Hyperspectral imaging for VIS-SWIR classification of post-consumer plastic packaging products by polymer and color

2020 ◽  
Author(s):  
Silvia Serranti ◽  
Paola Cucuzza ◽  
Giuseppe Bonifazi
Keyword(s):  
Hyperspectral Imaging ◽  
Plastic Packaging

Classification of fresh Atlantic salmon (Salmo salar L.) fillets stored under different atmospheres by hyperspectral imaging

2012 ◽  
Vol 109 (3) ◽  
pp. 482-489 ◽  
Author(s):  
Izumi Sone ◽  
Ragnar L. Olsen ◽  
Agnar H. Sivertsen ◽  
Guro Eilertsen ◽  
Karsten Heia
Keyword(s):  
Atlantic Salmon ◽  
Hyperspectral Imaging ◽  
Salmo Salar ◽  
Atlantic Salmon Salmo Salar

Classification of Heterogeneous Solids Using Infrared Hyperspectral Imaging

2009 ◽  
Vol 63 (2) ◽  
pp. 172-179 ◽  
Author(s):  
Helen T. Rutlidge ◽  
Brian J. Reedy
Keyword(s):  
Hyperspectral Imaging ◽  
Heterogeneous Solids

scholarly journals Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2281 ◽  
Author(s):  
Anne-Katrin Mahlein ◽  
Elias Alisaac ◽  
Ali Al Masri ◽  
Jan Behmann ◽  
Heinz-Wilhelm Dehne ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Hyperspectral Imaging ◽  
Optical Sensors ◽  
Fusarium Culmorum ◽  
Maximum Temperature ◽  
Support Vector ◽  
Head Blight ◽  
Maximum Temperature Difference ◽  
Simple Ratio

Optical sensors have shown high capabilities to improve the detection and monitoring of plant disease development. This study was designed to compare the feasibility of different sensors to characterize Fusarium head blight (FHB) caused by Fusarium graminearum and Fusarium culmorum. Under controlled conditions, time-series measurements were performed with infrared thermography (IRT), chlorophyll fluorescence imaging (CFI), and hyperspectral imaging (HSI) starting 3 days after inoculation (dai). IRT allowed the visualization of temperature differences within the infected spikelets beginning 5 dai. At the same time, a disorder of the photosynthetic activity was confirmed by CFI via maximal fluorescence yields of spikelets (Fm) 5 dai. Pigment-specific simple ratio PSSRa and PSSRb derived from HSI allowed discrimination between Fusarium-infected and non-inoculated spikelets 3 dai. This effect on assimilation started earlier and was more pronounced with F. graminearum. Except the maximum temperature difference (MTD), all parameters derived from different sensors were significantly correlated with each other and with disease severity (DS). A support vector machine (SVM) classification of parameters derived from IRT, CFI, or HSI allowed the differentiation between non-inoculated and infected spikelets 3 dai with an accuracy of 78, 56 and 78%, respectively. Combining the IRT-HSI or CFI-HSI parameters improved the accuracy to 89% 30 dai.

scholarly journals Hyperspectral Imaging Using Laser Excitation for Fast Raman and Fluorescence Hyperspectral Imaging for Sorting and Quality Control Applications

2018 ◽  
Vol 4 (10) ◽  
pp. 110 ◽  
Author(s):  
Florian Gruber ◽  
Philipp Wollmann ◽  
Wulf Grählert ◽  
Stefan Kaskel
Keyword(s):  
Quality Control ◽  
Hyperspectral Imaging ◽  
Lead Zirconate Titanate ◽  
Imaging System ◽  
Measurement Data ◽  
Machine Learning Algorithms ◽  
Area Measurement ◽  
Large Area ◽  
Sorting Tasks

A hyperspectral measurement system for the fast and large area measurement of Raman and fluorescence signals was developed, characterized and tested. This laser hyperspectral imaging system (Laser-HSI) can be used for sorting tasks and for continuous quality monitoring. The system uses a 532 nm Nd:YAG laser and a standard pushbroom HSI camera. Depending on the lens selected, it is possible to cover large areas (e.g., field of view (FOV) = 386 mm) or to achieve high spatial resolutions (e.g., 0.02 mm). The developed Laser-HSI was used for four exemplary experiments: (a) the measurement and classification of a mixture of sulphur and naphthalene; (b) the measurement of carotenoid distribution in a carrot slice; (c) the classification of black polymer particles; and, (d) the localization of impurities on a lead zirconate titanate (PZT) piezoelectric actuator. It could be shown that the measurement data obtained were in good agreement with reference measurements taken with a high-resolution Raman microscope. Furthermore, the suitability of the measurements for classification using machine learning algorithms was also demonstrated. The developed Laser-HSI could be used in the future for complex quality control or sorting tasks where conventional HSI systems fail.

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