Neural networks to estimate the water content of imidazolium-based ionic liquids using their refractive indices

Torrecilla, José S.; Tortuero, César; Cancilla, John C.; Díaz-Rodríguez, Pablo

doi:10.1016/j.talanta.2013.04.047

Cited by 25 publications

(18 citation statements)

References 24 publications

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“…However, if the NNHL is too high, the resulting ANN may be over-fitted to the dataset employed [ 38 ]. Moreover, the neurons in output layer were determined based on the independent variable that was employed in the model output [ 39 ]. In this paper, the three-layer ANN was developed to build a nonlinear model for the prediction of Cd 2+ concentration in the presence of Cu 2+ .…”

Section: Methodsmentioning

confidence: 99%

Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network

Zhao

Wang

Liu

2017

Sensors

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In this study, a novel method based on a Bi/glassy carbon electrode (Bi/GCE) for quantitatively and directly detecting Cd2+ in the presence of Cu2+ without further electrode modifications by combining square-wave anodic stripping voltammetry (SWASV) and a back-propagation artificial neural network (BP-ANN) has been proposed. The influence of the Cu2+ concentration on the stripping response to Cd2+ was studied. In addition, the effect of the ferrocyanide concentration on the SWASV detection of Cd2+ in the presence of Cu2+ was investigated. A BP-ANN with two inputs and one output was used to establish the nonlinear relationship between the concentration of Cd2+ and the stripping peak currents of Cu2+ and Cd2+. The factors affecting the SWASV detection of Cd2+ and the key parameters of the BP-ANN were optimized. Moreover, the direct calibration model (i.e., adding 0.1 mM ferrocyanide before detection), the BP-ANN model and other prediction models were compared to verify the prediction performance of these models in terms of their mean absolute errors (MAEs), root mean square errors (RMSEs) and correlation coefficients. The BP-ANN model exhibited higher prediction accuracy than the direct calibration model and the other prediction models. Finally, the proposed method was used to detect Cd2+ in soil samples with satisfactory results.

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Section: Methodsmentioning

confidence: 99%

Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network

Zhao

Wang

Liu

2017

Sensors

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“…In contrast, when the HNN is too high, the resulting ANN may be over-fit toward the employed dataset [ 31 ]. Finally, the output neurons are selected depending on the dependent variables that are trying to be estimated [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Optimization of Stripping Voltammetric Sensor by a Back Propagation Artificial Neural Network for the Accurate Determination of Pb(II) in the Presence of Cd(II)

Zhao

Wang

Liu

et al. 2016

Sensors

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An easy, but effective, method has been proposed to detect and quantify the Pb(II) in the presence of Cd(II) based on a Bi/glassy carbon electrode (Bi/GCE) with the combination of a back propagation artificial neural network (BP-ANN) and square wave anodic stripping voltammetry (SWASV) without further electrode modification. The effects of Cd(II) in different concentrations on stripping responses of Pb(II) was studied. The results indicate that the presence of Cd(II) will reduce the prediction precision of a direct calibration model. Therefore, a two-input and one-output BP-ANN was built for the optimization of a stripping voltammetric sensor, which considering the combined effects of Cd(II) and Pb(II) on the SWASV detection of Pb(II) and establishing the nonlinear relationship between the stripping peak currents of Pb(II) and Cd(II) and the concentration of Pb(II). The key parameters of the BP-ANN and the factors affecting the SWASV detection of Pb(II) were optimized. The prediction performance of direct calibration model and BP-ANN model were tested with regard to the mean absolute error (MAE), root mean square error (RMSE), average relative error (ARE), and correlation coefficient. The results proved that the BP-ANN model exhibited higher prediction accuracy than the direct calibration model. Finally, a real samples analysis was performed to determine trace Pb(II) in some soil specimens with satisfactory results.

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“…The verification stage is in charge of avoiding this phenomenon, and occurs just after the training step. In this case, the weight values remain fixed and the verification dataset is used to test the generalization capability of the model, or, in other words, the capability of the model to correctly perform accurately when facing external data [25]. Once this whole process has finished, a training cycle, or epoch, is completed.…”

Section: Ann Optimizationmentioning

confidence: 99%

Spectroscopic determination of the photodegradation of monovarietal extra virgin olive oils and their binary mixtures through intelligent systems

Torrecilla

Vidal

Aroca-Santos

et al. 2015

Talanta

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Neural networks to estimate the water content of imidazolium-based ionic liquids using their refractive indices

Cited by 25 publications

References 24 publications

Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network

Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network

Optimization of Stripping Voltammetric Sensor by a Back Propagation Artificial Neural Network for the Accurate Determination of Pb(II) in the Presence of Cd(II)

Spectroscopic determination of the photodegradation of monovarietal extra virgin olive oils and their binary mixtures through intelligent systems

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