AI Takes Over Taste Testing and Restaurant Analytics

New artificial intelligence (AI) tools are changing how restaurants analyze customer data, test food safety and even reproduce scents in laboratories. The technologies include a natural language assistant for querying loyalty programs, an electronic tongue that achieved 95% accuracy in detecting food issues and a system that can capture and recreate specific scents in different locations.

Restaurants Tap New AI Tool to Decode Customer Data

Paytronix unveiled an AI assistant this week that helps restaurants and convenience stores understand their loyalty program data through simple questions and answers.

The new tool lets staff query customer information in plain language — like asking about top-selling items or tracking reward redemptions — instead of digging through complex reports and analytics.

“PX Assistant brings essential data summaries and loyalty program improvement recommendations to our program in seconds,” Christine Cocce, director of marketing at Legal Seafoods, who tested the system during its beta phase, said in a news release.

The AI assistant analyzes spending patterns and suggests marketing campaigns based on actual customer behavior. It can track daily, weekly and monthly loyalty spending across store locations, helping businesses spot trends in how customers use rewards.

The tool could help level the playing field for smaller operations without dedicated analytics teams by simplifying access to customer insights. The assistant draws from each brand’s transaction data to offer relevant suggestions.

While AI tools are becoming more common in restaurants’ tech stacks, Paytronix aims to make this one particularly accessible by focusing on natural language queries that don’t require technical expertise.

AI Shows It’s Got Better Taste Than Humans in Food Safety Tech

In a surprising twist that could revolutionize food safety testing, AI has proven it has better “taste” than its human creators. Penn State researchers found that when their AI-powered electronic tongue was allowed to make its own decisions about how to analyze foods and beverages, it achieved a remarkable 95% accuracy rate — outperforming the 80% accuracy rate achieved using human-specified parameters.

The breakthrough device, detailed in Nature, combines a graphene-based sensor with neural networks to detect everything from watered-down milk to spoiled juice. However, the real revelation came when researchers let the AI define its assessment criteria instead of using human-designated parameters.

Using game theory, the team gained unprecedented insight into the AI’s decision-making process, revealing that it analyzes data holistically rather than checking individual parameters as humans do. This more nuanced approach could transform food safety testing and medical diagnostics, offering a faster, more accurate alternative to traditional testing methods.

Scientists Use AI to Capture and Recreate Scents Remotely

Scientists at Osmo have developed a system that can analyze and reproduce scents in different locations, combining AI with molecular analysis technology.

The process uses gas chromatography-mass spectrometry (GCMS) to identify molecular components of smells, which are then mapped and reconstructed using AI-guided formulation robots. Initial tests successfully recreated the scent of coconut across laboratory spaces.

“Our ongoing data collection process is an effort to reduce the number of mystery molecules and find new ways to recreate them,” Alex Wiltschko, Osmo founder and CEO, said in a news release. The team has built what they claim is the largest AI-compatible scent database to date.

The system faces significant technical hurdles, particularly with subtle compounds that are difficult to detect but important to overall scent profiles. Sulphur compounds found in tropical fruits have proven especially challenging to capture and reproduce accurately.

The technology combines several AI approaches: basic algorithms process existing scent data, while machine learning models predict formulas from GCMS readings. A “principal odor map” plots scents in multidimensional space to guide recreation. While mostly automated, the process still requires some human oversight.

For molecular analysis, liquid samples are injected directly into the GCMS system, while solid objects like fruits require headspace analysis — capturing airborne molecules around the sample. The data is uploaded to cloud storage, where AI systems interpret the molecular patterns and direct robotic systems to mix appropriate compounds for recreation.

The research team plans to begin limited public testing, though they acknowledge that perfecting the technology requires more development. The work could have implications for various industries, from perfume manufacturing to food science, where precise scent analysis and recreation are valuable tools.