SERS-based lateral flow assay combined with machine learning for highly sensitive quantitative analysis of Escherichia c
- PDF / 3,110,047 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 62 Downloads / 198 Views
RESEARCH PAPER
SERS-based lateral flow assay combined with machine learning for highly sensitive quantitative analysis of Escherichia coli O157:H7 Shuaishuai Yan 1 & Cheng Liu 1 & Shuiqin Fang 1 & Junfei Ma 1 & Jingxuan Qiu 1 & Dongpo Xu 1 & Li Li 2 & Jiaping Yu 2 & Daixi Li 1 & Qing Liu 1,3 Received: 9 July 2020 / Revised: 13 August 2020 / Accepted: 27 August 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In the present study, surface-enhanced Raman scattering–based lateral flow assay (SERS-LFA) strips were applied to promptly and sensitively detect Escherichia coli O157:H7 (E. coli O157:H7) to ensure food safety. The SERS nanotags were prepared by connecting peculiar monoclonal antibody (McAb) against E. coli O157:H7 directly onto the surfaces of gold-silver core-shell nanostructures loaded with two-layer Raman reporter molecules of 5,5′-dithiobis-(2-nitrobenzoic acid) (DTNB). The Raman signal intensity at 1335 cm−1 on the test line (T line) of SERS-LFA strips was detected in the wide range of 101–109 colonyforming units/mL (CFU/mL), and regression models based on machine learning were combined to accurately and quantitatively analyze E. coli O157:H7. The limit of detection (LOD) of the extreme gradient boosting regression (XGBR) based on the Raman signal intensity of DTNB was 6.94 × 101 CFU/mL for E. coli O157:H7, which was approximately four orders of magnitude lower than that of visual limits. In addition, although E. coli O157:H7 was spiked into the food matrices including milk and beef at an ultra-low dose of 10 CFU/mL, the SERS-LFA combined with XGBR was able to successfully explore E. coli O157:H7 from the mixture that was incubated for only 2 h, in which the recoveries were mainly distributed between 86.41 and 128.25%. In summary, these results demonstrated that the SERS-LFA had a significant potential as a powerful tool for the point-of-care testing (POCT) of E. coli O157:H7 in the early food contamination stage. Keywords E. coli O157:H7 . Lateral flow assay . Surface-enhanced Raman scattering . Quantitative analysis . Machine learning
Introduction One of the top five pathogens resulting in hospitalization was Escherichia coli O157:H7 (E. coli O157:H7), which can infect water, milk, juice, fruits, and vegetables [1, 2] and constitute ongoing public health concern and food safety problems [3]. Up until the present moment, many different detection
* Daixi Li [email protected] * Qing Liu [email protected] 1
School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2
Shanghai DIAN Medical Laboratory Co., Ltd, Shanghai 200433, China
3
Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, Shandong, China
assays have been established for E. coli O157:H7 determination, including the gold standard approaches for bacterial colony counting [4], polymerase chain reaction (PCR) [5], enzyme-linked immunosorbent assay (ELISA)
Data Loading...
