Op Ed: Camila Perussello PhD, Food Engineer & Author, on What Alt Protein Can Learn From Formula 1

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Camila Perussello, PhD, is an extensively published food engineer who has worked as a scientist and food-industry consultant in different parts of the world. Dr. Perussello is an expert in food processing engineering, modelling & simulation in food manufacturing, and valorization of agrifood by-products. Her latest book, Food for Thought: Planetary Healing Begins on Our Plate, was released in 2022. Dr. Perussello’s work aims to advance a sustainable, healthy, and nonviolent food system.

What the Alt Protein Sector Should Learn From Formula 1

By Camila Perussello, PhD

Both the agrifood and motorsports industries rely on technology and engineering. However, there is one primary thing Formula 1 has been doing right that the alt-protein sector should get inspiration from: modelling & simulation (M&S).

Trial and error is obviously not an efficient way to produce anything, from food to high-performance cars. Think of a food product – pasta, for example. Every time a company tests new formulations, packaging, equipment or process conditions, they use time and resources. Imagine doing that experimentally for all combinations of ingredients and processing methods towards the desired outcomes! A sector that has arisen as an alternative to the wasteful and polluting traditional system of producing food must act smarter than that.

Figure 1 – Alt-protein foods. Source: The Good Food Institute

We already know that alt-protein foods (Figure 1) are more sustainable and healthier than what they replace. But to accelerate positive change, plant-based, cultivated, and precision fermentation products need to be affordable and appealing. The sector would greatly benefit from more skilled professionals using computational tools to predict and optimise results across the supply chain.

To change the global food system for good, the alt-protein space needs more engineers with expertise in modelling & simulation to boost industry competitiveness, just like Formula 1 (F1) does.

How F1 became F1

In just over 70 years, F1 has evolved from a sport displaying the bravery and skills of drivers to the pinnacle of engineering innovation (Figure 2). Despite being a tiny niche in the automotive industry, with only 20 cars participating in every race, the innovations proposed in F1 reach far beyond the evolution of cars and engines. Their solutions are now employed across many industrial sectors, including the development of electric vehicles and medical devices that were fundamental during the COVID-19 pandemic.

Formula 1
Figure 2 – Evolution of F1 cars throughout the decades. Photo by Edwin van Nes.

The ultimate goal in F1 has never changed: winning races. We are talking about an industry where milliseconds can make a difference, and engineers have always sought ways to achieve a competitive advantage with the tools available. In the very beginning, this meant conceiving ideas, applying them directly to the car, and relying on the driver to tame it (Figure 3). Sadly, many lives were lost due to things not going as planned. From the need to create safer ways of validating ideas before implementation, a significant learning curve started with the use of prototypes, test benches, data acquisition techniques, and numerical simulation, particularly since the 1980s.

Figure 3 – Risky aerodynamics experiments: F1 car in 1968. Source: Lynn Lindhurst.

Computer simulation played a significant role in transforming early racing cars into the sophisticated machinery running on tracks worldwide today (Figure 4). As F1 benefitted from higher performance and safety as a result of numerical modelling, both related and unrelated industry sectors were positively affected, including the chemical, materials, automotive, finance, and pharmaceutical industries.

F1 revolutionized the automotive industry by investing in engineering solutions and computer simulation. We should do the same to revolutionise the agrifood industry.

Why change our food system?

Overwhelming evidence shows that a plant-centred food system is necessary to prevent a climate collapse and put us on track for a decent future. A plant-based food system can reduce carbon emissions and diet-related diseases, save water and land, restore forests, boost biodiversity and feed our growing population nutritious food. And most importantly, an animal-free food system would cease to reduce trillions of living beings to inanimate objects every year. Taking a life is a serious business.

A recent study published in the prestigious journal Nature on the impact of different diets included 55,504 people and 38,000 farms in 119 countries. Vegan diets resulted in 75% fewer carbon emissions while using only a quarter of the land and less than half of the water. Vegan diets also have a significantly lower impact on eutrophication and biodiversity loss.

Figure 4 – Performance evolution of F1 cars through computational fluid dynamics (CFD). Source: Siemens.

And yes, the study includes variations in food production methods and sourcing. So far, the mitigation strategies proposed by animal agriculture – such as regenerative grazing, genetically modified cows and methane reduction devices – have failed to show results of the same magnitude as the ones brought by cutting down on animal products. What we eat is far more important than where it came from and how it was produced. This has been proven time and again.

“If we are serious about the climate crisis, we cannot dismiss the power of diet change.”

Notably, a shift away from animal-sourced foods could save USD 7.3 trillion worth of production-related diseases and ecosystem degradation while curbing carbon emissions, according to another Nature paper. These are hidden costs of the average diet.

A plant-centred food system: the clock is ticking

Every day we get closer to the 1.5°C warming limit set in the Paris Agreement – as I write, the planet is over 1.1°C warmer than before the Industrial Revolution. Another study published in Nature shows that the rapid phaseout of animal agriculture could achieve half of the carbon emissions reductions needed to meet the Paris Agreement targets (Figure 5). If we are serious about the climate crisis, we cannot dismiss the power of diet change. We need to act fast, and computer simulation can help.

Figure 5 – Diet change must be at the forefront of strategies for averting environmental collapse. Source: Eisen & Brown (2022).

Modelling & simulation: a world of possibilities

The use of M&S is widely recognised in all domains of engineering. Nevertheless, there remains significant room for mathematical tools and computer simulation in the food industry, especially alt proteins.

Okay, but what is modelling and simulation in the first place? In simple terms, models are a representation of reality. Mathematical models can be anything from a linear equation to a set of physics-based equations that represent a process, product, system, phenomenon or population. These models associate variables with responses, for instance, how a drying process affects the colour and texture of a food product. Think of the pasta example mentioned before. Computer simulations are the tool by which multiple input variables are tested using a computer to calculate the outputs of the mathematical model.

M&S provides pivotal evidence for technical and managerial decision-making using a limited set of experiments. What could take months of experimentation to generate responses can be accomplished in a day of computer simulation.

“M&S can take alt-proteins from an emerging field to a mature industrial sector”

This does not mean that experiments are an obsolete way of generating insight. Well-designed experiments are still needed to build, feed, calibrate and validate the model, but reliable conclusions can be derived from a much smaller set of expensive and time-consuming experiments.

Computer simulation can be used in a multitude of ways in the food industry, such as to develop new products, predict results in a factory line, scale up production, bring down production costs, optimise processes for lower environmental impact, produce safer, healthier and more appealing products, gain deeper insight on consumer behaviour and market trends, extend the shelf-life of a food or beverage, test novel technologies, increase the digestibility of a food item, choose the best-performing packaging, reduce food waste and food loss, and so on. This tool can literally improve any aspect of the food industry you can think of, from field to fork and beyond.

Figure 6 – Applications of physics-based simulation in the food industry: A) optimising the hydration of barley for malt production, B) understanding the relationship between food mechanical properties and consumer acceptance, and C) choosing the best cold chain scenarios for fresh produce. Applications of physics-based simulation in the food industry: A) optimising the hydration of barley for malt production, B) understanding the relationship between food mechanical properties and consumer acceptance, and C) choosing the best cold chain scenarios for fresh produce. Sources: Montanuci et al. (2014), Samaras et al. (2023) and Han et al. (2023)

Data science vs physics-based modelling

Most people are familiar with data science, the process of extracting insights from unstructured data. It is important to note that modelling is not limited to data science, especially in the food industry. Foods and beverages are complex systems that undergo a wide range of processing stages before they reach the consumer table. To properly represent a system, you need engineers with deep knowledge not only of computational tools and data analysis, but also physics, chemistry and microbiology. A single problem in food processing engineering can include heat transfer, mass transfer, fluid flow, chemical reactions, microbial growth, electricity, and deformation (Figure 6). Not by accident, food engineering is one of the most multidisciplinary fields in engineering.

Poor understanding of the underlying processes involved in food manufacturing leads to an inadequate representation of reality – that is what we call “rubbish in, rubbish out”. So yes, mathematical tools and computational power can solve real-world problems fast and cheaply, but you need experts to do that.

Next-generation alt proteins

The global plant-based meat sector is projected to grow from USD 8.6 billion in 2020 to USD 42.7 billion by 2028, and the cellular agriculture sector is also growing. According to the Good Food Institute, 156 companies were dedicated to cultivated meat and seafood by the end of 2022, with investments totalling USD 2.8 billion.

However, like any other niche in its infancy, the alt-protein industry faces challenges with R&D, scaling-up, regulation and commercialization, as well as the competition against a consolidated industry (animal ag) benefiting from established supply chains, large-scale production, tax breaks, governmental subsidies and traditional eating habits. But M&S can take alt-proteins from an emerging field to a mature industrial sector (Figure 7). The US-based Cultivated Meat Modeling Consortium is already working on computer modelling techniques to advance the sector.

Figure 7 – Modelling & simulation can boost the alt-protein sector. Source: Perussello (2023)

Food system transformation is inevitable (and will benefit everyone), but it is imperative to accelerate the process. Changing consumer behaviour is just part of the game, as the industry itself must change its mindset and invest in highly skilled engineers to transform vision into reality. Hard yet exciting times to be alive on this planet!

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