Machine learning powered detection of biological toxins in association with confined lateral flow immunoassay (c-LFA).

Biological weapons, primarily dispersed as aerosols, can spread not only to the targeted area but also to adjacent regions following the movement of air driven by wind. Thus, there is a growing demand for toxin analysis because biological weapons are among the most influential and destructive. Specifically, such a technique should be hand-held, rapid, and easy to use because current methods require more time and well-trained personnel. Our study demonstrates the use of a novel lateral flow immunoassay, which has a confined structure like a double barbell in the detection area (so called c-LFA) for toxin detection such as staphylococcal enterotoxin B (SEB), ricinus communis (Ricin), and botulinum neurotoxin type A (BoNT-A). Additionally, we have explored the integration of machine learning (ML), specifically, a toxin chip boosting (TOCBoost) hybrid algorithm for improved sensitivity and specificity. Consequently, the ML powered c-LFA concurrently categorized three biological toxin types with an average accuracy as high as 95.5%. To our knowledge, the sensor proposed in this study is the first attempt to utilize ML for the assessment of toxins. The advent of the c-LFA orchestrated a paradigm shift by furnishing a versatile and robust platform for the rapid, on-site detection of various toxins, including SEB, Ricin, and BoNT-A. Our platform enables accessible and on-site toxin monitoring for non-experts and can potentially be applied to biosecurity.