Content area

Abstract

Meat adulteration is a form of economic fraud and a global issue diminishing consumer confidence. Consequently, a sensitive and reliable technique for meat species identification is needed to protect meat quality. The present work proposes a rapid, nucleic acid, isothermal amplification method, with SYBR Green I visualization called saltatory rolling circle amplification (SRCA) to identify horsemeat in beef products. Primers were designed to specifically amplify the mitochondrial cytochrome b gene regions of different lengths of horsemeat DNAs. Fresh muscle tissue samples from ten different species were used to confirm the specificity of SRCA. The sensitivity of SRCA was evaluated and found to be 6.3 × 101 fg/μL. The detection limit of SRCA was 0.01% when evaluated with artificially contaminated beef in horsemeat. Compared with conventional PCR approaches, SRCA assay achieved at least 100-fold higher sensitivity and tenfold lower detection limits. In addition, the sensitivity, specificity, and accuracy of the SRCA system were calculated to be 100.00, 98.41, and 98.48%, respectively. Consequently, the present study offers a highly sensitive and visual technique for determining meat adulteration by discriminating horsemeat.

Details

Title
Saltatory rolling circle amplification (SRCA) for sensitive visual detection of horsemeat adulteration in beef products
Author
Hu Xuejia 1 ; Xu, Hui 2 ; Zhang Yunzhe 1 ; Lu, Xin 2 ; Yang, Qian 2 ; Zhang, Wei 3 

 Hebei Agricultural University, College of Food Science and Technology, Baoding, China (GRID:grid.274504.0) (ISNI:0000 0001 2291 4530) 
 Hebei Agricultural University, College of Science and Technology, Cangzhou, China (GRID:grid.274504.0) (ISNI:0000 0001 2291 4530) 
 Hebei Agricultural University, College of Food Science and Technology, Baoding, China (GRID:grid.274504.0) (ISNI:0000 0001 2291 4530); Hebei Agricultural University, College of Science and Technology, Cangzhou, China (GRID:grid.274504.0) (ISNI:0000 0001 2291 4530); Hebei Agricultural University, College of Life Sciences, Baoding, China (GRID:grid.274504.0) (ISNI:0000 0001 2291 4530) 
Pages
2667-2676
Publication year
2021
Publication date
Nov 2021
Publisher
Springer Nature B.V.
ISSN
14382377
e-ISSN
14382385
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2576586909
Copyright
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.