Rapid quantification of flavonoids in propolis and previous study for classification of propolis from different origins by using near infrared spectroscopy

2012 ◽  
Vol 4 (8) ◽  
pp. 2388 ◽  
Author(s):  
Rui Cai ◽  
Shisheng Wang ◽  
Yu Meng ◽  
Qinggang Meng ◽  
Weijie Zhao
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Different Origins ◽  
Rapid Quantification

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

Rapid Quantification of Kinematical Viscosity in Aviation Kerosene by Near-Infrared Spectroscopy

2006 ◽  
Vol 20 (6) ◽  
pp. 2486-2488 ◽  
Author(s):  
Zhi-na Xing ◽  
Ju-xiang Wang ◽  
Yong Ye ◽  
Gang Shen
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Aviation Kerosene ◽  
Rapid Quantification

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

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.

scholarly journals Discrimination and Content Analysis of Fritillaria Using Near Infrared Spectroscopy

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yu Meng ◽  
Shisheng Wang ◽  
Rui Cai ◽  
Bohai Jiang ◽  
Weijie Zhao
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Partial Least Square ◽  
Least Square ◽  
Traditional Chinese Herbal Medicine ◽  
Total Alkaloids ◽  
Rapid Discrimination ◽  
Different Origins

Fritillaria is a traditional Chinese herbal medicine which can be used to moisten the lungs. The objective of this study is to develop simple, accurate, and solvent-free methods to discriminate and quantify Fritillaria herbs from seven different origins. Near infrared spectroscopy (NIRS) methods are established for the rapid discrimination of seven different Fritillaria samples and quantitative analysis of their total alkaloids. The scaling to first range method and the partial least square (PLS) method are used for the establishment of qualitative and quantitative analysis models. As a result of evaluation for the qualitative NIR model, the selectivity values between groups are always above 2, and the mistaken judgment rate of fifteen samples in prediction sets was zero. This means that the NIR model can be used to distinguish different species of Fritillaria herbs. The established quantitative NIR model can accurately predict the content of total alkaloids from Fritillaria samples.

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