Battery-Free Meat Freshness Monitoring with High Efficient RF Energy Scavenging

Abstract

The public has become more conscious of the importance of food quality for human health. Consuming the expired food can lead to serious health problems, and as a result spoilt food is eliminated, therefore leads to the waste of money as well as the emission of greenhouse gas, contributing to global warming. According to the statistics of Food and Agriculture Organization, approximate 40% of food in the US is wasted while around 33% of the food produced for human consumption is lost or wasted every year. Natural Resources Defense Council reckons that Americans waste the equivalent of $165 billion each year for throwing out food. Therefore a food freshness monitoring system that is harmless, inexpensive, and highly accurate is necessary to ensure that the customers may choose the food packages with the best quality as well as consuming food before the expired date. This thesis presents a method to monitor and indicate the qualitative meat packages by using a smart sensor tag that has small size and consists of three ultra-low power consumption sensors. Meat freshness will be monitored and assessed by a variety of factors including temperature, humidity, concentration of ammonia gas and hydrogen sulfide gas. These low-consumption power sensors consume less than 4 mW of power. Therefore an RF energy scavenging circuit integrated on the smart sensor tag can harvest energy from radio waves at high frequency of 13.56 MHz to supply the power for the operation of all tag components. The energy harvester can obtain efficiently energy within a distance of around 40 cm from a reader that has transmitted an approximate 4 W of power. This design allows the smart sensor tag to operate without any batteries, as batteries cause the increase of the cost due to frequent battery replacement. In this thesis book, meat is chosen to make the experiments due to its popularity in the meals every day. The experiments were monitored continuously during a one-week period in two different conditions: room temperature and refrigerator temperature that are the most usual places to store food. Based on the experimental results, the food assessment is classified into four categories: fresh, normal, low and spoilt. The monitored results of these sensors and estimated levels of food quality are shown on a developed user interface on a server PC. This research provides a good, safe and precise solution for the prediction of food freshness and can be applied in supermarket, food stores or customer’s homes.