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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| Published in: | Food chemistry Vol. 498; no. Pt 1; p. 147098 |
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| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
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England
Elsevier Ltd
01.01.2026
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| ISSN: | 0308-8146, 1873-7072, 1873-7072 |
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| Abstract | 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.
[Display omitted]
•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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| AbstractList | 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
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. 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.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. 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. [Display omitted] •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. |
| ArticleNumber | 147098 |
| Author | Chen, Zhenzhen Yu, Sha Chen, Jiawei Xia, Yudi Lai, Weihua Peng, Juan Xiao, Xiaoyue |
| Author_xml | – sequence: 1 givenname: Jiawei surname: Chen fullname: Chen, Jiawei organization: State Key Laboratory of Food Science and Resources, Nanchang University, 235 East Nanjing Road, Nanchang 330047, China – sequence: 2 givenname: Zhenzhen surname: Chen fullname: Chen, Zhenzhen organization: State Key Laboratory of Food Science and Resources, Nanchang University, 235 East Nanjing Road, Nanchang 330047, China – sequence: 3 givenname: Sha surname: Yu fullname: Yu, Sha organization: State Key Laboratory of Food Science and Resources, Nanchang University, 235 East Nanjing Road, Nanchang 330047, China – sequence: 4 givenname: Xiaoyue surname: Xiao fullname: Xiao, Xiaoyue organization: State Key Laboratory of Food Science and Resources, Nanchang University, 235 East Nanjing Road, Nanchang 330047, China – sequence: 5 givenname: Juan surname: Peng fullname: Peng, Juan organization: State Key Laboratory of Food Science and Resources, Nanchang University, 235 East Nanjing Road, Nanchang 330047, China – sequence: 6 givenname: Yudi surname: Xia fullname: Xia, Yudi email: yudi511@xmu.edu.cn organization: School of Journalism and Communication, Xiamen University, 422 Siming South Road, Xiamen 361102, China – sequence: 7 givenname: Weihua surname: Lai fullname: Lai, Weihua email: talktolaiwh@163.com organization: State Key Laboratory of Food Science and Resources, Nanchang University, 235 East Nanjing Road, Nanchang 330047, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41270623$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3168/jds.2018-16048 10.1016/j.foodchem.2023.135511 10.1002/smll.202207949 10.1002/adma.201703643 10.1016/j.cej.2024.153327 10.1016/j.tifs.2021.02.045 10.1038/s41467-024-46069-2 10.3390/foods11111609 10.1016/j.foodchem.2022.135175 10.1016/j.foodchem.2023.137328 10.1002/dta.1367 10.1016/j.foodchem.2021.129514 10.1080/19440049.2021.1881623 10.1016/j.foodchem.2020.128255 10.1016/j.trac.2019.115769 10.1016/j.bios.2024.117068 10.1016/j.fbio.2024.103905 10.1016/j.snb.2022.131450 10.1021/acs.analchem.4c06582 10.1016/j.foodchem.2024.139050 10.1080/09540105.2019.1585417 10.1021/acssensors.3c02250 10.1016/j.forsciint.2019.02.014 10.1021/acssensors.2c02165 10.1038/s41564-018-0295-3 10.1021/acs.chemrev.1c01012 10.7150/thno.67184 10.1021/acs.chemmater.2c03741 |
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| Keywords | Random forest algorithm Tadalafil detection Lateral flow immunoassay Machine learning |
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| References | Hnasko, Jackson, Lin, Haff, McGarvey (bb0040) 2021; 355 Monakhova, Kuballa, Löbell-Behrends, Maixner, Kohl-Himmelseher, Ruge, Lachenmeier (bb0090) 2013; 5 Ye, Yan, Zhang, Duan, Chen, Song, Gao (bb0140) 2022; 11 Niu, Zhang, Shi, Park, Xie, Kwok, Tang (bb0095) 2020; 123 Xiao, Hu, Lai, Peng, Lai (bb0130) 2021; 111 Liu, Chen, Li, Tan, Wang, Zhang, Tang (bb0075) 2025; 271 Lee, Park, Park, Kim, Park, Kang (bb0055) 2019; 298 Dong, Zhang, Liu, Zhu, Peng, Hu, Zhang, Chen (bb0020) 2024; 449 Zhang, Li, Mao, Dang, Huang, Wang, Yang, Bai, Zhang (bb0150) 2023; 407 Land, Boeras, Chen, Ramsay, Peeling (bb0050) 2019; 4 Atta, Zhao, Yampolsky, Sanchez, Vo-Dinh (bb0005) 2024; 496 Sun, Wu, Fang, Wang, Xu, Yan, Wang (bb0110) 2025; 97 Guan, Shen, Jiang, Zhao, Liang, Liu, Shen, Li, Xu, Lei (bb0030) 2022; 358 Sena-Torralba, Álvarez-Diduk, Parolo, Piper, Merkoçi (bb0105) 2022; 122 Lee, Park, Woo, Yoo, Lee, Chung, Lee (bb0065) 2024; 15 Renzi, Piper, Nastri, Merkoçi, Lombardi (bb0100) 2023; 19 Yang, Ho, Gao, Chen, Chen, Zhu, Zhang (bb0135) 2025 Suryoprabowo, Liu, Kuang, Cui, Xu (bb0120) 2021; 342 Zhang, Zhang, Lai, Su, He, Lai, Deng (bb0145) 2023; 35 Wei, Zhang, Huang, Yang, Tian, Shen (bb0125) 2024; 59 Lee, Min, Young, Park, Han, Yang, Baek (bb0060) 2021; 38 Li, Qin, Xu, Qian, Tang (bb0070) 2017; 29 Chen, Ding, Huang, Xiong (bb0015) 2022; 12 Hu, Fang, Huang, Chen, Liu, Xing, Peng, Lai (bb0045) 2019; 102 Supianto, Yoo, Hwang, Oh, Jhung, Lee (bb0115) 2024; 9 Chen, Zhang, Xiao, Liu, Peng, Xiong, Lai (bb0010) 2024; 282 Dou, Li, Wang, Shen, Yu (bb0025) 2022; 7 Lu, Ding, Liu, Xu, Kuang, Xu, Guo (bb0085) 2024; 433 He, Zou, Yang, Wang, Deng, Tian, Shen (bb0035) 2019; 30 Liu, Lai, Guo, Zhang, Zhang, Wu, Lai (bb0080) 2023; 411 Land (10.1016/j.foodchem.2025.147098_bb0050) 2019; 4 Hnasko (10.1016/j.foodchem.2025.147098_bb0040) 2021; 355 Liu (10.1016/j.foodchem.2025.147098_bb0075) 2025; 271 Suryoprabowo (10.1016/j.foodchem.2025.147098_bb0120) 2021; 342 Guan (10.1016/j.foodchem.2025.147098_bb0030) 2022; 358 He (10.1016/j.foodchem.2025.147098_bb0035) 2019; 30 Lee (10.1016/j.foodchem.2025.147098_bb0065) 2024; 15 Xiao (10.1016/j.foodchem.2025.147098_bb0130) 2021; 111 Chen (10.1016/j.foodchem.2025.147098_bb0010) 2024; 282 Lee (10.1016/j.foodchem.2025.147098_bb0060) 2021; 38 Zhang (10.1016/j.foodchem.2025.147098_bb0150) 2023; 407 Li (10.1016/j.foodchem.2025.147098_bb0070) 2017; 29 Wei (10.1016/j.foodchem.2025.147098_bb0125) 2024; 59 Lu (10.1016/j.foodchem.2025.147098_bb0085) 2024; 433 Chen (10.1016/j.foodchem.2025.147098_bb0015) 2022; 12 Atta (10.1016/j.foodchem.2025.147098_bb0005) 2024; 496 Niu (10.1016/j.foodchem.2025.147098_bb0095) 2020; 123 Renzi (10.1016/j.foodchem.2025.147098_bb0100) 2023; 19 Sun (10.1016/j.foodchem.2025.147098_bb0110) 2025; 97 Yang (10.1016/j.foodchem.2025.147098_bb0135) 2025 Dou (10.1016/j.foodchem.2025.147098_bb0025) 2022; 7 Sena-Torralba (10.1016/j.foodchem.2025.147098_bb0105) 2022; 122 Lee (10.1016/j.foodchem.2025.147098_bb0055) 2019; 298 Supianto (10.1016/j.foodchem.2025.147098_bb0115) 2024; 9 Liu (10.1016/j.foodchem.2025.147098_bb0080) 2023; 411 Ye (10.1016/j.foodchem.2025.147098_bb0140) 2022; 11 Hu (10.1016/j.foodchem.2025.147098_bb0045) 2019; 102 Dong (10.1016/j.foodchem.2025.147098_bb0020) 2024; 449 Zhang (10.1016/j.foodchem.2025.147098_bb0145) 2023; 35 Monakhova (10.1016/j.foodchem.2025.147098_bb0090) 2013; 5 |
| References_xml | – volume: 4 start-page: 46 year: 2019 end-page: 54 ident: bb0050 article-title: REASSURED diagnostics to inform disease control strategies, strengthen health systems and improve patient outcomes publication-title: Nature Microbiology – volume: 433 year: 2024 ident: bb0085 article-title: Immunochromatographic assay for rapid detection of flupyradifurone in grape, blueberry, and tomato samples publication-title: Food Chemistry – volume: 7 start-page: 3243 year: 2022 end-page: 3257 ident: bb0025 article-title: AIEgens: Next generation signaling source for immunoassays? publication-title: ACS Sensors – volume: 19 year: 2023 ident: bb0100 article-title: An artificial miniaturized peroxidase for signal amplification in lateral flow immunoassays publication-title: Small – volume: 97 start-page: 4824 year: 2025 end-page: 4831 ident: bb0110 article-title: Dual-mode colorimetric/SERS lateral flow immunoassay with machine learning-driven optimization for ultrasensitive mycotoxin detection publication-title: Analytical Chemistry – volume: 11 start-page: 11 year: 2022 ident: bb0140 article-title: Detection of pesticide residue level in grape using hyperspectral imaging with machine learning publication-title: Foods – volume: 411 year: 2023 ident: bb0080 article-title: Sensitive lateral flow immunoassay strips based on Fe3+−chelated polydopamine nanospheres for the detection of kanamycin publication-title: Food Chemistry – volume: 298 start-page: 10 year: 2019 end-page: 19 ident: bb0055 article-title: Screening of illegal sexual enhancement supplements and counterfeit drugs sold in the online and offline markets between 2014 and 2017 publication-title: Forensic Science International – volume: 15 start-page: 1695 year: 2024 ident: bb0065 article-title: Rapid deep learning-assisted predictive diagnostics for point-of-care testing publication-title: Nature Communications – volume: 35 start-page: 3494 year: 2023 end-page: 3502 ident: bb0145 article-title: Highly sensitive and Ultrastable lateral flow immunoassay based on polydopamine-coated aggregation-induced emission fluorescent microspheres with excellent fluorescence performance and biofriendly coupling strategy publication-title: Chemistry of Materials – volume: 30 start-page: 349 year: 2019 end-page: 368 ident: bb0035 article-title: Development of a skeleton-specific antibody and au nanoparticle-based immunochromatographic sensor for simultaneous detection of various tadalafil adulterants in health food publication-title: Food and Agricultural Immunology – volume: 111 start-page: 68 year: 2021 end-page: 88 ident: bb0130 article-title: Developmental trend of immunoassays for monitoring hazards in food samples: A review publication-title: Trends in Food Science & Technology – volume: 355 year: 2021 ident: bb0040 article-title: A rapid and sensitive lateral flow immunoassay (LFIA) for the detection of gluten in foods publication-title: Food Chemistry – volume: 59 year: 2024 ident: bb0125 article-title: Rapid detection of multiple sildenafil and tadalafil adulterants in dietary supplements by dual-labeled probe time-resolved fluorescence immunochromatography assay publication-title: Food Bioscience – volume: 38 start-page: 769 year: 2021 end-page: 781 ident: bb0060 article-title: Detection of 94 compounds related to sexual enhancement including sildenafil, tadalafil, vardenafil and their analogues in various formulations of dietary supplements and food samples using HPLC and LC-MS/MS publication-title: Food Additives & Contaminants: Part A – volume: 358 year: 2022 ident: bb0030 article-title: An ultrasensitive microfluidic chip-based immunoassay for multiplex determination of 11 PDE-5 inhibitors in adulterated health foods publication-title: Sensors and Actuators B: Chemical – volume: 12 start-page: 574 year: 2022 end-page: 602 ident: bb0015 article-title: Tailoring noble metal nanoparticle designs to enable sensitive lateral flow immunoassay publication-title: Theranostics – volume: 9 start-page: 1321 year: 2024 end-page: 1330 ident: bb0115 article-title: Linker-preserved iron metal–organic framework-based lateral flow AsSAY for SENSITIVE transglutaminase 2 Detection in urine through machine learning-assisted colorimetric analysis publication-title: ACS Sensors – volume: 5 start-page: 400 year: 2013 end-page: 411 ident: bb0090 article-title: Standardless 1H NMR determination of pharmacologically active substances in dietary supplements and medicines that have been illegally traded over the internet publication-title: Drug Testing and Analysis – volume: 29 year: 2017 ident: bb0070 article-title: AIE nanoparticles with high stimulated emission depletion efficiency and photobleaching resistance for long-term super-resolution bioimaging publication-title: Advanced Materials – volume: 102 start-page: 6037 year: 2019 end-page: 6046 ident: bb0045 article-title: Using molecular descriptors for assisted screening of heterologous competitive antigens to improve the sensitivity of ELISA for detection of enrofloxacin in raw milk publication-title: Journal of Dairy Science – volume: 122 start-page: 14881 year: 2022 end-page: 14910 ident: bb0105 article-title: Toward next generation lateral flow assays: Integration of nanomaterials publication-title: Chemical Reviews – volume: 496 year: 2024 ident: bb0005 article-title: Nanoengineered plasmonics-enhanced photothermal tags for sensitive detection of cardiac biomarker troponin I using lateral flow immunoassay publication-title: Chemical Engineering Journal – year: 2025 ident: bb0135 article-title: Machine learning: An effective tool for monitoring and ensuring food safety, quality, and nutrition publication-title: Food Chemistry – volume: 342 year: 2021 ident: bb0120 article-title: Fluorescence based immunochromatographic sensor for rapid and sensitive detection of tadalafil and comparison with a gold lateral flow immunoassay publication-title: Food Chemistry – volume: 407 year: 2023 ident: bb0150 article-title: Production of high-affinity monoclonal antibody and development of immunoassay for 3-methyl-quinoxaline-2-carboxylic acid detection in swine muscle and liver publication-title: Food Chemistry – volume: 449 year: 2024 ident: bb0020 article-title: A metal-organic framework signaling probe-mediated immunosensor for the economical and rapid determination of enrofloxacin in milk publication-title: Food Chemistry – volume: 282 year: 2024 ident: bb0010 article-title: Bifunctional bovine serum albumin modification driven sensitivity-enhanced lateral flow immunoassay for small molecule hazards monitoring in food publication-title: International Journal of Biological Macromolecules – volume: 271 year: 2025 ident: bb0075 article-title: AIE nanoparticle with enhanced fluorescence for ultrasensitive lateral flow immunoassays and point-of-care diagnosis of interstitial lung disease publication-title: Biosensors and Bioelectronics – volume: 123 year: 2020 ident: bb0095 article-title: AIE luminogens as fluorescent bioprobes publication-title: TrAC Trends in Analytical Chemistry – volume: 102 start-page: 6037 issue: 7 year: 2019 ident: 10.1016/j.foodchem.2025.147098_bb0045 article-title: Using molecular descriptors for assisted screening of heterologous competitive antigens to improve the sensitivity of ELISA for detection of enrofloxacin in raw milk publication-title: Journal of Dairy Science doi: 10.3168/jds.2018-16048 – volume: 411 year: 2023 ident: 10.1016/j.foodchem.2025.147098_bb0080 article-title: Sensitive lateral flow immunoassay strips based on Fe3+−chelated polydopamine nanospheres for the detection of kanamycin publication-title: Food Chemistry doi: 10.1016/j.foodchem.2023.135511 – volume: 19 issue: 51 year: 2023 ident: 10.1016/j.foodchem.2025.147098_bb0100 article-title: An artificial miniaturized peroxidase for signal amplification in lateral flow immunoassays publication-title: Small doi: 10.1002/smll.202207949 – volume: 29 issue: 43 year: 2017 ident: 10.1016/j.foodchem.2025.147098_bb0070 article-title: AIE nanoparticles with high stimulated emission depletion efficiency and photobleaching resistance for long-term super-resolution bioimaging publication-title: Advanced Materials doi: 10.1002/adma.201703643 – volume: 496 year: 2024 ident: 10.1016/j.foodchem.2025.147098_bb0005 article-title: Nanoengineered plasmonics-enhanced photothermal tags for sensitive detection of cardiac biomarker troponin I using lateral flow immunoassay publication-title: Chemical Engineering Journal doi: 10.1016/j.cej.2024.153327 – volume: 111 start-page: 68 year: 2021 ident: 10.1016/j.foodchem.2025.147098_bb0130 article-title: Developmental trend of immunoassays for monitoring hazards in food samples: A review publication-title: Trends in Food Science & Technology doi: 10.1016/j.tifs.2021.02.045 – volume: 15 start-page: 1695 issue: 1 year: 2024 ident: 10.1016/j.foodchem.2025.147098_bb0065 article-title: Rapid deep learning-assisted predictive diagnostics for point-of-care testing publication-title: Nature Communications doi: 10.1038/s41467-024-46069-2 – volume: 11 start-page: 11 issue: 11 year: 2022 ident: 10.1016/j.foodchem.2025.147098_bb0140 article-title: Detection of pesticide residue level in grape using hyperspectral imaging with machine learning publication-title: Foods doi: 10.3390/foods11111609 – volume: 407 year: 2023 ident: 10.1016/j.foodchem.2025.147098_bb0150 article-title: Production of high-affinity monoclonal antibody and development of immunoassay for 3-methyl-quinoxaline-2-carboxylic acid detection in swine muscle and liver publication-title: Food Chemistry doi: 10.1016/j.foodchem.2022.135175 – volume: 433 year: 2024 ident: 10.1016/j.foodchem.2025.147098_bb0085 article-title: Immunochromatographic assay for rapid detection of flupyradifurone in grape, blueberry, and tomato samples publication-title: Food Chemistry doi: 10.1016/j.foodchem.2023.137328 – volume: 5 start-page: 400 issue: 6 year: 2013 ident: 10.1016/j.foodchem.2025.147098_bb0090 article-title: Standardless 1H NMR determination of pharmacologically active substances in dietary supplements and medicines that have been illegally traded over the internet publication-title: Drug Testing and Analysis doi: 10.1002/dta.1367 – volume: 355 year: 2021 ident: 10.1016/j.foodchem.2025.147098_bb0040 article-title: A rapid and sensitive lateral flow immunoassay (LFIA) for the detection of gluten in foods publication-title: Food Chemistry doi: 10.1016/j.foodchem.2021.129514 – volume: 38 start-page: 769 issue: 5 year: 2021 ident: 10.1016/j.foodchem.2025.147098_bb0060 article-title: Detection of 94 compounds related to sexual enhancement including sildenafil, tadalafil, vardenafil and their analogues in various formulations of dietary supplements and food samples using HPLC and LC-MS/MS publication-title: Food Additives & Contaminants: Part A doi: 10.1080/19440049.2021.1881623 – volume: 342 year: 2021 ident: 10.1016/j.foodchem.2025.147098_bb0120 article-title: Fluorescence based immunochromatographic sensor for rapid and sensitive detection of tadalafil and comparison with a gold lateral flow immunoassay publication-title: Food Chemistry doi: 10.1016/j.foodchem.2020.128255 – volume: 123 year: 2020 ident: 10.1016/j.foodchem.2025.147098_bb0095 article-title: AIE luminogens as fluorescent bioprobes publication-title: TrAC Trends in Analytical Chemistry doi: 10.1016/j.trac.2019.115769 – volume: 271 year: 2025 ident: 10.1016/j.foodchem.2025.147098_bb0075 article-title: AIE nanoparticle with enhanced fluorescence for ultrasensitive lateral flow immunoassays and point-of-care diagnosis of interstitial lung disease publication-title: Biosensors and Bioelectronics doi: 10.1016/j.bios.2024.117068 – volume: 59 year: 2024 ident: 10.1016/j.foodchem.2025.147098_bb0125 article-title: Rapid detection of multiple sildenafil and tadalafil adulterants in dietary supplements by dual-labeled probe time-resolved fluorescence immunochromatography assay publication-title: Food Bioscience doi: 10.1016/j.fbio.2024.103905 – volume: 358 year: 2022 ident: 10.1016/j.foodchem.2025.147098_bb0030 article-title: An ultrasensitive microfluidic chip-based immunoassay for multiplex determination of 11 PDE-5 inhibitors in adulterated health foods publication-title: Sensors and Actuators B: Chemical doi: 10.1016/j.snb.2022.131450 – volume: 97 start-page: 4824 issue: 9 year: 2025 ident: 10.1016/j.foodchem.2025.147098_bb0110 article-title: Dual-mode colorimetric/SERS lateral flow immunoassay with machine learning-driven optimization for ultrasensitive mycotoxin detection publication-title: Analytical Chemistry doi: 10.1021/acs.analchem.4c06582 – volume: 449 year: 2024 ident: 10.1016/j.foodchem.2025.147098_bb0020 article-title: A metal-organic framework signaling probe-mediated immunosensor for the economical and rapid determination of enrofloxacin in milk publication-title: Food Chemistry doi: 10.1016/j.foodchem.2024.139050 – volume: 30 start-page: 349 issue: 1 year: 2019 ident: 10.1016/j.foodchem.2025.147098_bb0035 article-title: Development of a skeleton-specific antibody and au nanoparticle-based immunochromatographic sensor for simultaneous detection of various tadalafil adulterants in health food publication-title: Food and Agricultural Immunology doi: 10.1080/09540105.2019.1585417 – volume: 9 start-page: 1321 issue: 3 year: 2024 ident: 10.1016/j.foodchem.2025.147098_bb0115 article-title: Linker-preserved iron metal–organic framework-based lateral flow AsSAY for SENSITIVE transglutaminase 2 Detection in urine through machine learning-assisted colorimetric analysis publication-title: ACS Sensors doi: 10.1021/acssensors.3c02250 – volume: 282 year: 2024 ident: 10.1016/j.foodchem.2025.147098_bb0010 article-title: Bifunctional bovine serum albumin modification driven sensitivity-enhanced lateral flow immunoassay for small molecule hazards monitoring in food publication-title: International Journal of Biological Macromolecules – volume: 298 start-page: 10 year: 2019 ident: 10.1016/j.foodchem.2025.147098_bb0055 article-title: Screening of illegal sexual enhancement supplements and counterfeit drugs sold in the online and offline markets between 2014 and 2017 publication-title: Forensic Science International doi: 10.1016/j.forsciint.2019.02.014 – volume: 7 start-page: 3243 issue: 11 year: 2022 ident: 10.1016/j.foodchem.2025.147098_bb0025 article-title: AIEgens: Next generation signaling source for immunoassays? publication-title: ACS Sensors doi: 10.1021/acssensors.2c02165 – volume: 4 start-page: 46 issue: 1 year: 2019 ident: 10.1016/j.foodchem.2025.147098_bb0050 article-title: REASSURED diagnostics to inform disease control strategies, strengthen health systems and improve patient outcomes publication-title: Nature Microbiology doi: 10.1038/s41564-018-0295-3 – volume: 122 start-page: 14881 issue: 18 year: 2022 ident: 10.1016/j.foodchem.2025.147098_bb0105 article-title: Toward next generation lateral flow assays: Integration of nanomaterials publication-title: Chemical Reviews doi: 10.1021/acs.chemrev.1c01012 – year: 2025 ident: 10.1016/j.foodchem.2025.147098_bb0135 article-title: Machine learning: An effective tool for monitoring and ensuring food safety, quality, and nutrition publication-title: Food Chemistry – volume: 12 start-page: 574 issue: 2 year: 2022 ident: 10.1016/j.foodchem.2025.147098_bb0015 article-title: Tailoring noble metal nanoparticle designs to enable sensitive lateral flow immunoassay publication-title: Theranostics doi: 10.7150/thno.67184 – volume: 35 start-page: 3494 issue: 9 year: 2023 ident: 10.1016/j.foodchem.2025.147098_bb0145 article-title: Highly sensitive and Ultrastable lateral flow immunoassay based on polydopamine-coated aggregation-induced emission fluorescent microspheres with excellent fluorescence performance and biofriendly coupling strategy publication-title: Chemistry of Materials doi: 10.1021/acs.chemmater.2c03741 |
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| Title | Integrating machine learning with lateral flow immunoassay for ultrafast and sensitive tadalafil detection |
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