Influence of feed ingredients upon the prediction of protein in animal feed-mixes by near-infrared reflectance spectroscopy

1980 ◽  
Vol 31 (11) ◽  
pp. 1201-1213 ◽  
Author(s):  
Philip C. Williams ◽  
Patricia M. Starkey
Keyword(s):  
Near Infrared ◽  
Animal Feed ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Feed Ingredients ◽  
Infrared Reflectance Spectroscopy

The use of near infrared reflectance spectroscopy (NIRS) to predict the nutritive value of standard commercial diets for post-weaned pigs

2009 ◽  
Vol 2009 ◽  
pp. 108-108
Author(s):  
M E E McCann ◽  
R Park ◽  
M J Hutchinson ◽  
B Owens ◽  
V E Beattie
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Feed Ingredients ◽  
Infrared Reflectance Spectroscopy ◽  
Whole Wheat ◽  
Complete Diet

In order to assess the nutritive value of pig diets, performance and digestibility trials must be conducted as there is no accurate alternative to predict nutritive value. However, the use of near infrared reflectance spectroscopy (NIRS) to predict performance from feed ingredients has been shown to have potential. Owens et al (2007) investigated the use of NIRS to predict the performance of broilers offered wheat-based diets, through scanning of whole wheat, and observed that NIRS accurately predicted liveweight gain and gain:feed. The aim of this study was to investigate if NIRS could be used to predict the performance of pigs, through scanning of the complete diet.

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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