Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Cuiping Shi"'
Autor:
Junyu Pan, Zhongna Yu, Hongning Jiang, Cuiping Shi, Qijing Du, Rongbo Fan, Jun Wang, Latiful Bari, Yongxin Yang, Rongwei Han
Publikováno v:
Journal of Dairy Science, Vol 107, Iss 5, Pp 2774-2784 (2024)
ABSTRACT: The distribution of mineral elements in milk is crucial for their absorption and utilization, however, there has been limited attention given to the status of mineral elements in goat milk. In this study, goat milk was collected at 4 lactat
Externí odkaz:
https://doaj.org/article/ed122cf6184244c5aa94bd8fcf2b6115
Publikováno v:
npj Science of Food, Vol 8, Iss 1, Pp 1-11 (2024)
Abstract The effects of gelatin type (porcine skin gelatin, PSG; bovine skin gelatin, BSG; fish gelatin, FG; or cold-water fish skin gelatin, CFG) and concentration on the preparation and properties of fish oil powders were investigated in this work.
Externí odkaz:
https://doaj.org/article/58c54ae5aa1b4693ba5b244f9e9915a6
Publikováno v:
Food Chemistry: X, Vol 22, Iss , Pp 101250- (2024)
Herein, six types of polyphenol-crosslinked gelatin conjugates (PGCs) with ≥ two gelatin molecules were prepared using a covalent crosslinking method with two types of polyphenols (tannic acid and caffeic acid) and three types of gelatins (bovine b
Externí odkaz:
https://doaj.org/article/620a9d9a0b28423db83d77627fbd770f
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 999-1015 (2024)
In recent years, convolutional neural network (CNN) based methods have been widely used in remote sensing image compression tasks. However, CNN is commonly used to extract local information and does not fully utilize global contextual information. Th
Externí odkaz:
https://doaj.org/article/bd4de1d3f5454f538790248f4199ab8b
Publikováno v:
CAAI Transactions on Intelligence Technology, Vol 8, Iss 4, Pp 1288-1307 (2023)
Abstract In the past, convolutional neural network (CNN) has become one of the most popular deep learning frameworks, and has been widely used in Hyperspectral image classification tasks. Convolution (Conv) in CNN uses filter weights to extract featu
Externí odkaz:
https://doaj.org/article/0419ce86087949168dcc380ec61d3ea5
Publikováno v:
Egyptian Journal of Remote Sensing and Space Sciences, Vol 26, Iss 3, Pp 839-850 (2023)
Recently, hyperspectral image (HSI) classification methods based on deep-learning have attracted widespread attention. Convolutional neural networks, as a crucial deep-learning technique, have exhibited outstanding performance in HSI classification.
Externí odkaz:
https://doaj.org/article/6fbf12850a844e95ba2dede42be765cc
Publikováno v:
Remote Sensing, Vol 16, Iss 16, p 2942 (2024)
Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have made considerable advances in hyperspectral image (HSI) classification. However, most CNN-based methods learn features at a single-scale in HSI data, which may be insuf
Externí odkaz:
https://doaj.org/article/5b5be57aa3464f56ad12571765e57c82
Publikováno v:
npj Science of Food, Vol 7, Iss 1, Pp 1-10 (2023)
Abstract The blending of surfactants might change the properties of alginate-based oil encapsulation preparations. Herein, the effects of Tween series (Tween 20, 40, 60, and 80) blending on the fish oil-encapsulated sodium alginate dispersions and ca
Externí odkaz:
https://doaj.org/article/cef75a5e48344fa0a8ff04d6b87c3706
Publikováno v:
Symmetry, Vol 16, Iss 4, p 494 (2024)
In recent years, with the rapid development of deep learning technology, a large number of excellent convolutional neural networks (CNNs) have been proposed, many of which are based on improvements to classical methods. Based on the Inception family
Externí odkaz:
https://doaj.org/article/6083f4e01074421c9b15c6f6f5d8e852
Publikováno v:
Food Chemistry: X, Vol 18, Iss , Pp 100748- (2023)
This work studied the physicochemical properties and odor profiles of tilapia muscles after exposure to four types of thermal processing methods: microwaving, roasting, boiling, or steaming. The effect of thermal processing on textural properties fol
Externí odkaz:
https://doaj.org/article/752831b1e1eb460f9f557f2a2ce57dc9