Determination of dry matter and crude protein contents of undried forages by near-infrared reflectance spectroscopy

2002 ◽  
Vol 82 (4) ◽  
pp. 380-384 ◽  
Author(s):  
Daniel Cozzolino ◽  
Marcel Labandera
Keyword(s):  
Crude Protein ◽  
Dry Matter ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

Determination of Acid-Detergent Fiber and Crude Protein in Forages by Near-Infrared Reflectance Spectroscopy: Collaborative Study

1988 ◽  
Vol 71 (6) ◽  
pp. 1162-1167 ◽  
Author(s):  
Franklin E Barton II ◽  
William R Windham
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Collaborative Study ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Acid Detergent Fiber ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

Abstract A Collaborative Study Was Conducted To Determine The Standard Error Of Difference Among Laboratories For Near-Infrared Reflectance Spectroscopic (Nirs) Determination Of Acid-Detergent Fiber (Adf) And Crude Protein In Forages. The 6 Participating Laboratories Were Members Of The Usda/Ars National Near-Infrared Reflectance Spectroscopy Forage Research Project. The Nirs Calibration Equations Were Developed In The Associate Referee's Laboratory For Crude Protein And Adf And Were Transferred To The Instrument In Each Of The Other Collaborating Laboratories. The Calibration Set Included Over 650 Diverse Forage Samples With Crude Protein And Adf Calibration Data; The Validation Set Included 94 Samples Of Bermudagrass. Amonglaboratory Reproducibility For The Nirs Method, Calculated As The Relative Standard Deviation For Reproducibility (Rsdr), Was 1.14% For Adf And 0.42% For Crude Protein. The Variance Component For Among-Laboratory Variation (Coefficient Of Variation) Was 2.54% For Adf And 2.89% For Crude Protein. These Results Confirm That It Is Possible To Calibrate, Validate, And Transfer (Nirs) Equations And Data Among Laboratories For The Accurate Determination Of Adf And Crude Protein, And Thereby Demonstrate That Nirs Can Be Used As A Standard Method For The Analysis Of Forages. The Method Has Been Adopted Official First Action

scholarly journals Applicability of near‐infrared reflectance spectroscopy ( NIRS ) for determination of crude protein content in cowpea ( V igna unguiculata ) leaves

2012 ◽  
Vol 1 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Erick K. Towett ◽  
Merle Alex ◽  
Keith D. Shepherd ◽  
Severin Polreich ◽  
Ermias Aynekulu ◽  
...  
Keyword(s):  
Protein Content ◽  
Crude Protein ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Crude Protein Content ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

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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