When it comes to the “smell test,” the nose isn’t always the best judge of food quality. Now in a study appearing in ACS’ journal Nano Letters, scientists report that they have developed a wireless tagging device that can send signals to smartphones warning consumers and food distributors when meat and other perishables have spoiled.
They say this new sensor could improve the detection of rotten food so it is tossed before consumers eat it.
Every year, 48 million people in the U.S. get sick from foodborne illnesses, according to the U.S. Centers for Disease Control and Prevention. Of these, about 125,000 people are hospitalized and 3,000 die. Traditionally, many consumers just smell a food to detect spoilage, but this technique is only as reliable as the sniffer’s nose. At the other end of the spectrum, food inspectors often use bulky, expensive equipment to detect harmful microbes. Scientists are investigating other approaches, including near field communication (NFC) labeling, that are both portable and dependable. NFC devices wirelessly transmit information over short distances — usually less than 4 inches. They are similar to the radio frequency identification products retailers use to track inventory and shipments. Building on this idea, Lijia Pan, Yi Shi, Guihua Yu and colleagues sought to incorporate a sensitive switch into NFC labeling tags to detect food spoilage using a smartphone.
The scientists created a nanostructured, conductive, polymer-based gas sensor that can detect substances called biogenic amines (BAs), which give decomposing meat its bad odor. They embedded these sensors into NFCs placed next to meats. After the meats had been stored for 24 hours at 86 degrees Fahrenheit, the researchers found that the sensors successfully detected significant amounts of BAs. The sensors then switched on the NFCs so they could transmit this information to a nearby smartphone.
Receive an email update when we add a new FOODBORNE ILLNESSES DETECTION article.
The Latest on: Foodborne illnesses detection
via Google News
The Latest on: Foodborne illnesses detection
- Award-Winning Purdue Technology Speeds Up Pathogen Detection on November 12, 2018 at 11:33 am
... to make giant leaps in health and preventing more people from being sickened by foodborne illnesses,” Ladisch said. Solutions such as this foodborne detection technology are also a focus of Purdue ... […]
- Food protection: Award-winning Purdue technology speeds up the process to detect salmonella, E. coli, other foodborne illnesses on November 12, 2018 at 10:57 am
WEST LAFAYETTE, Ind. – An award-winning Purdue University technology is showing increasing promise in helping to detect foodborne pathogens in real time. It’s a problem that the Centers for Disease Co... […]
- Supermarket produce contains transferable antibiotic resistant genes on November 12, 2018 at 10:20 am
Common supermarket produce is a reservoir for transferable antibiotic resistance genes that may be missed by traditional detection methods, researchers found. “Recently, several foodborne disease outb... […]
- Awards for food microbiologist, law students on November 10, 2018 at 8:00 am
This is for her research in developing a real-time method to detect dangerous bacteria causing foodborne diseases in raw chicken. Several studies in Malaysia have shown that nine out of 10 raw chicken... […]
- Experts gather at BfR to discuss viruses and antimicrobial resistance on November 9, 2018 at 9:29 pm
The number of foodborne diseases caused by viruses is increasing ... It is currently held by Livsmedelsverket (National Food Agency, Sweden). “Even though the detection methods for viruses in foods ha... […]
- Google creates AI to detect foodborne illnesses on November 8, 2018 at 4:16 pm
A team of Google researchers and public health experts are working on an artificial intelligence model that detects the spread of foodborne illnesses on the city level. Researchers from Google created ... […]
- Google & Harvard's FINDER AI Tackles Restaurant Safety on November 8, 2018 at 2:10 pm
The model is called FINDER, short for Foodborne Illness DEtector in Real time. The model's primary focus is to detect possible foodborne illness sources and outbreaks faster and more accurately than t... […]
- Sources of Food-borne Illnesses Identified by a Computer Model on November 7, 2018 at 7:15 pm
whereas the overall rate of detection of unsafe restaurants via routine inspections across the two cities was 22.7%. Interestingly, the study showed that in 38% of all cases identified by this model, ... […]
- Computer model more accurate at identifying potential sources of foodborne illnesses than traditional methods on November 6, 2018 at 8:12 am
Funding for this study came in part from the U.S. Centers for Disease Control and Prevention cooperative agreement 1U01EH001301-01. “Machine-Learned Epidemiology: Real-time Detection of Foodborne Illn... […]
via Bing News