Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral imaging
•Black goji berries total anthocyanins, flavonoids and phenolics were predicted by HSI.•SPA, CARS and PCA, WT were compared to obtain conventional features for prediction.•CNN models for regression were proposed and compared.•CNN and DAE were used and compared as feature extraction methods for predi...
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| Vydáno v: | Food chemistry Ročník 319; s. 126536 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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England
Elsevier Ltd
30.07.2020
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| ISSN: | 0308-8146, 1873-7072, 1873-7072 |
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| Abstract | •Black goji berries total anthocyanins, flavonoids and phenolics were predicted by HSI.•SPA, CARS and PCA, WT were compared to obtain conventional features for prediction.•CNN models for regression were proposed and compared.•CNN and DAE were used and compared as feature extraction methods for prediction.•Proposed deep learning approaches could be used to determine chemical compositions.
Black goji berry (Lycium ruthenicum Murr.) has great commercial and nutritional values. Near-infrared hyperspectral imaging (NIR-HSI) was used to determine total phenolics, total flavonoids and total anthocyanins in dry black goji berries. Convolutional neural networks (CNN) were designed and developed to predict the chemical compositions. These CNN models and deep autoencoder were used as supervised and unsupervised feature extraction methods, respectively. Partial least squares (PLS) and least-squares support vector machine (LS-SVM) as modelling methods, successive projections algorithm and competitive adaptive reweighted sampling (CARS) as wavelength selection methods, and principal component analysis (PCA) and wavelet transform (WT) as feature extraction methods were studied as conventional approaches for comparison. Deep learning approaches as modelling methods and feature extraction methods obtained good and equivalent performances to the conventional methods. The results illustrated that deep learning had great potential as modelling and feature extraction methods for chemical compositions determination in NIR-HSI. |
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| AbstractList | Black goji berry (Lycium ruthenicum Murr.) has great commercial and nutritional values. Near-infrared hyperspectral imaging (NIR-HSI) was used to determine total phenolics, total flavonoids and total anthocyanins in dry black goji berries. Convolutional neural networks (CNN) were designed and developed to predict the chemical compositions. These CNN models and deep autoencoder were used as supervised and unsupervised feature extraction methods, respectively. Partial least squares (PLS) and least-squares support vector machine (LS-SVM) as modelling methods, successive projections algorithm and competitive adaptive reweighted sampling (CARS) as wavelength selection methods, and principal component analysis (PCA) and wavelet transform (WT) as feature extraction methods were studied as conventional approaches for comparison. Deep learning approaches as modelling methods and feature extraction methods obtained good and equivalent performances to the conventional methods. The results illustrated that deep learning had great potential as modelling and feature extraction methods for chemical compositions determination in NIR-HSI. •Black goji berries total anthocyanins, flavonoids and phenolics were predicted by HSI.•SPA, CARS and PCA, WT were compared to obtain conventional features for prediction.•CNN models for regression were proposed and compared.•CNN and DAE were used and compared as feature extraction methods for prediction.•Proposed deep learning approaches could be used to determine chemical compositions. Black goji berry (Lycium ruthenicum Murr.) has great commercial and nutritional values. Near-infrared hyperspectral imaging (NIR-HSI) was used to determine total phenolics, total flavonoids and total anthocyanins in dry black goji berries. Convolutional neural networks (CNN) were designed and developed to predict the chemical compositions. These CNN models and deep autoencoder were used as supervised and unsupervised feature extraction methods, respectively. Partial least squares (PLS) and least-squares support vector machine (LS-SVM) as modelling methods, successive projections algorithm and competitive adaptive reweighted sampling (CARS) as wavelength selection methods, and principal component analysis (PCA) and wavelet transform (WT) as feature extraction methods were studied as conventional approaches for comparison. Deep learning approaches as modelling methods and feature extraction methods obtained good and equivalent performances to the conventional methods. The results illustrated that deep learning had great potential as modelling and feature extraction methods for chemical compositions determination in NIR-HSI. Black goji berry (Lycium ruthenicum Murr.) has great commercial and nutritional values. Near-infrared hyperspectral imaging (NIR-HSI) was used to determine total phenolics, total flavonoids and total anthocyanins in dry black goji berries. Convolutional neural networks (CNN) were designed and developed to predict the chemical compositions. These CNN models and deep autoencoder were used as supervised and unsupervised feature extraction methods, respectively. Partial least squares (PLS) and least-squares support vector machine (LS-SVM) as modelling methods, successive projections algorithm and competitive adaptive reweighted sampling (CARS) as wavelength selection methods, and principal component analysis (PCA) and wavelet transform (WT) as feature extraction methods were studied as conventional approaches for comparison. Deep learning approaches as modelling methods and feature extraction methods obtained good and equivalent performances to the conventional methods. The results illustrated that deep learning had great potential as modelling and feature extraction methods for chemical compositions determination in NIR-HSI.Black goji berry (Lycium ruthenicum Murr.) has great commercial and nutritional values. Near-infrared hyperspectral imaging (NIR-HSI) was used to determine total phenolics, total flavonoids and total anthocyanins in dry black goji berries. Convolutional neural networks (CNN) were designed and developed to predict the chemical compositions. These CNN models and deep autoencoder were used as supervised and unsupervised feature extraction methods, respectively. Partial least squares (PLS) and least-squares support vector machine (LS-SVM) as modelling methods, successive projections algorithm and competitive adaptive reweighted sampling (CARS) as wavelength selection methods, and principal component analysis (PCA) and wavelet transform (WT) as feature extraction methods were studied as conventional approaches for comparison. Deep learning approaches as modelling methods and feature extraction methods obtained good and equivalent performances to the conventional methods. The results illustrated that deep learning had great potential as modelling and feature extraction methods for chemical compositions determination in NIR-HSI. |
| ArticleNumber | 126536 |
| Author | Wu, Wenyan Cheng, Huan Ye, Xingqian He, Yong Zhang, Chu Zhou, Lei |
| Author_xml | – sequence: 1 givenname: Chu surname: Zhang fullname: Zhang, Chu organization: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China – sequence: 2 givenname: Wenyan surname: Wu fullname: Wu, Wenyan organization: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China – sequence: 3 givenname: Lei surname: Zhou fullname: Zhou, Lei organization: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China – sequence: 4 givenname: Huan orcidid: 0000-0002-1192-3228 surname: Cheng fullname: Cheng, Huan organization: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China – sequence: 5 givenname: Xingqian surname: Ye fullname: Ye, Xingqian email: psu@zju.edu.cn organization: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China – sequence: 6 givenname: Yong surname: He fullname: He, Yong email: yhe@zju.edu.cn organization: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32146292$$D View this record in MEDLINE/PubMed |
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| Keywords | Regression issue Total flavonoids Black goji berry Total anthocyanins Convolutional neural network Near-infrared hyperspectral imaging Total phenolics Deep autoencoder |
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| Snippet | •Black goji berries total anthocyanins, flavonoids and phenolics were predicted by HSI.•SPA, CARS and PCA, WT were compared to obtain conventional features for... Black goji berry (Lycium ruthenicum Murr.) has great commercial and nutritional values. Near-infrared hyperspectral imaging (NIR-HSI) was used to determine... |
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| SubjectTerms | anthocyanins Black goji berry chemical composition Convolutional neural network Deep autoencoder hyperspectral imagery least squares Lycium ruthenicum Near-infrared hyperspectral imaging neural networks nutritive value phenolic compounds principal component analysis Regression issue selection methods support vector machines Total anthocyanins Total flavonoids Total phenolics wavelengths wavelet |
| Title | Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral imaging |
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