IoT-based Beverage Fraud Detection: A Theoretical Review

Abraham Usman Usman; Stephen Seyi Oyewobi; Abdulkadir Olayinka Abdulbaki; Bala Alhaji Salihu; Thomas A Mamman; Johnson A Ajiboye

2025, Vol: 2, Issue: 2

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Online Published: 29 Jun 2025

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IoT-based Beverage Fraud Detection: A Theoretical Review

Abraham Usman Usman, Stephen Seyi Oyewobi, Abdulkadir Olayinka Abdulbaki, Bala Alhaji Salihu, Thomas A Mamman, Johnson A Ajiboye.

Abstract
This theoretical review explores the foundational theories, frameworks, and models relevant to the design of an Artificial Intelligence (AI) and Internet of Things (IoT)-based system for detecting counterfeit and expired carbonated beverages in the Nigerian market. The growing incidence of beverage fraud, including dilution, mislabeling, and expiration concealment, necessitates the adoption of advanced detection mechanisms. This study explores the use of Bayesian Linear Regression (BLR) to analyze caffeine and CO₂ concentrations within established theories from food safety, sensor analytics, and machine learning. By comparing and critiquing traditional and modern approaches, the review highlights the strengths of integrated IoT and Machine Learning (ML) technologies for scalable, real-time quality monitoring. The key finding from the review is that integrating IoT-enabled sensors, BLR, and ML within the Fake Beverages Detection Systems (FaBEDs) framework offers a scalable, real-time, and interpretable approach for detecting counterfeit and expired carbonated beverages, particularly suitable for low-resource settings like Nigeria. This work contributes a critical perspective on how theoretical models can inform practical implementations for safeguarding public health and ensuring beverage supply chain integrity in developing economies

Key words: Bayesian Linear Regression, Beverage, Counterfeit, Fraud, Internet of Things, Machine Learning

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How to Cite this Article

Pubmed Style

Usman AU, Oyewobi SS, Abdulbaki AO, Salihu BA, Mamman TA, Ajiboye JA. IoT-based Beverage Fraud Detection: A Theoretical Review. CUJOSTECH. Online First: 29 Jun, 2025.

Web Style

Usman AU, Oyewobi SS, Abdulbaki AO, Salihu BA, Mamman TA, Ajiboye JA. IoT-based Beverage Fraud Detection: A Theoretical Review. https://www.cujostech.com.ng/?mno=266497 [Access: June 30, 2025].

AMA (American Medical Association) Style

Usman AU, Oyewobi SS, Abdulbaki AO, Salihu BA, Mamman TA, Ajiboye JA. IoT-based Beverage Fraud Detection: A Theoretical Review. CUJOSTECH. Online First: 29 Jun, 2025.

Vancouver/ICMJE Style

Usman AU, Oyewobi SS, Abdulbaki AO, Salihu BA, Mamman TA, Ajiboye JA. IoT-based Beverage Fraud Detection: A Theoretical Review. CUJOSTECH, [cited June 30, 2025]; Online First: 29 Jun, 2025.

Harvard Style

Usman, A. U., Oyewobi, . S. S., Abdulbaki, . A. O., Salihu, . B. A., Mamman, . T. A. & Ajiboye, . J. A. (0) IoT-based Beverage Fraud Detection: A Theoretical Review. CUJOSTECH, Online First: 29 Jun, 2025.

Turabian Style

Usman, Abraham Usman, Stephen Seyi Oyewobi, Abdulkadir Olayinka Abdulbaki, Bala Alhaji Salihu, Thomas A Mamman, and Johnson A Ajiboye. 0. IoT-based Beverage Fraud Detection: A Theoretical Review. Confluence University Journal of Science and Technology, Online First: 29 Jun, 2025.

Chicago Style

Usman, Abraham Usman, Stephen Seyi Oyewobi, Abdulkadir Olayinka Abdulbaki, Bala Alhaji Salihu, Thomas A Mamman, and Johnson A Ajiboye. "IoT-based Beverage Fraud Detection: A Theoretical Review." Confluence University Journal of Science and Technology Online First: 29 Jun, 2025.

MLA (The Modern Language Association) Style

APA (American Psychological Association) Style

Usman, A. U., Oyewobi, . S. S., Abdulbaki, . A. O., Salihu, . B. A., Mamman, . T. A. & Ajiboye, . J. A. (0) IoT-based Beverage Fraud Detection: A Theoretical Review. Confluence University Journal of Science and Technology, Online First: 29 Jun, 2025.

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