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

doi:10.1007/s11783-021-1472-9

Front. Environ. Sci. Eng.

2022, Vol. 16

Issue (3) : 38 https://doi.org/10.1007/s11783-021-1472-9

RESEARCH ARTICLE

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

Yicai Huang¹, Jiayuan Chen¹, Qiannan Duan², Yunjin Feng¹, Run Luo¹, Wenjing Wang¹, Fenli Liu¹, Sifan Bi¹, Jianchao Lee¹(

)

¹. Laboratory of Environmental Aquatic Chemistry, Department of Environmental Science, School of Geography and Tourism, Shaanxi Normal University, Xi’an 710062, China
². Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China

Download: PDF(3709 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

• 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.

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.

Keywords Antibiotic contamination Spectral detection Machine learning

Corresponding Author(s): Jianchao Lee

Issue Date: 13 July 2021

Cite this article:

Yicai Huang,Jiayuan Chen,Qiannan Duan, et al. A fast antibiotic detection method for simplified pretreatment through spectra-based machine learning[J]. Front. Environ. Sci. Eng., 2022, 16(3): 38.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-021-1472-9
https://academic.hep.com.cn/fese/EN/Y2022/V16/I3/38

Fig.1 Overall scheme for the analysis of mixed antibiotics based on the machine learning of spectra. The main spectral equipment is denoted by a–e. The equipment includes a light source a, a homogeneous sheet b, a filter chip c, a sample cell d, and a highly sensitive camera e. f shows the characteristic spectral image taken by the camera. g shows the ML training model using the characteristic spectral image.

Fig.2 (A) Learning and prediction process of the Inception v1 model. (B) The GoogLeNet Inception v1 structure, consisting of 9 basic modules and 22 main parameter layers. The model was retrained using the dataset, and the parameters for all layers were fine-tuned.

Fig.3 Performances of various concentrations and chromogenic systems. (A) and (B) show the prediction effects of three mixed antibiotics at a concentration of 10 g/L under chromogenic systems I and II, respectively. (C) and (D) show the prediction effects of three mixed antibiotics at a concentration of 5 g/L under chromogenic systems I and II, respectively. The average R² and RMSE of each plot are annotated separately, and the dashed line indicates the y= x line. The data in the plots include only the test set.

Fig.4 (A) Plots of the true and predicted values for “symmetric pattern” chips. (a) and (b) show the predicted performances of three mixed antibiotics using “symmetric pattern” chips in color systems I and II, respectively. The average R² and RMSE are used to evaluate the prediction performance of different filter chips, and the dashed line indicates the y=x line. (B) Plots of “symmetric pattern” and “random pattern” filter chips.

Fig.5 Performances of various background solutions. (A) Plots of the predicted effects of antibiotics in the sewage and urine background for chromogenic system I. (B) Plots of the predicted effects of antibiotics in the sewage and urine background for chromogenic system II. The average R² and RMSE of each plot are annotated separately, and the dashed line indicates the y= x line. The data in the plots include only the test set.

Fig.6 Construction of grayscale contour plots of antibiotics at different moments. The characteristic spectral images were transformed into grayscale patterns, and the grayscale values were extracted to draw contour plots. When the antibiotic concentration changed, the change was effectively reflected in the characteristic spectral images.

Fig.7 (A) Predictive performances of the trained Inception v1 model on the three antibiotics. The average R² and RMSE of each plot are annotated, the dashed line indicates the y=x line, and the solid line indicates the best-fit curve. (B) Line chart of the relative error. The relative error shows a decreasing trend as the concentration of antibiotics increases. (C) Box line plots of the relative error distribution for each concentration range of antibiotics (0–15, 15–50, 50–200 and 200–800 mg/L). The data in the plots include only the test set.

1	D S Aga, M Lenczewski, D Snow, J Muurinen, J B Sallach, J S Wallace (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 https://doi.org/10.2134/jeq2015.07.0393
2	S Ahmed, J Ning, G Cheng, I Ahmad, J Li, L Mingyue, W Qu, M Iqbal, M A B Shabbir, Z Yuan (2017). Receptor-based screening assays for the detection of antibiotics residues: A review. Talanta, 166: 176–186 https://doi.org/10.1016/j.talanta.2017.01.057
3	S Ahmed, J Ning, D Peng, T Chen, I Ahmad, A Ali, Z Lei, M Abu Bakr Shabbir, G Cheng, Z Yuan (2020). Current advances in immunoassays for the detection of antibiotics residues: A review. Food and Agricultural Immunology, 31(1): 268–290 https://doi.org/10.1080/09540105.2019.1707171
4	O Y Al-Jarrah, P D Yoo, S Muhaidat, G K Karagiannidis, K Taha (2015). Efficient machine learning for big data: A review. Big Data Research, 2(3): 87–93 https://doi.org/10.1016/j.bdr.2015.04.001
5	M A Alsheikh, S Lin, D Niyato, H Tan (2014). Machine learning in wireless sensor networks: Algorithms, strategies, and applications. IEEE Communications Surveys and Tutorials, 16(4): 1996–2018 https://doi.org/10.1109/COMST.2014.2320099
6	S M Anwar, M Majid, A Qayyum, M Awais, M Alnowami, M K Khan (2018). Medical image analysis using convolutional neural networks: A review. Journal of Medical Systems, 42(11): 226 https://doi.org/10.1007/s10916-018-1088-1
7	A Bielen, A Šimatović, J Kosić-Vukšić, I Senta, M Ahel, S Babić, T Jurina, J J González Plaza, M Milaković, N Udiković-Kolić (2017). Negative environmental impacts of antibiotic-contaminated effluents from pharmaceutical industries. Water Research, 126: 79–87 https://doi.org/10.1016/j.watres.2017.09.019
8	A L Blum, P Langley (1997). Selection of relevant features and examples in machine learning. Artificial Intelligence, 97(1–2): 245–271 https://doi.org/10.1016/S0004-3702(97)00063-5
9	M Capra, B Bussolino, A Marchisio, M Shafique, G Masera, M Martina (2020). An updated survey of efficient hardware architectures for accelerating deep convolutional neural networks. Future Internet, 12(7): 113 https://doi.org/10.3390/fi12070113
10	H Çelebi, T Bahadır, İ Şimşek, Ş Tulun (2019). Antibiotic applications in fish farms and environmental problems. Turkish Journal of Engineering, 3(2): 60–67 doi:10.31127/tuje.452921
11	H Çelebi, O Gök, D T Sponza (2016). Removals of non-analogous OTC and BaP in AMCBR with and without primary substrate. Environmental Technology, 37(14): 1768–1781 https://doi.org/10.1080/09593330.2015.1131752
12	H Çelebi, D T Sponza (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 https://doi.org/10.2166/wst.2012.293
13	H Chen, L Xu, W Ai, B Lin, Q Feng, K Cai (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 https://doi.org/10.1016/j.scitotenv.2020.136765
14	C Cristea, M Tertis, R Galatus (2017). Magnetic nanoparticles for antibiotics detection. Nanomaterials (Basel, Switzerland), 7(6): 119 https://doi.org/10.3390/nano7060119
15	M C Danner, A Robertson, V Behrends, J Reiss (2019). Antibiotic pollution in surface fresh waters: Occurrence and effects. Science of the Total Environment, 664: 793–804 https://doi.org/10.1016/j.scitotenv.2019.01.406
16	B J Erickson, P Korfiatis, Z Akkus, T L Kline (2017). Machine learning for medical imaging. Radiographics, 37(2): 505–515 https://doi.org/10.1148/rg.2017160130
17	Y J Fan, Y C Hsiao, Y L Weng, Y H Chen, P Y Chiou, H J Sheen (2020). Development of a parallel three-dimensional microfluidic device for high-throughput cytometry. Sensors and Actuators. B, Chemical, 320: 128255 https://doi.org/10.1016/j.snb.2020.128255
18	M Favorskaya, A Pakhirka (2019). Animal species recognition in the wildlife based on muzzle and shape features using joint CNN. Procedia Computer Science, 159: 933–942 https://doi.org/10.1016/j.procs.2019.09.260
19	J J Hanak (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 https://doi.org/10.1007/BF00558177
20	Y B Ho, M P Zakaria, P A Latif, N Saari (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 https://doi.org/10.1016/j.chroma.2012.09.024
21	M I Jordan, T M Mitchell (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245): 255–260 https://doi.org/10.1126/science.aaa8415
22	C W Knapp, J Dolfing, P A I Ehlert, D W Graham (2010). Evidence of increasing antibiotic resistance gene abundances in archived soils since 1940. Environmental Science & Technology, 44(2): 580–587 https://doi.org/10.1021/es901221x
23	P Kovalakova, L Cizmas, T J Mcdonald, B Marsalek, M Feng, V K Sharma (2020). Occurrence and toxicity of antibiotics in the aquatic environment: A review. Chemosphere, 251: 126351 https://doi.org/10.1016/j.chemosphere.2020.126351
24	F J Lara, M Del Olmo-Iruela, C Cruces-Blanco, C Quesada-Molina, A M García-Campaña (2012). Advances in the determination of β-lactam antibiotics by liquid chromatography. Trends in Analytical Chemistry, 38: 52–66 https://doi.org/10.1016/j.trac.2012.03.020
25	L Liu, J Chen, P Fieguth, G Y Zhao, R Chellappa, M Pietikainen (2019). From BoW to CNN: Two decades of texture representation for texture classification. International Journal of Computer Vision, 127(1): 74–109 https://doi.org/10.1007/s11263-018-1125-z
26	Q Luo, N Yu, C Shi, X Wang, J Wu (2016). Surface plasmon resonance sensor for antibiotics detection based on photo-initiated polymerization molecularly imprinted array. Talanta, 161: 797–803 https://doi.org/10.1016/j.talanta.2016.09.049
27	Z Mao, J Wang, Y Gong, H Yang, S Zhang (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 https://doi.org/10.3390/mi9110606
28	J L Martinez (2009). Environmental pollution by antibiotics and by antibiotic resistance determinants. Environmental Pollution, 157(11): 2893–2902 https://doi.org/10.1016/j.envpol.2009.05.051
29	S Masmoudi, H Elghazel, D Taieb, O Yazar, A Kallel (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 https://doi.org/10.1016/j.scitotenv.2020.136991
30	S Mullainathan, J Spiess (2017). Machine learning: An applied econometric approach. Journal of Economic Perspectives, 31(2): 87–106 https://doi.org/10.1257/jep.31.2.87
31	A Pamreddy, M Hidalgo, J Havel, V Salvadó (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 https://doi.org/10.1016/j.chroma.2013.05.014
32	M Qiao, G G Ying, A C Singer, Y G Zhu (2018). Review of antibiotic resistance in China and its environment. Environment International, 110: 160–172 https://doi.org/10.1016/j.envint.2017.10.016
33	W Qiu, X Liu, F Yang, R Li, Y Xiong, C Fu, G Li, S Liu, C Zheng (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 https://doi.org/10.1016/j.scitotenv.2020.137062
34	S A Rahimi, H Sajedi (2019). Monitoring air pollution by deep features and extreme learning machine. Journal of Experimental & Theoretical Artificial Intelligence, 31(4): 517–531 https://doi.org/10.1080/0952813X.2019.1572658
35	R Ramprasad, R Batra, G Pilania, A Mannodi-Kanakkithodi, C Kim (2017). Machine learning in materials informatics: Recent applications and prospects. NPJ Computational Materials, 3(1): 54 https://doi.org/10.1038/s41524-017-0056-5
36	A Rashid, S H Mazhar, Q Zeng, C Kiki, C P Yu, Q Sun (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 https://doi.org/10.1016/j.jchromb.2020.122103
37	W Rawat, Z Wang (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural Computation, 29(9): 2352–2449 https://doi.org/10.1162/neco_a_00990
38	J C F Silva, R M Teixeira, F F Silva, S H Brommonschenkel, E P B Fontes (2019). Machine learning approaches and their current application in plant molecular biology: A systematic review. Plant Science, 284: 37–47 https://doi.org/10.1016/j.plantsci.2019.03.020
39	J C Snyder, M Rupp, K Hansen, K R Müller, K Burke (2012). Finding density functionals with machine learning. Physical Review Letters, 108(25): 253002 https://doi.org/10.1103/PhysRevLett.108.253002
40	L Šoltés (1999). Aminoglycoside antibiotics—two decades of their hplc bioanalysis. Biomedical Chromatography, 13(1): 3–10 https://doi.org/10.1002/(SICI)1099-0801(199902)13:1<3::AID-BMC811>3.0.CO;2-T
41	L Song, C Wang, Y Wang (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	D T Sponza, H Çelebi (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 https://doi.org/10.1002/jctb.3706
43	S Thangadurai (2015). Gas chromatographic–mass spectrometric determination of azithromycin in biological fluids. Journal of Analytical Science and Technology, 6(1): 18 https://doi.org/10.1186/s40543-015-0059-0
44	F Toldrá, M Reig (2006). Methods for rapid detection of chemical and veterinary drug residues in animal foods. Trends in Food Science & Technology, 17(9): 482–489 https://doi.org/10.1016/j.tifs.2006.02.002
45	K Wan, W Lin, S Zhu, S Zhang, X Yu (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	J Wu, Y Zhao (2019). Machine learning technology in the application of genome analysis: A systematic review. Gene, 705: 149–156 https://doi.org/10.1016/j.gene.2019.04.062
47	N Wu, W Zhang, S Xie, M Zeng, H Liu, J Yang, X Liu, F Yang (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	Q Yang, Y Liu, T Chen, Y Tong (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2): 1–19 https://doi.org/10.1145/3298981
49	S Yang, X Zhu, J Wang, X Jin, Y Liu, F Qian, S Zhang, J Chen (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 https://doi.org/10.1016/j.biortech.2015.06.083
50	G Zeng, Y Liu, X Ma, Y Fan (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	B Zhao, X Li, X Lu, Z Wang (2018). A CNN–RNN architecture for multi-label weather recognition. Neurocomputing, 322: 47–57 https://doi.org/10.1016/j.neucom.2018.09.048

[1]

FSE-21050-OF-HYC_suppl_1

Download

Viewed

Full text

Abstract

Cited

Shared

Discussed