Time takes its toll: Detection of organic binder media in ochre paints with visible near-infrared and short-wave infrared reflectance spectroscopy

2018 ◽  
Vol 21 ◽  
pp. 10-20 ◽  
Author(s):  
Karen R. Horn
Keyword(s):  
Near Infrared ◽  
Short Wave ◽  
Reflectance Spectroscopy ◽  
Organic Binder ◽  
Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Short Wave Infrared

Visible and short-wave infrared reflectance spectroscopy of REE fluorocarbonates

2014 ◽  
Vol 99 (7) ◽  
pp. 1335-1346 ◽  
Author(s):  
D. J. Turner ◽  
B. Rivard ◽  
L. A. Groat
Keyword(s):  
Short Wave ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Short Wave Infrared

Visible and short-wave infrared reflectance spectroscopy of REE phosphate minerals

2016 ◽  
Vol 101 (10) ◽  
pp. 2264-2278 ◽  
Author(s):  
David J. Turner ◽  
Benoit Rivard ◽  
Lee A. Groat
Keyword(s):  
Short Wave ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Short Wave Infrared

Visible and short-wave infrared reflectance spectroscopy of selected REE-bearing silicate minerals

2018 ◽  
Vol 103 (6) ◽  
pp. 927-943 ◽  
Author(s):  
David J. Turner ◽  
Benoit Rivard ◽  
Lee A. Groat
Keyword(s):  
Short Wave ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Silicate Minerals ◽  
Infrared Reflectance Spectroscopy ◽  
Short Wave Infrared

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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