U.S. Department of Agriculture: Agricultural Research Service, Lincoln, Nebraska
Date of this Version
2014
Citation
Microchemical Journal 117 (2014) 178–182; http://dx.doi.org/10.1016/j.microc.2014.06.030
Abstract
Near infrared (NIR) reflectance spectroscopy has been applied to the problemof differentiating four genotypes of durum wheat: ‘waxy’, Wx A1 null null, wx-B1 null and wild type. The test data consisted of 95 NIR reflectance spectra ofwheat samples obtained froma USDA-ARSwheat breeding program. A two-step procedure for pattern recognition analysis ofNIR spectral data was employed. First, thewavelet packet transform[14,15] was applied to the NIR reflectance data usingwavelet filters at different scales to extract and separate low-frequency signal components from high frequency noise components. By applying these filters, each reflectance spectrum was decomposed into wavelet coefficients that represented the sample's constituent frequencies. Second, wavelet coefficients characteristic of the waxy condition of the wheat samples were identified using a genetic algorithm for pattern recognition. The pattern recognition GA employed both supervised and unsupervised learning to identify wavelet coefficients that optimized clustering of the spectra by genotype in a plot of the two largest principal components of the data. By sampling key feature subsets, scoring their PC plots, and tracking those genotypes and samples that were difficult to classify, the pattern recognition GA was able to identify a set of wavelet coefficientswhose PC plot showed clustering of thewheat samples on the basis of their ‘waxy’ condition. Object validation was also performed to assess the predictive ability of the proposed NIR method to identify the ‘waxy’ condition of the wheat. An overall classification success rate of 78% was achieved for the spectral data.
Comments
U.S. Government Work