Abstract
Aflatoxin B1 (AFB1), as the main metabolite, found in aflatoxin contaminated food is highly toxic and strictly controlled in many countries. Aiming to construct a simplified, highly sensitive, and accurate quantitative detection platform of AFB1 in ordinary conditions, a one-step competitive quantum dot-labeled immunosorbent assay (cQLISA) was developed using biocompatible CdSe/ZnS core/shell quantum dots (QDs). The quantitative detection of AFB1 can be achieved by adding food sample and QD-AFB1 antibody simultaneously, with an analytical range between 1 and 40 ng/mL. The half maximal inhibitory concentration (IC50) of this method is 542 pg/mL and the limit of detection (LOD) is 56 pg/mL for standard samples, demonstrating a twofold improvement in sensitivity compared with that of the commercial enzyme-linked immunosorbent assay (ELISA) test kit. The recovery rates of negative corn spike-in samples range from 87.01 to114.7% (CV < 15%). A strong correlation was found between cQLISA and commercialized reference platform using real contaminated corns (R2 = 0.9916). The current method demonstrating a shorter operation time due to reduced steps, and a higher accuracy and consistency. The cQLISA showed great potential for convenient, accurate, and sensitive quantitative detection of batch analytes in food safety monitoring in ordinary condition.
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Acknowledgements
The authors gratefully acknowledge the financial support from the research project of National Natural Science Foundation of China (Grant Nos. 81902158 and 21671058), the Science and Technology Department of Henan Province (Grant No. 192102310043), and SZU Top Ranking Project (Grant No. 86000000210).
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Yanbing Lv, Yifan Yang, Ruili Wu, Yanxia Xu, Jinjie Li, Ning Li, Huaibin Shen, Yujuan Chai, and Lin Song Li declare that they have no conflict of interest.
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Lv, Y., Yang, Y., Wu, R. et al. A CdSe/ZnS core/shell competitive quantum dot-based fluorescence-linked immunosorbent assay for the sensitive and accurate detection of aflatoxin B1 in corn sample. Food Measure 16, 857–866 (2022). https://doi.org/10.1007/s11694-021-01223-y
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DOI: https://doi.org/10.1007/s11694-021-01223-y