The 'electronic nose' (e-nose) comprises a 'barcode' that changes colour over time in reaction to the gases produced by meat as it decays, and a barcode 'reader' in the form of a smartphone app powered by artificial intelligence (AI). The e-nose has been trained to recognise and predict meat freshness from a large library of barcode colours.

When tested on commercially packaged chicken, fish and beef meat samples that were left to age, the team found that their deep convolutional neural network AI algorithm that powers the e-nose predicted the freshness of the meats with a 98.5 per cent accuracy. As a comparison, the research team assessed the prediction accuracy of a commonly used algorithm to measure the response of sensors like the barcode used in this e-nose. This type of analysis showed an overall accuracy of 61.7 per cent.

The e-nose, described in a paper published in the scientific journal Advanced Materials in October, could help to reduce food wastage by confirming to consumers whether meat is fit for consumption, more accurately than a 'Best Before' label could, said the research team from NTU Singapore, who collaborated with scientists from Jiangnan University, China, and Monash University, Australia.

Co-lead author Professor Chen Xiaodong, the Director of Innovative Centre for Flexible Devices at NTU, said: "Our proof-of-concept artificial olfactory system, which we tested in real-life scenarios, can be easily integrated into packaging materials and yields results in a short time without the bulky wiring used for electrical signal collection in some e-noses that were developed recently.

"These barcodes help consumers to save money by ensuring that they do not discard products that are still fit for consumption, which also helps the environment. The biodegradable and non-toxic nature of the barcodes also means they could be safely applied in all parts of the food supply chain to ensure food freshness."

A patent has been filed for this method of real-time monitoring of food freshness, and the team is now working with a Singapore agribusiness company to extend this concept to other types of perishables.

A nose for freshness

The e-nose developed by NTU scientists and their collaborators comprises two elements: a coloured 'barcode' that reacts with gases produced by decaying meat; and a barcode 'reader' that uses AI to interpret the combination of colours on the barcode. To make the e-nose portable, the scientists integrated it into a smartphone app that can yield results in 30 seconds.

The e-nose mimics how a mammalian nose works. When gases produced by decaying meat bind to receptors in the mammalian nose, signals are generated and transmitted to the brain. The brain then collects these responses and organises them into patterns, allowing the mammal to identify the odour present as meat ages and rots.

In the e-nose, the 20 bars in the barcode act as the receptors. Each bar is made of chitosan (a natural sugar) embedded on a cellulose derivative and loaded with a different type of dye. These dyes react with the gases emitted by decaying meat and change colour in response to the different types and concentrations of gases, resulting in a unique combination of colours that serves as a 'scent fingerprint' for the state of any meat.

For instance, the first bar in the barcode contains a yellow dye that is weakly acidic. When exposed to nitrogen-containing compounds produced by decaying meat (called bioamines), this yellow dye changes into blue as the dye reacts with these compounds. The colour intensity changes with an increasing concentration of bioamines as meat decays further.

For this study, the scientists first developed a classification system (fresh, less fresh, or spoiled) using an international standard that determines meat freshness. This is done by extracting and measuring the amount of ammonia and two other bioamines found in fish packages wrapped in widely-used transparent PVC (polyvinyl chloride) packaging film and stored at 4°C (39°Fahrenheit) over five days at different intervals.

They concurrently monitored the freshness of these fish packages with barcodes glued on the inner side of the PVC film without touching the fish. Images of these barcodes were taken at different intervals over five days.

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Materials provided by Nanyang Technological University. Note: Content may be edited for style and length.

Journal Reference:

1. Lingling Guo, Ting Wang, Zhonghua Wu, Jianwu Wang, Ming Wang, Zequn Cui, Shaobo Ji, Jianfei Cai, Chuanlai Xu, Xiaodong Chen. Portable Food‐Freshness Prediction Platform Based on Colorimetric Barcode Combinatorics and Deep Convolutional Neural Networks. Advanced Materials, 2020; 32 (45): 2004805 DOI: 10.1002/adma.202004805