Deep Dive: Precision palate

As protein sources diversify, the future of food will be shaped not just by taste, but by data. From molecular profiling to regulatory precision, Lloyd Fuller explores how analytical mastery is redefining the next frontier of food innovation

‘Data is the new oil’ might be a tech-bro cliché, but in the high-stakes arena of next-gen proteins, it’s less a metaphor and more a survival strategy – with a critical caveat. As the sharp minds on Protein Production Technology International’s April 2025 webinar panel made clear, raw data isn’t gold dust. It’s potential energy. And in the world of alternative proteins, that energy only becomes useful when captured, contextualized, and acted upon with near real-time precision. This isn’t just about numbers on a spreadsheet – it’s about building the digital backbone of a food revolution.

Our recent virtual gathering for Analytics in Action: Tackling the Complexities of Next-Gen Protein Sources, hosted by Xylem Lab Solutions and Gerhardt Analytical Systems, brought together scientific disruptors and industrial heavyweights. Their goal? To break open the black box of protein analytics and explore how evolving methods, regulatory shifts, and automation are rewriting the rules for how we understand and commercialize novel proteins. What emerged was a narrative threaded with chitin’s molecular sleight of hand, the relentless drive for accuracy, and a clear-eyed view of the regulatory terrain. In short: the future of food is being built one data point at a time.

Reexamining a trusted yardstick

For more than a century, the Kjeldahl and Dumas methods have been cornerstones of protein quantification. Long entrenched across sectors, these techniques estimate protein content by measuring organic nitrogen in the case of Kjeldahl and total nitrogen in the case of Dumas, then applying the Jones conversion factor – typically 6.25 – to estimate protein content. But as Michelle Kuzio, Product Manager at Xylem Lab Solutions, warns, that logic doesn’t always hold up when faced with the weird and wonderful world of alt proteins.

“When you’re measuring protein using Kjeldahl or Dumas, you’re really measuring nitrogen,” she says. “But not all nitrogen equates to usable protein.”

Take chitin as an example – a nitrogen-rich structural polysaccharide found in insect and crustacean exoskeletons. It’s biologically present, but nutritionally irrelevant. As Dr Lukas Brieger of C. Gerhardt explains, “Some nitrogen sources – such as chitin – aren’t bioavailable to humans or animals. Consequently, the crude protein value obtained through these traditional methods might not always directly correlate with the actual nutritional protein content.”

In an industry where supply chain decisions hinge on protein content, such discrepancies aren’t academic – they’re financial. Dr BJ Bench, Managing Director of FSQA & Laboratories at Tyson Foods, frames it in real-world terms. “You’re often paying a set price based on protein, so understanding the relationship between total nitrogen and bioavailable protein through appropriate analytical methods is crucial for accurate valuation.”

Beyond economics, there’s the issue of nutritional integrity. “Every ingredient – whether it’s from plants, insects, or something else – can have a different nitrogen-to-protein conversion factor,” Bench continues. “While 6.25 is commonly used, it’s not always the right number. Determining the correct Jones factor for new ingredients up front is essential.”

It’s a subtle but powerful insight. In the realm of alternative proteins, trust begins with numbers – but credibility demands context.

Seeing what others miss

So, what’s next after crude protein? A new generation of scientists is chasing a more elusive target: ‘true protein’, or the fraction that’s actually bioavailable and nutritionally relevant. This isn’t just semantics – it’s a shift in analytical focus from the convenient to the meaningful.

Brieger shares a breakthrough: a technique developed with the Research Association for Feed Technology in Braunschweig, Germany, that selectively isolates chitin from the rest of the protein matrix. “We analyze the nitrogen content specifically in the chitin, multiply that by 6.25, and subtract that from the total crude protein,” he says. “What you’re left with is a much more accurate measurement of true, bioavailable protein.”

It sounds simple but it wasn’t. “It actually took quite a bit of time to identify the right parameters to ensure we weren’t extracting the chitin along with the digestible protein,” Brieger adds. The goal was surgical precision – remove the noise without distorting the signal.

That’s especially important in sectors such as insect and fungal proteins, where whole-organism processing is common. While crustacean shells may be discarded in shrimp cocktails, Brieger notes their importance in formulations such as fish meal or shrimp meal. “Analyzing the chitin content becomes relevant,” he says.

Here, analytical accuracy isn’t just a scientific ambition – it’s a commercial imperative.

Regulation: the great data bottleneck

But even the most elegant data sets can’t sidestep bureaucracy. Regulation looms large in the alternative protein narrative, often acting as the invisible ceiling on innovation. Rodrigo Ledesma-Amaro, Director of the Bezos Centre for Sustainable Protein/Microbial Food Hub at Imperial College London, doesn’t sugarcoat it. “Regulation remains one of the biggest bottlenecks for innovation in this space, especially for startups,” he says.

Ledesma-Amaro’s talking about timeframes, risk, and return on investment. Without clear paths to approval, companies are stuck playing regulatory roulette. And the global patchwork of standards only complicates the game. “Regulatory frameworks differ from country to country,” he explains, noting that many startups strategically pivot to the USA or Singapore, where regulatory environments are more fluid.

That said, change is afoot – particularly in the UK, where post-Brexit regulatory autonomy could become a lever for progress. The UK’s proposed frameworks around precision breeding and novel food approval signal a shift toward more agile, data-informed systems. “These initiatives are designed to explore how we can regulate more efficiently and in a way that supports innovation, while still ensuring safety,” Ledesma-Amaro explains.

What’s more, regulators are starting to embrace collaboration. By investing in ‘sandboxes’ that bring together scientists, regulators, and companies, the UK is experimenting with a model that treats regulation not as a barrier, but as part of the innovation lifecycle. And that shift – from gatekeeping to partnership – might just change everything.

Reproducibility: the non-negotiable

We all know that science runs on trust. And trust, in turn, runs on reproducibility. For Ledesma-Amaro, this principle is non-negotiable. “Without reproducibility, it’s hard to
build trust in findings or make meaningful progress.”

To that end, the Bezos Centre is diving deep into standardization, especially in protein expression and engineering biology. Automation, unsurprisingly, is playing a starring role. “We believe the future lies in robots executing experiments while humans focus on designing and interpreting them,” Ledesma-Amaro says.

That’s not just sci-fi optimism. Automation reduces variability, boosts precision, and paves the way for the kind of high-resolution data needed to make sense of complex protein matrices. Through its work on the Periodic Table of Food Initiative, the Bezos Centre is contributing to a future where food composition is mapped with the same rigor as atomic particles.

But even with automation, access remains an issue. Priera Panescu-Scott, Lead Scientist – Plant-Based Specialist at The Good Food Institute, points out that tools such as AOAC and AACC provide critical standards – but those standards aren’t always easy to access. “One of the biggest bottlenecks is making those standards more widely accessible and usable across the board,” she believes.

The cost of instrumentation, the opacity of methods, and the scarcity of shared data can hobble even the most innovative startups. Hence Panescu-Scott argues for a more open-source mindset. “A rising tide lifts all boats,” she suggests. “That’s what we need – a high tide to get everyone moving in the same direction.”

Formulation in the data age

As the demand for clean-label, nutritionally optimized alternatives surges, formulation becomes both science and art – and increasingly, a data-driven one. However, Lisa Zychowski, Head of Formulations & Strategic Procurement at Planted, sees a problem: current analytical methods don’t go far enough.

Take particle size analysis, for instance. “Sieve analysis tells you a little, but not enough to predict texture or viscosity in the final product,” she explains. In a world chasing perfect mouthfeel, “a little” simply doesn’t cut it.

Compounding the issue is a lack of standardization across companies. “One company might test an ingredient one way, another does it differently,” Zychowski explains. The result? A tower of Babel where data comparisons fall apart.

Her solution is a push for richer, more consistent methodologies – laser diffraction, advanced rheometry – tools that might have been overkill in the past, but are now essential for precision formulation. “We want robust reproducibility and a deep understanding of functionality,” she says, especially under the extreme conditions of high-moisture extrusion.

The consumer is changing, too. Nutritional superiority isn’t a bonus – it’s a demand. “What we’re seeing now is a push toward products that not only meet clean label expectations but also offer superior nutritional profiles – ideally even better than meat,” Zychowski notes. At the end of the day, better food starts with better data.

Automation: the quiet revolution

While moonshot technologies grab headlines, it’s the quiet revolution of lab automation that’s transforming how teams work with protein. For budget-conscious startups, Xylem Lab Solutions’ Kuzio suggests keeping it simple. “Pick one or two tasks you can automate cost-effectively,” she advises.

That might be a titrator, a distillation unit, or even an autosampler – but even modest steps can pay off in consistency and efficiency.

Zychowski, though, imagines a future where automation goes deeper, integrating nutritional profiling and ingredient functionality into one seamless workflow. She points to AI models such as those developed by Givaudan as early prototypes. “I’d love to see a future where formulation and nutrition are automated together,” she says. “That kind of integration could really help accelerate development.”

C. Gerhardt’s Brieger sees continuity more than disruption. “At the end of the day, we’re typically working with powders, liquids, or pastes – these are forms that are already compatible with Kjeldahl or Dumas methods,” he notes. With the right mindset, even legacy labs can pivot toward the future.

The speed-precision paradox is breaking

Traditionally, labs had to choose between speed and accuracy, but that trade-off is fading. As instrumentation improves, real-time analytics are becoming sharper. “We’re seeing more methods that deliver faster results without compromising on precision,” Kuzio continues.

Brieger points to Dumas technology: once slow and laborious, it now matches Kjeldahl in accuracy – at a fraction of the time. But he’s especially excited about what’s next: Near-Infrared Spectroscopy (NIR) and Nuclear Magnetic Resonance (NMR), both capable of delivering rich, multidimensional data with impressive speed. For BJ Bench, context is key. “Matrix matters,” he insists. Every ingredient has challenges. Without proper validation, even the best instruments can produce misleading data. And in large-scale operations, small anomalies can mean waste, lost revenue, or compromised quality. Fast is great. Fast and accurate is best.

The protein transition is underway – but progress depends on data, not promises. Ledesma-Amaro puts it plainly. “The fact that researchers like me are now actively involved in these discussions is a good sign. It shows that regulation is no longer just a bureaucratic hurdle – it’s becoming part of the innovation process itself.”

With scientists, industry, and regulators in open dialogue – and reproducible analytics at the core – the next chapter of food innovation is taking shape. One where numbers don’t just support decisions – they define the future.

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