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Simultaneous identification of animal-derived components in meats using high-throughput sequencing in combination with a custom-built mitochondrial genome database
Currently, the inspection and supervision of animal ingredients relies primarily upon specific amplification-dependent methods, whose efficiency and accuracy are being seriously challenged by the increasing diversity and complexity of meat products. High-throughput sequencing (HTS) technology was em...
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Published in: | Scientific reports 2020-06, Vol.10 (1), p.8965, Article 8965 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | Currently, the inspection and supervision of animal ingredients relies primarily upon specific amplification-dependent methods, whose efficiency and accuracy are being seriously challenged by the increasing diversity and complexity of meat products. High-throughput sequencing (HTS) technology was employed to develop an alternative method to detect animal-derived ingredients in meat products. A custom-built database containing 2,354 complete mitochondrial genomic sequences from animals, an identification analysis pipeline based on short-sequence alignment, and a web-based server were built to facilitate this detection. The entire process, including DNA extraction, gene amplification, and sequencing, was established and optimized for both marker gene (part of the
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gene)-based detection and total DNA-based detection. Using simulated samples containing various levels of pig, cattle, sheep, chicken, rabbit, and mice ingredients, the detection capability and accuracy of this method were investigated. The results of this study indicated that the method is capable of detecting animal components in meats that are present at levels as low as 1%. Our method was then tested using 28 batches of real meat products such as raw meat slices, raw meat mince, cooked dried meat, cooked meat sausage, and other supermarket samples, with a traditional qPCR method as the control. The results demonstrated an accuracy of 97.65% for the qualitative detection method, which indicate that the developed method is reliable for the detection of animal components. The method is also effective for the identification of unknown food samples containing mixed animal components, which suggests a good future in application. |
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ISSN: | 2045-2322 2045-2322 |
DOI: | 10.1038/s41598-020-65724-4 |