The automatic identification (location, segmentation , and classification) by UAV- based optical imaging of spills of transparent floating Hazardous and Noxious Substances (HNS) benefits the on-site response to spill incidents, but it is also challenging. With a focus on the on-site optical imaging of HNS, this study explores the potential of single spectral imaging for HNS identification using the Faster R-CNN architecture. Images at 365 nm (narrow UV band), blue channel images (visible broadband of ∼400–600 nm), and RGB images of typical HNS (benzene, xylene, and palm oil) in different scenarios were studied with and without Faster R-CNN. Faster R-CNN was applied to locate and classify the HNS spills. The segmentation using Faster R-CNN-based methods and the original masking methods, including Otsu, Max entropy, and the local fuzzy thresholding method (LFTM), were investigated to explore the optimal wavelength and corresponding image processing method for the optical imaging of HNS. We also compared the classification and segmentation results of this study with our previously published studies on multispectral and whole spectral images. The results demonstrated that single spectral UV imaging at 365 nm combined with Faster R-CNN has great potential for the automatic identification of transparent HNS floating on the surface of the water. RGB images and images using Faster R-CNN in the blue channel are capable of HNS segmentation.
Ya Zhao,Xinnan Zhao,Tao Li,Xiao Wang,Cheng Zhong,Xiupin Wang,Peiwu Li
Glycerophospholipids (GPs) have a wide variety and complex structure, which makes their identification challenging. Our software affords a novel tool for the automated identification of non-target GPs in biological mixtures. Here, we explored the multi-stage fragmentation processes of GPs in positive and negative ion modes, and then constructed multi-stage fragment ion databases. This database includes 8214 simulated GP molecules from a random combination of fatty acids corresponding to 42,439 self-built predicted multi-stage fragment ions in positive ion mode and 31,487 self-built predicted multi-stage fragment ions in negative ion mode (MS ≤ 3). The automatic GP identification (AGPI) software can screen out GP candidates utilizing the MS 1 accurate mass. The isomers of fatty acid chains and the phosphoryl head group can be distinguished using the MS 2 and MS 3 fragment spectra in positive-ion and negative-ion modes. All of the selected 45 GP standards were putatively identified using AGPI software; however, there were false positives because the software cannot distinguish positional isomers of fatty acids. Therefore, the AGPI software could be applied to identify GPs in samples, such as cancer cells; we successfully identified 41 GPs in cancer cells.
Shuting Chen,Weijia Wang,Shaohua Xu,Caili Fu,Shuyi Ji,Fang Luo,Cuiying Lin,Bin Qiu,Zhenyu Lin
In this work, an auto-identify sensor was constructed for rapid and high-precision detection of L-histidine. The proposed strategy is based on the auto-identify algorithm and the aggregation of alkynyl and azide functionalized gold nanoparticles induced by the Cu + catalyzed azides and alkynes cycloaddition (CuAAC) reaction. Specially, the color of scattering light spots for the aggregated gold nanoparticle (AuNPs) caused by CuAAC reaction was quite different from that of the monomers . However, L-histidine can bind to Cu 2+ and inhibits the production of Cu + , hence preventing the aggregation of AuNPs. Therefore, there is a distinct change of color as the addition of L-histidine under dark-field microscopy. Then, L-histidine can be quantitatively detected by combining the color change with the Meanshift algorithm accurately and automatically. Such proposed method has been successfully applied for the detection of L-histidine in serum sample with satisfying result.
危险品化学品经营许可证（带存储）