Polysaccharide determination and habitat classification for fresh <i>Dendrobiums</i> with hyperspectral imagery and modified RBFNN

Wei, Yuzhen; Hu, Wenjun; Wu, Feiyue; He, Yi

doi:10.1039/d1ra08577h

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…The strategy included visible near-infrared and shortwave-infrared fusion, spectral and image fusion, and full data fusion, to identify the origin of Rhizoma Atractylodis Macrocephalae . Similarly, HSI was used to identify the origin of Dendrobium and detect the mannose and polysaccharides it contains [ 103 ]. Zhang et al.…”

Section: Hyperspectral Imaging Technologymentioning

confidence: 99%

Development status of novel spectral imaging techniques and application to traditional Chinese medicine

Wang

Zhang

Yang

2023

Journal of Pharmaceutical Analysis

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Section: Hyperspectral Imaging Technologymentioning

confidence: 99%

Development status of novel spectral imaging techniques and application to traditional Chinese medicine

Wang

Zhang

Yang

2023

Journal of Pharmaceutical Analysis

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“…All other swarm intelligence algorithms refer to the mechanism of GA [34]. For the operation of GA in this research, each spectrum sample is treated as a chromosome encoded in a binary sequence, and each band in the spectrum is treated as a gene that takes the value of either 0 or 1 [35]. Fifty chromosomes are selected to construct the initial population.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning

Wei

Liu

et al. 2023

Processes

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The classification of GM and non-GM maize kernels is fundamental for further analysis of the gene action in maize. Therefore, a complete and novel detection scheme based on near-infrared spectra was designed to distinguish GM and non-GM maize kernels. Hyperspectral images (935–1720 nm) of 777 maize kernels from 3 kinds were captured, and the average spectra of the maize kernels were extracted for modeling analysis. The classical modeling methods based on feature engineering were first studied, and the backpropagation neural network–genetic algorithm model showed the best performance with a prediction accuracy of 0.861. Then, novel modeling methods based on deep learning were developed. To dig out the interactive information between different bands and match the application scenarios, the original spectra were transformed into two-dimensional matrices before establishing the deep learning models. A modified convolution neural network (i.e., VGG net) with dilated convolution was finally constructed to classify the maize kernels, and the prediction accuracy reached 0.961. This research provides a referential and novel way to detect GM maize kernels. Future research will improve the detection scheme for monitoring unauthorized GM organisms by introducing the visualization technology of deep learning.

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Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng

Bai,

You,

Wang

et al. 2024

Heliyon

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Polysaccharide determination and habitat classification for fresh Dendrobiums with hyperspectral imagery and modified RBFNN

Abstract: This research aimed to study the visual and nondestructive detection of mannose (MN) and Dendrobium polysaccharides (DP) in Dendrobiums by using hyperspectral imaging technology.

Cited by 7 publications

References 26 publications

Development status of novel spectral imaging techniques and application to traditional Chinese medicine

Development status of novel spectral imaging techniques and application to traditional Chinese medicine

Classification for GM and Non-GM Maize Kernels Based on NIR Spectra and Deep Learning

Hyperspectral reflectance imaging for visualizing reducing sugar content, moisture, and hollow rate in red ginseng

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