'Electronic nose'

 In order to reduce food waste and check the validity of foods in stores, a joint research team from Singapore, China and Australia has developed an electronic nose that is able to examine the validity of fish, poultry and beef meat and determine its validity.

The electronic nose called "PEGS" works by placing a special barcode on the foods. The color of this barcode changes by interacting with the gases resulting from the decomposition of the food. After that, this barcode is read by a smart phone application and determines the validity of the foods.

According to the "Advance Material" website, today, Thursday, the results of the accuracy of this innovation are 98.5%, and the result appears within 30 seconds.

"This barcode helps consumers save money by ensuring they purchase consumable products, which also helps preserve the environment by reducing meat production by slaughtering animals," said Professor Chen Xiaodong, Director of the Innovation Center for Flexible Devices at National Taiwan University.

In mammals' sense of smell, the gases produced by rotting flesh bind to specific receptors in the nose, and this generates signals for the brain to decode them.

The brain then collects these impulses and organizes them into patterns, allowing mammals to choose putrid scents.

Each bar in the barcode acts as a receptor similar to human receptors, and it is made of chitosan (a type of complex sugar) incorporated into a cellulose derivative, and loaded with different types of dye, then these dyes interact with the gases resulting from the rotting and change their color according to the gases' concentrations.

Scientists have classified food with three designations: fresh, less fresh and spoiled, which is an international standard system.

For the test, researchers measured levels of ammonia and biogenic amines in fish containers coated with transparent PVC and stored at 40 degrees Fahrenheit (4 degrees Celsius) for five days.

Next, the researchers used a type of algorithm called a "deep convolutional neural network," and trained it using different barcode images to identify patterns associated with different smell fingerprints.

The test result was 100% accurate in identifying spoiled meats, and 96-99% accurate in fresh and less fresh meats.