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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Food chemistry Ročník 319; s. 126536
Hlavní autoři:	Zhang, Chu, Wu, Wenyan, Zhou, Lei, Cheng, Huan, Ye, Xingqian, He, Yong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Elsevier Ltd 30.07.2020
Témata:	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 Regression issue Total flavonoids Black goji berry Total anthocyanins Convolutional neural network Near-infrared hyperspectral imaging Total phenolics Deep autoencoder
ISSN:	0308-8146, 1873-7072, 1873-7072
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	•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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0308-8146 1873-7072 1873-7072
DOI:	10.1016/j.foodchem.2020.126536