AI, VR, and E-noses are revolutionizing food science

Virtual and augmented reality have emerged as key tools in consumer behavior studies, providing immersive, interactive environments that simulate real-world settings. These systems allow researchers to evaluate how environmental cues, like lighting or ambiance, impact taste perception. For instance, a VR simulation might present a wine tasting in a virtual vineyard or fine-dining restaurant, influencing how people rate flavor intensity or enjoyment.

From digitizing taste to decoding emotion, virtual reality, artificial intelligence, and electronic sensory systems are revolutionizing the global food industry’s approach to understanding taste, smell, and texture. A new review published in Applied Sciences under the title “Innovative Approaches in Sensory Food Science: From Digital Tools to Virtual Reality,” synthesizes recent research to show how multisensory technologies are changing how scientists and companies analyze and design food products.

At a time when product success hinges on rapidly shifting consumer preferences, the integration of smart sensory systems offers food scientists a more objective, efficient, and data-driven means of decoding how people perceive flavor. With AI now central to predicting consumer liking, and with VR and AR reshaping how foods are tested and marketed, the industry is moving away from traditional panels and toward an era of real-time emotional feedback and algorithmic insight.

What technologies are redefining food sensory evaluation?

Virtual and augmented reality have emerged as key tools in consumer behavior studies, providing immersive, interactive environments that simulate real-world settings. These systems allow researchers to evaluate how environmental cues, like lighting or ambiance, impact taste perception. For instance, a VR simulation might present a wine tasting in a virtual vineyard or fine-dining restaurant, influencing how people rate flavor intensity or enjoyment.

The review cites studies showing that VR affects perceived saltiness or sweetness depending on visual congruence, such as matching beverage color with expected flavor. Augmented reality (AR), on the other hand, overlays digital stimuli onto real-world items, helping researchers test how changing a product’s appearance or label in real time affects consumer choice. Though AR cannot replicate full eating experiences, it proves valuable in visualizing serving sizes and nutrient content.

In addition, biometric tools like eye tracking and emotion recognition software (notably FaceReader) are gaining traction. Eye tracking allows scientists to measure how long consumers fixate on packaging features, such as nutrition labels or branding elements. FaceReader goes deeper by interpreting subtle emotional cues, such as surprise or disgust, during taste tests. These tools quantify unconscious responses, offering data far beyond what traditional taste panels can capture.

Together, these immersive and biometric technologies increase the ecological validity of food studies. Rather than relying solely on sterile lab setups, researchers now emulate real-life eating environments, giving them a more accurate picture of how food is experienced in everyday contexts.

How does artificial intelligence enhance sensory prediction and personalization?

Artificial intelligence is playing a pivotal role in refining food product development by making sense of complex sensory data. Machine learning models can now predict how consumers will rate aroma, taste, and texture based on chemical compositions or visual traits. Neural networks, decision trees, support vector machines, and convolutional models are applied across food types - from dry-aged meats and yogurts to wines and juices.

These systems not only streamline research but reduce the reliance on large human panels, which are expensive, time-intensive, and prone to subjective bias. AI compensates by identifying key flavor molecules, mapping texture preferences, and classifying products based on sensory signatures. It can even factor in how sensory preferences change over time, thanks to recurrent models analyzing storage or fermentation conditions.

Crucially, the integration of AI into sensory evaluation extends to personalized nutrition. By analyzing user-specific data, including genetics, health conditions, and historical preferences, AI can tailor diets or product recommendations. This has implications for developing functional foods, plant-based alternatives, and custom-formulated products aligned with lifestyle or health goals.

However, the study stresses that model reliability hinges on the quality and diversity of training datasets. Without rigorous validation, predictions may skew toward bias or misinterpret sensory responses. As such, data preprocessing, noise filtering, and adaptive recalibration mechanisms are essential to ensure trustworthy outcomes.

What roles do electronic sensory systems play in food quality control?

Beyond immersive experiences and predictive analytics, sensory science is being physically reengineered through the deployment of electronic noses, tongues, and eyes, collectively known as “e-sensing” technologies. These systems mimic the human senses but offer higher precision, consistency, and real-time performance.

The electronic nose (E-nose) uses gas sensors to detect volatile compounds in food products. It can discern freshness, identify spoilage, or differentiate aroma profiles in cheeses, wines, or meats. Paired with AI, E-nose systems use neural networks and boosting algorithms like XGBoost to classify aroma fingerprints faster and more reliably than human panels.

Similarly, the electronic tongue (E-tongue) evaluates taste using electrochemical sensors. Its hybrid designs, combining potentiometric and voltammetric detection, enable it to analyze sweetness, bitterness, and even spicy compounds. It’s already being applied to detect milk fermentation, wine maturity, and apple juice authenticity, among others.

The electronic eye (E-eye) captures and analyzes visual properties such as color, brightness, and opacity. Especially useful for assessing ripeness, beverage color consistency, or meat quality, E-eyes use image sensors and color models to ensure uniformity and appeal. Integrated with machine learning, these systems can monitor color degradation, flag product inconsistencies, and predict consumer preferences based on visual cues.

Together, these tools enable fast, objective assessments of product attributes that were once subject to human subjectivity. They allow quality control teams to monitor products throughout processing, storage, and shelf display, reducing reliance on expensive analytical equipment or sensory panels.

Importantly, combining data from E-nose, E-tongue, and E-eye systems, known as multimodal fusion, yields a comprehensive sensory profile. Through pattern recognition and chemometric modeling, this triad of technologies enhances the reliability of food evaluations while minimizing cross-reactivity between similar compounds.