Application of near-infrared reflectance spectroscopy for predicting carotenoid content in summer squash fruit

2014 ◽  
Vol 108 ◽  
pp. 71-79 ◽  
Author(s):  
Damián Martínez-Valdivieso ◽  
Rafael Font ◽  
María Teresa Blanco-Díaz ◽  
José Manuel Moreno-Rojas ◽  
Pedro Gómez ◽  
...  
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Carotenoid Content ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Summer Squash

Application of near infrared reflectance spectroscopy to the determination of carotenoid content in tritordeum for breeding purposes

2005 ◽  
Vol 56 (1) ◽  
pp. 85 ◽  
Author(s):  
S. G. Atienza ◽  
C. M. Avila ◽  
M. C. Ramírez ◽  
A. Martín
Keyword(s):  
Standard Error ◽  
Near Infrared ◽  
Cross Validation ◽  
Reflectance Spectroscopy ◽  
Carotenoid Content ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Non Destructive

For pasta production, the yellow colour, mainly caused by carotenoids, is a worldwide requirement. Hexaploid tritodeums are the amphiploids derived from the cross between Hordeum chilense and Triticum turgidum. They show a higher carotenoid content than their wheat parents. This work aimed to develop a non-destructive method for carotenoid content determination to assist the tritordeum breeding program. We assessed the ability of near infrared reflectance spectroscopy (NIRS) to predict carotenoid content in whole grains of tritordeum. In total, 285 samples were scanned by NIRS. After non-destructive NIRS scanning, the seeds were analysed for carotenoid content and a calibration equation was developed. It is characterised by a coefficient of multiple determination (R2) of 0.85. This equation was initially evaluated by cross validation showing an r2 of 0.81 and a standard error of cross validation (SECV) of 1.49. It was further evaluated using external validation with a different set of samples not included in the calibration. This analysis showed an r2 of 0.81 and a standard error of performance (SEP) of 1.51. This equation allows discrimination between low and high carotenoid content lines in a non-destructive way. These results constitute a substantial advance for tritordeum breeding programs whose final aim is to develop high carotenoid content tritordeums useful for durum wheat breeding.

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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