Monitoring of multiple solid-state transformations at tablet surfaces using multi-series near-infrared hyperspectral imaging and multivariate curve resolution

2015 ◽  
Vol 93 ◽  
pp. 224-230 ◽  
Author(s):  
Guilherme L. Alexandrino ◽  
Milad R. Khorasani ◽  
José M. Amigo ◽  
Jukka Rantanen ◽  
Ronei J. Poppi
Keyword(s):  
Solid State ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Multivariate Curve Resolution ◽  
Curve Resolution

Detection of chocolate powder adulteration with peanut using near-infrared hyperspectral imaging and Multivariate Curve Resolution

Food Control ◽  
2021 ◽  
Vol 119 ◽  
pp. 107454
Author(s):  
Antoine Laborde ◽  
Francesc Puig-Castellví ◽  
Delphine Jouan-Rimbaud Bouveresse ◽  
Luc Eveleigh ◽  
Christophe Cordella ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Multivariate Curve Resolution ◽  
Curve Resolution

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.

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