The use of near infrared reflectance spectroscopy in the control of flour protein content

2007 ◽  
Vol 36 (8) ◽  
pp. 364-366 ◽  
Author(s):  
B. G. Osborne
Keyword(s):  
Protein Content ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Flour Protein

Evaluation of seed components of wild soybean (Glycine soja) collected in Japan using near-infrared reflectance spectroscopy

2017 ◽  
Vol 16 (2) ◽  
pp. 94-102 ◽  
Author(s):  
Chi-Do Wee ◽  
Masatsugu Hashiguchi ◽  
Genki Ishigaki ◽  
Melody Muguerza ◽  
Chika Oba ◽  
...  
Keyword(s):  
Protein Content ◽  
Near Infrared ◽  
Glycine Soja ◽  
Wild Soybean ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Lipid Contents

AbstractSeed composition, including the protein, lipid and sucrose contents of 334 accessions of wild soybean (Glycine soja) collected in Japan, was evaluated using near-infrared reflectance spectroscopy (NIRS) technology. The distribution of protein, lipid and sucrose contents and correlations among these three classes of seed components were determined. Protein, lipid and sucrose levels ranged in accessions from 48.6 to 57.0, 9.0 to 14.3 and 1.24 to 3.53%, respectively. Average levels of protein, lipid and sucrose in the accessions were 54, 11 and 2.5%, respectively. High negative correlations were observed between the protein and lipid contents, and the protein and sucrose contents. Mean levels of the three constituents were compared among collection sites classified by climatic conditions. The total protein content of accessions from regions with a high annual mean temperature was high. The protein content of accessions from the II-1 region was higher than those from the III-3 region, and the sucrose content from the II-1 region was lower than those from regions III-2 and IV-3. The lipid content of plants from the II-1 region was lower than those from other regions, and the accessions in region II had a higher protein content and lower sucrose and lipid contents than the other regions. These results provide diverse and wide-ranged protein, lipid and sucrose contents information of Japanese wild soybean resources according to climatic region; thus, providing a foundation for the future development and selection of new soybean varieties with desired traits in global environmental changes.

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.

Sign in / Sign up

Export Citation Format

Share Document