Application of near Infrared Reflectance Spectroscopy to a Durum Wheat Breeding Programme

2006 ◽  
Vol 14 (1) ◽  
pp. 17-25 ◽  
Author(s):  
Mike Sissons ◽  
Brian Osborne ◽  
Shaylene Sissons
Keyword(s):  
Durum Wheat ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Wheat Breeding ◽  
Breeding Programme ◽  
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.

scholarly journals Predicting quality, texture and chemical content of yam (Dioscorea alata L.) tubers using near infrared spectroscopy

2021 ◽  
pp. 096703352110075
Author(s):  
Adou Emmanuel Ehounou ◽  
Denis Cornet ◽  
Lucienne Desfontaines ◽  
Carine Marie-Magdeleine ◽  
Erick Maledon ◽  
...  
Keyword(s):  
High Throughput ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Quality Traits ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Texture Attributes

Despite the importance of yam ( Dioscorea spp.) tuber quality traits, and more precisely texture attributes, high-throughput screening methods for varietal selection are still lacking. This study sets out to define the profile of good quality pounded yam and provide screening tools based on predictive models using near infrared reflectance spectroscopy. Seventy-four out of 216 studied samples proved to be moldable, i.e. suitable for pounded yam. While samples with low dry matter (<25%), high sugar (>4%) and high protein (>6%) contents, low hardness (<5 N), high springiness (>0.5) and high cohesiveness (>0.5) grouped mostly non-moldable genotypes, the opposite was not true. This outline definition of a desirable chemotype may allow breeders to choose screening thresholds to support their choice. Moreover, traditional near infrared reflectance spectroscopy quantitative prediction models provided good prediction for chemical aspects (R2 > 0.85 for dry matter, starch, protein and sugar content), but not for texture attributes (R2 < 0.58). Conversely, convolutional neural network classification models enabled good qualitative prediction for all texture parameters but hardness (i.e. an accuracy of 80, 95, 100 and 55%, respectively, for moldability, cohesiveness, springiness and hardness). This study demonstrated the usefulness of near infrared reflectance spectroscopy as a high-throughput way of phenotyping pounded yam quality. Altogether, these results allow for an efficient screening toolbox for quality traits in yams.

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