Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy

Chen, Huazhou; Xu, Lili; Ai, Wu; Lin, Bin; Feng, Qinghua; Cai, Ken

doi:10.1016/j.scitotenv.2020.136765

Cited by 44 publications

(26 citation statements)

References 37 publications

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“…The SVR modelling accuracy can be improved with the right choice of kernel function as different kernel functions have different mapping capabilities. The four kernel functions given in Equations (16) to (19) are most commonly used in the SVR algorithm [ 22 , 35 , 36 , 37 , 38 , 39 , 41 ]:

where

is the transpose of

, r is a constant term,

is the polynomial order, and

is a RBF kernel parameter that controls the spread of the data while transforming to higher dimensions.…”

Section: Methodsmentioning

confidence: 99%

“…Many studies indicated that machine learning has potential for the analysis of single or multi-wavelength spectral data [ 10 , 20 , 21 , 22 , 23 ]. For instance, using UV absorbance spectrometry in the 250–300-nm region, Kim et al [ 24 ] used a multiple linear regression model to detect organic compounds in water.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, using multi-wavelength absorbance spectrometry with a feed-forward neural network, Alves et al [ 20 ] attempted to determine a river water quality index. In contrast, Chen et al [ 22 ] assessed near-infrared (NIR) spectra using the least squares support vector machine (LSSVM) to develop a method for the quantitative determination of COD. Li and Hur [ 25 ] investigated the dynamics, fate, and distribution of dissolved organic matter (DOM) in various aquatic environments.…”

Section: Introductionmentioning

confidence: 99%

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Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Hossain

Chow

Hewa

et al. 2020

Sensors

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The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) can be better achieved by separating its spectra from the combined spectra. In this paper, two major focuses are (i) the separation of monochloramine spectra from the combined spectra and (ii) assessment of the application of the machine learning algorithm in real-time detection of monochloramine. The support vector regression (SVR) model was developed using multi-wavelength ultraviolet-visible (UV-Vis) absorbance spectra and online amperometric monochloramine residual measurement data. The performance of the SVR model was evaluated by using four different kernel functions. Results show that (i) particles or turbidity in water have a significant effect on UV-Vis spectral measurement and improved modelling accuracy is achieved by using particle compensated spectra; (ii) modelling performance is further improved by compensating the spectra for natural organic matter (NOM) and nitrate (NO3) and (iii) the choice of kernel functions greatly affected the SVR performance, especially the radial basis function (RBF) appears to be the highest performing kernel function. The outcomes of this research suggest that disinfectant residual (monochloramine) can be measured in real time using the SVR algorithm with a precision level of ± 0.1 mg L−1.

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where

is the transpose of

, r is a constant term,

is the polynomial order, and

is a RBF kernel parameter that controls the spread of the data while transforming to higher dimensions.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Hossain

Chow

Hewa

et al. 2020

Sensors

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show abstract

“…These results suggested a great impact of the normalization operation on the SVM model and suggested an effective removal of the dimensional inuence of the original spectral data by the normalization. [30][31][32] Furthermore, two thirds of the sample data were randomly selected as the training set, and the remaining data were used as the verication set. The average recognition effect of the moving window selection and spectral band feature selection was investigated in ten random experiments.…”

Section: Tablementioning

confidence: 99%

The statistical fusion identification of dairy products based on extracted Raman spectroscopy

Zhang

2020

RSC Adv.

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“…Besides, due to individual differences and instrument noise, it is difficult to detect subtle differences in the peak position of NIRS. To reduce the influence of these factors and extract useful information, it is necessary to combine spectral information with a machine learning method to establish a diagnostic model for the accurate diagnosis of pre-diabetes, which can reduce the influence of these factors and extract effective information (Huazhou et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Early Diagnosis of Type 2 Diabetes Based on Near-Infrared Spectroscopy Combined With Machine Learning and Aquaphotomics

Guo²,

et al. 2020

Front. Chem.

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Early diagnosis is important to reduce the incidence and mortality rate of diabetes. The feasibility of early diagnosis of diabetes was studied via near-infrared spectra (NIRS) combined with a support vector machine (SVM) and aquaphotomics. Firstly, the NIRS of entire blood samples from the population of healthy, pre-diabetic, and diabetic patients were obtained. The spectral data of the entire spectra in the visible and near-infrared region (400–2,500 nm) were used as the research object of the qualitative analysis. Secondly, several preprocessing steps including multiple scattering correction, variable standardization, and first derivative and second derivative steps were performed and the best pretreatment method was selected. Finally, for the early diagnosis of diabetes, models were established using SVM. The first overtone of water (1,300–1,600 nm) was used as the research object for an aquaphotomics model, and the aquagram of the healthy group, pre-diabetes, and diabetes groups were drawn using 12 water absorption patterns for the early diagnosis of diabetes. The results of SVM showed that the highest accuracy was 97.22% and the specificity and sensitivity were 95.65 and 100%, respectively when the pretreatment method of the first derivative was used, and the best model parameters were c = 18.76 and g = 0.008583.The results of the aquaphotomics model showed clear differences in the 1,400–1,500 nm region, and the number of hydrogen bonds in water species (1,408, 1,416, 1,462, and 1,522 nm) was evidently correlated with the occurrence and development of diabetes. The number of hydrogen bonds was the smallest in the healthy group and the largest in the diabetes group. The suggested reason is that the water matrix of blood changes with the worsening of blood glucose metabolic dysfunction. The number of hydrogen bonds could be used as biomarkers for the early diagnosis of diabetes. The result show that it is effective and feasible to establish an accurate and rapid early diagnosis model of diabetes via NIRS combined with SVM and aquaphotomics.

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Kernel functions embedded in support vector machine learning models for rapid water pollution assessment via near-infrared spectroscopy

Cited by 44 publications

References 37 publications

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach

The statistical fusion identification of dairy products based on extracted Raman spectroscopy

Early Diagnosis of Type 2 Diabetes Based on Near-Infrared Spectroscopy Combined With Machine Learning and Aquaphotomics

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