Food has long been recognized as chemically rich, but its complexity extends far beyond molecular diversity alone. A single food matrix may contain thousands of volatile and non-volatile compounds, macromolecules, and trace constituents, many of which interact nonlinearly during processing and consumption. These interactions ultimately give rise to emergent properties such as flavor, texture, nutritional value, and perceived quality. To conceptualize this multifaceted nature, food complexity can be structured into three interconnected layers: chemical complexity, component interaction complexity, and perceptual complexity^[1].

Chemical Complexity of Food

The first layer of food complexity arises from its chemical composition. Foods contain a vast array of small molecules, including nutrients, flavor compounds, additives, and processing-induced products. Although more than tens of thousands of food-related chemicals have been reported, only a small fraction have been systematically annotated with sensory, bioactivity, or safety information^[2]. Minor structural differences, such as stereochemistry or functional group substitutions, can lead to pronounced changes in odor, taste, or biological effects.

Machine learning has emerged as a powerful tool for navigating this high-dimensional chemical space. By learning structure–property relationships directly from molecular representations, AI models can predict sensory attributes, receptor interactions, and functional properties of food-related compounds^[3]. These approaches enable rapid screening of uncharacterized molecules and help prioritize candidates for experimental validation, thereby accelerating discovery within the vast and largely unexplored chemical landscape of foods.

Component Interaction Complexity

Beyond individual molecules, food properties are shaped by interactions among components within complex matrices. Sugars, acids, lipids, proteins, and aroma compounds interact dynamically, with outcomes influenced by concentration, processing conditions, and physical structure^[4][5]. Such interactions often generate emergent behaviors that cannot be inferred from single-component analysis alone. Well-known examples include aroma release modulation by lipids or texture–flavor interactions in emulsions and gels.

Capturing interaction-driven complexity remains challenging due to matrix effects, nonlinear dependencies, and experimental variability. Advances in instrumental analysis—such as gas chromatography–mass spectrometry, liquid chromatography–mass spectrometry, and sensor-based technologies—generate rich datasets that reflect collective molecular behavior. When combined with machine learning and data fusion strategies, these datasets enable more comprehensive modeling of food quality, authenticity, and sensory attributes. Multimodal integration of chemical fingerprints and sensor responses provides a scalable pathway to decode interaction patterns that are difficult to model mechanistically.

Perceptual Complexity and Human Response

The third layer of food complexity emerges from human perception. Flavor perception is inherently multisensory, integrating taste, smell, trigeminal sensations, texture, visual cues, and cognitive factors. The same chemical stimulus can elicit distinct perceptual responses across individuals, shaped by genetics, cultural background, and prior experience. As a result, perceptual outcomes cannot be fully predicted from chemical composition alone.

Neuroinformatics technologies, including electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and functional magnetic resonance imaging (fMRI), provide new avenues for probing perceptual complexity. When coupled with machine learning, these modalities enable the modeling of neural responses associated with flavor perception and preference^[6]. Such approaches offer a quantitative bridge between molecular stimuli and subjective experience, opening opportunities for personalized food design and sensory optimization.

Integrating the Three Layers of Food Complexity

Although each layer of food complexity can be studied independently, their true significance lies in their interconnections. Chemical composition constrains possible interactions, interactions shape sensory-relevant signals, and perception feeds back into food design and consumer choice. Artificial intelligence plays a key role in integrating these layers by enabling multimodal data fusion and cross-scale modeling.

Unified frameworks that combine molecular data, instrumental measurements, and neurophysiological signals can reveal patterns that remain hidden within isolated datasets. These integrative approaches are particularly valuable for addressing challenges such as data sparsity, heterogeneity, and noise, which are common in food research. As datasets expand and modeling techniques mature, AI-driven analysis is expected to deepen scientific understanding of food complexity and support applications ranging from flavor innovation to nutrition and sustainability assessment^[7][8].

Outlook

Food complexity represents both a scientific challenge and an opportunity. By framing food systems across chemical, interaction, and perceptual layers, researchers gain a structured lens for exploring how food properties emerge and how they can be predicted or designed. Continued progress will depend on the development of standardized datasets, interpretable models, and interdisciplinary collaboration. Advances in artificial intelligence are poised to play a central role in this effort, transforming food complexity from an abstract concept into a quantitatively tractable framework with broad implications for food science and industry.

This entry is adapted from: 10.1038/s41538-026-00730-w