From Speed and Sensitivity to Robust Reliability: Addressing the Detection Bias of Loop-Mediated Isothermal Amplification (LAMP) in Food Authentication
Food adulteration poses severe threats to the integrity and safety of global food supply chains. Innovations in analytical techniques, especially the rapid development of LAMP-based detection methods, are indispensable for addressing these risks. However, the dominant research paradigm, which has long prioritized accelerated amplification kinetics and ultralow detection limits, largely obscures a critical limitation: insufficient detection reliability when deployed in complex field environments. This review consolidates state-of-the-art advances targeting reliability optimization and delineates key technical strategies to construct robust, improved-reliability LAMP platforms for real-food testing. Specifically, we summarize critical factors at the reaction and readout levels that compromise the reliability of LAMP under realistic scenarios, such as inexperienced operators, degraded samples, matrices containing inhibitory substances, and uncontrolled environments. We further elaborate on effective solutions to bridge the reliability gap between laboratory validation and on-site deployment, which requires coordinated technical improvements throughout the full LAMP testing pipeline. Ultimately, we propose a forward-looking developmental principle for next-generation LAMP assays: analytical reliability and operational robustness should be elevated to equal priority alongside the traditionally pursued ultra-rapid amplification and high sensitivity. This balanced technical framework may help pave the way toward large-scale standardization of LAMP-enabled food authentication, although its presumptive results must be confirmed by an independent validated method before they can support regulatory enforcement or other formal decisions.
Authors
- Xiong Xiong (ORCID: https://orcid.org/0000-0001-7965-5859)
- Yahui Zhang (ORCID: https://orcid.org/0009-0002-0280-2472)
- Yan Guo
- Mengxuan Liu
- Ying Yang
Institutions
- Nanjing Tech University (CN)
- Anhui Agricultural University (CN)
Publication Details
- Journal
- Foods
- Published
- 2026-09-29
- DOI
- https://doi.org/10.3390/foods15193483
- Primary Topic
- Biosensors and Analytical Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00