Ultraviolet-Visible, near Infrared and Mid Infrared Reflectance Spectroscopy of Turquoise

2006 ◽  
Vol 14 (4) ◽  
pp. 241-250 ◽  
Author(s):  
B. Jagannatha Reddy ◽  
Ray L. Frost ◽  
Matt L. Weier ◽  
Wayde N. Martens
Keyword(s):  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Infrared Reflectance Spectroscopy

Near versus Mid-Infrared Spectroscopy for Quantitative Analysis of Chlorite Treated Forages and By-Products

1994 ◽  
Vol 2 (3) ◽  
pp. 153-162 ◽  
Author(s):  
James B. Reeves
Keyword(s):  
Infrared Spectroscopy ◽  
Diffuse Reflectance ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Sodium Chlorite ◽  
Infrared Reflectance ◽  
Mid Infrared ◽  
Infrared Reflectance Spectroscopy ◽  
Grating Monochromator ◽  
By Products

The objective of this work was to explore the relative merits of near and mid-infrared diffuse reflectance spectroscopy in determining the composition of sodium chlorite treated forages and by-products. Sixteen feed-stuffs (174 total samples treated at one of 11 levels of sodium chlorite, 0 to 0.394 g per gram of feedstuff) were examined in the near and mid-infrared spectral regions using diffuse reflectance on a Fourier transform spectrometer, and in the near infrared region using a grating monochromator. Samples were scanned as is and as 5% sample in KBr on the Fourier spectrometer and as is on the grating monochromator. Samples were analysed chemically and spectroscopically for neutral and acid detergent fibre, in vitro digestibility, permanganate lignin, crude protein and lignin nitrobenzene oxidation products. Results showed that diffuse mid-infrared reflectance spectroscopy can perform as well as, and sometimes better than, diffuse near infrared reflectance spectroscopy in determining the composition of chlorite-treated forages and by-products. In addition, Fourier near infrared spectroscopy did not perform as well as either near infrared using a grating monochromator or the Fourier mid-infrared spectrometer. Finally, diluting samples with KBr was often beneficial for mid-infrared based determinations.

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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