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Articles by Jian Qin
Total Records ( 2 ) for Jian Qin
  Jafar Muhammad El-Qudah , Jian Qin and Guangwen Tang
  Mallow (Corchorus olitorius) is a green vegetable, which is widely consumed either fresh or dry by Middle East population. This study was carried out to determine the contents of major carotenoids quantitatively in mallow, by using a High Performance Liquid Chromatography (HPLC) equipped with a Bischoff C30 column. Neoxanthin and violaxanthin in mallow were characterized by Liquid Chromatography-Mass Spectrometry (LC-MS) with Atmospheric Pressure Chemical Ionization (APCI) as an ionization interphase. The total carotenoid content of mallow was estimated as 16.9 mg/100 g fresh weight. In which, major carotenoids in mallow are: lutein (5.3 mg/100 g), anhydrolutein (4.4 mg/100 g), β-carotene (3.7 mg/100 g), cis-anhydrolutein (1.0 mg/100 g), neoxanthin (1.6 mg/100 g), violaxanthin (0.3 mg/100 g), 9-cis-β- carotene (0.4 mg/100 g) and 13-cis-β-carotene (0.09 mg/100 g). Therefore, mallow is a rich source of carotenoids. As a popular vegetable consumed by the Middle East and surrounding populations, our finding of this study confirmed that mallow is an important source for carotenoids including provitamin A carotenoids.
  Xiao-Ping Zeng , Yong-Ming Li and Jian Qin
  In this paper, one novel genetic algorithm dynamic chain-like agent genetic algorithm (CAGA) is proposed for solving global numerical optimization problem and feature selection problem. The CAGA combines the chain-like agent structure with dynamic neighboring genetic operators to get higher optimization capability. An agent in chain-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and can use knowledge to increase energies. Global numerical optimization problem and feature selection problem are the most important problems for evolutionary algorithm, especially for genetic algorithm. Hence, the experiments of global numerical optimization and feature selection are necessary to verify the performance of genetic algorithms. Corresponding experiments have been done and show that CAGA is suitable for real coding and binary coding optimization problems, and has more precise and more stable optimization results.
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