Measuring Grain Protein Concentration with In-line Near Infrared Reflectance Spectroscopy

2008 ◽  
Vol 100 (2) ◽  
pp. 247-252 ◽  
Author(s):  
D. S. Long ◽  
R. E. Engel ◽  
M. C. Siemens
Keyword(s):  
Protein Concentration ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Grain Protein ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Grain Protein Concentration ◽  
Infrared Reflectance Spectroscopy

Development of novel technological approaches for a reliable crop characterization under changing environmental conditions

NIR news ◽  
2020 ◽  
Vol 31 (7-8) ◽  
pp. 14-19
Author(s):  
Omar Vergara-Díaz ◽  
Shawn Kefauver ◽  
José Luis Araus ◽  
Iker Aranjuelo
Keyword(s):  
Precision Agriculture ◽  
Grain Quality ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Grain Protein ◽  
World Population ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

The expansion of world population requires the development of new strategies and tools for agriculture. Extensive breeding and agronomic efforts over the past 50 years have been responsible for tripling cereal yields, while advances in grain quality have been less evident. Continuing advances in the techniques available to breeders offer the potential to increase the rate of genetic improvement aiming to develop resilient crop and better (more resource use efficient) varieties. Plant breeders want to be able to phenotype large numbers of lines rapidly and accurately identify the best progeny. For this purpose, different methodological approaches have been proposed to evaluate these traits in the field: (1) proximal (remote) sensing and imaging, (2) laboratory analyses of samples, and (3) lab-based near-infrared reflectance spectroscopy analysis in the harvestable part of the crop. However, near-infrared reflectance spectroscopy-based field evaluation of yield and grain quality is currently a real option. Thus the development of new technological approaches, such as the use of hyperspectral imaging sensors or near-infrared reflectance spectroscopy under field conditions may be critical as a phenotypic approach for efficient breeding as well as in field management of crops. This article reports the description of the CropYQualT-CEC project funded by the H2020-MSCA-RISE program. This project pursues the main objective of generating a common solid knowledge basis within the context of precision agriculture and digital farming. Further, within the project context, the article also provides a case study in which prediction models for total grain protein content, based on the reflectance spectrum of wheat canopies, are presented. Measurements were performed at around anthesis, using a full range near-infrared reflectance spectroscopy field spectrometer. Several models explaining >60% of grain protein variance in field trials illustrate the predictive capacity and robustness of this methodology for inferring grain quality traits well in advance of harvest.

Diet quality and liveweight gain of steers grazing Leucaena - grass pasture estimated with faecal near infrared reflectance spectroscopy (F.NIRS)

2008 ◽  
Vol 48 (7) ◽  
pp. 835 ◽  
Author(s):  
R. M. Dixon ◽  
D. B. Coates
Keyword(s):  
Diet Quality ◽  
Crude Protein ◽  
Protein Concentration ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Additional Reference

Three drafts of Bos indicus cross steers (initially 178–216 kg) grazed Leucaena–grass pasture [Leucaena leucocephala subspecies glabrata cv. Cunningham with green panic (Panicum maximum cv. trichoglume)] from late winter through to autumn during three consecutive years in the Burnett region of south-east Queensland. Measured daily weight gain (DWGActual) of the steers was generally 0.7–1.1 kg/day during the summer months. Estimated intakes of metabolisable energy and dry matter (DM) were calculated from feeding standards as the intakes required by the steers to grow at the DWGActual. Diet attributes were predicted from near infrared reflectance spectroscopy spectra of faeces (F.NIRS) using established calibration equations appropriate for northern Australian forages. Inclusion of some additional reference samples from cattle consuming Leucaena diets into F.NIRS calibrations based on grass and herbaceous legume–grass pastures improved prediction of the proportion of Leucaena in the diet. Mahalanobis distance values supported the hypothesis that the F.NIRS predictions of diet crude protein concentration and DM digestibility (DMD) were acceptable. F.NIRS indicated that the percentage of Leucaena in the diet varied widely (10–99%). Diet crude protein concentration and DMD were usually high, averaging 12.4 and 62%, respectively, and were related asymptotically to the percentage of Leucaena in the diet (R2 = 0.48 and 0.33, respectively). F.NIRS calibrations for DWG were not satisfactory to predict this variable from an individual faecal sample since the s.e. of prediction were 0.33–0.40 kg/day. Cumulative steer liveweight (LW) predicted from F.NIRS DWG calibrations, which had been previously developed with tropical grass and grass–herbaceous legume pastures, greatly overestimated the measured steer LW; therefore, these calibrations were not useful. Cumulative steer LW predicted from a modified F.NIRS DWG calibration, which included data from the present study, was strongly correlated (R2 = 0.95) with steer LW but overestimated LW by 19–31 kg after 8 months. Additional reference data are needed to develop robust F.NIRS calibrations to encompass the diversity of Leucaena pastures of northern Australia. In conclusion, the experiment demonstrated that F.NIRS could improve understanding of diet quality and nutrient intake of cattle grazing Leucaena–grass pasture, and the relationships between nutrient supply and cattle growth.

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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