A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning

Highlights

• A spectral machine learning approach is proposed for predicting mixed antibiotic.

• Pretreatment is far simpler than traditional detection methods.

• Performance of the model is compared in different influencing factors.

• Spectral machine learning is promising in the detection of complex substances.

Abstract

Antibiotics are widely used in medicine and animal husbandry. However, due to the resistance of antibiotics to degradation, large amounts of antibiotics enter the environment, posing a potential risk to the ecosystem and public health. Therefore, the detection of antibiotics in the environment is necessary. Nevertheless, conventional detection methods usually involve complex pretreatment techniques and expensive instrumentation, which impose considerable time and economic costs. In this paper, we proposed a method for the fast detection of mixed antibiotics based on simplified pretreatment using spectral machine learning. With the help of a modified spectrometer, a large number of characteristic images were generated to map antibiotic information. The relationship between characteristic images and antibiotic concentrations was established by machine learning model. The coefficient of determination and root mean squared error were used to evaluate the prediction performance of the machine learning model. The results show that a well-trained machine learning model can accurately predict multiple antibiotic concentrations simultaneously with almost no pretreatment. The results from this study have some referential value for promoting the development of environmental detection technologies and digital environmental management strategies.

Graphical abstract

Keywords

Antibiotic contamination / Spectral detection / Machine learning

References

[1] Aga D S, Lenczewski M, Snow D, Muurinen J, Sallach J B, Wallace J S (2016). Challenges in the measurement of antibiotics and in evaluating their impacts in agroecosystems: A critical review. Journal of Environmental Quality, 45(2): 407–419

[2] Ahmed S, Ning J, Cheng G, Ahmad I, Li J, Mingyue L, Qu W, Iqbal M, Shabbir M A B, Yuan Z (2017). Receptor-based screening assays for the detection of antibiotics residues: A review. Talanta, 166: 176–186

[3] Ahmed S, Ning J, Peng D, Chen T, Ahmad I, Ali A, Lei Z, Abu Bakr Shabbir M, Cheng G, Yuan Z (2020). Current advances in immunoassays for the detection of antibiotics residues: A review. Food and Agricultural Immunology, 31(1): 268–290

[4] Al-Jarrah O Y, Yoo P D, Muhaidat S, Karagiannidis G K, Taha K (2015). Efficient machine learning for big data: A review. Big Data Research, 2(3): 87–93

[5] Alsheikh M A, Lin S, Niyato D, Tan H (2014). Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys and Tutorials, 16(4): 1996–2018

[6] Anwar S M, Majid M, Qayyum A, Awais M, Alnowami M, Khan M K (2018). Medical image analysis using convolutional neural networks: A review. Journal of Medical Systems, 42(11): 226

[7] Bielen A, Šimatović A, Kosić-Vukšić J, Senta I, Ahel M, Babić S, Jurina T, González Plaza J J, Milaković M, Udiković-Kolić N (2017). Negative environmental impacts of antibiotic-contaminated effluents from pharmaceutical industries. Water Research, 126: 79–87

[8] Blum A L, Langley P (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1–2): 245–271

[9] Capra M, Bussolino B, Marchisio A, Shafique M, Masera G, Martina M (2020). An updated survey of efficient hardware architectures for accelerating deep convolutional neural networks. Future Internet, 12(7): 113

[10] Çelebi H, Bahadır T, Şimşek İ, Tulun Ş (2019). Antibiotic applications in fish farms and environmental problems. Turkish Journal of Engineering, 3(2): 60–67 doi:10.31127/tuje.452921

[11] Çelebi H, Gök O, Sponza D T (2016). Removals of non-analogous OTC and BaP in AMCBR with and without primary substrate. Environmental Technology, 37(14): 1768–1781

[12] Çelebi H, Sponza D T (2012). Comparison of the sensitivities of fish, Microtox and Daphnia-magna bioassays to amoxycillin in anaerobic/aerobic sequential reactor systems. Water Science and Technology, 66(5): 1117–1131

[13] Chen H, Xu L, Ai W, Lin B, Feng Q, Cai K (2020). Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy. Science of the Total Environment, 714: 136765

[14] Cristea C, Tertis M, Galatus R (2017). Magnetic nanoparticles for antibiotics detection. Nanomaterials (Basel, Switzerland), 7(6): 119

[15] Danner M C, Robertson A, Behrends V, Reiss J (2019). Antibiotic pollution in surface fresh waters: Occurrence and effects. Science of the Total Environment, 664: 793–804

[16] Erickson B J, Korfiatis P, Akkus Z, Kline T L (2017). Machine learning for medical imaging. Radiographics, 37(2): 505–515

[17] Fan Y J, Hsiao Y C, Weng Y L, Chen Y H, Chiou P Y, Sheen H J (2020). Development of a parallel three-dimensional microfluidic device for high-throughput cytometry. Sensors and Actuators. B, Chemical, 320: 128255

[18] Favorskaya M, Pakhirka A (2019). Animal species recognition in the wildlife based on muzzle and shape features using joint CNN. Procedia Computer Science, 159: 933–942

[19] Hanak J J (1970). The “multiple-sample concept” in materials research: Synthesis, compositional analysis and testing of entire multicomponent systems. Journal of Materials Science, 5(11): 964–971

[20] Ho Y B, Zakaria M P, Latif P A, Saari N (2012). Simultaneous determination of veterinary antibiotics and hormone in broiler manure, soil and manure compost by liquid chromatography–tandem mass spectrometry. Journal of Chromatography. A, 1262: 160–168

[21] Jordan M I, Mitchell T M (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245): 255–260

[22] Knapp C W, Dolfing J, Ehlert P A I, Graham D W (2010). Evidence of increasing antibiotic resistance gene abundances in archived soils since 1940. Environmental Science & Technology, 44(2): 580–587

[23] Kovalakova P, Cizmas L, Mcdonald T J, Marsalek B, Feng M, Sharma V K (2020). Occurrence and toxicity of antibiotics in the aquatic environment: A review. Chemosphere, 251: 126351

[24] Lara F J, Del Olmo-Iruela M, Cruces-Blanco C, Quesada-Molina C, García-Campaña A M (2012). Advances in the determination of β-lactam antibiotics by liquid chromatography. Trends in Analytical Chemistry, 38: 52–66

[25] Liu L, Chen J, Fieguth P, Zhao G Y, Chellappa R, Pietikainen M (2019). From BoW to CNN: Two decades of texture representation for texture classification. International Journal of Computer Vision, 127(1): 74–109

[26] Luo Q, Yu N, Shi C, Wang X, Wu J (2016). Surface plasmon resonance sensor for antibiotics detection based on photo-initiated polymerization molecularly imprinted array. Talanta, 161: 797–803

[27] Mao Z, Wang J, Gong Y, Yang H, Zhang S (2018). A set of platforms with combinatorial and high-throughput technique for gas sensing, from material to device and to system. Micromachines, 9(11): 606

[28] Martinez J L (2009). Environmental pollution by antibiotics and by antibiotic resistance determinants. Environmental Pollution, 157(11): 2893–2902

[29] Masmoudi S, Elghazel H, Taieb D, Yazar O, Kallel A (2020). A machine-learning framework for predicting multiple air pollutants’ concentrations via multi-target regression and feature selection. Science of the Total Environment, 715: 136991

[30] Mullainathan S, Spiess J (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2): 87–106

[31] Pamreddy A, Hidalgo M, Havel J, Salvadó V (2013). Determination of antibiotics (tetracyclines and sulfonamides) in biosolids by pressurized liquid extraction and liquid chromatography–tandem mass spectrometry. Journal of Chromatography. A, 1298: 68–75

[32] Qiao M, Ying G G, Singer A C, Zhu Y G (2018). Review of antibiotic resistance in China and its environment. Environment International, 110: 160–172

[33] Qiu W, Liu X, Yang F, Li R, Xiong Y, Fu C, Li G, Liu S, Zheng C (2020). Single and joint toxic effects of four antibiotics on some metabolic pathways of Zebrafish (Danio rerio) larvae. Science of the Total Environment, 716: 137062

[34] Rahimi S A, Sajedi H (2019). Monitoring air pollution by deep features and extreme learning machine. Journal of Experimental & Theoretical Artificial Intelligence, 31(4): 517–531

[35] Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C (2017). Machine learning in materials informatics: Recent applications and prospects. NPJ Computational Materials, 3(1): 54

[36] Rashid A, Mazhar S H, Zeng Q, Kiki C, Yu C P, Sun Q (2020). Simultaneous analysis of multiclass antibiotic residues in complex environmental matrices by liquid chromatography with tandem quadrupole mass spectrometry. Journal of Chromatography. B, Analytical Technologies in the Biomedical and Life Sciences, 1145: 122103

[37] Rawat W, Wang Z (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural Computation, 29(9): 2352–2449

[38] Silva J C F, Teixeira R M, Silva F F, Brommonschenkel S H, Fontes E P B (2019). Machine learning approaches and their current application in plant molecular biology: A systematic review. Plant Science, 284: 37–47

[39] Snyder J C, Rupp M, Hansen K, Müller K R, Burke K (2012). Finding density functionals with machine learning. Physical Review Letters, 108(25): 253002

[40] Šoltés L (1999). Aminoglycoside antibiotics—two decades of their hplc bioanalysis. Biomedical Chromatography, 13(1): 3–10

[41] Song L, Wang C, Wang Y (2020). Optimized determination of airborne tetracycline resistance genes in laboratory atmosphere. Frontiers of Environmental Science & Engineering, 14(6): 95 doi:10.1007/s11783-020-1274-5

[42] Sponza D T, Çelebi H (2012). Removal of oxytetracycline (OTC) in a synthetic pharmaceutical wastewater by sequential anaerobic multichamber bed reactor (AMCBR)/completely stirred tank reactor (CSTR) system: Biodegradation and inhibition kinetics. Journal of Chemical Technology and Biotechnology (Oxford, Oxfordshire), 87(7): 961–975

[43] Thangadurai S (2015). Gas chromatographic–mass spectrometric determination of azithromycin in biological fluids. Journal of Analytical Science and Technology, 6(1): 18

[44] Toldrá F, Reig M (2006). Methods for rapid detection of chemical and veterinary drug residues in animal foods. Trends in Food Science & Technology, 17(9): 482–489

[45] Wan K, Lin W, Zhu S, Zhang S, Yu X (2020). Biofiltration and disinfection codetermine the bacterial antibiotic resistome in drinking water: A review and meta-analysis. Frontiers of Environmental Science & Engineering, 14(1): 10 doi:10.1007/s11783-019-1189-1

[46] Wu J, Zhao Y (2019). Machine learning technology in the application of genome analysis: A systematic review. Gene, 705: 149–156

[47] Wu N, Zhang W, Xie S, Zeng M, Liu H, Yang J, Liu X, Yang F (2020). Increasing prevalence of antibiotic resistance genes in manured agricultural soils in northern China. Frontiers of Environmental Science & Engineering, 14(1): 1 doi:10.1007/s11783-019-1180-x

[48] Yang Q, Liu Y, Chen T, Tong Y (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2): 1–19

[49] Yang S, Zhu X, Wang J, Jin X, Liu Y, Qian F, Zhang S, Chen J (2015). Combustion of hazardous biological waste derived from the fermentation of antibiotics using TG–FTIR and Py–GC/MS techniques. Bioresource Technology, 193: 156–163

[50] Zeng G, Liu Y, Ma X, Fan Y (2021). Fabrication of magnetic multi-template molecularly imprinted polymer composite for the selective and efficient removal of tetracyclines from water. Frontiers of Environmental Science & Engineering, 15(5): 107 doi:10.1007/s11783-021-1395-5

[51] Zhao B, Li X, Lu X, Wang Z (2018). A CNN–RNN architecture for multi-label weather recognition. Neurocomputing, 322: 47–57

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 50309011), the Research Project of Shaanxi Province (2011K17-03-06), the Natural Science Basic Research Plan in the Shaanxi Province of China (No. 2021JQ436) and the Scientific Research Foundation for the Retuned Overseas Chinese Scholars (08501041585).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Electronic Supplementary Material

Supplementary material is available in the online version of this article athttps://doi.org/10.1007/s11783-021-1472-9 and is accessible for authorized users.

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