Prediction of in situ degradation characteristics of neutral detergent fibre (aNDF) in temperate grasses and red clover using near-infrared reflectance spectroscopy (NIRS)

2007 ◽  
Vol 139 (1-2) ◽  
pp. 92-108 ◽  
Author(s):  
Hege Nordheim ◽  
Harald Volden ◽  
Gustav Fystro ◽  
Tor Lunnan
2004 ◽  
Vol 5 (1) ◽  
pp. 49 ◽  
Author(s):  
Diana Rocío Vásquez ◽  
Beatriz Abadía ◽  
Luis Carlos Arreaza

<p>El objetivo fue estandarizar la técnica de Espectroscopía de Reflectancia en el Infrarrojo Cercano (<em>Near Infrared Reflectance Spectroscopy</em>, NIRS) a fin de obtener la valoración nutricional del pasto Guinea (<em>Panicum maximum</em>) y del grano de maíz (<em>Zea maiz</em>) procedentes de la región Caribe (microregiones de Valle del Cesar y Valle del Sinú, respectivamente). Este objetivo se cumplió en cuatro etapas, así: Inicialmente se hicieron análisis químicos de las muestras para los siguientes componentes nutricionales: Materia Seca (MS), cenizas, extracto etéreo, proteína cruda, fibra detergente neutra, fibra detergente ácida, lignina, digestibilidad <em>in situ</em>, carbohidratos no estructurales y nitrógeno no proteico, sobre 70 muestras de pasto Guinea y 193 muestras de maíz. La segunda etapa se obtuvieron espectros de absorción de las muestras en el espectrofotómetro (NIRS). El tercer paso consistió en correlacionar los datos químicos con el barrido de absorción para cada uno de los componentes (ecuaciones de calibración); por último, se realizó la validación de las ecuaciones mediante análisis de 22 muestras adicionales de pasto Guinea y 55 de maíz. Según los resultados finales, el método NIRS funcionó para la mayoría de los componentes, excepto los carbohidratos no estructurales, puesto que éstos no se diferencian de la celulosa al sobreponerse sus espectros en diferentes longitudes de onda; así mismo, el nitrógeno no proteico, ya que está constituido por una mezcla de diferentes compuestos como aminas, amidas, aminoácidos, péptidos (que no precipitan con TCA), ácidos nucleicos, cierta cantidad de nitritos y nitratos (parte inorgánica), los cuales aparentemente no fueron absorbidos en la zona evaluada del infrarrojo cercano.</p><p><strong><br /></strong></p><p><strong>Near Infrared Reflectance Spectroscopy (NIRS) applied to nutritional characterization of Guinea grass and corn grain.</strong></p><p>In this study, the use of Near Infrared Reflectance Spectroscopy (NIRS) as a method to estimate the nutritional quality of Guinea grass (<em>Panicum Maximum</em>) and corn (<em>Zea Mays</em>), was evaluated. These forage species were collected in the Cesar and Sinú valleys of the Colombian Caribbean. Activities included chemical analysis (content of dry matter, ash, ether extract, crude protein, neutral detergent fiber, acid detergent fiber, lignin, non structural carbohydrate and non protein nitrogen as well as the <em>in situ </em>digestibility) on 70 Guinea grass and 193 corn samples; generation of the samples absorption spectra; correlation of the chemical and absorption spectra data for each component (calibration equations) and validation of the calibration equations with 22 Guinea grass and 55 corn samples. Predictions obtained with the NIRS method were similar to the chemical measurements, except for non-structural carbohydrates, which did not relate properly due to overlapping of their spectra with those from cellulose  and from non protein nitrogen because this component involves many compounds (amines, amides, amino acids, peptides that no precipitate with TCA, nucleic acids, nitrites and nitrates), that apparently do not absorb at the near  infrared region tested.</p>


2021 ◽  
pp. 096703352110075
Author(s):  
Adou Emmanuel Ehounou ◽  
Denis Cornet ◽  
Lucienne Desfontaines ◽  
Carine Marie-Magdeleine ◽  
Erick Maledon ◽  
...  

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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