International Journal of Modern Research in Engineering & Management (IJMREM) ||Volume|| 2 ||Issue|| 3 ||Pages|| 18-22 || March 2019 || ISSN: 2581-4540
Detection of COD Value in Aquaculture Water by Near Infrared Spectroscopy 1,
Zhu Chengyun, 2,Chen Jie, 3,Wang Rong, 4,Liu Yan, 5Zhu Lijuan
School of New Energy and Electronic Engineering, Yancheng Teachers University, China --------------------------------------------------ABSTRACT-------------------------------------------------------Chemical oxygen demand (COD) is an important parameter of aquaculture water quality, which seriously affects the safety of aquaculture. This paper studies the COD detection technology of aquaculture water based on near infrared spectroscopy. In this paper, standard normal variate (SNV) transformation as a spectral pretreatment algorithm is used to pretreat the near infrared original spectrum of water samples, and the COD prediction model is established by partial least squares (PLS) algorithm. In order to reduce the computational complexity of the model and improve the computational speed of the model to achieve rapid detection, genetic algorithm (GA) is used to filter the spectral characteristic wavelengths, and a GA-siPLS model is established. The training set and the prediction set were tested respectively with the established model, and the prediction set Rp = 0.9924, RMSEP = 3.15; the training set Rc = 0.9937, RMSECV = 2.95. The results show that the genetic algorithm can significantly reduce the dimension of input variables, reduce the amount of calculation and ensure the prediction accuracy.
KEYWORDS: Aquaculture tail water, COD, near infrared spectroscopy, GA-si PLS model --------------------------------------------------------------------------------------------------------------------------------------Date of Submission: Date, 11 March 2019 Date of Accepted: 17. March 2019 ---------------------------------------------------------------------------------------------------------------------------------------
I.
INTRODUCTION
Dissolved organic matter is the most important and most common pollutant in industrial aquaculture wastewater (Badiola, Albaum, Curtin, Gartzia, & Mendiola, 2016). Because the density of industrial aquaculture is usually relatively high, in order to improve economic efficiency, feed is usually increased, which will inevitably produce feed residue. The food is mainly processed from protein, organic acid and fat. With the accumulation of the residual bait in the water, the turbidity and glue strength of the water body increase, on the one hand, it will cause the fish to breathe and metabolize difficulty and reduce food intake; on the other hand, the residual bait will rot and deteriorate when soaked in the water for a long time, and will produce. It will produce NH3, CH4, H2S, organic acids, aldehydes, alcohols and other toxic substances to fish, when the accumulation of these organic substances in the water to a certain extent, the water will become black and odorous, will lead to fish poisoning and death. The composition of dissolved organic compounds in water is complex. Chemical oxygen demand (COD) is usually used to measure the content of dissolved organic compounds in water (Chen et al., 2017; Jørpeland, Imsland, Stien, Bleie, & Roth, 2015). Therefore, the detection of organic matter in aquaculture water will be converted into the detection of COD, and the rapid detection of COD content in aquaculture water will greatly reduce the risk of aquaculture. For the detection of COD, the traditional methods include fast digestion spectrophotometry and potassium dichromate titration. In recent years, new methods such as flow injection analysis and electrochemical methods have been gradually applied to the detection of COD, but there are some shortcomings in these methods, mainly the use of chemical reagents in the measurement process, which will cause secondary pollution, and the detection time is relatively long. Near infrared (NIR) is an electromagnetic wave with wavelengths ranging from 700 nm to 2500 nm and wavenumbers ranging from 12500 cm to 4000 cm-1. Nearly all organic substances can find absorption signals in the near infrared spectra, and the spectra are relatively stable (Fei, Dong, Zhao, & Zheng, 2017; Zhong, Xia, & Lian, 2017). Near infrared spectroscopy (NIRS) is a fastdeveloping nondestructive testing technology in recent years. It takes less time and can reflect the state of the object quickly and real-time. It does not destroy the sample, does not need to consume chemicals and does not cause secondary pollution to the environment. It belongs to green analysis technology and is suitable for field testing. It is widely used in medicine, chemical industry, agricultural products, feed, food, pesticide residue detection and other fields. In this paper, near infrared spectroscopy (NIR) is applied to the rapid measurement of organic matters in aquaculture water (Nath, Patel, & Dave, 2016; Soonthonhut & Christy, 2017).
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Detection of COD Value in Aquaculture Water… II.
MATERIALS AND METHODS
Acquisition of experimental data: The experimental base of this subject is in Jiangsu Zhenjiang Yang Zhong Huantai Fishery Co., Ltd. The experimental fish pond is shown in Fig. 1. Four of the ponds were used in the experiment. The cultured fish were Malay red bream. The cultured densities were about 30kg/m3, 40kg/m3, 50kg/m3 and 60kg/m3, respectively. The whole experiment time is 12 days. The national standard HJ494-2009 is used as the guide. Because the water flow is fast and the volume is small, the dissolved organic matter in the fish pond distributes evenly, so the sampling point is selected at the center of the fish pond 1.5m under water. Water samples were collected once a day at 7:00, 12:00 and 18:00 in each pond. Twelve groups of water samples were obtained daily, so 144 samples were collected during the whole experimental period. Every time the water sample is collected, it is left for 30 minutes to fully precipitate, and then filtered out the particulate matter magazine through a filter screen. Then the water sample is divided into two parts, one of which is used to collect water samples by near infrared spectroscopy, the other is used for physical and chemical experiments. If the spectrum collection and physicochemical experiment of the water sample can not be carried out in time, the water sample will be preserved according to the national standard HJ493-2009. 144 water samples were randomly divided into two groups, one group of 100 samples as the training set, the remaining 44 samples as the prediction set.
Fig. 1 Industrialized aquaculture pond COD value determination physicochemical experiment: The COD value of water sample was determined by dichromate method. Under certain conditions, the water sample is oxidized with appropriate amount of potassium dichromate, dissolved organic matter or granular organic matter in water chemical reaction consumption of dichromate corresponding to the mass concentration of oxygen. The COD values of the measured samples are shown in Table 1. Table 1 Statistical data of COD values in physical and chemical experiments of water samples Sample set
Sample number
Average value （mg/L）
Minimum value （mg/L）
Maximum value （mg/L）
standard deviation （mg/L）
Training set
100
162.48
65.76
234.51
36.14
Prediction set
44
162.63
72.18
241.28
37.68
All samples
144
162.51
65.76
241.28
36.51
Near infrared spectrum acquisition of water samples: Near infrared spectroscopy data of aquaculture water samples were collected by Antaris II Fourier Transform Near Infrared Spectrometer (Thermo Science, USA). With the built-in parameters as the background, the water samples were collected at room temperature of 26℃ and humidity was kept constant. The sweep spectrum wavenumber ranged from 10000 to 4000cm-1, the near infrared spectrum resolution was set to 8.0 cm-1, the spectral sampling interval was set to 3.865 cm-1, and the scanning times were set to 32. Each spectrum contained 1557 data Point. Each water sample is sampled once at three different angles, and the average spectral value is taken as the original spectrum of the sample. The original spectrum is shown in Figure 2.
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Detection of COD Value in Aquaculture Water‌ 3.5
Absorbance
3 2.5 2 1.5 1 0.5 4000
5000
6000 7000 8000 Wavenumber /cm-1
9000
10000
Spectral preprocessing The SNV algorithm is one of the most widely used NIRS pretreatment methods (R. J Barnes, M. S Dhanoa, & Susan J Lister, 2016; R. J. Barnes, M. S. Dhanoa, & Susan J. Lister, 2016; Genkawa et al., 2015). Using SNV near infrared spectroscopy to pretreat each spectrum is a separate correction, so this method has a very strong correction ability. Therefore, this paper chooses SNV pretreatment algorithm to pretreat the near-infrared spectrum of water samples. The result of pretreatment of water sample is shown in Figure 3.
SNV preprocessing spectra
2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5 4000
5000
6000 7000 8000 Wavenumber /cm-1
9000
10000
Fig. 3 Near infrared spectra of water samples are preprocessed by SNV algorithm Spectral optimum feature wavelength variable screening : The experimental results show that there are 1557 wavenumber variables in each spectrum, because there is noise information in the original spectrum. In order to eliminate the noise information, genetic algorithm is used to screen the spectral eigenvectors. Because the genetic algorithm is a probabilistic search algorithm with a certain randomness, the optimal wavenumber variables screened by the algorithm may also have a certain randomness. In this paper, the GA algorithm is used to select the optimal wavenumber variables in five experiments, and the best one is selected as the final result(Ding et al., 2016; Kang et al., 2016; Marques, Castro, Costa, Neto, & Lima, 2016). GA algorithm was used to carry out 100 iterative screening for the optimal joint subinterval of water samples. The frequency of the selected wavenumber variable was shown in figure 4. In order to reduce randomness, a total of 5 experiments were conducted. The variation coefficient (CV) varies with the increase of the number of variables selected for the COD value of the water sample during the selection iteration of the optimal joint subregion of the water sample by GA algorithm. The curve is shown in Fig.5. When the cumulative number of selected variables reaches 107, the variation coefficient reaches the maximum value of 98.9568%. After that, the variation coefficient is maintained. The COD value of the water sample selected in this paper is constant, and the optimal number of variables is 107. Frequency of selections after 100 runs
20 (a) 15
10
5
0 0
100
200
300
400
500
Fig. 4 Frequency spectrum of spectral variables selected for COD value of water samples
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Detection of COD Value in Aquaculture Water‌ C.V. as a function of the number of selected variables
100 107 80 60 40 20 (a) 0 0
50
100
150
Fig. 5 Variation coefficient trend chart of spectral value of water sample COD value The COD value selected from the optimal joint interval by genetic algorithm is used to predict the optimal number of wavenumber variables. The specific wavenumber variables are shown in Table 2. Table 2 the best wave number for GA-siPLS modeling Water quality parameters
COD value
Selected wavenumber variables (cm-1) 8719.24; 8026.63; 8039.68; 8824.89; 8838.67; 7849.35; 7959.62; 8031.19; 8249.76; 8761.23; 8848.92; 8022.32; 8039.87; 8618.76; 8727.62; 8893.35; 7845.68; 7875.98; 8027.63; 8035.84; 8139.21; 8248.23; 8307.91; 8754.52; 8868.23; 7893.68; 8227.48; 8720.14; 8775.42; 8768.63; 8771.38; 7836.41; 7847.84; 7878.36; 8245.48; 8262.96; 8274.83; 8355.47; 8705.74; 8823.16; 8865.64; 8916.37; 8922.84; 9343.68; 9995.49; 7842.67; 7988.29; 8047.68; 8189.31; 8474.18; 8515.64; 8614.76; 8653.37; 8707.69; 8734.23; 8753.71; 8772.53; 8784.61; 8787.88; 8811.64; 8893.51; 8953.72; 8985.91; 9016.07; 9128.65; 9274.39; 9498.71; 9726.38; 9957.15; 7828.64; 7998.62; 8015.23; 8025.56; 8064.64; 8082.28; 8093.49; 8108.73; 8157.61; 8245.76; 8254.32; 8268.18; 8282.37; 8295.69; 8317.43; 8363.93; 8386.48; 8545.24; 8617.39; 8633.42; 8698.99; 8725.62; 8808.88; 8806.45; 8823.97; 8902.07; 8908.67; 8914.59; 8947.81; 9033.63; 9077.92; 9112.47; 9159.23; 9536.41; 9652.36; 9705.78; 9877.08; 9972.34
III.
Number of variables
107
CONCLUSIONS
The GA-siPLS model for predicting COD value of water samples was established by selecting 107 optimal wavenumber variables from the optimal joint sub-interval. The training set and prediction set are tested with the established model. The scatter plots of the predicted and actual values are shown in Figure 6. The prediction focuses on Rp=0.9924, RMSEP=3.15; the training focuses on Rc=0.9937, RMSECV=2.95. The results show that, compared with the joint interval partial least squares model, the correlation coefficient of prediction set and training set is slightly improved, and the error is slightly reduced, while the number of variables is reduced from 1557 to 107, which greatly reduces the computational load of the model and improves the efficiency of the model.
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Detection of COD Value in Aquaculture Water‌ Fig. 6 Scatter plots of the predicted values of the water samples and the COD values of the training set, the actual measured values and the predicted values. (a) Prediction results for prediction sets; (b) Prediction results for training set
ACKNOWLEDGEMENTS This work is supported by Natural Science Foundation of the Higher Education Institutions of Jiangsu Province, China (17KJD240002)
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