TY - JOUR
T1 - A WPT/NFC-Based Sensing Approach for Beverage Freshness Detection Using Supervised Machine Learning
AU - Rodriguez, Daniel
AU - Saed, Mohammad A.
AU - Li, Changzhi
N1 - Funding Information:
Manuscript received July 6, 2020; accepted July 28, 2020. Date of publication August 3, 2020; date of current version December 4, 2020. This work was supported by the National Science Foundation (NSF) under Grant CNS-1718483 and Grant ECCS-1808613. The associate editor coordinating the review of this article and approving it for publication was Prof. Boby George. (Corresponding author: Daniel Rodriguez.) The authors are with the Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX 79409 USA (e-mail: daniel-fernando.rodriguez@ttu.edu; mohammad.saed@ttu.edu; changzhi.li@ttu.edu). Digital Object Identifier 10.1109/JSEN.2020.3013506
Publisher Copyright:
© 2001-2012 IEEE.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - The massive deployment of wireless sensors is a fundamental piece in the growing internet of things (IoT) industry. Therefore, it is imperative to use already existing hardware to realize new sensing functions with very few or no hardware added. As wireless power transfer (WPT) and near field communication (NFC) become standard features in smart phones, this article investigates beverage freshness sensing based on the WPT/NFC technology compatible with smart phones. A circuit model for the beverage-coil interaction was developed and the performance of features from different nature (e.g., magnitude, amplitude, phase) for classification was analyzed and tested. Accuracies up to 96.7% were achieved using supervised machine learning for milk freshness classification, when 5 different types of milk were used and up to 100% when just 2% fat milk was used for classification. Additionally, the radio frequency bandwidth needed for classification was reduced to 10 MHz using singular value decomposition (SVD) and boxplot analysis without affecting the classification accuracy for two different methods of feature extraction.
AB - The massive deployment of wireless sensors is a fundamental piece in the growing internet of things (IoT) industry. Therefore, it is imperative to use already existing hardware to realize new sensing functions with very few or no hardware added. As wireless power transfer (WPT) and near field communication (NFC) become standard features in smart phones, this article investigates beverage freshness sensing based on the WPT/NFC technology compatible with smart phones. A circuit model for the beverage-coil interaction was developed and the performance of features from different nature (e.g., magnitude, amplitude, phase) for classification was analyzed and tested. Accuracies up to 96.7% were achieved using supervised machine learning for milk freshness classification, when 5 different types of milk were used and up to 100% when just 2% fat milk was used for classification. Additionally, the radio frequency bandwidth needed for classification was reduced to 10 MHz using singular value decomposition (SVD) and boxplot analysis without affecting the classification accuracy for two different methods of feature extraction.
KW - Machine learning
KW - RF/microwave interaction with biomaterials
KW - near field communication
KW - sensor
KW - singular value decomposition
KW - wireless power transfer
UR - http://www.scopus.com/inward/record.url?scp=85097710589&partnerID=8YFLogxK
U2 - 10.1109/JSEN.2020.3013506
DO - 10.1109/JSEN.2020.3013506
M3 - Article
AN - SCOPUS:85097710589
VL - 21
SP - 733
EP - 742
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
SN - 1530-437X
IS - 1
M1 - 9154353
ER -