Abstract
For rapid real-time detection of veterinary drug residues in paddy field water, we developed a novel sensor system using interdigitated electrodes as detection probes and the STM32F405RGT6 microcontroller as the core processing unit. The hardware architecture integrates multiple functional modules including excitation signal generation, signal detection, signal processing, LoRa coupled with 4G wireless communication, voltage regulation, and lithium battery charging. The system acquires three types of measurement data (amplitude ratio, phase difference, and their combination) from water samples containing sulfamethazine, ofloxacin, doxycycline hydrochloride and tetracycline hydrochloride across a broad frequency spectrum from 200 Hz to 100 MHz. Through Competitive Adaptive Reweighted Sampling (CARS) for feature selection and artificial neural network modeling, we established a multi-input multi-output concentration prediction model. Comparative analysis demonstrated superior performance when using phase difference data as model input, achieving prediction coefficients of determination (R2) between 0.7831 and 0.8713 with root mean square errors of prediction (RMSEP) ranging from 22.0759 to 28.1526 mg/L. Studies showed that this sensor device could effectively detect the contents of four veterinary drugs, namely sulfamethazine, doxycycline hydrochloride, ofloxacin, and tetracycline hydrochloride, in paddy field water, thus realizing the rapid and real-time monitoring of veterinary drugs in paddy field water.
Similar content being viewed by others
Data availability
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
References
Chen, J. et al. Study on the harm and detection methods of veterinary drug residues in livestock products. China Food Saf. Mag. 29, 161–163 (2024).
Stolker, A., Zuidema, T. & Nielen, M. Residue analysis of veterinary drugs and growth-promoting agents. TrAC Trends Anal. Chem. 26, 967–979 (2007).
Lim, S. J. et al. Occurrence and ecological hazard assessment of selected veterinary medicines in Livestock wastewater treatment plants. J. Environ. Sci. Health B 48, 658–670 (2013).
Wei, R. et al. Occurrence of veterinary antibiotics in animal wastewater and surface water around farms in Jiangsu province, China. Chemosphere 82, 1408–1414 (2011).
Lin, J. J. China resources comprehensive utilization. China Resour. Compr. Util. 43, 62–64 (2025).
Chen, X. H., Bu, N. X. & Chen, J., et al. Current situation and control measures of veterinary drug residues in animal-derived foods. Chin. Foreign Food Ind. 55–57 (2024). .
Kaczala, F. & Blum, S. E. The occurrence of veterinary pharmaceuticals in the environment: A review. Curr. Anal. Chem. 12, 169–182 (2016).
Li, W. C. Occurrence, sources, and fate of pharmaceuticals in aquatic environment and soil. Environ. Pollut. 187, 193–201 (2014).
Pan, X. & Tang, Y. Research progress on the resistance mechanisms of quinolone antibacterial drugs. Chin. J. Infect. Chemother. 3, 187–191 (2005).
Xia, R., Guo, X., Zhang, Y. & Xu, H. Research progress on quinolone drugs and bacterial resistance mechanisms. Chin. J. Antibiot. 35, 255–261 (2010).
Yi, D. et al. Occurrence and fate of antibiotics in the aqueous environment and their removal by constructed wetlands in china: a review. Pedosphere 27, 42–51 (2010).
Wang, C., Luo, Y. & Mao, D. Sources, fate, ecological risks, and mitigation strategies of antibiotics in soil environments. Environ. Chem. 33, 19–29 (2014).
Yan, Q. et al. Different concentrations of doxycy-cline in swine manure affect the microbiome and degradation of doxycycline residue in soil. Front. Microbiol. 9, 3129 (2018).
Wei, R. et al. Occurrence of seventeen veterinary antibiotics and resistant bacterias in manure-fertilized vegetable farm soil in four provinces of China. Chemosphere 215, 234–240 (2019).
Chen, Q., Fang, K. Y., Zhao, C. M. & Deng, X. J. Advances in detection techniques for veterinary drug residues in feed. Mod. Food Sci. Technol. 34(10), 281–290 (2018).
Zhou, J., Zou, X., Song, S. & Chen, G. Quantum dots applied to methodology on detection of pesticide and veterinary drug residues. J. Agric. Food. Chem. 66(6), 1307–1319 (2018).
Wang, W. Detection of veterinary drug residues in food inspection and testing. Modern Food 30(20), 201–203 (2024).
Zang, H. Common veterinary drugs in feed and their detection technologies. Anim. Husb. Vet. Sci. Inf. 2, 181–183 (2022).
Fu, S. X. et al. Detection methods and key considerations for common veterinary drug residues in eggs. China Anim. Health. 26(3), 121–122 (2024).
Song, S., Gao, Z., Guo, X. & Chen, G. Aptamer-based detection methodology studies in food safety. Food Anal. Methods 12, 966–990 (2019).
Zhao, T. Z., Song, H. X., He, G. C., Liang, Y. & Song, C. J. Simultaneous determination of 22 antibiotic residues including sulfonamides, quinolones, and tetracyclines in aquaculture sediments using pass-through solid-phase extraction coupled with ultra-performance liquid chromatography-tandem mass spectrometry. Anhui Chem. Ind. 50(2), 170–176 (2024).
Tan, T., Liu, Y. J., Tang, Q., Zhong, W. W. & Lan, Z. P. Simultaneous separation and detection of multiple antibiotics in water by capillary electrophoresis. Chongqing Med. 47(35), 4530–4533 (2018).
Chen, M. et al. A novel multiplexed fluorescence polarisation immunoassay based on a recombinant bi-specific single-chain diabody for simultaneous detection of fluoroquinolones and sulfonamides in milk. Food Addit. Contaminants Part A 31(12), 1959–1967 (2014).
Xu, F., Zhang, L. B., Ji, S. M. & Ke, D. G. Study on nondestructive detection method of fruit quality based on dielectric properties. J. Zhejiang Univ. Technol. 29, 20–24 (2001).
Ren, T. Study on Moisture Content Detection of Cron Stalk Compost Based on Dielectric Properties (Shenyang Agricultural University, 2023).
Lv, L. M. Development of a Prototype for Detecting Internal Quality of Apples Based on Dielectric Characteristics (Northwest A&F University, 2021).
Guo, W. C. et al. Nondestructive detection of soluble solids content in postharvest apples based on dielectric spectroscopy. Trans. Chin. Soc. Agric. Mach. 44, 132–137 (2013).
Wang, X., Jin, Q. H., Zou, J., & Jian, J. W, Research on an interdigital electrode sensor for water quality detection. Wireless Communication Technology. 29(3), 56–61 (2020).
Zhao, H., Yan, L. & Hou, Z. et al. Error analysis strategy for long-term correlated network systems: generalized nonlinear stochastic processes and dual-layer filtering architecture. IEEE Internet Things J. (2025).
Yang, C., Zhan, P. F. & He, L. Visual detection of moisture content in mulberry leaves based on hyperspectral imaging technology. Sci. Seric. 49(05), 430–437 (2023).
Li, M., Tao, G. L. & Liao, H. G. Estimation of disease index of Camellia oleifera anthracnose based on canopy hyperspectral data. J. Jiangsu For. Sci. Technol. 2023(50), 25–29 (2023).
Funding
This work was supported in part by Key R&D Project of Jiangxi Province Science and Technology Plan under Grant 20212BBF61014.
Author information
Authors and Affiliations
Contributions
Conceptualization, J.Z. and J.H.; methodology, B.H., and S.H.; investigation, B.H., and S.H.; validation, B.H.; software, X.W., and S.H.; writing—original draft preparation, J.H.; writing—review and editing, X.W., and J.Z.; supervision, J.Z. and X.W.; funding acquisition, J.Z.. All authors have read and agreed to the published version of the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Huang, J., Huang, B., Huang, S. et al. Design and predictive modeling of a veterinary drug detection sensor in paddy field water based on artificial neural networks. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38752-9
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-38752-9
