
%0 Journal Article
%A Ferrio, J. P. 
%A Bertran, E. 
%A Nachit, M. 
%A Royo, C. 
%A Araus, J. L. 
%T Near infrared reflectance spectroscopy as a potential surrogate method for the analysis of <font face="symbol">D</font><emph type="7">13</emph>C in mature kernels of durum wheat
%J Australian Journal of Agricultural Research
%D 2001/07/25
%V 52
%N 8
%P 809-816
%K NIRS, carbon isotope discrimination, PCR, PLSR, multivariate analysis, regression.
%R https://doi.org/10.1071/AR00116
%U https://www.publish.csiro.au/paper/AR00116 
%X Carbon isotope discrimination (&#916;13C) 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 &#916;13C 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 &#916;13C values in grains (from about 12.9&#8240; to 17.6&#8240;). 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 &#916;13C. 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 &#60; 0.001) were found between NIRS-predicted &#916;13C and measured &#916;13C 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 &#916;13C values. Local models also performed well when selecting samples from the other 2 trials. The advantages and possible limitations of NIRS are further discussed. 
