Design and development of a rapid meat detection system based on RPA-CRISPR/cas12a-lfd
In recent years, meat adulteration safety incidents have occurred frequently, triggering widespread attention and discussion. Although there are a variety of meat quality identification methods, conventional assays require high standards for personnel and experimental conditions and are not suitable...
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Veröffentlicht in: | Current research in food science 2023-01, Vol.7, p.100609-100609, Article 100609 |
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Sprache: | eng |
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Zusammenfassung: | In recent years, meat adulteration safety incidents have occurred frequently, triggering widespread attention and discussion. Although there are a variety of meat quality identification methods, conventional assays require high standards for personnel and experimental conditions and are not suitable for on-site testing. Therefore, there is an urgent need for a rapid, sensitive, high specificity and high sensitivity on-site meat detection method. This study is the first to apply RPA combined with CRISPR/Cas12a technology to the field of multiple meat identification. The system developed by parameter optimization can achieve specific detection of chicken, duck, beef, pork and lamb with a minimum target sequence copy number as low as 1 × 10° copies/μL for 60 min at a constant temperature. LFD test results can be directly observed with the naked eye, with the characteristics of fast, portable and simple operation, which is extremely in line with current needs. In conclusion, the meat identification RPA-CRISPR/Cas12a-LFD system established in this study has shown promising applications in the field of meat detection, with a profound impact on meat quality, and provides a model for other food safety control programs.
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•This study is the first to apply RPA combined with CRISPR/Cas12a technology to the field of multiple meat identification.•The proposed RPA-CRISPR/Cas12a-LFD meat identification platform is very appropriate for exploiting on-site large quantities of meat. |
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ISSN: | 2665-9271 2665-9271 |
DOI: | 10.1016/j.crfs.2023.100609 |