Integrating machine learning with lateral flow immunoassay for ultrafast and sensitive tadalafil detection
Tadalafil, a phosphodiesterase type 5 inhibitor frequently detected in functional foods and dietary supplements, poses significant risks. To enable sensitive on-site detection, an anti-tadalafil monoclonal antibody (3C5) with an IC₅₀ of 0.32 ng mL−1 was prepared. Antibody-antigen binding kinetics re...
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| Vydané v: | Food chemistry Ročník 498; číslo Pt 1; s. 147098 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
England
Elsevier Ltd
01.01.2026
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| Predmet: | |
| ISSN: | 0308-8146, 1873-7072, 1873-7072 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Tadalafil, a phosphodiesterase type 5 inhibitor frequently detected in functional foods and dietary supplements, poses significant risks. To enable sensitive on-site detection, an anti-tadalafil monoclonal antibody (3C5) with an IC₅₀ of 0.32 ng mL−1 was prepared. Antibody-antigen binding kinetics revealed distinct variations in the early detection stage, prompting the integration of lateral flow immunoassay with machine learning (LFIA@ML) to further speed up testing. A Random Forest model trained on multi-dimensional signal features achieved fast detection and high accuracy, with a coefficient of determination of 0.995 and a mean absolute error of 0.038. For 140 samples, the LFIA@ML demonstrated a 94.3 % prediction accuracy and significantly reduced detection time to 3 min. The assay exhibited a strong correlation with LC-MS/MS, with recoveries ranging from 92.3 % to 112 % (coefficient variations ≤15.37 %). The LFIA@ML showcased the ability to reduce detection time by using the Random Forest algorithm and was validated in wine samples.
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•Sensitive monoclonal antibodies against tadalafil were developed.•A fluorescent lateral flow immunoassay detecting tadalafil was developed.•LFIA@ML achieves ultrafast detection (<3 min) versus conventional 10–20 min.•The results showed that the LFIA@ML correlated well with LC-MS. |
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| Bibliografia: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0308-8146 1873-7072 1873-7072 |
| DOI: | 10.1016/j.foodchem.2025.147098 |