Quantification of nitrogen concentration in perennial ryegrass and red fescue using near-infrared reflectance spectroscopy (NIRS) and chemometrics

2004 ◽  
Vol 88 (2-3) ◽  
pp. 269-277 ◽  
Author(s):  
R Gislum ◽  
E Micklander ◽  
J.P Nielsen
Keyword(s):  
Perennial Ryegrass ◽  
Near Infrared ◽  
Nitrogen Concentration ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Red Fescue

The distribution of errors associated with genotype and environment during the prediction of the water-soluble carbohydrate concentration of perennial ryegrass cultivars using near infrared reflectance spectroscopy

2000 ◽  
Vol 51 (4) ◽  
pp. 481 ◽  
Author(s):  
K. F. Smith ◽  
G. A. Kearney
Keyword(s):  
Perennial Ryegrass ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Water Soluble ◽  
Soluble Carbohydrate ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Water Soluble Carbohydrate

Significant deviations associated with site or cultivars within sites were detected in 4 of 6 independent near infrared reflectance spectroscopy (NIRS) calibrations developed to predict water-soluble carbohydrate (WSC) concentrations in perennial ryegrass herbage harvested from 2 sites. These effects were observed both when calibration subsets were selected on the basis of spectral characteristics, and when calibration sets were balanced with respect to a priori knowledge of the structure of the data set. However, there were also instances when non-random deviations were not significant, demonstrating that it was possible to develop broadly based NIRS calibrations to predict WSC in perennial ryegrass. Deviations between NIRS predictions and reference values should be monitored, with reference to the structure of the experiment from which the samples were derived, before NIRS estimates of WSC concentration are used in agronomy or plant breeding.

Measurement of the magnesium concentration in perennial ryegrass (Lolium perenne) using near infrared reflectance spectroscopy

1991 ◽  
Vol 42 (8) ◽  
pp. 1399 ◽  
Author(s):  
KF Smith ◽  
SE Willis ◽  
PC Flinn
Keyword(s):  
Lolium Perenne ◽  
Perennial Ryegrass ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Magnesium Concentration ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Calibration Equations

Near infrared reflectance spectroscopy (NIR) was used to develop calibration equations to measure the magnesium concentration in perennial ryegrass herbage (Lolium perenne). A subset of 72 samples was selected on the basis of spectral variation from 400 samples grown in 1988-1989. Three alternative equations were chosen using stepwise multiple linear regression, with standard errors ranging from 0.4 to 0.3 g/kg DM with corresponding squared multiple correlation coefficients ( R2) of 0.68 to 0.82. The equations had 2, 4 and 4 wavelength terms respectively. When these equations were tested on an independent population of perennial ryegrass samples, a significant bias existed when using the 4 term equations but there was no bias when the 2 term equation was used. We conclude that NIR can be used to screen large numbers of perennial ryegrass plants for magnesium concentration. However, for the calibration equations to be used for the analysis of other populations equation performance must be monitored by comparing reference and NIR analyses on a small number of samples.

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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