Comparison and data fusion of an electronic nose and near-infrared reflectance spectroscopy for the discrimination of ginsengs

2016 ◽  
Vol 8 (6) ◽  
pp. 1265-1273 ◽  
Author(s):  
Jiacheng Miao ◽  
Zhiyuan Luo ◽  
You Wang ◽  
Guang Li
Keyword(s):  
Electronic Nose ◽  
Sensor Array ◽  
Near Infrared ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Metal Oxide Sensors ◽  
Electronic Nose System

This paper reports a hybrid system consisting of a homemade electronic nose system (E-nose) with a sensor array of 16 metal-oxide sensors and near-infrared reflectance spectroscopy (NIRS) system for discriminating different kinds of ginsengs.

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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