What if we could decipher information about tomatoes and apples otherwise hidden from our senses, making businesses more sustainable? As a matter of fact, we can, from anything emitting gases, thanks to unique multi-gas analyser technology from Tunable.
Food waste and greenhouse gas (GHG) emissions are well established complex problems that require many solutions. Faced with regulations and market demand, businesses and industries adopt new technologies and redefine processes striving to minimize pollution, GHG emissions and reducing waste of water, resources and energy. Consider this:
Up to 40 % of the world’s food is wasted or lost annually, with fruits and vegetables being one of the largest contributors.
The water consumption from food loss accounts for ¼ of the world’s fresh water supply
Almost 50% of all food waste occurs post-harvest, with a worth of 300 BUSD.
Food waste alone generates more GHG emissions than all single countries in the world, excluding the US and China.
In a world of almost 8 billion people wasting food makes no sense – economically, environmentally or ethically.
Decode the language of air-borne molecules
Tunable is currently exploring a wide range of applications for their multi-gas analysers, exceeding the natural sense of smell. Together with BAMA and ASKO, who are among the largest distributors of fresh produce in Norway, Tunable is taking groundbreaking strides in developing a solution for detection and monitoring of packaged fresh produce quality.
There is a lot of information to be extracted from natural processes that can be fed to decision makers or even automated systems. The information carrier is simply all kinds of volatile organic compounds (VOCs), emitted from anything organic. Gases naturally leave patterns that can be digitally mapped to particular states.
Enlightenment at the distribution center
To players like BAMA and ASKO it is of great interest to monitor the status of their fresh produce, at different stages of their supply chain. Knowing when to do what with fresh produce is of utmost importance. Tunable aim to equip producers, distributors and retailers with a tool that can help them improve both product quality and profitability. This is how Tunable contribute to sustainability forreal and making it more than just an empty phrase.
On a business level, the Tunable technology is expected to reduce costs by optimizing logistics and the shelf-life of the products. Other motivations for fruit and vegetable players are reduced food loss, improved carbon footprint and higher customer satisfaction.
At their distribution centers in Norway, BAMA and ASKO receives and handles thousands of pallets of fresh produce daily. Making the Tunable detection systems work efficiently on a large scale is key. Speed and accuracy are of the essence.
A more sustainable business
Gaining access to gas information opens a new space for learning and improvements, and high-quality gas data is an essential prerequisite for effective AI applications. The Tunable technology itself is a small investment and is highly versatile, meaning that it can easily be installed to serve several different purposes.
In the case of detecting spoiled fruits and vegetables through gas sensing at distribution centers, these insights have direct implications for where shipments go, returns, and operational efficiency.
Tunable will share some of their work within quality detection and monitoring of fresh produce at the Startup Stage at Fruit Logistica in Berlin, the premier exhibition for the fruit and vegetable industry, on the 9th of February 2024. More information is available here: https://www.fruitlogistica.com/en/events/start-up-day/.
Replicating nature´s sense of smell
All human senses, except the Sense of Smell, have been artificially improved, digitalised and miniaturised. Tunable has a groundbreaking solution for a miniaturised analyser replicating nature´s sense of smell.
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Tunable is expanding the information universe. We are building the tools to include real time and historic gas data, across applications and categories.