TY - JOUR
T1 - VibMilk
T2 - Nonintrusive Milk Spoilage Detection via Smartphone Vibration
AU - Wu, Yuezhong
AU - Song, Wei
AU - Wang, Yanxiang
AU - Ma, Dong
AU - Xu, Weitao
AU - Hassan, Mahbub
AU - Hu, Wen
PY - 2024/5/15
Y1 - 2024/5/15
N2 - 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. Second, 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 article: 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). © 2014 IEEE.
AB - 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. Second, 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 article: 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). © 2014 IEEE.
KW - Food safety
KW - liquid testing
KW - milk spoilage
KW - neural networks
KW - nonintrusive sensing
KW - smartphone
KW - vibration
UR - http://www.scopus.com/inward/record.url?scp=85184806321&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85184806321&origin=recordpage
U2 - 10.1109/JIOT.2024.3359049
DO - 10.1109/JIOT.2024.3359049
M3 - RGC 21 - Publication in refereed journal
SN - 2327-4662
VL - 11
SP - 17184
EP - 17197
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 10
ER -