Electrochemical fingerprinting combined with machine learning algorithm for closely related medicinal plant identification

Xiao, Qi; Zhou, Zhenzeng; Shen, Zijie; Chen, Jiandan; Gu, Chunchuan; Li, Lihua; Chen, Fenglong; Liu, Hongying

doi:10.1016/j.snb.2022.132922

Cited by 11 publications

(2 citation statements)

References 40 publications

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“…Deep neural network (DNN) and various architectures of machine learning (ML) have been successfully used in many scientific areas such as physics, [12,13] astronomy, [14][15][16] chemistry, [17][18][19][20] biology, [21][22][23][24] and electrochemistry. [25][26][27][28] For instance, Gareth et al demonstrated identifying reaction mechanisms in the electrochemical system using DNN based on images of cyclic voltammograms and showed high performance in classification tasks. [28] This study indicates that DNN is very useful not only in common image recognition but it is also able to recognize the spectrum in the scientific area with a high degree of confidence.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks

Doonyapisut

Kannan

Kim

et al. 2023

Advanced Intelligent Systems

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Technology advancements in energy storage, photocatalysis, and sensors have generated enormous impedimetric data. Electrochemical impedance spectroscopy (EIS) results play an essential role in analyzing the interfacial properties of materials. Nonetheless, in many situations, the data is misinterpreted due to the complexity of the electrochemical system or the compromise between the experimental result and the theoretical model, resulting in partiality in the interpretation process, especially for the impedimetric results. Typically, the experimenter interprets impedimetric results using a searching approach based on a theoretical model until the best‐fitting model is obtained, which is a time‐consuming process, and errors can occur. To reduce misinterpretation by the experimenter, herein, the machine‐learning strategy is demonstrated for the classification of an EIS circuit model and parameter prediction using a deep neural network (DNN). The DNN model shows a highly accurate classifier for the commonly used EIS circuit with an average area under the receiver operating characteristic curve of more than 0.95. Additionally, the model demonstrates high accuracy in the prediction of EIS parameters on a complex EIS system, with a maximum R2 of 0.999. These reveal that the machine‐learning strategy may open a new room for studying electrochemical systems.

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Section: Introductionmentioning

confidence: 99%

Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks

Doonyapisut

Kannan

Kim

et al. 2023

Advanced Intelligent Systems

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“…In [10], voltammetric measurements were applied using the cyclic voltammetry (CV) method with the use of an electronic tongue (ET) to create a database of vinegar fingerprints for subsequent chemometric analysis. Utilizing the differential pulse voltammetry technique (DPV) and a glassy carbon electrode (GC), the data was obtained during the study of medical plant profiles [11]. The same technique, but in combination with ET, was used during the analysis of seasonal changes in honeys [12] and while observing the maturation process of young wine [13].…”

Section: Introductionmentioning

confidence: 99%

The Application of Picein Wax Carbon Composite Electrode for Plant-Based Milk Profiling

Wójcik,

Wyrwa,

Ciepiela

et al. 2023

Chemosensors

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This work demonstrates the successful application of the picein wax carbon composite electrode (PWCCE) for profiling both commercial and homemade plant milks. Picein wax was utilized as an unconventional binder. The resulting electrode paste exhibited a solidified and hard texture, enabling its use in a manner analogous to that of the glassy carbon electrode. Differential pulse voltammetry (DPV) with an automated measurement and recording procedure was employed to obtain plant-based milk profiles. The utilization of operator-independent measurement procedures yielded high-quality electrochemical fingerprints suitable for subsequent calculations. To interpret the data, unsupervised machine learning methods were implemented, such as principal component analysis (PCA) and cluster analysis. These chemometric techniques confirmed the electrode effectiveness of the construction for this type of research. Moreover, they proved valuable in distinguishing between plant-based milk and cow’s milk, including two different variants: whole milk and lactose-free milk.

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Research on meat adulteration based on one-dimensional convolutional neural network (1DCNN) combined with electrochemical technology

2024

Food Measure

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Electrochemical fingerprinting combined with machine learning algorithm for closely related medicinal plant identification

Cited by 11 publications

References 40 publications

Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks

Analysis of Electrochemical Impedance Data: Use of Deep Neural Networks

The Application of Picein Wax Carbon Composite Electrode for Plant-Based Milk Profiling

Research on meat adulteration based on one-dimensional convolutional neural network (1DCNN) combined with electrochemical technology

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