Simultaneous Rapid Analysis of Multiple Nitrogen Compounds in Polluted River Treatment Using Near-Infrared Spectroscopy and a Support Vector Machine

被引:2
作者
Huang, Jian [1 ,2 ]
Zhang, Xiong [2 ]
Sun, Qingye [1 ]
Zhang, Hua [2 ]
Yu, Xiaokun [2 ]
Wu, Zhaoliang [2 ]
机构
[1] Anhui Univ, Sch Resources & Environm Engn, Hefei 230601, Anhui, Peoples R China
[2] Anhui Jianzhu Univ, Key Lab Anhui Prov Water Pollut Control & Wastewa, Hefei 230601, Anhui, Peoples R China
来源
POLISH JOURNAL OF ENVIRONMENTAL STUDIES | 2017年 / 26卷 / 05期
关键词
near infrared spectroscopy; support vector machine; principal component analysis; intermittent aeration; REGRESSION; SEDIMENTS; AERATION; CHINA;
D O I
10.15244/pjoes/70002
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
An intermittent aerobic process has been developed to effectively remove nitrogen from polluted rivers. In addition, a chemometric model was developed to achieve simultaneous rapid analysis of total nitrogen, ammonia nitrogen, and nitrite nitrogen based on near-infrared spectroscopy data combined with a support vector machine. An intermittent aeration process showed that total nitrogen decreased from 54.25 mg.L-1 to 0.64 mg.L-1. Ammonia nitrogen decreased significantly in the aeration stage, but increased in the non-aeration stage. Eventually, ammonia nitrogen decreased from 45.04 mg.L-1 to 0.57 mg.L-1. Nitrite nitrogen and nitrate nitrogen increased in the aeration stage, but decreased in the non-aeration stage. The concentration ranges of nitrite nitrogen and nitrate nitrogen were, respectively, 0.05 similar to 31.40 mg.L-1 and 0 similar to 0.38 mg.L-1. The 138 water samples were collected during the intermittent aeration process, of which 116 samples were used as the calibration set and the remaining 22 samples were used as a test set in modeling. The actual concentration values and the near-infrared spectroscopy data were used as input of the models. Then the corresponding calibration values and predication values were output by the models. All the samples were scanned with near-infrared spectroscopy from 4,000 similar to 12,500 cm(-1) and measured by chemical methods. Principal component analysis of raw near-infrared spectral data showed that the matrix dimension of spectral data was significantly reduced, which decreased from 2,203x106 to 6x106. Support vector machine models of total nitrogen, ammonia nitrogen, and nitrite nitrogen showed that the calibration correlation coefficient (R-2) of calibration values and actual values were, respectively, 0.9561, 0.9661, and 0.9702, with the root mean square error of cross validation (RMSECV) being 0.09372, 0.04749, and 0.03187. The test results of support vector machine models of total nitrogen, ammonia nitrogen, and nitrite nitrogen showed that the predication correlation coefficient (R-2) of prediction values and actual values were, respectively, 0.9616, 0.9410, and 0.9284, with the root mean square error of prediction (RMSEP) being 0.09420, 0.08227, and 0.06770. This study indicated that nitrogen in a polluted river can be removed through the intermittent aerobic process. Moreover, simultaneous rapid determination of total nitrogen, ammonia nitrogen, and nitrite nitrogen may be achieved with near-infrared spectroscopy and a support vector machine. The results showed that the proposed methods provided effective treatment and detection technology for a polluted river.
引用
收藏
页码:2013 / 2019
页数:7
相关论文
共 50 条
[31]   Plastic solid waste identification system based on near infrared spectroscopy in combination with support vector machine [J].
Zhu S. ;
Chen H. ;
Wang M. ;
Guo X. ;
Lei Y. ;
Jin G. .
Advanced Industrial and Engineering Polymer Research, 2019, 2 (02) :77-81
[32]   Study on Estimation of Fall Dormancy in Alfalfa by Near Infrared Reflectance Spectroscopy and Support Vector Machine Model [J].
Wang Hong-liu ;
Yue Zheng-wen ;
Lu Xin-shi .
SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31 (06) :1510-1513
[33]   Classification of fungal infected wheat kernels using near-infrared reflectance hyperspectral imaging and support vector machine [J].
Zhang, H. ;
Paliwal, J. ;
Jayas, Digvir S. ;
White, N. D. G. .
TRANSACTIONS OF THE ASABE, 2007, 50 (05) :1779-1785
[34]   Near infrared reflectance spectroscopy analysis of compost products using nonlinear support vector machine with RBF nucleus [J].
Huang, Guangqun ;
Han, Lujia .
Guangxue Xuebao/Acta Optica Sinica, 2009, 29 (12) :3556-3560
[35]   Rapid and simultaneous analysis of five alkaloids in four parts of Coptidis Rhizoma by near-infrared spectroscopy [J].
Xue Jintao ;
Liu Yufei ;
Ye Liming ;
Li Chunyan ;
Yang Quanwei ;
Wang Weiying ;
Jing Yun ;
Zhang Minxiang ;
Li Peng .
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2018, 188 :611-618
[36]   Estimation of Andrographolides and Gradation of Andrographis paniculata Leaves Using Near Infrared Spectroscopy Together With Support Vector Machine [J].
Sing, Dilip ;
Banerjee, Subhadip ;
Jana, Shibu Narayan ;
Mallik, Ranajoy ;
Dastidar, Sudarshana Ghosh ;
Majumdar, Kalyan ;
Bandyopadhyay, Amitabha ;
Bandyopadhyay, Rajib ;
Mukherjee, Pulok K. .
FRONTIERS IN PHARMACOLOGY, 2021, 12
[37]   Research on the Quantitative Analysis of Near Infrared Spectroscopy of Asiatic Moonseed Based on Support Vector Machine and Wavelet Transform [J].
Yong, Zhang ;
Hua, Yu Fan .
2016 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC), VOL. 1, 2016, :85-88
[38]   Application of Support Vector Machine in identifying inside moldy chestnut using Near Infrared Spectroscopy [J].
Liu, Jie ;
Li, Xiaoyu ;
Wang, Wei ;
Zhang, Jun .
AUTOMATIC CONTROL AND MECHATRONIC ENGINEERING III, 2014, 615 :169-172
[39]   Rapid screening of fumonisins in maize using near-infrared spectroscopy (NIRS) and machine learning algorithms [J].
Carbas, Bruna ;
Sampaio, Pedro ;
Barros, Silvia Cruz ;
Freitas, Andreia ;
Silva, Ana Sanches ;
Brites, Carla .
FOOD CHEMISTRY-X, 2025, 27
[40]   Fast qualitative analysis of textile fiber in near infrared spectroscopy based on support vector machine [J].
Wang, DH ;
Jin, SZ ;
Gan, B ;
Feng, HX .
2ND INTERNATIONAL CONFERENCE ON ADVANCED OPTICAL MANUFACTURING AND TESTING TECHNOLOGIES: ADVANCED OPTICAL MANUFACTURING TECHNOLOGIES, 2006, 6149