Prediction of the nutritional composition of the crop contents of free-living scarlet macaw chicks by near-infrared reflectance spectroscopy

2012 ◽  
Vol 39 (3) ◽  
pp. 230 ◽  
Author(s):  
Juan Cornejo ◽  
Ryan Taylor ◽  
Thomas Sliffe ◽  
Christopher A. Bailey ◽  
Donald J. Brightsmith

Context It is difficult to determine with accuracy the nutrition of bird diets through observation and analysis of dietary items. Collection of the ingested material from the birds provides an alternative but it is often limited by the small sizes of samples that can be obtained. Aims We tested the efficacy of near-infrared reflectance spectroscopy (NIRS) to assess the nutritional composition of very small samples of growing-parrot crop content. Methods We used 30 samples of the crop content of free-living scarlet macaw (Ara macao) chicks. Samples were scanned with a near-infrared reflectance analyser, and later analysed by traditional wet laboratory methods for crude protein/N, fat, ash, neutral detergent fibre, P, K, Ca, Mg, Cu, Zn and S. A calibration model was developed using principal components analysis. Key results Coefficients of determination in the calibration (R2) and standard errors of cross-validation (SECV) for most of the nutrients showed a good performance (mean R2 of 0.91 ± 0.11 s.d., n = 10) when excluding Zn (R2 of 0.15, SECV = 25.37). Conclusions The present results established NIRS as a valid technique for the non-destructive, low-cost prediction of a variety of nutritional attributes of avian crop contents as small as 0.5-g dry weight. Implications The use of NIRS expands the possibilities of wild-animal nutrition research.

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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