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| Study on Infrared Spectral Recognition of Microplastics in Fishmeal Based on XGBoost Algorithm |
| XU Xiao-dong, ZHANG Hui-min, LIU Jia-le, HAN Lu-jia, YANG Zeng-ling, LIU Xian* |
Department of Agricultural Engineering, College of Engineering, China Agricultural University, Beijing 100083, China
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Abstract As one of the four emerging pollutants, the harm caused by “microplastics” has become increasingly prominent. The detection and identification of microplastics are the keys to pollution assessment and risk management prevention and control. This paper uses microplastics (including PA, PE, PET, PP, PS, and PVC) in fishmeal as the research objects. The XGBoost algorithm studies and constructs the qualitative recognition models of near-infrared and infrared spectroscopy. The XGBoost algorithm studies and constructs the qualitative recognition models of near-infrared and infrared spectroscopy. Optimising the main hyperparameters of the XGBoost model using the GridSearchCV toolkit. The hyperparameter optimization results of the near-infrared spectroscopy model were n_estimators: 300, learning_rate: 0.08, gamma: 0, max_depth: 4, min_child_weight: 1. The hyperparameter optimization results of infrared spectroscopy are n_estimators: 100, learning_rate: 0.02, gamma: 0.20, max_depth: 4, and min_child_weight: 1. The average Precision of the NIR qualitative recognition model constructed based on the optimized hyperparameters was 0.985, the average Recall was 0.977, and the average F1 score was 0.978, which improved by 40.17%, 51.00%, and 50.00% compared with the model before optimization. The average precision, average recall, and average F1 scores of the infrared qualitative recognition model were all 1.000, and the optimized model effect improved by 20.67%, 27.50%, and 26.33%, respectively. Further comparative analysis with the PLS-DA model shows that the XGBoost model of the infrared spectrum is the same as that of the PLS-DA model, and the effect of each parameter (Accuracy, Precision, Recall, F1 score) of the XGBoost model of the near-infrared spectrum is better than that of PLS-DA model to varying degrees. In summary, the XGBoost algorithm can effectively identify different types of microplastics in fishmeal. This study provides theoretical and technical support for rapidly detecting and identifying microplastics in fishmeal.
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Received: 2023-03-17
Accepted: 2023-11-05
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Corresponding Authors:
LIU Xian
E-mail: lx@cau.edu.cn
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[1] Veerasingam S, Saha M, Suneel V, et al. Chemosphere, 2016, 159: 496.
[2] Thompson R C. Science, 2004, 304(5672): 838.
[3] Hale S E, Arp H P H, Schliebner I, et al. Environmental Science & Technology, 2020, 54(23): 14790.
[4] Bhat M A, Gedik K, Gaga E O. Air Quality, Atmosphere & Health, 2023, 16(2): 233.
[5] Amato-Lourenço L F, Carvalho-Oliveira R, Júnior G R, et al. Journal of Hazardous Materials, 2021, 416: 126124.
[6] Ragusa A, Svelato A, Santacroce C, et al. Environment International, 2021, 146: 106274.
[7] Tang B L. Frontiers in Environmental Science, 2017, 7(1): 46687.
[8] Jenner L C, Rotchell J M, Bennett R T, et al. Science of The Total Environment, 2022, 831: 154907.
[9] Leslie H A, van Velzen M J M, Brandsma S H, et al. Environment International, 2022, 163: 107199.
[10] Ragusa A, Svelato A, Santacroce C, et al. Environment International, 2021, 146: 106274.
[11] WU Xue, FENG Wei-wei, CAI Zong-qi, et al(吴 雪，冯巍巍，蔡宗岐, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2022, 42(11): 3501.
[12] LUO Yong-ming, SHI Hua-hong, TU Chen, et al(骆永明，施华宏，涂 晨, 等). Chinese Science Bulletin(科学通报), 2021, 66(13): 1547.
[13] Peñalver R, Arroyo-Manzanares N, López-García I, et al. Chemosphere, 2020, 242: 125170.
[14] Castelvetro V, Corti A, Bianchi S, et al. Environmental Pollution, 2021, 273: 115792.
[15] Thiele C J, Hudson M D, Russell A E, et al. Scientific Reports, 2021, 11(1): 2045.
[16] Walkinshaw C, Tolhurst T J, Lindeque P K, et al. Marine Pollution Bulletin, 2022, 185: 114189.
[17] Chen T, Guestrin C. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, 785.
[18] ZHAO Xin, LIU Xin, WANG Yun-peng, et al(赵 昕，刘 鑫，王韵彭, 等). Science and Technology of Food Industry(食品工业科技), 2022, 43(21): 294.
[19] Zontov Y V, Rodionova O Y, Kucheryavskiy S V, et al. Chemometrics and Intelligent Laboratory Systems, 2020, 203: 104064.
[20] Santos Y J S, Malegori C, Colnago L A, et al. Critical Reviews in Food Science and Nutrition, 2022, 64(10): 11.
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