The Canberra biotech startup is helping processors detect spoilage risks before milk reaches the shelf.
Updated
August 13, 2026 1:01 AM

CYBERTONGUE® Food Testing System, developed by PPB Technology. IMAGE: PPB Technology
Anyone who has opened a carton of milk before its expiry date only to find that it smells off or tastes bitter has experienced the problem firsthand. For dairy processors, the same issue can play out across thousands of litres. Milk may pass routine quality checks, make it through production and still spoil before reaching the end of its expected shelf life.
CYBERTONGUE®, a biosensing platform, is designed to give dairy processors an earlier warning.
Developed by Canberra-based biotech startup PPB Technology, the compact food testing system allows processors to analyse milk on-site and receive results within minutes. Its first commercial application detects protease, an enzyme linked to spoilage, shorter shelf life and production losses across dairy products.
Dairy companies already test milk for fat, protein, bacteria, antibiotics and other indicators. Samples from individual farms may be sent to central laboratories, while tanker loads are tested again when they reach processing plants. These checks provide useful information, but they do not always show whether milk will remain stable in a UHT carton, meet a supermarket’s shelf-life requirements or produce the expected amount of cheese.
Even after heat treatment, protease can continue affecting milk. For processors making UHT milk, fresh milk or mature cheese, that creates a costly blind spot.
“Right now, dairy processors are working blind on this,” PPB Technology founder Stephen Trowell told Ventureport. “It’s becoming more of a problem as customers expect longer shelf lives and higher quality.”
CYBERTONGUE® uses engineered biosensing proteins that emit blue and green light. The balance between the two colours changes when the protein reacts with a target substance. The reader measures that shift and calculates how much of the target is present.

Trowell describes himself as a protein engineer at heart. He spent around three decades at CSIRO, Australia's national science and technology agency, and close to four decades in research before becoming a startup founder. The technology behind CYBERTONGUE® grew out of years of biological research, including earlier experiments involving wine aromas and explosives detection.

PPB initially developed its technology to measure plasmin, an enzyme derived from cows. After feedback from the dairy industry, the company shifted its focus to bacterial proteases, which were identified as a more pressing spoilage problem. The platform can make some existing tests faster, but PPB chose to focus first on a measurement previously unavailable to processors.
“Where we’ve chosen to lead is with a measurement nobody else can do at all,” he said. “The blue ocean, if you like.”
That focused entry point shapes PPB’s wider strategy. CYBERTONGUE® could eventually test for lactose, allergens, microbial toxins and other targets. Yet launching everything at once would stretch a six-person company too far. Protease gives it a clear route into dairy, where the financial value is easier to demonstrate.
A processor that detects high protease activity may redirect a batch of milk away from long-life products and use it for yoghurt, curd or another product where the enzyme causes less damage. PPB also says its tests can help trace problems in the milk supply and detect biofilms on processing equipment, allowing factories to take corrective cleaning measures.
Fresh milk presents another sizeable opportunity, with the global market estimated at around US$81 billion in 2026 and projected to grow at 6.83% annually through 2035. At that scale, even small gains in shelf-life consistency could prevent substantial losses. Supermarkets often require milk to have a minimum amount of shelf life remaining on delivery. Products that fall short may be rejected, discounted or discarded at the processor’s expense. While Australian processors typically aim for a shelf life of 19 to 21 days, they do not always achieve it. CYBERTONGUE® could help them reach that target more consistently.
Cheese producers face a related issue. Protease can create bitter flavours in hard cheeses and reduce production yield. Cheesemakers add another enzyme, chymosin, during production, often without knowing how much protease activity is already present in the milk. Better information could help them adjust the process and get more value from the same raw material.
PPB sells the reader as laboratory equipment, but its long-term business model centres on repeat testing. Customers buy the machine, then purchase a PPB reagent cartridge each time they run an assay. Future tests are intended to work on the same reader, making the system more useful as the catalogue expands.
CYBERTONGUE®’s compact design reflects PPB’s focus on practical onsite testing. The device is small enough to fit into two cupped hands and comes with a tablet running PPB’s software.

At the time of the interview, 13 to 15 systems had been installed with paying customers. PPB had made sales in 11 countries across five continents, with Europe as its largest market.
Trowell describes PPB as “born global”. While Australia and New Zealand have sophisticated dairy industries, Trowell thinks CYBERTONGUE® may offer even more value in regions where milk quality is less consistent, transport routes are longer or refrigeration is harder to maintain.
Trowell also believes PPB has a strong competitive moat, with the combination of manufacturing both the hardware and consumable reagents. Patents form one layer, but he does not consider legal protection sufficient on its own. A competitor would need expertise in protein engineering, reagent production, optical hardware, software and dairy processing.
Calibration adds another barrier. For each new box of reagents, PPB securely uploads the required calibration data to the customer’s machine. A third party could not simply manufacture a cartridge with the same dimensions and expect it to work correctly.
“We own the whole stack,” Trowell said, referring to everything from the sensing molecule to the hardware, software and cloud platform.
That said, he accepts that no technology is impossible to copy. His view is that PPB’s mix of scientific knowledge, integrated system and experience in dairy gives it enough of a head start to build a meaningful position before competitors catch up.
The technology could eventually reach beyond food. Trowell sees possible uses in environmental testing, veterinary care and medical diagnostics, including the analysis of blood, serum or saliva. PPB has already discussed potential applications with organisations outside the food industry.
For now, however, moving directly into clinical diagnostics would create more risk than opportunity. Medical products can require years of trials, extensive regulatory work and large development budgets. PPB would also need to build new sales channels and choose between many possible clinical uses without knowing which one would attract the strongest demand.
For these reasons, food testing offers PPB a clearer path. The regulatory barriers are generally lower, customers have immediate operational problems, and PPB has already built relationships in dairy. Once the platform has proved itself commercially, the company could enter other sectors through partnerships or licensing.
Trowell has held that view for years. The immediate goal is to build a successful food testing company rather than chase the prestige often attached to medical biotechnology.
The opportunity may also expand as food production becomes more complex. Dairy factories increasingly process oat, soy and other alternatives on equipment that also handles cow’s milk. This creates cross-contamination and allergen risks, offering another potential use for CYBERTONGUE®.
PPB still has plenty to prove. It must secure repeat reagent orders, convert early trials into wider deployments and expand its test catalogue without compromising accuracy. Scaling an integrated biotech hardware business across international markets will not be simple.
Still, its approach offers a useful lesson for other deep-tech startups. Instead of beginning with every possible application, PPB picked a specific problem that costs customers money today. It built a product around that pain point, then used the same technology as the foundation for a broader platform.
As Trowell put it, customers “need it fast, and they need it right.” CYBERTONGUE® is betting that food testing can deliver both.
Keep Reading
Huawei is betting that the future of AI infrastructure will depend as much on energy systems as on computing power
Updated
August 10, 2026 5:56 PM

Blue light painting with a lightbulb. PHOTO: UNSPLASH
As AI companies build larger models and deploy more AI agents, the industry is running into a new constraint: electricity. The challenge is no longer just about computing power. It is increasingly about how to supply, manage and sustain the energy needed to run AI infrastructure at scale.
That was the central argument behind Huawei’s latest AI data center strategy unveiled at its Global AIDC Industry Summit in Dongguan.
The company introduced what it calls a grid-interactive AIDC strategy, focused on redesigning AI data centers around power supply, cooling systems and energy management. AIDC refers to AI data centers built specifically for large-scale AI computing workloads.
The announcement reflects a broader shift happening across the industry. As AI systems grow larger, data centers are consuming more electricity and generating more heat than traditional computing infrastructure was designed to handle. Companies are now being forced to rethink not just chips and servers, but the physical systems supporting them.
Huawei argues that future AI infrastructure will need closer coordination between computing systems and energy grids. The company says traditional data center designs are struggling to keep up with fluctuating AI workloads, rising power density and the growing use of renewable energy sources.
Hou Jinlong, Director of the Board of Huawei and President of Huawei Digital Power, said: "The booming AI industry, widely adopted large models, and numerous AI agents are creating huge energy demands, set to boost the global AIDC capacity. Electricity is essential for computing; energy is the foundation for AI long-term development. Computing and electricity will deeply synergize and empower each other, progressively building an integrated framework that brings together new power systems and AI infrastructure."
A large part of Huawei’s strategy focuses on power architecture. AI workloads can create sudden spikes in electricity demand, especially in high-density computing environments. To manage that, Huawei says it plans to develop new power systems that combine grid-friendly UPS infrastructure with energy storage technologies.
Cooling is becoming another major pressure point. AI servers generate significantly more heat than traditional enterprise systems and Huawei says liquid cooling is now becoming essential for large-scale AI deployments. The company introduced a liquid cooling system designed to improve long-term thermal management inside high-density AI environments.
Huawei is also pushing modular construction methods to reduce deployment times for AI data centers. Instead of building infrastructure entirely onsite, parts of the system can be prefabricated and tested in factories before installation.
Bob He, Vice President of Huawei Digital Power, said: "The global AI industry is booming, and the token demand surges. As such, the AIDC industry is entering the Token era."
As part of that shift, Huawei introduced a proposed measurement system called the TokEnergy Index. The company says the metric is designed to measure the relationship between energy consumption and AI computing output, rather than relying only on traditional data center efficiency metrics such as PUE.
The broader message behind the strategy is that AI infrastructure is becoming an energy engineering problem as much as a computing problem. As global demand for AI continues to rise, companies across the sector are beginning to realise that the future of AI may depend not only on better models, but also on whether power grids and data centers can keep up with them.