Electrochemical detection combined with artificial neural networks for the simultaneous intelligent sensing of caffeine and chlorogenic acid

Caffeine (CAF) is a common central nervous system stimulant. However, the excessive intake of CAF can cause physical discomfort to consumers and affect the health of drinkers. Chlorogenic acid (CGA) is a powerful antioxidant with antiinflammatory and antiobesity properties. Here, we used an artifici...

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Bibliographic Details
Published inElectrochimica acta Vol. 463; p. 142820
Main Authors Gu, Bing-Chen, Chung, Kuan-Jung, Chen, Bo-Wei, Dai, Yu-Han, Wu, Chia-Che
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 20.09.2023
Subjects
Caffeine
Chlorogenic acid
Electrochemistry
Machine learning
Electrochemistry
Chlorogenic acid
Machine learning
Caffeine
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Summary:Caffeine (CAF) is a common central nervous system stimulant. However, the excessive intake of CAF can cause physical discomfort to consumers and affect the health of drinkers. Chlorogenic acid (CGA) is a powerful antioxidant with antiinflammatory and antiobesity properties. Here, we used an artificial neural network (ANN) for the intelligent sensing and electrochemical measurements of CAF and CGA by differential pulse voltammetry and linear sweep voltammetry. The measurement error of the electrochemical method for detecting CAF concentrations could be eliminated using a large amount of electrochemical measurement data for ANN training. The CAF and CGA concentrations were sensed with an accuracy of nearly 90%. A sample of real coffee was also sensed with an accuracy rate of over 90%. The results showed that this method can effectively eliminate the errors of electrochemical measurement methods and instruments, and the accuracy rate of calibration line measurements exceeded that of the traditional electrochemical method.
ISSN:0013-4686
1873-3859
DOI:10.1016/j.electacta.2023.142820