Combination of Multivariate Curve Resolution with Factorial Discriminant Analysis for detection of grapevine diseases using Hyperspectral imaging. Case study: Flavescence Dorée

The Analyst ◽  
2021 ◽  
Author(s):  
Silvia MAS Mas Garcia ◽  
Maxime Ryckewaert ◽  
Florent Abdelghafour ◽  
Maxime Metz ◽  
Daniel Moura ◽  
...  
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Multivariate Curve Resolution ◽  
Viral Diseases ◽  
Huge Amount ◽  
Flavescence Dorée ◽  
Curve Resolution ◽  
Non Destructive ◽  
Factorial Discriminant Analysis

Hyperspectral imaging is an emergent technique in viticulture that can potentially detect viral diseases in a non-destructive manner. However, the main problem is to handle the huge amount of information...

scholarly journals Hyperspectral photoluminescence imaging of defects in solar cells

2016 ◽  
Author(s):  
Ingunn Burud ◽  
Torbjørn Mehl ◽  
Andreas Flo ◽  
Dominik Lausch ◽  
Espen Olsen
Keyword(s):  
Solar Cells ◽  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Silicon Crystal ◽  
Multivariate Curve Resolution ◽  
Curve Resolution ◽  
Analysis Techniques ◽  
Promising Tool ◽  
Photovoltaic Applications

The present work is a demonstration of how near infrared (NIR) hyperspectral photoluminescence imaging can be used to detect defects in silicon wafers and solar cells. Chemometric analysis techniques such as multivariate curve resolution (MCR) and partial least squares discriminant analysis (PLS-DA) allow various types of defects to be classified and cascades of radiative defects in the samples to be extracted. It is also demonstrated how utilising a macro lens yields a spatial resolution of 30 µm on selected regions of the samples, revealing that some types of defect signals originate in grain boundaries of the silicon crystal, whereas other signals show up as singular spots. Combined with independent investigation techniques, hyperspectral imaging is a promising tool for determining origins of defects in silicon samples for photovoltaic applications.

Study of chemical compound spatial distribution in biodegradable active films using NIR hyperspectral imaging and multivariate curve resolution

2019 ◽  
Vol 34 (1) ◽  
Author(s):  
Larissa R. Terra ◽  
Jussara V. Roque ◽  
Cícero C. Pola ◽  
Igor M. Gonçalves ◽  
Nilda de Fátima F. Soares ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Hyperspectral Imaging ◽  
Chemical Compound ◽  
Multivariate Curve Resolution ◽  
Curve Resolution
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