scholarly journals Classification of the waxy condition of durum wheat by near infrared reflectance spectroscopy using wavelets and a genetic algorithm

2014 ◽  
Vol 117 ◽  
pp. 178-182 ◽  
Author(s):  
Barry K. Lavine ◽  
Nikhil Mirjankar ◽  
Stephen Delwiche
Keyword(s):  
Genetic Algorithm ◽  
Durum Wheat ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

Near infrared reflectance spectroscopy as a potential surrogate method for the analysis of D13C in mature kernels of durum wheat

2001 ◽  
Vol 52 (8) ◽  
pp. 809 ◽  
Author(s):  
J. P. Ferrio ◽  
E. Bertran ◽  
M. Nachit ◽  
C. Royo ◽  
J. L. Araus
Keyword(s):  
Durum Wheat ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Local Models ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Δ13c Values ◽  
Infrared Reflectance Spectroscopy ◽  
Wide Range

Carbon isotope discrimination (Δ13C) in grain is a potentially useful trait in breeding programs that aim to increase the yield of wheat and other cereals. Near infrared reflectance spectroscopy (NIRS) is used in routine assays to determine grain and flour quality. This study assesses the ability of NIRS to predict Δ13C in mature kernels of durum wheat. Plants were grown in north-west Syria as this location provided 3 distinct Mediterranean trials that covered a wide range for Δ13C values in grains (from about 12.9‰ to 17.6‰). We measured the spectral reflectance signature between 1100 and 2500 nm in samples from the same flour used in the conventional (i.e. mass spectrometry) determinations of Δ13C. By using principal components regression and partial least squares regression (PLSR), a model of the association between conventional laboratory analysis and these spectra was produced. Global regressions, which included samples from all 3 trials, and local models, which used samples from only one trial, were built and then validated with sample sets not included in calibration procedures. In global models, strong significant correlations (P < 0.001) were found between NIRS-predicted Δ13C and measured Δ13C values. PLSR gave r 2 values of 0.86 and 0.82 for calibration and validation sets, respectively. Although less strongly correlated, all local models selected for a subset of samples with significantly higher Δ13C values. Local models also performed well when selecting samples from the other 2 trials. The advantages and possible limitations of NIRS are further discussed.

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