8. Technological innovations and scientific advances

Technological revolution is transforming the agrifood systems. Scientific advances are being employed to chase the fundamental goal of producing more food with less – lower use of agrochemicals, reduced water utilization – in addition to improved land use and benefiting farmers economically. Remote sensing technologies (drones, satellites, and so on), new and improved technologies with analytical and traceability functions, innovations that allow data to be moved between the field and computational cloud, and technologies that allow processing of large volumes of information have ushered in the age of digital agricultural revolution (Delgado et al., 2019; FAO, 2019; Lovell, 2021; World Bank, 2019).

Innovations and technological advancements in the food industry are also rapidly evolving the food safety arena (FAO and WHO, 2018a). New and emerging technologies in food production, processing and packaging are providing better tools for improving traceability, detecting contaminants in food and for investigating outbreaks. Below a few select technologies and innovations that have implications for food safety are briefly described in no particular order. The full breadth of opportunities and challenges associated with these technologies and innovations are not fully understood, and some of them still remain in their infancy.

Packaging

Appropriate packaging is designed to preserve the quality of food and makes it easier to transport, store and display at retail stores. Packages can also be used to communicate the nutritional content and potential safety issues associated with the food product inside to consumers through written texts or labels affixed on the outside. Loss of food quality does occur during storage or distribution due to various biological or chemical processes, and appropriate packaging can help to slow down these processes. Preserving food quality is not only linked to protecting the health of consumers against foodborne illnesses but also contributes to food security by minimizing food loss and waste. Some of the key food safety issues associated with food packaging are discussed in Chapter 6.

Active packaging and intelligent packaging are two new concepts that have emerged in response to the fast pace of globalization, longer distribution chains, greater awareness of food waste as well as changing consumer preferences. Active packaging is intended to extend the shelf-life of food products via the addition of various components to the packaging material. These components (oxygen and ethylene scavengers, moisture regulators, controlled release of antioxidants and antimicrobial agents, among others) either absorb or release substances in response to changes in the ambient environment both inside and outside the package thereby maintaining the quality and safety of food products.

Intelligent packaging includes materials that can monitor the condition of packaged food as well as the environment inside the package, alerting manufacturers, retailers or consumers when a product has been compromised or contaminated, for instance, indicating food spoilage by a change of colour of the package (BBC News, 2021). Intelligent packaging can also include “smart” labels that can track products as they move through the supply chain, confirm that products have not been tampered with and allow quick identification of products in a supply chain in case of contamination. Smart labels can also provide additional information that are not present on the physical label such as the sourcing of the food products and allergen information, among others.²⁵

Nanotechnology

While this technology itself is not new, the use of nanotechnology in the food industry has started to garner renewed attention by offering a slew of novel applications and benefits in food packaging, processing, nutrition as well as safety. For instance, the technology can be used as nanocarriers to encapsulate and deliver nutrients like vitamin supplements and other food additives such as anticaking agents and antimicrobial agents. Nanocomposites can improve the mechanical strength and barrier properties of food packaging materials. Nanotechnology also has potential in food nanosensing, as part of active packaging, which can be used to monitor for pathogen detection, thereby improving food safety and quality (Singh et al., 2017). There is also potential for low-cost nanofilters in wastewater treatment to improve the quality and safety of water used in agriculture, aquaculture and for human consumption (FAO and WHO, 2012).

There is ongoing research about the fate of nanoparticles in the human body and any potential safety issues associated with them when ingested. In addition, the disposal of such materials at the end of their life cycle is another area of concern – whether nanomaterials are degradable, interact with/accumulate chemicals in the environment, among others (EFSA Scientific Committee et al., 2018; FAO and WHO, 2010; FAO and WHO, 2013). Low absorption and accumulation of titanium dioxide (food additive E171) nanoparticles after ingestion has been reported by a recent safety assessment carried out by the EFSA Panel on Food Additives and Flavourings (2021).

3D printing of food

The first instance of using additive manufacturing or 3D printing to produce edible forms from liquid or semi-solid food materials was reported in 2007 (Malone and Lipson, 2007). Most 3D printers for food applications are extrusion based, i.e. a moving nozzle extrudes an edible formulation or “ink” in a pattern predetermined by a 3D model (Godoi, Prakash and Bhandari, 2016). Some equipment also allows for the simultaneous printing and cooking of food (Blutinger et al., 2021; Gibbs, 2015). Apart from some of the more common materials (chocolate, cheese, sugar, starch-based food products) used for 3D printing, other alternative raw materials, such as seaweed, insect flour, fruits and vegetables, among others, are also gaining attention.

There are several food-based applications that 3D printing can render itself to – from confectioners printing desserts to creating edible food commodities from food waste (Banis, 2018; Garber, 2014). 3D printing can also diversify and personalize food products by allowing the mixing of several different ingredients, including encapsulated probiotics and vitamins, through co-extrusion printing. As the popularity of plant-based diets grow, 3D bioprinting can be used to create “meat”-like textures with plant-based ingredients (Moon, 2020). A recent advancement now allows 3D bioprinting of steak using a culture of live animal tissues, which can propel the field of cell-based food products even further (Bandoim, 2021). Taking 3D-printing of food a step ahead, now four-dimensional food printing is under development. The principal applications of 4D printing are changes in colour, shape or flavour of food in response to stimuli such as pH, heat, moisture and so on. For instance, Ghazal et al. (2021) reported colour changes in a 4D-printed potato-starch based meal from anthocyanins responding to pH stimulus.

3D printed chocolate products.
©Shutterstock/Maksym Kaharlyk

However, the widespread commercialization of this technology, either for domestic use or at the retail level, will require thorough assessment of potential food safety risks, and there is currently limited scientific research on the various food safety aspects of 3D-printed foods. Some potential food safety issues involve the migration of chemicals from the 3D printer to food. To reduce such concerns, it would be important to use food-grade materials to construct parts that come in contact with food (Azimi et al., 2016). The ability to thoroughly disassemble and clean a 3D printer will help reduce risks of microbiological contamination from the equipment and prevent cross-contamination issues (Severini et al., 2018).

Handheld devices

Food-sensing technologies on portable analysers can identify various contaminants in food in real time making it quicker than laboratory-based testing. It also enables people, who are not food safety professionals, to operate the devices; for instance, farmers can check for pesticide residue levels on their crops, or supermarkets can check for various contaminants before displaying produce for retail (Chai et al., 2013; EC, 2019; World Bank, 2019).

Point of care diagnostics allow consumers to carry out instant on-site testing of their food for certain ingredients, such as food allergens (like eggs, gluten or peanuts). With food allergies becoming an important public health issue, these devices can also be used in clinical settings where rapid, low-cost detection of food allergies can be performed. Since many allergic individuals often suffer from more than one food allergy, a range of allergen detection is a likely desired feature of such devices (Albrecht, 2019; Neethirajan et al., 2018; Rateni, Dario and Cavallo, 2017).

However, some devices may be limited by their inability to detect substances beyond a certain depth from the surface. In certain cases, results from screening tests may require further confirmation through validated instrumental analysis, a protocol not always followed. Moreover, a lack of international standards on threshold detection limits can also be a challenge.

Distributed Ledger Technologies (DLT)

Blockchain, which is one of most well-known uses of DLT,²⁶ comprises an extensive set of encrypted blocks of shared data that are strung together chronologically (Karthika and Jaganathan, 2019; Mistry et al., 2020; Nakamoto, 2009). This data is a record of transactions that is shared among members of a network, allowing greater access to information and preventing manipulation (Atzori, 2015; Cai and Zhu, 2016; Underwood, 2016). The decentralized nature of such databases enables all members participating in a network to validate and record digital data with no central authority over them.

The application of DLT, Blockchain in particular, in the food sector is an emerging area and holds much promise in food safety control (Li et al., 2020; Pearson et al., 2019). Food traceability is a major application with Blockchain providing a mechanism to securely record every step of a food product’s journey through a supply chain making it easier to trace it from origin to end-point (Aung and Chang, 2014; Pearson et al., 2019). Enhanced transparency and traceability afforded by such technologies can reduce the response time when contaminated foods are discovered, making it easier and faster to selectively recall food products (Li et al., 2020; Yiannis, 2018). According to a major retailer in the United States of America, upon implementation of Blockchain technology, time taken to track the origin of a mango went from one week to 2.2 seconds (Kamath, 2018; Unuvar, 2017). By ensuring food traceability, Blockchain technology can also build consumer trust in food safety. In addition, it may be even possible to prevent or suppress fraud in some food supply chains (Cai and Zhu, 2016; Li et al., 2020; Yiannis, 2018).

However, it is important to point out that the ability of DLT itself to judge data quality is limited. Data can be entered from untrustworthy sources or may be incorrect, allowing erroneous data to be permanently recorded. The decentralized nature of DLT make its governance different from existing governance structures that have hierarchies. Governance of a digital domain can be complex; however, the successful implementation of DLT will depend on constructing an appropriate governance structure, particularly when it comes to issues pertaining to data rights, privacy and protection (van Pelt, 2020). Another important aspect is the need for interoperability, that enables seamless flow of data across disparate networks, in the food industry. The lack of this aspect can lead to information asymmetry and fragmentation within food supply chains that may employ a number of different DLTs. The need to preserve the decentralized properties within the boundaries of a single network complicates the notion of interoperability (Deshpande et al., 2017). In addition, high energy usage of certain types of Blockchains due to requirements for substantial computational power may complicate implementation given the current emphasis on environmental sustainability (Kaplan, 2021). Therefore, more assessment studies that help provide deeper understanding of the various environmental perspectives associated with these new and emerging technologies are needed (Köhler and Pizzol, 2019).

Internet of Things (IoT)

Various sensors (for temperature, humidity, pH, and so on) embedded into a vast network of devices that are spread across different aspects of a food chain connect and share data on a platform called the Internet of Things (IoT). The IoT platform integrates the data received from various sources, enables further analytics to be performed, followed by extraction of valuable information as per requirements which can then be streamed or shared with relevant recipients remotely (Bouzembrak et al., 2019). The application of this can be observed in food traceability where food distributors can track and document the journey of food products while ensuring that they have been stored at the right temperature along the way (Cece, 2019). At the consumer level, smart appliances connected to IoT are revolutionizing kitchens, for instance, smart refrigerators can scan and categorize food items and store them efficiently. They can also guide homeowners to organize their groceries and help plan their meals to minimize food loss (Landman, 2018).

Remote sensing

Today, high-resolution satellite imagery and drones carrying cameras and sophisticated sensors are revolutionizing agriculture by allowing food producers to remotely collect valuable information in real time, such as crop health, growth and maturity, soil conditions, as well as monitoring unanticipated weather conditions. On the back end, machine-learning algorithms can scan the images to provide deeper analytic data. Remote sensing also allows early detection of pest damage and disease outbreaks thereby providing opportunities to prevent overuse of agro-chemicals (pesticides, fertilizers, antibiotics) by facilitating targeted treatment of crops (Delgado et al., 2019; Raza et al., 2020; World Bank, 2019). Such a way of farming, also called precision agriculture, requires a technological network over which multiple instruments interact with each other, which is where IoT comes into play.

Linking geographic information systems with predictive risk-assessment models can help to forecast when, where and under what conditions microbial or chemical contamination of crops are likely to occur, thereby taking a functional role in early warning systems and preventing food safety issues downstream (Mateus et al., 2019).

Big data

Put simply, big data refers to a large volume of data gathered rapidly from a variety of sources. In food safety, this data can be from databases, sensors, handheld devices, social media, omics profiling, among many others (Donaghy et al., 2021). Big data can alert us to food safety risks in the food supply chain through new technologies like IoT, whole genome sequencing, next-generation sequencing, and Blockchain. These technologies generate large amounts of highly variable data that require tools to process the information to enable effective and timely decision-making, particularly in situations such as source identification during foodborne illness outbreaks, analysing food safety risks based on climate data, and so on (Donaghy et al., 2021; Marvin et al., 2017).

However, the use of big data in food safety is not straightforward as food safety information and data tend to be scattered across multiple sectors – food, health and agriculture. Food safety data traditionally collected through monitoring and surveillance can be limited and not always harmonized among different regions. The application of big data in food safety will require establishment of appropriate platforms for collection, storage and analyses of a diversity of data along with implementing safeguards for data rights and usage (Marvin et al., 2017).

Artificial Intelligence (AI)

AI incorporates advancements in machine learning to detect and predict patterns based on large data sets. New AI-based algorithms applied to conventional forecasting techniques can strengthen and enhance foresight capabilities of the actors in a food chain. AI can help track products from farm to consumers, forecast market fluctuations, facilitate autonomous farming, predict health code violations, and even be tailored to carry out foodborne disease surveillance.

AI also brings the power of machine learning and decision making to IoT thereby playing a major role in the growth of IoT applications and deployments in the food industry. For instance, AI-powered IoT (sometimes referred to as Artificial Intelligence of Things or AIoT) can improve operational efficiency through predictive analytics, such as by indicating when equipment needs maintenance or is closer to end-of-life and requires replacement, thereby enhancing risk management and maintaining performance. AIoT can help detect defective ingredients during food processing; in food manufacturing plants AIoT can ensure that workers are complying with food safety regulations, among many other applications (Friedlander and Zoellner, 2020). However, while this technology holds promise for food safety, it is not without risks – human bias, data inaccuracy, as well as security issues arising from cyberattacks – and should therefore be adopted keeping controls in place.

Automation

In an effort to better manage the risks that human workers can pose to food safety, advancements in robotics technology, coupled with AIoT, can be used to improve food safety, for instance by preventing cross-contamination issues. While previously robots were mainly limited to last step packaging tasks, today they are being increasingly used to handle unpackaged goods (Mohan, 2020). Soft robots, built from softer and flexible materials, to facilitate efficient handling of delicate food commodities without bruising, are being employed by some food producers to harvest fruits, by food manufacturers to run automated warehouses and by processing plants to handle a variety of food products (Jones et al., 2021). To ensure that the robots themselves are not contributing towards contamination, an additional set of robots have been developed to wash down the entire working area in such processing plants (Jarrett, 2020; Newton, 2021). Collaborative robots, or cobots, are a new generation of robots made to work alongside humans, under limited supervision. Cobots can be used for tasks that are carried out in areas which pose health hazards for human employees or are mundane and repetitive while ensuring adequate and consistent quality control (So, 2019).

Automation used in agriculture.
©Shutterstock/Microgen

Scientific advances improve risk assessment of chemical mixtures

The provision of scientific advice by FAO and WHO is the foundation for the development of international standards by the Codex Alimentarius (FAO and WHO, 2018b). As science is constantly evolving, it is important to keep pace with these advancements to maintain and improve the reliability, robustness and relevance of food safety risk assessments, which in turn facilitate the establishment of appropriate regulatory frameworks and food safety standards.

Methodologies used for food safety risk assessments largely depend on the purpose of the assessment as well as the quantity and quality of scientific data available on the substances being evaluated at the time. This implies that food safety risk assessments are in continuous evolution to match the state of scientific knowledge at a given time period and this is explained in the context of food safety risk assessments for combined exposure to chemical mixtures.

Assessment of risks of combined exposure to chemical mixtures is a notion that has been developing over the last few years. While risk assessments of chemical hazards in food usually tend to evaluate individual compounds,²⁷ humans are typically exposed to multiple low-levels of chemicals (not all of which pose appreciable negative health impacts), with various sources including food and water contributing to these exposures (Drakvik et al., 2020).

In 2019, FAO and WHO convened an expert consultation to develop guidelines for a pragmatic step-wise decision-making process for undertaking risk assessment for combined exposure to multiple chemicals (FAO and WHO, 2019). The experts agreed that if a substance under consideration was not part of an established chemical group previously considered, the Joint FAO/WHO Meeting on Pesticide Residues (JMPR) or the Joint FAO/WHO Expert Committee on Food Additives (JECFA) would then determine if there was need to include it in a risk assessment of combined exposure to multiple chemicals. Both JECFA and JMPR are expected to pilot the agreed-upon guidelines prior to the general implementation of the methodology (FAO and WHO, 2019). A number of other organizations, including the European Food Safety Authority (EFSA), Organisation for the Economic Co-operation and Development (OECD) and the United States Environmental Protection Agency have also published guidance and methodologies for combined exposure to chemical mixtures (EFSA Scientific Committee, 2019; OECD, 2018; US EPA 2000; 2003; 2008; 2016).

As the evaluation of chemical mixtures is an evolving area, it is vital to keep monitoring it and to update, as appropriate, the risk assessment processes, to ensure soundness and relevance of the advice that is provided.

What is the way forward?

Technological innovations are transforming the agrifood sector, including the field of food safety. Digitalization, scientific innovations, and technical advancements can facilitate international trade that is faster, more cost-effective, with greater market access and inclusivity, increased food safety along food chains, and reduced vulnerabilities to fraud. However, emerging technologies, by definition, come with both opportunities and challenges, and a critical view is needed to balance the benefits with the risks. Promotion of standardization and best practices, access to reliable and curated reference databases, communication of lessons learned, and transparency in data sharing across stakeholders will be needed to implement and apply emerging technologies and innovations. Rapid advances in technology often outpace the development of appropriate regulations needed to provide oversight. In addition, technological advancements will continue to provide opportunities to collaborate and have access to large amounts of diverse data from a wide variety of sources within the food sector. With rules on the governance of this data often unclear and inadequate, it raises trust and transparency concerns regarding data rights, privacy, sharing and may provide opportunities for misuse (Jacobs et al., 2021).

Translation of cutting-edge technologies across the global agrifood systems is not uniform. Excluding those that lack access and affordability can reinforce and accelerate inequalities. If adoption of such technologies requires substantial investments and capacity development, low- and middle-income actors in the food chain can get left out. For instance, if retailers required all suppliers to adhere to real-time traceability for food safety by implementing blockchain technology, it would raise supplier entry costs, and those unable to meet these requirements can get excluded from market access. Countries most affected by foodborne illnesses, where innovations in analytical tools might be most beneficial, often do not have access to these technologies or sufficient resources to realize their development. To promote equitable implementation of scientific advances, the international community will need to contribute more to help LMICs close the technological divide. This can be done through measures such as investments in infrastructure – roads, electricity, post-harvest storage facilities and so on – which can be some of the major constraints faced by farmers, developing capacities and training in technical expertise to facilitate understanding of new technologies as well as increasing user capabilities.

Finally, it is worthwhile to iterate that science is central to food safety. Development and application of sound scientific principles underpin the formulation of appropriate food safety regulatory frameworks and policies that are needed to safeguard public health amid ever-changing agrifood systems. The interconnection between science, risk assessment and risk management in food safety has always been complex, and it is even more so in an era with rapid scientific advances and technological innovations