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Research progress on sample pretreatment technology for the analysis of new pollutants in food using composite covalent organic framework materials
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Zeyi SUN, Guangnian YUAN, Yuanye NIU, Jiping MA*
Chinese Journal of Chromatography | 2026, 44(7) : 731 - 740
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Chinese Journal of Chromatography | 2026, 44(7): 731-740
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Research progress on sample pretreatment technology for the analysis of new pollutants in food using composite covalent organic framework materials
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Zeyi SUN, Guangnian YUAN, Yuanye NIU, Jiping MA*
Affiliations
  • 1School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China
Published: 2026-07-08 doi: 10.3724/SP.J.1123.2025.08002
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New pollutants are recently identified or recognized chemical substances. They pose risks to ecosystems or human health. Many are not yet regulated or lack effective control measures. These pollutants show biological toxicity, environmental persistence, and bioaccumulation. They threaten human health. In recent years, their detection frequency in food has increased. Efficient detection technologies are urgently needed. Sample pretreatment is key for analyzing new pollutants in food. The core of pretreatment lies in the preparation and selection of adsorbent materials. Covalent organic frameworks (COFs) are porous crystalline materials. They are formed by light elements linked through covalent bonds. COFs have highly ordered crystal structures. Their pore sizes can be adjusted. Surface properties are functionalizable. They show excellent chemical and thermal stability. Composite covalent organic framework materials combine COFs with other materials. This is achieved through physical or chemical methods. The composites exhibit synergistic effects. They retain the unique properties of both COFs and the other materials. This article reviews common sample pretreatment techniques for new pollutants in food. These include solid-phase extraction (SPE), solid-phase microextraction (SPME), stir bar sorptive extraction (SBSE), dispersive solid-phase extraction (DSPE), and magnetic solid-phase extraction (MSPE). SPE is a chromatographic technique. It removes impurities from solid or liquid samples. It also enriches target compounds. SPE offers high enrichment factors and low solvent use. It is easy to automate. SPME balances samples between solid and liquid phases. It integrates sampling, extraction, and concentration. SPME uses little or no solvent. It is simple and automatable. It can be coupled with other techniques online. SBSE evolved from SPME. It has a larger stationary phase volume and higher capacity. SBSE uses a stir bar with a magnetic core. The bar is coated with an extraction layer. Stirring ensures full contact with analytes. SBSE is solvent-free or uses minimal solvent. It is accurate, fast, and easy to automate. DSPE disperses adsorbents into sample matrices. It increases contact area between adsorbents and analytes. DSPE simplifies sample processing. It avoids sample loss. MSPE uses magnetic or magnetizable materials as adsorbents. It captures target analytes efficiently. MSPE is simple to prepare and separate. Pipette-tip-SPE (PT-SPE) is a newer technique. It packs adsorbents into pipette tips. PT-SPE is flexible, low-cost, and needs small sample volumes. This article details types of composite COF materials. These include magnetic COF (MCOF), sponge-COF, molecularly imprinted polymer-COF (MIP-COF), metal-organic framework-COF (MOF-COF), and electrospun-COF. MCOF combines COFs with magnetic nanoparticles. It enables quick separation under a magnetic field. Sponge-COF grows COFs on sponge fibers. It enhances adsorption capacity and mass transfer. MIP-COF integrates molecularly imprinted polymers with COFs. It offers specific recognition sites. MOF-COF combines metal-organic frameworks with COFs. It introduces metal active sites. Electrospun-COF embeds COFs into polymer nanofibers. It improves mechanical performance and stability. These composite materials are utilized in the pretreatment of food samples, where they effectively enrich trace levels of new pollutants, including perfluoroalkyl substances, antibiotics, personal care products, endocrine disruptors, and flame retardants, within complex food matrices. When coupled with analytical techniques such as high-performance liquid chromatography (HPLC), HPLC-tandem mass spectrometry (HPLC-MS/MS), gas chromatography (GC), gas chromatography-mass spectrometry (GC-MS), molecular fluorescence spectroscopy, and Raman spectroscopy, they facilitate highly accurate and sensitive detection of these new pollutants in food products. Future directions include improving synthesis methods. Current methods are time-consuming and costly. New techniques like water-phase synthesis are promising. Multifunctional composites are needed. They should adsorb multiple pollutant types. Automated and high-throughput extraction technologies will be developed. Green and scalable production processes are essential for industrial applications. In conclusion, composite COF materials show great potential. They enhance the efficiency and accuracy of food pollutant analysis. Further research will expand their applications and improve performance.

composite covalent organic framework materials  /  food samples  /  sample pretreatment techniques  /  new pollutants
孙泽艺, 袁光年, 牛媛烨, 马继平. 复合共价有机框架材料用于食品中新污染物分析的样品前处理技术研究进展. 色谱, 2026 , 44 (7) : 731 -740 . DOI: 10.3724/SP.J.1123.2025.08002
Zeyi SUN, Guangnian YUAN, Yuanye NIU, Jiping MA. Research progress on sample pretreatment technology for the analysis of new pollutants in food using composite covalent organic framework materials[J]. Chinese Journal of Chromatography, 2026 , 44 (7) : 731 -740 . DOI: 10.3724/SP.J.1123.2025.08002
Year 2026 volume 44 Issue 7
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doi: 10.3724/SP.J.1123.2025.08002
  • Receive Date:2025-08-04
  • Online Date:2026-10-27
  • Published:2026-07-08
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  • Received:2025-08-04
Affiliations
    1School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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