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.

Broad-based calibrations of in vitro gas production of forages by near-infrared reflectance spectroscopy

1998 ◽  
Vol 22 ◽  
pp. 234-237
Author(s):  
M. Herrero ◽  
N. S. Jessop
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Gas Production ◽  
Grass Species ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Wide Range

There is increasing demand to obtain fast and accurate dynamic nutritional information from forages. Near-infrared reflectance spectroscopy (NIRS) offers the possibility for obtaining such information for a range of nutritional constituents of foods. Herrero et al. (1996 and 1997) calibrated in vitro gas production measurements of a single grass species by NIRS. There would be greater practical benefit if the gas production predictions could be obtained using calibrations derived from a wide range of plant species, since a single equation could be used for all forages. The objective of this study was to investigate if in vitro gas production measurements of a broad based sample population could be calibrated by NIRS.

Determination of macro elements in alfalfa and white clover by near-infrared reflectance spectroscopy

2002 ◽  
Vol 139 (4) ◽  
pp. 413-423 ◽  
Author(s):  
A. MORÓN ◽  
D. COZZOLINO
Keyword(s):  
White Clover ◽  
Near Infrared ◽  
Cross Validation ◽  
Dry Weight ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Wide Range

Near-infrared reflectance spectroscopy was used to assess the mineral composition of both alfalfa (Medicago sativa L.) and white clover (Trifolium repens L.). Alfalfa (n=230) and white clover (n=97) plant samples from different locations in Uruguay representing a wide range of soil types were analysed. The samples were scanned for reflectance in a NIRSystems 6500 monochromator (NIRSystems, Silver Spring, MD, USA). Predictive equations were developed using modified partial least squares (MPLS) with cross validation to avoid overfitting. The coefficients of determination in calibration (R_{\rm cal}^{2}) and the standard errors in cross validation (SECV) were 0·93 (SECV: 1·6), 0·95 (SECV: 1·3), 0·93 (SECV: 1·9), 0·88 (SECV: 2·7), 0·82 (SECV: 0·3) and 0·75 (SECV: 4·7) for alfalfa and 0·98 (SECV: 0·8), 0·52 (SECV: 0·8), 0·97 (SECV: 2·7), 0·83 (SECV: 3·1), 0·82 (SECV: 1·9) and 0·45 (SECV: 2·6) for white clover, for N, Ca, K, P, Mg and S in g/kg on a dry weight respectively. Calcium, nitrogen and potassium were well predicted by NIRS in both alfalfa and white clover samples.

GENETIC STUDIES ON KERNEL HARDNESS IN WHEAT USING GRINDING TIME AND NEAR INFRARED REFLECTANCE SPECTROSCOPY

1983 ◽  
Vol 63 (4) ◽  
pp. 825-832 ◽  
Author(s):  
D. R. SAMPSON ◽  
D. W. FLYNN ◽  
P. JUI
Keyword(s):  
Near Infrared ◽  
Single Gene ◽  
Reflectance Spectroscopy ◽  
Kernel Hardness ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Wide Range ◽  
Grinding Time

Kernel hardness was measured in 600 random lines from five crosses and in seven control cultivars of spring wheat grown in 2 yr, using grinding time and near infrared reflectance spectroscopy. Both methods clearly differentiated soft from hard cultivars. Grinding time was the more accurate method because it gave lower coefficients of variation and higher correlations between years but it required five times more grain. The parents of the five crosses represented a range in hardness and were themselves from the cross Opal (hard) × Pitic 62 (soft). A hard × hard cross gave only hard lines; a medium × soft cross gave mostly soft; three soft or medium × hard crosses gave a wide range of hardness types that in two crosses suggested a single gene difference between hard and soft. Heritability of grinding time in standard units ranged from 55 to 92% per progeny and on average was 10% greater for soft lines than for hard.Key words: Wheat, kernel hardness, grinding time, Technicon InfraAlyzer, genetics

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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