scholarly journals Application and interpretation of deep learning methods for the geographical origin identification of Radix Glycyrrhizae using hyperspectral imaging

RSC Advances ◽  
2020 ◽  
Vol 10 (68) ◽  
pp. 41936-41945
Author(s):  
Tianying Yan ◽  
Long Duan ◽  
Xiaopan Chen ◽  
Pan Gao ◽  
Wei Xu
Keyword(s):  
Deep Learning ◽  
Hyperspectral Imaging ◽  
Geographical Origin ◽  
Learning Methods ◽  
Radix Glycyrrhizae ◽  
Origin Identification

Hyperspectral imaging provides an effective way to identify the geographical origin of Radix Glycyrrhizae to assess its quality.

scholarly journals Application of Near-Infrared Hyperspectral Imaging with Machine Learning Methods to Identify Geographical Origins of Dry Narrow-Leaved Oleaster (Elaeagnus angustifolia) Fruits

Foods ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 620 ◽  
Author(s):  
Pan Gao ◽  
Wei Xu ◽  
Tianying Yan ◽  
Chu Zhang ◽  
Xin Lv ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Support Vector ◽  
Elaeagnus Angustifolia ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Discriminant Models ◽  
Second Derivative Spectra ◽  
Origin Identification

Narrow-leaved oleaster (Elaeagnus angustifolia) fruit is a kind of natural product used as food and traditional medicine. Narrow-leaved oleaster fruits from different geographical origins vary in chemical and physical properties and differ in their nutritional and commercial values. In this study, near-infrared hyperspectral imaging covering the spectral range of 874–1734 nm was used to identify the geographical origins of dry narrow-leaved oleaster fruits with machine learning methods. Average spectra of each single narrow-leaved oleaster fruit were extracted. Second derivative spectra were used to identify effective wavelengths. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to build discriminant models for geographical origin identification using full spectra and effective wavelengths. In addition, deep convolutional neural network (CNN) models were built using full spectra and effective wavelengths. Good classification performances were obtained by these three models using full spectra and effective wavelengths, with classification accuracy of the calibration, validation, and prediction set all over 90%. Models using effective wavelengths obtained close results to models using full spectra. The performances of the PLS-DA, SVM, and CNN models were close. The overall results illustrated that near-infrared hyperspectral imaging coupled with machine learning could be used to trace geographical origins of dry narrow-leaved oleaster fruits.

scholarly journals Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1274
Author(s):  
Xingpeng Li ◽  
Hongzhe Jiang ◽  
Xuesong Jiang ◽  
Minghong Shi
Keyword(s):  
Hyperspectral Imaging ◽  
Geographical Origin ◽  
Principal Component ◽  
Brand Value ◽  
Support Vector ◽  
Successive Projections Algorithm ◽  
One Dimensional ◽  
Machine Learning Methods ◽  
Origin Identification ◽  
Successive Projections

The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of 400–1000 nm was applied to identify a total of 417 Chinese chestnuts from three different geographical origins. Principal component analysis (PCA) was preliminarily used to investigate the differences of average spectra of the samples from different geographical origins. A deep-learning-based model (1D-CNN, one-dimensional convolutional neural network) was developed first, and then the model based on full spectra and optimal wavelengths were established for various machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The optimal results based on full spectra for 1D-CNN, PLS-DA, and PSO-SVM models were 97.12%, 97.12%, and 95.68%, respectively. Competitive adaptive reweighted sampling (CARS) and a successive projections algorithm (SPA) were individually utilized for wavelengths selection, and the results of simplified models generally improved. The contrasting results demonstrated that the prediction accuracies of SPA-PLS-DA and 1D-CNN both reached 97.12%, but 1D-CNN presented a higher Kappa coefficient value than SPA-PLS-DA. Meanwhile, the sensitivities and specificities of SPA-PLS-DA and 1D-CNN models were both above 90% for the samples from each geographical origin. These results indicated that both SPA-PLS-DA and 1D-CNN models combined with HSI have great potential for the geographical origin identification of Chinese chestnuts.

scholarly journals Application of Convolutional Neural Network-Based Feature Extraction and Data Fusion for Geographical Origin Identification of Radix Astragali by Visible/Short-Wave Near-Infrared and Near Infrared Hyperspectral Imaging

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4940
Author(s):  
Qinlin Xiao ◽  
Xiulin Bai ◽  
Pan Gao ◽  
Yong He
Keyword(s):  
Neural Network ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Geographical Origin ◽  
Short Wave ◽  
Infrared Range ◽  
Radix Astragali ◽  
Origin Identification ◽  
Level Fusion ◽  
Near Infrared Range

Radix Astragali is a prized traditional Chinese functional food that is used for both medicine and food purposes, with various benefits such as immunomodulation, anti-tumor, and anti-oxidation. The geographical origin of Radix Astragali has a significant impact on its quality attributes. Determining the geographical origins of Radix Astragali is essential for quality evaluation. Hyperspectral imaging covering the visible/short-wave near-infrared range (Vis-NIR, 380–1030 nm) and near-infrared range (NIR, 874–1734 nm) were applied to identify Radix Astragali from five different geographical origins. Principal component analysis (PCA) was utilized to form score images to achieve preliminary qualitative identification. PCA and convolutional neural network (CNN) were used for feature extraction. Measurement-level fusion and feature-level fusion were performed on the original spectra at different spectral ranges and the corresponding features. Support vector machine (SVM), logistic regression (LR), and CNN models based on full wavelengths, extracted features, and fusion datasets were established with excellent results; all the models obtained an accuracy of over 98% for different datasets. The results illustrate that hyperspectral imaging combined with CNN and fusion strategy could be an effective method for origin identification of Radix Astragali.

In vivo detection of head and neck tumors by hyperspectral imaging combined with deep learning methods

2021 ◽  
Author(s):  
Dennis Eggert ◽  
Marcel Bengs ◽  
Stephan Westermann ◽  
Nils Gessert ◽  
Andreas O.H. Gerstner ◽  
...  
Keyword(s):  
Deep Learning ◽  
Head And Neck ◽  
Hyperspectral Imaging ◽  
Head And Neck Tumors ◽  
Learning Methods ◽  
Neck Tumors ◽  
In Vivo Detection

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review
Sign in / Sign up

Export Citation Format

Share Document