A WPT/NFC-Based Sensing Approach for Beverage Freshness Detection Using Supervised Machine Learning

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.

Automated summary

This article focuses on utilizing the WPT/NFC technology compatible with smart phones for beverage freshness sensing, achieving accuracies up to 96.7% for milk freshness classification using supervised machine learning. Additionally, the radio frequency bandwidth needed for classification was reduced to 10 MHz without affecting the classification accuracy for two different methods of feature extraction.