Study on intracellular polymers using near infrared spectroscopy and extreme learning machine in denitrifying phosphorus removal process
ZHANG Hua1,2, QUAN Gui-jun1,2, HUANG Jian1,2, HUANG Xian-huai1,2, YAN Sheng1,2, LIU Pei-ran1,2, LIU Hang1,2, TIAN Ji-yu1,2
1. School of Environment and Energy Engineering, Anhui Jianzhu University, Hefei 230601, China;
2. Key Laboratory of Anhui Province of Water Pollution Control and Wastewater Reuse, Hefei 230601, China
In order to realize rapid determination of intracellular poly-β-hydroxybutyrate (PHB), polyphosphate (Poly-P) and glycogen (Gly) in denitrifying phosphorus removal process with near infrared spectroscopy, the calibration models (ELM models) of PHB, Poly-P, Gly were established by multiple scatter correction preprocessing and extreme learning machine algorithm. The preprocessing results showed that the multiple scattering correction can eliminate the scattering effects on the raw near infrared spectral data of PHB, Poly-P and Gly. The ELM models of PHB, Poly-P and Gly were established with preprocessed spectral data by extreme learning machine. The principal component numbers of ELM models of PHB, Poly-P and Gly were respectively 6, 6 and 7, with the nodes number of hidden layer being 18, 12 and 17 respectively. The ELM models of PHB, Poly-P and Gly showed that the correlation coefficients (rc) were respectively 0.9835, 0.9499, 0.9589, with the root mean square errors of cross validation (RMSECV) being 0.0541, 0.0579, 0.0489 respectively. The prediction results of ELM models of PHB, Poly-P and Gly indicated that the correlation coefficient (rp) were respectively 0.9683, 0.9288, 0.9488, with the root mean square errors of prediction (RMSEP) being 0.0668, 0.0776, 0.0501. It showed that ELM models of PHB, Poly-P and Gly had better prediction performance for the contents of PHB, Poly-P and Gly. This study provides a convenient method for rapid determination of PHB, Poly-P and Gly in denitrifying phosphorus removal process with near infrared spectroscopy and extreme learning machine.
张华, 全桂军, 黄健, 黄显怀, 闫升, 刘沛然, 刘航, 田纪宇. 近红外光谱和极限学习机分析反硝化除磷中胞内聚合物[J]. 中国环境科学, 2017, 37(5): 1823-1830.
ZHANG Hua, QUAN Gui-jun, HUANG Jian, HUANG Xian-huai, YAN Sheng, LIU Pei-ran, LIU Hang, TIAN Ji-yu. Study on intracellular polymers using near infrared spectroscopy and extreme learning machine in denitrifying phosphorus removal process. CHINA ENVIRONMENTAL SCIENCECE, 2017, 37(5): 1823-1830.
Zhao W H, Zhang Y, Lv D M, et al. Advanced nitrogen and phosphorus removal in the pre-denitrification anaerobic/anoxic/aerobic nitrification sequence batch reactor (pre-A2NSBR) treating low carbon/nitrogen (C/N) wastewater [J]. Chemical Engineering Journal, 2016,302:296-304.
Wei Z, Wang X, Li B, et al. Nitritation and denitrifying phosphorus removal via nitrite pathway from domestic wastewater in a continuous MUCT process [J]. Bioresource Technology, 2013,143(9):187-195.
[5]
Nielsen P H, Saunders A M, Hansen A A, et al. Microbial communities involved in enhanced biological phosphorus removal from wastewater—a model system in environmental biotechnology [J]. Current Opinion in Biotechnology, 2012,23(3): 452-459.
[6]
Wang Y Y, Geng J J, Peng Y Z, et al. A comparison of endogenous processes during anaerobic starvation in anaerobic end sludge and aerobic end sludge from an anaerobic/anoxic/oxic sequencing batch reactor performing denitrifying phosphorus removal [J]. Bioresource Technology, 2012,104(1):19-27.
[7]
Zeng W, Zhang J, Wang A Q, et al. Denitrifying phosphorus removal from municipal wastewater and dynamics of"Candidatus Accumulibacter"and denitrifying bacteria based on genes of ppk1, narG, nirS and nirK [J]. Bioresource Technology, 2016,207:322-331.
[8]
Pan Y W, Cheng K Y, Kaksonen A H, et al. A novel post denitrification configuration for phosphorus recovery using polyphosphate accumulating organisms [J]. Water Research, 2013,47(17):6488-6495.
Wang Y Y, Guo G, Wang H, et al. Long-term impact of anaerobic reaction time on the performance and granular characteristics of granular denitrifying biological phosphorus removal systems [J]. Water Research, 2013,47(14):5326-5337.
Pan T, Han Y, Chen J M, et al. Optimal partner wavelength combination method with application to near-infrared spectroscopic analysis [J]. Chemometrics & Intelligent Laboratory Systems, 2016,156:217-223.
[16]
Chen L, Yuan H F, Zhao Z, et al. A new multivariate calibration model transfer method of near-infrared spectral analysis [J]. Chemometrics & Intelligent Laboratory Systems, 2016,153:51-57.
Zhao M, Downey G, O'Donnell C P. Exploration of microwave dielectric and near infrared spectroscopy with multivariate data analysis for fat content determination in ground beef [J]. Food Control, 2016,68:260-270.
Zhu F L, Yong H E, Shao Y N. Application of Near-Infrared Hyperspectral Imaging to Predicting Water Content in Salmon Flesh [J]. Spectroscopy & Spectral Analysis, 2015,35(1):113-117.
[21]
Páscoa R N M J, Lopo M, Santos C A T D, et al. Exploratory study on vineyards soil mapping by visible/near-infrared spectroscopy of grapevine leaves [J]. Computers & Electronics in Agriculture, 2016,127:15-25.
Nan Q U, Zhu M C, Dou S. Application of Near-and Mid-infrared Diffuse Reflectance Spectroscopic Techniques in Soil Analysis [J]. Journal of Instrumental Analysis, 2015,34(1): 120-126.
[24]
Pan T, Li M, Chen J. Selection method of quasi-continuous wavelength combination with applications to the near-infrared spectroscopic analysis of soil organic matter [J]. Applied Spectroscopy, 2014,68(3):263-271.
[25]
Pan T, Chen Z H, Chen J M, et al. Near-infrared spectroscopy with waveband selection stability for the determination of COD in sugar refinery wastewater [J]. Analytical Methods, 2012,4(4): 1046-1052.
[26]
Zangerlé A, Hissler C, McKey D, et al. Using near infrared spectroscopy (NIRS) to identify the contribution of earthworms to soil macroaggregation in field conditions [J]. Applied Soil Ecology, 2016,104:138-147.
[27]
Majed N, Matthäus C, Diem M, et al. Evaluation of intracellular polyphosphate dynamics in enhanced biological phosphorus removal process using raman microscopy [J]. Environmental Science & Technology, 2009,43(14):5436-5642.
[28]
Mesquita D P, Leal C, Cunha J R, et al. Prediction of intracellular storage polymers using quantitative image analysis in enhanced biological phosphorus removal systems [J]. Analytica Chimica Acta, 2013,770(3):36-44.
Jovi? O. Durbin-Watson partial least-squares regression applied to MIR data on adulteration with edible oils of different origins [J]. Food Chemistry, 2016,213:791-798.