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Peptidomics was employed to systematically analyze the characteristic peptides in Galli Gigerii Endothelium Corneum and its adulterants and establish a method for distinguishing Galli Gigerii Endothelium Corneum from its adulterants, including the gizzard membranes from ducks, geese, and pigeons. UPLC-Q-Exactive Orbitrap-MS was combined with multivariate statistical analysis to analyze the peptides in Galli Gigerii Endothelium Corneum and its adulterants. The structures of peptides were identified by pNovo combined with manual recognition of spectra, and synthetic peptide standards were used for validation. LC-MS/MS was used to optimize the sample pre-processing conditions, including the extraction procedure, extraction time, extraction solvents, and solvent volumes, for the characteristic peptide LESY in Galli Gigerii Endothelium Corneum. Multiple reaction monitoring(MRM) in the ESI~+ mode with m/z 511.24→269.11 and 511.24→243.13 as detection ions was employed for qualitative and quantitative analyses. The established UPLC-MS/MS method demonstrated good specificity, stability, and durability. The content of LESY in 16 batches of Galli Gigerii Endothelium Corneum samples ranged from 55.03 to 113.36 μg·g~(-1). Additionally, a qualitative detection method for the common peptide RDPVLVSR in adulterants was established with m/z 471.28→785.45 and 471.28→670.41 as the detection ions. This study established a convenient, rapid, and accurate detection method for the characteristic peptides in Galli Gigerii Endothelium Corneum and its adulterants. The method possesses good specificity, stability, and durability, providing a valuable reference for the identification and quality control of Galli Gigerii Endothelium Corneum and other traditional Chinese medicines derived from animal sources.

The arid area is mainly composed of desert, with fragile eco-environment and being extremely vulnerable to the influence of natural and human perturbations. Based on the remote sen-sing ecological index (RSEI), the arid remote sensing ecological index (ARSEI) was formed to improve the remote sensing ecological index for arid area, which was coupled with the information of greenness, humidity, salinity, heat and land degradation to quantitatively evaluate the eco-environment quality. We used ARSEI and RSEI to dynamically monitor and evaluate the eco-environment quality of Ulan Buh Desert from 2000 to 2019, and analyzed their differences and their applicability in arid area. We further examined the characteristics and reasons of the temporal and spatial variations of the eco-environment quality of Ulan Buh Desert. The results showed that the ARSEI index had better applicability to the eco-environment quality in arid area than the RSEI, and it enhanced the role of land use changes in the ecological environment quality assessment. From 2000 to 2019, the overall eco-environmental quality of Ulan Buh Desert was worse. The parts under better, good, and medium grades were mainly distributed in the northern region, the parts with worse grades were mainly concentrated in the gobi and sandy land, and the poor ones were mainly located in area with low coverage vegetation. From 2000 to 2019, the overall quality of the eco-environment in the Ulan Buh Desert were becoming better. Meanwhile, the eco-environment quality of towns and farms in the northern part of the desert changed complexly, with deterioration and improvement alternately distributed. The main reason for the changes in the eco-environment of Ulan Buh Desert was the positive effects of ecological agriculture and sand industry.