Near-infrared hyperspectral imaging for detection and quantification of azodicarbonamide in flour

2017 ◽  
Vol 98 (7) ◽  
pp. 2793-2800 ◽  
Author(s):  
Xiaobin Wang ◽  
Chunjiang Zhao ◽  
Wenqian Huang ◽  
Qingyan Wang ◽  
Chen Liu ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Detection And Quantification

Online detection and quantification of ergot bodies in cereals using near infrared hyperspectral imaging

2012 ◽  
Vol 29 (2) ◽  
pp. 232-240 ◽  
Author(s):  
Ph. Vermeulen ◽  
J.A. Fernández Pierna ◽  
H.P. van Egmond ◽  
P. Dardenne ◽  
V. Baeten
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Online Detection ◽  
Detection And Quantification

Validation and transferability study of a method based on near-infrared hyperspectral imaging for the detection and quantification of ergot bodies in cereals

2013 ◽  
Vol 405 (24) ◽  
pp. 7765-7772 ◽  
Author(s):  
Ph. Vermeulen ◽  
J. A. Fernández Pierna ◽  
H. P. van Egmond ◽  
J. Zegers ◽  
P. Dardenne ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Detection And Quantification

Online detection and quantification of particles of ergot bodies in cereal flour using near-infrared hyperspectral imaging

2017 ◽  
Vol 34 (8) ◽  
pp. 1312-1319 ◽  
Author(s):  
Ph. Vermeulen ◽  
M. B. Ebene ◽  
B. Orlando ◽  
J. A. Fernández Pierna ◽  
V. Baeten
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Online Detection ◽  
Detection And Quantification ◽  
Cereal Flour

Pork Tissue Recognition Using Visible and Near Infrared Hyperspectral Imaging

2020 ◽  
Author(s):  
Chih-Cheng Pai ◽  
Yang-Chu Chen ◽  
Keng-Hao Liu ◽  
Yuan-Hsun Tsai ◽  
Po-Chi Hu ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared

scholarly journals Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires

2020 ◽  
Author(s):  
L. Granlund ◽  
M. Keinänen ◽  
T. Tahvanainen
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Support Vector ◽  
High Resolution Imaging ◽  
Factors Affecting ◽  
Vector Machines ◽  
Resolution Imaging ◽  
Shortwave Infrared ◽  
Type Classification ◽  
Wide Potential

Abstract Aims Hyperspectral imaging (HSI) has high potential for analysing peat cores, but methodologies are deficient. We aimed for robust peat type classification and humification estimation. We also explored other factors affecting peat spectral properties. Methods We used two laboratory setups: VNIR (visible to near-infrared) and SWIR (shortwave infrared) for high resolution imaging of intact peat profiles with fen-bog transitions. Peat types were classified with support vector machines, indices were developed for von Post estimation, and K-means clustering was used to analyse stratigraphic patterns in peat quality. With separate experiments, we studied spectral effects of drying and oxidation. Results Despite major effects, oxidation and water content did not impede robust HSI classification. The accuracy between Carex peat and Sphagnum peat in validation was 80% with VNIR and 81% with SWIR data. The spectral humification indices had accuracies of 82% with VNIR and 56%. Stratigraphic HSI patterns revealed that 36% of peat layer shifts were inclined by over 20 degrees. Spectral indices were used to extrapolate visualisations of element concentrations. Conclusions HSI provided reliable information of basic peat quality and was useful in visual mapping, that can guide sampling for other analyses. HSI can manage large amounts of samples to widen the scope of detailed analysis beyond single profiles and it has wide potential in peat research beyond the exploratory scope of this paper. We were able to confirm the capacity of HSI to reveal shifts of peat quality, connected to ecosystem-scale change.

Spatial distribution of total polyphenols in multi-type of tea using near-infrared hyperspectral imaging

LWT ◽  
2021 ◽  
pp. 111737
Author(s):  
Yujie Wang ◽  
Ying Liu ◽  
Yuyu Chen ◽  
Qingqing Cui ◽  
Luqing Li ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Total Polyphenols

A method using near infrared hyperspectral imaging to highlight the internal quality of apple fruit slices

2021 ◽  
Vol 175 ◽  
pp. 111497
Author(s):  
Weijie Lan ◽  
Benoit Jaillais ◽  
Catherine M.G.C. Renard ◽  
Alexandre Leca ◽  
Songchao Chen ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Apple Fruit ◽  
Internal Quality
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