Abstract
In this study, the goal was to develop a method for detecting and classifying organophosphorus pesticides (OPPs) in bodies of water. Sixty-five samples with different concentrations were prepared for each of the organophosphorus pesticides, namely chlorpyrifos, acephate, parathion-methyl, trichlorphon, dichlorvos, profenofos, malathion, dimethoate, fenthion, and phoxim, respectively. Firstly, the spectral data of all the samples was obtained using a UV–visible spectrometer. Secondly, five preprocessing methods, six manifold learning methods, and five machine learning algorithms were utilized to build detection models for identifying OPPs in water bodies. The findings indicate that the accuracy of machine learning models trained on data preprocessed using convolutional smoothing + first-order derivatives (SG + FD) outperforms that of models trained on data preprocessed using other methods. The backpropagation neural network (BPNN) model exhibited the highest accuracy rate at 99.95%, followed by the support vector machine (SVM) and convolutional neural network (CNN) models, both at 99.92%. The extreme learning machine (ELM) and K-nearest neighbors (KNN) models demonstrated accuracy rates of 99.84% and 99.81%, respectively. Following the application of a manifold learning algorithm to the full-wavelength data set for the purpose of dimensionality reduction, the data was then visualized in the first three dimensions. The results demonstrate that the t-distributed domain embedding (t-SNE) algorithm is superior, exhibiting dense clustering of similar clusters and clear classification of dissimilar ones. SG + FD-t-SNE-SVM ranks highest among the feature extraction models in terms of performance. The feature extraction dimension was set to 4, and the average classification accuracy was 99.98%, which slightly improved the prediction performance over the full-wavelength model. As shown in this study, the ultraviolet–visible (UV–visible) spectroscopy system combined with the t-SNE and SVM algorithms can effectively identify and classify OPPs in waterbodies.
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Data Availability
The data that support the findings of this study are available on request from the corresponding author, Ruijun Ma upon reasonable request.
References
Anowar F, Sadaoui S, Selim B (2021) Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne). Comput Sci Rev 40:100378. https://doi.org/10.1016/j.cosrev.2021.100378
Arduini F, Cinti S, Caratelli V, Amendola L, Palleschi G, Moscone D (2019) Origami multiple paper-based electrochemical biosensors for pesticide detection. Biosens Bioelectron 126:346–354. https://doi.org/10.1016/j.bios.2018.10.014
Badr AM (2020) Organophosphate toxicity: updates of malathion potential toxic effects in mammals and potential treatments. Environ Sci Pollut Res Int 27:26036–26057. https://doi.org/10.1007/s11356-020-08937-4
Banjare P, Matore B, Singh J, Roy PP (2021) In silico local qsar modeling of bioconcentration factor of organophosphate pesticides. In Silico Pharmacol 9:28. https://doi.org/10.1007/s40203-021-00087-w
Chai Z, Wang C, Bi H (2021) Rapid identification between two fish species using uv-vis spectroscopy for substitution detection. Molecules 26. https://doi.org/10.3390/molecules26216529
Chaudhari YS, Kumar P, Soni S et al (2023) An inclusive outlook on the fate and persistence of pesticides in the environment and integrated eco-technologies for their degradation. Toxicol Appl Pharmacol 466:116449. https://doi.org/10.1016/j.taap.2023.116449
Chawla P, Kaushik R, Shiva Swaraj VJ, Kumar N (2018) Organophosphorus pesticides residues in food and their colorimetric detection. Environ Nanotechnol Monit Manag 10:292–307. https://doi.org/10.1016/j.enmm.2018.07.013
Chen Z, Dong X, Liu C, Wang S, Dong S, Huang Q (2023) Rapid detection of residual chlorpyrifos and pyrimethanil on fruit surface by surface-enhanced Raman spectroscopy integrated with deep learning approach. Sci Rep 13:19855. https://doi.org/10.1038/s41598-023-45954-y
Cheng C, Wan L, Jiarong Z, Zhonghua J, Zhixiu Z, Weibin Z (2019) Monitoring chlorpyrifos and abamectin in water bodies of paddies and assessment of ecological risk to aquatic animals. Trans Chin Soc Agric Eng 35:195–205. https://doi.org/10.11975/j.issn.1002-6819.2019.11.023
Cruz-Alcalde A, Sans C, Esplugas S (2018) Priority pesticide dichlorvos removal from water by ozonation process: reactivity, transformation products and associated toxicity. Sep Purif Technol 192:123–129. https://doi.org/10.1016/j.seppur.2017.09.069
Dowgiallo AM, Guenther DA (2019) Determination of the limit of detection of multiple pesticides utilizing gold nanoparticles and surface enhanced Raman spectroscopy (sers). J Agric Food Chem. https://doi.org/10.1021/acs.jafc.9b01544
Ferretto N, Tedetti M, Guigue C, Mounier S, Redon R, Goutx M (2014) Identification and quantification of known polycyclic aromatic hydrocarbons and pesticides in complex mixtures using fluorescence excitation–emission matrices and parallel factor analysis. Chemosphere 107:344–353. https://doi.org/10.1016/j.chemosphere.2013.12.087
Guang-Bin Huang QZAC (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. IEEE
Hu B, Sun D, Pu H, Wei Q (2020) Rapid nondestructive detection of mixed pesticides residues on fruit surface using SERS combined with self-modeling mixture analysis method. Talanta 217:120998. https://doi.org/10.1016/j.talanta.2020.120998
Huang L, Ma R, Chen Y et al (2023) Experimental study on rapid detection of various organophosphorus pesticides in water by UV-vis spectroscopy and parallel factor analysis. Spectrosc Spect Anal 43:3452–3460
Jiang H, Ru Y, Chen Q, Wang J, Xu L (2021) Near-infrared hyperspectral imaging for detection and visualization of offal adulteration in ground pork. Spectrochim Acta Part A Mol Biomol Spectrosc 249:119307. https://doi.org/10.1016/j.saa.2020.119307
Junxian G, Yongjie M, Zhiming G, Hua H, Yong S, Jun Z (2020) Watercore identification of **njiang Fuji apple based on manifold learning algorithm and near infrared transmission spectroscopy. Guang Pu Xue Yu Guang Pu Fen ** 40:2415–2420. https://doi.org/10.3964/j.issn.1000-0593(2020)08-2415-06
Karimi-Maleh H, Darabi R, Baghayeri M et al (2023) Recent developments in carbon nanomaterials-based electrochemical sensors for methyl parathion detection. J Food Meas Charact 17:5371–5389. https://doi.org/10.1007/s11694-023-02050-z
Kumar P, Arshad M, Gacem A et al (2023) Insight into the environmental fate, hazard, detection, and sustainable degradation technologies of chlorpyrifos—an organophosphorus pesticide. Environ Sci Pollut Res Int 30:108347–108369. https://doi.org/10.1007/s11356-023-30049-y
Li Z (2023) Improved physiologically based kinetic (PBK) matrix for biotransfer modeling of pesticides in birds: the role of feather dynamics. Comput Toxicol 26:100268. https://doi.org/10.1016/j.comtox.2023.100268
Li Z, Fantke P (2022) Toward harmonizing global pesticide regulations for surface freshwaters in support of protecting human health. J Environ Manag 301:113909. https://doi.org/10.1016/j.jenvman.2021.113909
Li H, Qi F, Wang S (2005) A comparison of model selection methods for multi-class support vector machines. Comput Sci Appl 3483:1140–1148. https://doi.org/10.1007/11424925_119
Li Q, Huang Y, Zhang J, Min S (2021) A fast determination of insecticide deltamethrin by spectral data fusion of UV–vis and NIR based on extreme learning machine. Spectrochim Acta Part A Mol Biomol Spectrosc 247:119119. https://doi.org/10.1016/j.saa.2020.119119
Li H, Zhang L, Sun H, Rao Z, Ji H (2022a) Discrimination of unsound wheat kernels based on deep convolutional generative adversarial network and near-infrared hyperspectral imaging technology. Spectrochim Acta Part A Mol Biomol Spectrosc 268:120722. https://doi.org/10.1016/j.saa.2021.120722
Li Y, Ma B, Li C, Yu G (2022b) Accurate prediction of soluble solid content in dried Hami jujube using SWIR hyperspectral imaging with comparative analysis of models. Comput Electron Agric 193:106655. https://doi.org/10.1016/j.compag.2021.106655
Li P, Tang S, Chen S, Tian X, Zhong N (2023) Hyperspectral imaging combined with convolutional neural network for accurately detecting adulteration in Atlantic salmon. Food Control 147:109573. https://doi.org/10.1016/j.foodcont.2022.109573
Mali H, Shah C, Raghunandan BH et al (2023) Organophosphate pesticides an emerging environmental contaminant: pollution, toxicity, bioremediation progress, and remaining challenges. J Environ Sci (china) 127:234–250. https://doi.org/10.1016/j.jes.2022.04.023
Mesquita DP, Quintelas C, Amaral AL, Ferreira EC (2017) Monitoring biological wastewater treatment processes: recent advances in spectroscopy applications. Rev Environ Sci Biotechnol 16:395–424. https://doi.org/10.1007/s11157-017-9439-9
Pires NL, de Araújo EP, Oliveira-Filho EC, Caldas ED (2023) An ultrasensitive lc-ms/ms method for the determination of glyphosate, ampa and glufosinate in water – analysis of surface and groundwater from a hydrographic basin in the midwestern region of brazil. Sci Total Environ 875:162499. https://doi.org/10.1016/j.scitotenv.2023.162499
Pundir CS, Malik A, Preety (2019) Bio-sensing of organophosphorus pesticides: a review. Biosens Bioelectron 140. https://doi.org/10.1016/j.bios.2019.111348
Rodríguez-Cuesta MJ, Boqué R, Rius FX, Picón Zamora D, Martínez Galera M, Garrido Frenich A (2003) Determination of carbendazim, fuberidazole and thiabendazole by three-dimensional excitation–emission matrix fluorescence and parallel factor analysis. Anal Chim Acta 491:47–56. https://doi.org/10.1016/S0003-2670(03)00786-4
Ruijun M, Yali Z, Yu C, Zhi Q, **qing X (2019) Experimental study on detection of chlorpyrifos concentration in water by hyperspectral technique based on characteristic band. Guang Pu Xue Yu Guang Pu Fen ** 39:923–930. https://doi.org/10.3964/j.issn.1000-0593(2019)03-0923-08
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536. https://doi.org/10.1038/323533a0
Schleiffer M, Speiser B (2022) Presence of pesticides in the environment, transition into organic food, and implications for quality assurance along the European organic food chain – a review. Environ Pollut 313:120116. https://doi.org/10.1016/j.envpol.2022.120116
Shamsipur M, Yazdanfar N, Ghambarian M (2016) Combination of solid-phase extraction with dispersive liquid-liquid microextraction followed by GC-MS for determination of pesticide residues from water, milk, honey and fruit juice. Food Chem 204:289–297. https://doi.org/10.1016/j.foodchem.2016.02.090
Shao Y, Li Y, Jiang L, Pan J, He Y, Dou X (2016) Identification of pesticide varieties by detecting characteristics of chlorella pyrenoidosa using visible/near infrared hyperspectral imaging and Raman microspectroscopy technology. Water Res 104:432–440. https://doi.org/10.1016/j.watres.2016.08.042
Sharma A, Kumar V, Shahzad B et al (2019) Worldwide pesticide usage and its impacts on ecosystem. Sn Appl Sci 1. https://doi.org/10.1007/s42452-019-1485-1
Shi Z, Chow CWK, Fabris R, Liu J, ** B (2022) Applications of online uv-vis spectrophotometer for drinking water quality monitoring and process control: a review. Sensors (basel, Switzerland) 22:2987. https://doi.org/10.3390/s22082987
Umapathi R, Sonwal S, Lee MJ et al (2021) Colorimetric based on-site sensing strategies for the rapid detection of pesticides in agricultural foods: new horizons, perspectives, and challenges. Coord Chem Rev 446:214061. https://doi.org/10.1016/j.ccr.2021.214061
Vu CT, Le PT, Chu DB et al (2021) One-step purification/extraction method to access glyphosate, glufosinate, and their metabolites in natural waters. J Chromatogr A 1649:462188. https://doi.org/10.1016/j.chroma.2021.462188
Wang X, Jiang S, Liu Z et al (2024) Integrated surface-enhanced Raman spectroscopy and convolutional neural network for quantitative and qualitative analysis of pesticide residues on pericarp. Food Chem 440:138214. https://doi.org/10.1016/j.foodchem.2023.138214
Xu Z, Li X, Cheng W et al (2023) Data fusion strategy based on ultraviolet–visible spectra and near-infrared spectra for simultaneous and accurate determination of key parameters in surface water. Spectrochim Acta Part A Mol Biomol Spectrosc 302:123007. https://doi.org/10.1016/j.saa.2023.123007
Yang SI, Mingjun C, Jialiang Z et al (2023) Quantitative identification method of reservoir flow barriers based on self-organizing neural network and k-nearest neighbor algorithm. J China Univ Petr (edition of Natural Science) 47:35–47. https://doi.org/10.3969/j.issn.1673-5005.2023.04.004
Ye W, Yan T, Zhang C et al (2022) Detection of pesticide residue level in grape using hyperspectral imaging with machine learning. Foods 11:1609. https://doi.org/10.3390/foods11111609
Zhang Y, Qin P, Lu S et al (2021) Occurrence and risk evaluation of organophosphorus pesticides in typical water bodies of Bei**g, China. Environ Sci Pollut Res Int 28:1454–1463. https://doi.org/10.1007/s11356-020-10288-z
Zhou Z (2016) Machine learning. Tsinghua University Press, Bei**g
Funding
This research was supported by the 13th Five-Year National Key Research and Development Program [2016YFD0800901].
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C.S.: conceptualization, methodology, investigation, visualization, software, formal analysis, writing–original draft. Z.Y.: investigation, supervision, data curation. C.L.: software, supervision. Y.H.: software, supervision. Y.L.: supervision. Y.C.: conceptualization, writing–review and editing. R.M.: conceptualization, writing–review and editing.
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Shao, C., Ma, R., Yan, Z. et al. Basic research for identification and classification of organophosphorus pesticides in water based on ultraviolet–visible spectroscopy information. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-34182-0
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DOI: https://doi.org/10.1007/s11356-024-34182-0