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!

Buďte první, kdo okomentuje tento záznam!