Electrochemical Sensor to Detect Antibiotics in Milk Based on Machine Learning Algorithms

Aliev, Timur A.; Belyaev, Vadim E.; Pomytkina, Anastasiya V.; Nesterov, Pavel V.; Shityakov, Sergey; Sadovnichii, Roman V.; Novikov, Alexander S.; Orlova, Olga Yu.; Masalovich, Maria S.; Skorb, Ekaterina V.

doi:10.1021/acsami.3c12050

Cited by 7 publications

(6 citation statements)

References 59 publications

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“…To improve the identification of multiple antibiotics in milk, T. A. Aliev et al used a multi-electrode system composed of Cu, Ni, and C working electrodes (sharing a Cu counter electrode, Figure 3 A) that differently responded to the antibiotic molecules and generated a complementary dataset [ 20 ]. Metal elements have their own oxidation and reduction characteristics and result in unique cyclic voltammograms.…”

Section: Strategy I Composing a Set Of Electrodes That Differently In...mentioning

confidence: 99%

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Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A Focused Review

Kang,

Kim,

Kim

et al. 2024

Sensors

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In this review, recent advances regarding the integration of machine learning into electrochemical analysis are overviewed, focusing on the strategies to increase the analytical context of electrochemical data for enhanced machine learning applications. While information-rich electrochemical data offer great potential for machine learning applications, limitations arise when sensors struggle to identify or quantitatively detect target substances in a complex matrix of non-target substances. Advanced machine learning techniques are crucial, but equally important is the development of methods to ensure that electrochemical systems can generate data with reasonable variations across different targets or the different concentrations of a single target. We discuss five strategies developed for building such electrochemical systems, employed in the steps of preparing sensing electrodes, recording signals, and analyzing data. In addition, we explore approaches for acquiring and augmenting the datasets used to train and validate machine learning models. Through these insights, we aim to inspire researchers to fully leverage the potential of machine learning in electroanalytical science.

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Section: Strategy I Composing a Set Of Electrodes That Differently In...mentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A Focused Review

Kang,

Kim,

Kim

et al. 2024

Sensors

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“…When there are fewer hidden layer nodes, it is easy to cause overfitting. When new sample data is input, the mapping results in output values that do not match the actual situation, resulting in significant errors in the network [37,38]. According to the results, PSO-BPNN3 has high accuracy and can be used for predicting the corrosion depth of polymer anti-corrosion cement.…”

Section: Pso-bp Modelmentioning

confidence: 99%

Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network

Zhao,

Chen,

Liu

et al. 2023

Polymers

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Oil well cement is prone to corrosion and damage in carbon dioxide (CO2) acidic gas wells. In order to improve the anti-corrosion ability of oil well cement, polymer resin was used as the anti-corrosion material. The effect of polymer resin on the mechanical and corrosion properties of oil well cement was studied. The corrosion law of polymer anti-corrosion cement in an acidic gas environment was studied. The long-term corrosion degree of polymer anti-corrosion cement was evaluated using an improved neural network model. The cluster particle algorithm (PSO) was used to improve the accuracy of the neural network model. The results indicate that in acidic gas environments, the compressive strength of polymer anti-corrosion cement was reduced under the effect of CO2, and the corrosion depth was increased. The R2 of the prediction model PSO-BPNN3 is 0.9970, and the test error is 0.0136. When corroded for 365 days at 50 °C and 25 MPa pressure of CO2, the corrosion degree of the polymer anti-corrosion cement was 43.6%. The corrosion depth of uncorroded cement stone is 76.69%, which is relatively reduced by 33.09%. The corrosion resistance of cement can be effectively improved by using polymer resin. Using the PSO-BP neural network to evaluate the long-term corrosion changes of polymer anti-corrosion cement under complex acidic gas conditions guides the evaluation of its corrosion resistance.

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“…In recent years, artificial intelligence has developed rapidly, which has become a popular and dominant direction within drug discovery because of its superior performance and high efficiency. Moreover, many deep-learning methods [5,6] have been successfully applied to various tasks in drug discovery, including molecular property prediction [7], drug-target affinity prediction [8,9], and protein-protein interaction prediction [10].…”

Section: Introductionmentioning

confidence: 99%

Triple Generative Self-Supervised Learning Method for Molecular Property Prediction

Xu,

Xia,

Pan

et al. 2024

IJMS

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Molecular property prediction is an important task in drug discovery, and with help of self-supervised learning methods, the performance of molecular property prediction could be improved by utilizing large-scale unlabeled dataset. In this paper, we propose a triple generative self-supervised learning method for molecular property prediction, called TGSS. Three encoders including a bi-directional long short-term memory recurrent neural network (BiLSTM), a Transformer, and a graph attention network (GAT) are used in pre-training the model using molecular sequence and graph structure data to extract molecular features. The variational auto encoder (VAE) is used for reconstructing features from the three models. In the downstream task, in order to balance the information between different molecular features, a feature fusion module is added to assign different weights to each feature. In addition, to improve the interpretability of the model, atomic similarity heat maps were introduced to demonstrate the effectiveness and rationality of molecular feature extraction. We demonstrate the accuracy of the proposed method on chemical and biological benchmark datasets by comparative experiments.

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Electrochemical Sensor to Detect Antibiotics in Milk Based on Machine Learning Algorithms

Cited by 7 publications

References 59 publications

Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A Focused Review

Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A Focused Review

Corrosion Degree Evaluation of Polymer Anti-Corrosive Oil Well Cement under an Acidic Geological Environment Using an Artificial Neural Network

Triple Generative Self-Supervised Learning Method for Molecular Property Prediction

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