018
Citation: Delwiche, S.R., Qin, J., Graybosch, R.A., Rausch, S.R., Kim, M.S. 2018. Near-infrared hyperspectral imaging of blends of conventional and waxy hard wheats. Journal of Spectral Imaging. 7(a2):1-13.
Interpretive Summary: Starch present in wheat kernels consists of two large molecules, amylose and amylopectin, whose structures differ only in the way that their glucose building block units are joined. Wheat kernels that contain low levels of amylose are called waxy wheat. These starch structural differences impart unique processing and end-use properties that result in higher value for waxy wheat. With recent availability of new commercial U.S. waxy wheat varieties that have virtually zero amylose, and with these varieties often appearing indistinguishable to conventional varieties, the need exists for a rapid method to measure the purity or composition of waxy wheat in harvested lots of wheat. Our previous research demonstrated the potential of a technique known as near-infrared (NIR) spectroscopy to fill this need. The current research extends this work to another technique that combines NIR with digital imaging. Known as hyperspectral image analysis, this technique allows for the evaluation of hundreds of kernels within a sample at the kernel level for determining mixture levels of conventional and waxy wheats. We demonstrated that this technique can be used to accurately determine the percentage of waxy wheat in mixed samples and, because the technique can differentiate single wheat kernels, may be used in commercial wheat sorting operations. Wheat traders, millers, and processors stand to benefit from this research.
Technical Abstract: Recent development of hard winter waxy (amylose-free) wheat adapted to the North American climate has prompted the quest to find a rapid method that will determine mixture levels of conventional wheat in lots of identity preserved waxy wheat. Previous work documented the use of conventional near-IR reflection spectroscopy to determine the mixture level of conventional wheat in waxy wheat, with an examined range, through binary sample mixture preparation, of 0 to 100 percent (w conventional /w total). The current study examines the ability of near-IR hyperspectral imaging of intact kernels to determine mixture levels. Twenty-nine mixtures (0, 1, 2, 3, 4, 5, 10, 15, …, 85, 90, 95, 96, 97, 98, 99, 100 percent) were formed from known genotypes of waxy and conventional wheat. Two-class partial least squares discriminant analysis (PLSDA) and statistical pattern recognition classifier models were developed for identifying each kernel in the images as conventional or waxy. Along with these approaches, conventional PLS1 regression modeling was performed on means of kernel spectra within each mixture test sample. Results indicated close agreement between all three approaches, with standard errors of prediction for the better preprocess transformations (PLSDA models) or better classifiers (pattern recognition models) of approximately 9 percentage units. Although such error rates were slightly greater than ones previously published using non-imaging near-IR analysis of bulk whole kernel wheat and wheat meal, the HSI technique offers an advantage of its potential use in sorting operations.