Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Adsorption & Supply chain. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

Tracking phospholipid profiling of muscle from Ctennopharyngodon idellus during storage by shotgun lipidomics.

Abstract: This paper aims to study phospholipid (PL) profiling of muscle from Ctenopharyngodon idellus during room-temperature storage for 72 h by direct-infusion electrospray ionization tandem mass spectrometry (ESI–MS/MS). Five classes of PLs, including phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidylserine (PS), and sphingomyelin (SM), were analyzed. At least 110 molecular species of PLs were identified, including 32 species of PC, 34 species of PE, 24 species of PS, 18 species of PI, and 2 species of SM. The result showed that oxidation and hydrolysis are the two main causes for the deterioration of PLs in fish muscle during storage. Most content of PL molecular species increased and then decreased gradually. However, some special PE molecular species with former low abundance, such as PE 32:1, PE 34:2, and PE 34:1, emerged during the storage in quantity. It indicated that those PE molecular species may come from the microbe bred in the muscle. This phenomenon was...

Tree species classification using deep learning and RGB optical images obtained by an unmanned aerial vehicle

Abstract: The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification. The combination of deep learning methods and RGB optical images obtained by unmanned aerial vehicles (UAVs) provides a new research direction for urban tree species classification. We proposed an RGB optical image dataset with 10 urban tree species, termed TCC10, which is a benchmark for tree canopy classification (TCC). TCC10 dataset contains two types of data: tree canopy images with simple backgrounds and those with complex backgrounds. The objective was to examine the possibility of using deep learning methods (AlexNet, VGG-16, and ResNet-50) for individual tree species classification. The results of convolutional neural networks (CNNs) were compared with those of K-nearest neighbor (KNN) and BP neural network. Our results demonstrated: (1) ResNet-50 achieved an overall accuracy (OA) of 92.6% and a kappa coefficient of 0.91 for tree species classification on TCC10 and outperformed AlexNet and VGG-16. (2) The classification accuracy of KNN and BP neural network was less than 70%, while the accuracy of CNNs was relatively higher. (3) The classification accuracy of tree canopy images with complex backgrounds was lower than that for images with simple backgrounds. For the deciduous tree species in TCC10, the classification accuracy of ResNet-50 was higher in summer than that in autumn. Therefore, the deep learning is effective for urban tree species classification using RGB optical images.

An optical and rapid sandwich immunoassay method for detection of Salmonella pullorum and Salmonella gallinarum based on immune blue silica nanoparticles and magnetic nanoparticles

Abstract: An optical and rapid sandwich immunoassay of Salmonella pullorum and Salmonella gallinarum (S. pullorum and S. gallinarum) was designed using anti-S. pullorum and S. gallinarum antibody-functionalized blue silica nanoparticles (IgG-Blue-SiNPs) and magnetic nanoparticles (IgG-MNPs) as immunosensing probes in this article. The IgG-MNPs were used for enrichment of S. pullorum and S. gallinarum, IgG-Blue-SiNPs were used for signal amplification. The Blue-SiNPs were synthesized by doping C.I. reactive blue 14 into silica nanoparticles using an inverse microemulsion method. The morphology, surface charge and functional groups of Blue-SiNps were characterized by SEM, Zeta potential and FTIR spectroscopy. S. pullorum and S. gallinarum in sample solution was captured, enriched and separated by IgG-MNPs. Then IgG-Blue-SiNps were added into the above mixture solution, S. pullorum and S. gallinarum was sandwiched by IgG-MNPs and IgG-Blue-SiNps, forming a blue plaque. Under optimal conditions, the detection limit for pure S. pullorum and S. gallinarum was from 8.8 × 101. The detection limit for S. pullorum and S. gallinarum in milk powder was 8.8 × 102 CFU/ml. Besides, this qualitative detection method was economic, simple, rapid, specific and good stability. Such a simple optical sandwich immunoassay holds great potential as an on-site tool for clinical diagnosis of bacteria and viruses.