Thanks to an EU-funded consortium, the new system running under the GRACED project umbrella will allow workers to check for pesticides or bacteria by monitoring dozens of more samples of fruits and vegetables than are currently performed.

This new ultrasensitive detector harnesses light particles to spot the tiniest traces of pesticide or bacteria in minutes; that means 50 to 100 times quicker than existing technologies.

“From preparing a sample to detection, the new system can deliver a result in less than 30 minutes – a fraction of the time at present,” GRACED project experts added.

The developers took their inspiration from one of their existing sensors that examines water to detect microbiological or chemical contamination with a small number of pesticides.

“We developed the project as a ‘hard fork’ extension of a previous EU project we ran called WATERSPY. In a sense, the two projects are related: bio photonics-based technology to determine contamination in specific matrixes – in WATERSPY, it was drinking water; in GRACED, it can be anything – water used for feeding the plants, or the actual fruits and vegetables themselves. The core technologies, however, are entirely different. Everything is done on a single chip – we are working to detect seven different analytes simultaneously in less than 30 minutes (including sample preparation time).”

The System ‘Looks’ at the ‘Binding’ of the Contaminant to the Sensor Surface

The GRACED project experts specified that to produce an unequivocal bacteria or pesticide detection, the system works by looking at the ‘binding’ of the contaminant to the sensor surface – producing a new unique signal when a harmful constituent is present.

With their plasmo-photonic bimodal multiplexing sensor, the GRACED team uses one of the most sensitive detection technologies available to identify at the molecular level. Since the receptors on the sensor surface are specifically ‘tuned’ to a particular bacteria or chemical, only the analytes of interest are captured along the sensor.

Light traveling in the sensor generates a fully exposed evanescent field over the sensor surface. Here, receptors can recognize the contaminants when a sample passes through. This recognition event changes the speed of the laser light and therefore the interference pattern at the output.

This change can be measured and determined precisely against a set of existing values – and could give an instant diagnosis for a contaminant expected at the picomolar to attomolar (pM–aM) range without any need for amplification.

The sensor is part of a holistic, modular solution that exploits unique engineering designs, IoT concepts, and advanced data analytics to detect contaminations in fruit and vegetable value chains.

Given that food can degrade quickly and with lengthy safety checks at present, factories can often make fewer checks meaning consumers face a greater risk of exposure to poisons and bacteria – even in countries with very efficient monitoring techniques.

Typically, safety checks on fruit and vegetables are made in random batches then sent to a laboratory, a process that can take 2-3 days to get a result. Due to time and costs, these checks cannot be performed in critical parts of the value chain like supermarkets and restaurants.

Each year, 385 million people are unintentionally poisoned by eating fruit or vegetables that contain pesticides; 11,000 of these tragically lose their lives as a result. These pesticide residues consumed via food and water can also cause a whole host of deadly long-term conditions, including birth defects, cancers, genetic defects, blood disorders, and neurotoxicity.

GRACED is currently being coordinated by Cyprus research and innovation Centre (CyRIC) and includes a consortium of experts from all across Europe.

In 2024, the GRACED project will conduct future trials in France, Italy, and Hungary, covering different types of production systems (conventional open-air farming, novel urban farming, short agro-ecological value chain, semi-automatic farming). The three-and-a-half-year project received a grant of EUR4.9m from Horizon 2020 under the Research and Innovation action funding scheme.