Digital twins and biosensors are changing the game in the food sector, making food safety and supply chain efficiency better. These cool tech tools allow for spot-on simulations that help make quick, smart decisions and let us keep an eye on food quality in real time. This moves us towards a way of producing food that’s not just smarter but also kinder to our planet.

Safety is super important when it comes to our food supply chain. Regulatory bodies all over the globe are all in on making sure that the food we eat is safe and that the whole journey of our food, from farm to fork, doesn’t hurt our health, the environment, or ecosystems.

With supply chains getting more complex and the risks that come with them, it’s really key to have specific standards that guide the food industry. These standards lay down the safety measures and steps needed to keep our food safe and sound.

Public standards set by folks like the EFSA in the EU or the FDA in the US, as well as private standards from industry groups or NGOs, are super important for making our food supply chains more robust. The mix of public rules and private efforts is essential for tackling the food safety challenges that grow as food travels across borders and through different processing stages.

In this piece, we’re diving into how tech, especially biosensors and digital twins, can really amp up the efforts of those involved in getting our food from farm to table.

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TAKEAWAYS

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The complexity of the food supply chain

The food supply chain’s got a lot on its plate with big challenges in keeping quality up, cutting down waste, staying safe, and fighting off fakes. The thing is, this supply chain’s pretty unique and we’ve got to keep that in mind. For starters, lots of food goes bad fast, has a short shelf-life compared to stuff that lasts longer, and needs to be kept cold and moved quickly.

Then, to keep food quality and safety up to scratch, there are all these specific needs for storing and handling food, like keeping the right temperature and humidity. Plus, food often goes through more steps (like getting cut, preserved, cooked, and packed) than other goods, making the whole food supply chain more complex. On top of that, to make sure food is safe, there’s a need for tracking it all the way from start to finish, which is super important if there’s ever a need to recall food.

The whole agri-food supply chain is a big web of people working towards the same goals but facing a lot more uncertainty and risk. Stuff like the pandemic, the ongoing conflict in Ukraine, or new political changes have shown how fragile global supply networks can be. Outside forces can throw in unexpected delays, cost management headaches, issues with working together, keeping data in sync, rising transport costs, guessing demand, and port jams.

The agri-food supply chain is tapping into some high-tech tools to turn into a smart, flexible, and self-sufficient system powered by data. The latest in cloud computing, IoT, big data, blockchain, robotics, and AI are making smart connected systems even smarter, stepping up automation in this field and giving folks a chance to spot problems, find fixes, and smooth out their operations.

The role of tech in agri-food: biosensors

On the tech front for food safety, especially with biosensors, there’s a mix of tried-and-true and cutting-edge tech that’s being put to work for spotting risks early and keeping an eye on new threats.

With more pressure from regulators and worries about the bad press from any incidents, the agri-food industry is going beyond just showing they’re meeting standards with traditional monitoring data.

Things like precision farming, RFID, and wireless sensors let them gather real-time info on food safety or quality right from the start.

Meat and dairy chains are where you can see tech in action, with digital tags, RFID readers, and GPS trackers helping to watch for any food safety risks. Biosensors on farms or in the production line can check for nasty bugs, and camera watches in slaughterhouses can look for any signs of disease like lesions.

Biosensors are particularly nifty for picking up on pathogens, toxins, and viruses in different ways. Biosensors are analytical devices used to determine the quantity of a molecule in a sample, typically featuring a bioreceptor (enzyme, whole cell, antibody, aptamer, nucleic acid) linked to a transducer.

ùThe specific interaction between the target molecule and the biocomponent generates a physical-chemical or biological signal, converted into a measurable property by the transducer. The biosensor reacts only to the specific thing it’s looking for, ignoring anything else that might be in the mix.

In the agri-food sector, applied to livestock, biosensors could also measure biochemical parameters such as blood glucose levels, integrating video and audio surveillance methods already used in precision farming, like cameras, sound systems, and audio to track feeding and drinking behaviours and movements.

Furthermore, biosensors could be incorporated into packaging materials as a compliance tool for HACCP (Hazard Analysis and Critical Control Points) based food safety management systems, measuring dozens of data points at critical control points in real time.

Tech’s role in keeping food safe: digital twins

There’s a cool tech spot that research is zeroing in on, and that’s digital twins. Experts are digging into how making a virtual version can shake things up for managing the food supply chain.

They’re really into figuring out how digital twins could boost how we handle the quality of perishable food all the way down the supply chain. They want to pinpoint where and how to use them, spot opportunities and challenges, and get the design and deployment right, especially considering what this sector specifically needs.

A digital twin is like a virtual copy of a real-world system, capturing its surroundings and how it works. This tech keeps the real and the virtual buddies in constant chat, always swapping updates. A digital twin has three main bits: a digital sketch of the physical setup, real-world data mainly from IoT (Internet of Things) and live telemetry, and a data model that helps with making smart choices.

Though it’s a big deal in manufacturing, digital twins have the power to really transform how food supply chains are managed, making it easier to keep an eye on food quality and safety and to spot risks early.

Digital twins let us manage and control things from afar, guess and plan for changes in resources and processes with “what if” scenarios, which means better energy use, less waste, more consistent processes, and less downtime for fixes.

Digital twins and fresh food: a peek into a study

Fresh stuff like fruit and veg needs extra care through the supply chain: if we’re not careful or don’t keep track of how much we have properly, we could end up throwing away a lot of food that could have helped feed people in need.

The loss of food along the supply chain of fruits and vegetables due to poor quality and mismatches between demand and supply is an issue that needs addressing, as it leads to the wastage of still usable food nearing the end of its shelf life.

The study “Digital Twin for Inventory Planning of Fresh Produce” by Tsega Y. Melesse, Matteo Bollo, Valentina Di Pasquale, Stefano Riemma at the University of Salerno takes a deep dive into how new, clever solutions can make the perishable goods supply chain run smoother.

Using digital twins, which rely on solid simulations to keep an eye on the ins and outs of logistics in real time, provides a detailed mock-up of the supply chain. This uses up-to-the-minute data to forecast what’s going to happen next.

A real-world case with Banco Alimentare

The study delves into a trial conducted with Banco Alimentare Campania Onlus, one of the 21 operational regional food banks in Italy, which recovers and collects surplus food from various sources along the supply chain, redistributing it to over 350 small charitable organisations in the region.

The trial focused on perishable foods, particularly fruit, whose management is complicated by uncertainty in donations and demand from charitable organisations. These organisations tend to request donations irregularly, while donors typically contact the food bank within 48 hours to confirm their donation availability.

Due to a traditional approach in communication and inventory management, there is significant wastage of these perishable foods. The research, therefore, suggests adopting a digital twin to overcome these challenges, allowing real-time monitoring and better synchronisation of incoming donations with distribution needs.

Using a data model created and trained for the specific need with historical data from the food bank, the digital twin can predict the number of incoming donations and deliveries to charitable organisations, enabling the food bank to quickly re-plan and modify the inventory to address issues such as storage space and limited refrigeration capacity.

By tracking deviations from the set plan and reorganising logistical activities to meet the needs of charitable organisations, the digital twin improves forecast accuracy and reduces product loss. The proposed solution aims to synchronise demand and supply in the supply chain, enhancing collaboration between donors, food banks, and charitable organisations, and enabling rapid decisions based on near real-time analysis.

Digital twins: a perspective for the agri-food sector

As mentioned, the adoption of digital twins can have a significant impact on visibility and process monitoring in the agri-food supply chain, improving real-time transparency of the entire transport network and eliminating discrepancies between shortages and surpluses.

They allow continuous demand control, a better understanding of purchasing patterns, while the connection of real-time sensor data helps to monitor the quality and marketability of foods.

And this applies to all players in the supply chain: retail managers can use digital twins to assess the impact of temperature differences on product quality, while the food industry evaluates their use for traceability, monitoring environmental conditions, weight loss, and overall quality in the post-harvest supply chain.

In the agricultural world, from precision to open-field agriculture, the adoption of digital twins for continuous and real-time monitoring is being considered, offering growers vital information on fertilisers, pesticides, irrigation management, environmental protection, and crop growth forecasts.

These systems also allow for monitoring crop health and receiving real-time notifications on pests, diseases, and climate changes, supporting informed decisions on fertiliser use and agricultural activities. In the agri-food context, the essential components of digital twins include:

• physical entities (real products or systems represented)
• virtual entities (digital models that replicate the appearance, properties, and behaviours of the real entities)
• service platforms (for the execution of models)
• data models and informational links

In the agri-food supply chain, digital twins can be developed based on statistical models, work on data (mechanistic), or use physical models for their greater predictive precision. The latter can predict how and when food quality will change over time, particularly helpful in monitoring fresh fruits and vegetables to reduce product loss during transport.

Unlike statistical and data-driven models, which look for patterns in data to explain quality loss, physical digital twins offer a more accurate representation of the ongoing biochemical, microbiological, and physiological processes, providing detailed explanations for the causes of quality loss.

It is clear that the implementation of digital twins is showing promising results in addressing the global challenges of the agri-food supply chain. Despite this, the real-world application of this solution is still in its infancy and raises questions about its actual economic sustainability for businesses in the sector.

Glimpses of Futures

Looking ahead to future and alternative scenarios, we’re going to use the STEPS matrix to figure out the kind of impact that bringing digital twins into the agri-food sector might have, considering social, technological, economic, political, and sustainability angles.

S – SOCIAL: to handle the social and ethical impacts that rolling out digital twins in the agri-food sector might bring, we need to take a Responsible Research and Innovation (RRI) approach. This means getting researchers/innovators to work openly with all sorts of people, like citizens, politicians, businesses, or NGOs. It’s about kicking off a conversation to make sure science and tech do the best they can not just for solving today’s problems but for building a fair and safe future too. The goal isn’t just to hit some pre-set research and innovation targets in terms of social and economic outcomes, or to make sure these outcomes are on the right side of ethics and law, but also to ensure the whole research and innovation process is seen as fair and desirable by society. An RRI approach believes that considering the “human factor” is key in research and innovation because tech is seen as a game-changer for the world, deeply affecting human lives (social). The aim here is to widen and deepen how tech innovators (including scientists, techies, businesses, and sometimes politicians) see things, helping them make choices that consider societal and ethical issues.

T – TECHNOLOGICAL: despite the buzz, there are real technical and economic hurdles to getting digital twins up and running in the agri-food sector. For starters, creating strong virtual replicas requires good sensor coverage and a way to figure out how uncertain the model might be. Some folks suggest using Bayesian methods to build digital twins, but finding solid methods to deal with uncertainty is still a big challenge. Also, sensors can sometimes fail or not record data: relying on a predictive model with dodgy sensor data ups the chance of mistakes. This is a major roadblock to rolling them out on a large scale. Plus, there’s a lack of common standards for modeling digital twins, which could make it tricky to integrate models made separately.

E – ECONOMIC: tech innovation is huge for the agri-food sector, helping tackle big issues like climate change, rising production demands, and changing consumer needs. Digital tech is shaking up business models, creating new ways to make money and add value all along the value chain. Tech tools like artificial intelligence, new ways for humans and machines to interact, augmented reality, IoT, digital twins, and advanced robotics are set to revamp the farming sector, opening doors to new value creation, cost and waste reduction, and boosting the sector’s economic sustainability. But right now, the investment needed seems out of reach for many, especially in countries like ours where smaller businesses are the norm.

P – POLITICAL: the FAO points out that a key challenge in this tech transformation is bridging the gap between coming up with a tech or innovation and actually putting it to use. Making this work depends on creating the right conditions to make the most of the benefits while minimizing the challenges from potentially disruptive tech. There’s a need to tweak and tailor national, international, and beyond national research and development programs, alongside policies and investments in agri-food science, tech, and innovation.

S – SUSTAINABILITY: Making virtual versions of whole agricultural systems and pairing them with advanced decision-making tech could bring loads of benefits, especially when agri-food production systems and supply chains aren’t currently on track to meet the Sustainable Development Goals (SDGs). Using reinforced learning – a slice of AI that’s really good at tackling complex decisions – digital twins can come up with strategies to, say, boost crop yields, as well as run “what-if” forecasts and simulations to spot ways to make the agri-food sector more sustainable. Digitizing farm production might cut labor costs, boost operational efficiency, improve animal health, and help stop biodiversity loss. Yet, there are tech and economic barriers that limit their spread and put the aim of creating inclusive, productive, and sustainable agri-food systems at risk.