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9 results on '"WANG Xia-li"'

1. Determination of 57 organic pollutants in edible vegetable oil by gas chromatography-triple quadrupole mass spectrometry

Author

LIU Yu-xing, YI Shou-fu, XU Wen-yang, TANG Wan-li, KANG Shao-ying, and WANG Xia-li

Subjects
gas chromatography-triple quadrupole mass spectrometry, edible vegetable oil, polycyclic aromatic hydrocarbons, polychlorinated biphenyls, phthalates, benzene hexachloride, dichlorodiphenyltrichloroethane, Food processing and manufacture, TP368-456
Abstract

Objective: Establish a high throughput method for the determination of 57 organic pollutants in edible vegetable oil. Methods: The target compounds were extracted by acetonitrile-hexane solution and purified by disperse solid phase extraction and solid phase extraction, after that the contents of 16 polycyclic aromatic hydrocarbons, 17 phthalates, 8 organochlorines and 16 polychlorinated biphenyls in edible oil were detected by gas chromatography triple quadrupole mass spectrometry. Results: All target components had an excellent linearity within the linear range of 0.001~0.1 μg/mL （r2＞0.998）, and the limits of detection was between 0.16~2.0 μg/kg, meanwhile the average recovery rate of matrix addition was between 71.0%~119.5%, and the relative standard deviation was below 12.6% （n=6）. Furthermore, the method was used to analyze 24 baches of edible oils, and 25 types of target substances were founded. Conclusion: This method is high efficiency, accuracy, high sensitivity, which has great significance for the monitoring of persistent organic pollutants and phthalates in edible oil.

Published
2023
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2. Analysis of pesticide residues and heavy metal pollution in leek

Author

WANG Xia-li, YAN Jian, ZHANG Li, LI Tao, and ZHANG Ji-hong

Subjects
leek, pesticides residues, procymidone, cd, Food processing and manufacture, TP368-456
Abstract

Objective: This study aimed to help with the scientific management of leek food safety in Hunan Province. Methods: 24 kinds of pesticides and 5 kinds of heavy metals in 690 leek samples from Hunan Province were detected. Results: ① Pesticide residues were detected in 23.19% of the 690 samples, and the top three pesticides were determined to be carbendazim, procymidone and cypermethrin. The exceeding rate of pesticide residues was 5.80%, and the highest exceeding rate was 2.61% of procymidone. ② The detection rate of heavy metal elements was 77.68%, and the exceeding rate was 3.62%. The highest detection rate and exceeding rate of heavy metal elements was Cd. Conclusion: High pesticide residues in leek were confirmed, and the detection rate of heavy metal elements in leeks is much higher than that of pesticide residues. Among them, pesticide procymidone and heavy metal Cd are the main over-standard items of leeks. It is suggested to strengthen the continuous monitoring of pesticide residues and heavy metal pollution in leeks.

Published
2022
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8. Study on the polymorphisms of HLA‐ABCDQB1DRB1 alleles and haplotypes in Hubei Han population of China

Author

Yuan Yan Zhu, Jie Zou, Wang Xia Li, Wen Qiang, and Gang Shen

Subjects
China, Genes, MHC Class II, Immunology, Population, Genes, MHC Class I, Disease Association, HLA-C Antigens, Human leukocyte antigen, Biology, Asian People, Gene Frequency, HLA Antigens, Ethnicity, Genetics, Cluster Analysis, HLA-DQ beta-Chains, Humans, Typing, Allele, Han population, education, Molecular Biology, Allele frequency, Alleles, Genetics (clinical), education.field_of_study, HLA-A Antigens, Haplotype, General Medicine, Haplotypes, HLA-B Antigens, HLA-DRB1 Chains
Abstract

The present study aimed to analyse the frequencies of human leukocyte antigen HLA-ABCDQB1 and HLA-DRB1 alleles and haplotypes in a subset of 3,732 Han population from Hubei of China. All samples were typed in the HLA-ABCDQB1 and HLA-DRB1 loci using the sequence-based typing method; subsequently, the HLA polymorphisms were analysed. A total of 47 HLA-A, 89 HLA-B, 43 HLA-C, 49 HLA-DRB1 and 24 HLA-DQB1 alleles were identified in the Hubei Han population. The top three most frequent alleles in the HLA-ABCDQB1 and HLA-DRB1 were A*11:01 (0.2617), A*24:02 (0.1590), A*02:07 (0.1281); B*46:01 (0.1502), B*40:01 (0.1409) and B*58:01 (0.0616); C*01:02 (0.2023), C*07:02 (0.1691) and C*03:04 (0.1175); and DQB1*03:01 (0.2000), DQB1*03:03 (0.1900), DQB1*06:01 (0.1187); DRB1*09:01 (0.1790), DRB1*15:01 (0.1062) and DRB1*12:02 (0.0841), respectively. Meanwhile, the three most frequent two-loci haplotypes were A*02:07-C*01:02 (0.0929), B*46:01-C*01:02 (0.1366) and DQB1*03:03-DRB1*09:01 (0.1766). The three most frequent three-loci haplotypes were A*02:07-B*46:01-C*01:02 (0.0883), B*46:01-DQB1*03:03-DRB1*09:01 (0.0808) and C*01:02-DQB1*03:03-DRB1*09:01 (0.0837). The three most frequent four-loci haplotypes were A*02:07-B*46:01-C*01:02-DQB1*03:03 (0.0494), B*46:01-DRB1*09:01-C*01:02-DQB1*03:03 (0.0729) and A*02:07-B*46:01-DQB1*03:03-DRB1*09:01 (0.0501). The most frequent five-loci haplotype was A*02:07-B*46:01-C*01:02-DQB1*03:03-DRB1*09:01 (0.0487). Heat maps and multiple correspondence analysis based on the frequencies of HLA specificity indicated that the Hubei Han population might be described into Southern Chinese populations. Our results lay a certain foundation for future population studies, disease association studies and donor recruitment strategies.

Published
2020
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9. A Fast K-medoids Clustering Algorithm for Image Segmentation based Object Recognition

Author

Wang Xia Li and Wang Xiaochun

Subjects
Computer science, business.industry, K medoids clustering, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Cognitive neuroscience of visual object recognition, Segmentation, Pattern recognition, Artificial intelligence, Image segmentation, Cluster analysis, business, Image (mathematics)
Abstract

The segmentation of color images as a preprocessing to recognize objects is an important computer vision technique for robotic environment modeling. Linking image sequences to identify all the segments belonging to the same objects is a crucial and challenging problem, especially given the large volumes of image data. In this paper, we propose an aggregation-based fast K-medoids clustering algorithm as a solution for an efficient as well as reliable image segmentation and object recognition problem.

Published
2020
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