Geographical Classification of Honey Samples by Near-Infrared Spectroscopy: A Feasibility Study

2007 ◽  
Vol 55 (22) ◽  
pp. 9128-9134 ◽  
Author(s):  
Tony Woodcock ◽  
Gerard Downey ◽  
J. Daniel Kelly ◽  
Colm O’Donnell
Keyword(s):  
Infrared Spectroscopy ◽  
Feasibility Study ◽  
Near Infrared Spectroscopy ◽  
Near Infrared

Application of Near-Infrared Spectroscopy for the Classification of Fresh Pork Quality in Cooked Ham Production

2015 ◽  
Vol 8 (12) ◽  
pp. 2383-2391 ◽  
Author(s):  
Ellen Neyrinck ◽  
Stefaan De Smet ◽  
Liesbeth Vermeulen ◽  
Danny Telleir ◽  
Stefaan Lescouhier ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Pork Quality ◽  
Cooked Ham

Evaluation and classification of eucalypt charcoal quality by near infrared spectroscopy

2018 ◽  
Vol 112 ◽  
pp. 85-92 ◽  
Author(s):  
Lívia Ribeiro Costa ◽  
Paulo Fernando Trugilho ◽  
Paulo Ricardo Gherardi Hein
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared

scholarly journals Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2362 ◽  
Author(s):  
Alexander E. Hramov ◽  
Vadim Grubov ◽  
Artem Badarin ◽  
Vladimir A. Maksimenko ◽  
Alexander N. Pisarchik
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
High Efficiency ◽  
Brain Activity ◽  
Blood Oxygenation ◽  
Hemispheric Lateralization ◽  
Motor Execution ◽  
Functional Near Infrared Spectroscopy

Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes.

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