<i>Investigating the Development of Aspergillus flavus inoculated on Maize Kernels Using Visible and Near Infrared Hyperspectral Imaging</i>

2019 ◽  
Author(s):  
Beibei Jia ◽  
Wei Wang ◽  
Yi Yang ◽  
Xin Zhao ◽  
Hongzhe Jiang
Keyword(s):  
Hyperspectral Imaging ◽  
Aspergillus Flavus ◽  
Near Infrared ◽  
Maize Kernels

<i>Variety classification of maize kernels using near infrared (NIR) hyperspectral imaging</i>

2017 ◽  
Author(s):  
Xin Zhao ◽  
Wei Wang ◽  
Xuan Chu ◽  
Hongzhe Jiang ◽  
Beibei Jia ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Variety Classification ◽  
Maize Kernels

Classifying maize kernels naturally infected by fungi using near-infrared hyperspectral imaging

2020 ◽  
Vol 105 ◽  
pp. 103242 ◽  
Author(s):  
Xuan Chu ◽  
Wei Wang ◽  
Xinzhi Ni ◽  
Chunyang Li ◽  
Yufeng Li
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Maize Kernels

Application of near-infrared hyperspectral imaging for variety identification of coated maize kernels with deep learning

2020 ◽  
Vol 111 ◽  
pp. 103550
Author(s):  
Chu Zhang ◽  
Yiying Zhao ◽  
Tianying Yan ◽  
Xiulin Bai ◽  
Qinlin Xiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Variety Identification ◽  
Maize Kernels

scholarly journals Growth Identification of Aspergillus flavus and Aspergillus parasiticus by Visible/Near-Infrared Hyperspectral Imaging

2018 ◽  
Vol 8 (4) ◽  
pp. 513 ◽  
Author(s):  
Xuan Chu ◽  
Wei Wang ◽  
Xinzhi Ni ◽  
Haitao Zheng ◽  
Xin Zhao ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Aspergillus Flavus ◽  
Near Infrared ◽  
Aspergillus Parasiticus

Near-infrared hyperspectral imaging for detecting Aflatoxin B1 of maize kernels

Food Control ◽  
2015 ◽  
Vol 51 ◽  
pp. 347-355 ◽  
Author(s):  
Wei Wang ◽  
Kurt C. Lawrence ◽  
Xinzhi Ni ◽  
Seung-Chul Yoon ◽  
Gerald W. Heitschmidt ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Aflatoxin B1 ◽  
Near Infrared ◽  
Maize Kernels

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.

Sign in / Sign up

Export Citation Format

Share Document