Location: National Peanut Research LaboratoryTitle: Identification of wheat varieties with a parallel-plate capacitance sensor using fisher linear discriminant analysis
|Govindadajan, K - University Of Nebraska|
|Pupalla, Naveen - New Mexico State University|
|Settluri, V - Kle University|
|Reddy, R - Kle University|
Submitted to: IEEE Sensors Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/27/2013
Publication Date: 12/15/2013
Publication URL: http://www.hindawi.com/journals/js/2014/691898
Citation: Kandala, C., Govindadajan, K.N., Pupalla, N., Settluri, V., Reddy, R.S. 2013. Identification of wheat varieties with a parallel-plate capacitance sensor using fisher linear discriminant analysis. IEEE Sensors Journal. Available: http://www.hindawi.com/journals/js/2014/691898.
Interpretive Summary: Wheat (Triticum aestivum L.) is a prominent crop grown worldwide, and also is one of the most important food item consumed in different forms such as bread, cookies and pasta. It is also an important ingredient in hundreds of other food and drink preparations such as pizza, cakes, soups and beer. Many other varieties exist specific to the local cultures in different regions of the world. Wheat breeders developed hundreds of varieties to improve not only the yields, but also such agronomic and quality attributes such as resistance to pests and diseases, and stability in height and growth. Over the years, producers in different geographical areas started using particular wheat varieties, to produce end products such as bread or beer, according to the local tastes. Thus, variety identification plays an important role in selecting the right type of wheat for a particular product, and assures its quality. Many product manufactures such as bakeries and restaurants demand high levels of purity with respect to the variety. Techniques used presently for variety identification include gel electrophoresis and high performance liquid chromatography (HPLC). The CSIRO Plant Industry of Australia  developed a testing system using a set of DNA markers to identify wheat and barley varieties. However, these methods are time consuming and need some level of expertise to use. Thus, a physical method which is rapid and nondestructive would be useful for both the breeder and the industry in maintaining the required quality of the wheat and its products. In the present work, FLD models for wheat variety classification and identification were developed with the three variables C, ', and Z that were earlier used for MC determinations. The models were tested and validated on six varieties of wheat. The classification accuracy obtained over six varieties was over 95% by this method.
Technical Abstract: Fisher’s linear discriminant (FLD) models for wheat variety classification were developed and validated. The inputs to the FLD models were the capacitance (C), impedance (Z), and phase angle ('), measured at two frequencies. Classification of wheat varieties was obtained as output of the FLD models. Z and ' of a parallel-plate capacitance system holding the wheat samples were measured using an impedance meter, and the C value was computed. The best model developed classified the wheat varieties with an accuracy of 95.4% over the six wheat varieties tested. This method is simple, rapid and nondestructive and would be useful for the breeders and the industry.