Publication Type

Journal Article

Version

acceptedVersion

Publication Date

2-2024

Abstract

Quantifying the chemical process of milk spoilage is challenging due to the need for bulky, expensive equipment that is not user-friendly for milk producers or customers. This lack of a convenient and accurate milk spoilage detection system can cause two significant issues. First, people who consume spoiled milk may experience serious health problems. Secondly, milk manufacturers typically provide a “best before” date to indicate freshness, but this date only shows the highest quality of the milk, not the last day it can be safely consumed, leading to significant milk waste. A practical and efficient solution to this problem is proposed in this paper: a vibration-based milk spoilage detection method called VibMilk that utilizes the ubiquitous vibration motor and Inertial Measurement Unit (IMU) of off-the-shelf smartphones. The method detects spoilage based on the fact that the milk’s physical properties change, inducing different vibration responses at various stages of degradation. Using the InceptionTime deep learning model, VibMilk achieves 98.35% accuracy in detecting milk spoilage across 23 different stages, from fresh (pH = 6.6) to fully spoiled (pH = 4.4).

Keywords

Dairy products, Fats, Food Safety, Internet of Things, Liquid Testing, Liquids, Microorganisms, Milk Spoilage, Neural Networks, Non-intrusive Sensing, Proteins, Smartphone, Vibration, Vibrations

Discipline

Food Science | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Internet of Things Journal

First Page

1

Last Page

14

ISSN

2327-4662

Identifier

10.1109/JIOT.2024.3359049

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/JIOT.2024.3359049

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