Dual-Mode Colorimetric/SERS Lateral Flow Immunoassay with Machine Learning-Driven Optimization for Ultrasensitive Mycotoxin Detection
South China Normal University · Institute of Food Science & Technology · +1 more institution
Abstract
Detecting and quantifying mycotoxins using LFIA are challenging due to the need for high sensitivity and accuracy. To address this, a dual-mode colorimetric-SERS LFIA was developed for detecting deoxynivalenol (DON). Rhodium nanocores provided strong plasmonic properties as the SERS substrate, while silver nanoparticles created electromagnetic “hotspots” to enhance signal sensitivity. Finite element modeling optimized the electromagnetic field intensity, and Prussian blue generated a distinct signal at 2156 cm–1, effectively reducing background interference. This dual-mode LFIA achieved a detection limit of 4.21 pg/mL, 37 times lower than that of colloidal gold-based LFIA (0.156 ng/mL). Machine learning…
Citation impact
- FWCI
- 21.96
- Percentile
- 100%
- References
- 50
Authors
9- BSBoyang Sun
South China Normal University, Institute of Food Science & Technology, Northwest A&F University
- HWHaiyu Wu
South China Normal University, Institute of Food Science & Technology, Northwest A&F University
- TFTianrui Fang
South China Normal University, Institute of Food Science & Technology, Northwest A&F University
- ZWZihan Wang
South China Normal University, Institute of Food Science & Technology, Northwest A&F University
- KXKe Xu
South China Normal University, Institute of Food Science & Technology, Northwest A&F University
Topics & keywords
- Chemistry
- Mycotoxin
- Dual mode
- Immunoassay
- Chromatography
- Nanotechnology
- Aerospace engineering