Plant-based cheeses often fail to reproduce the texture of dairy cheese. Closing this gap is slow because the reference methods, texture profile analysis (TPA) and sensory panels, destroy the sample and are poorly suited to screening many formulations in a timely and cost-effective manner. Here we show that non-contact, air-coupled ultrasonic imaging combined with machine learning can characterise cheese texture rapidly and without damaging the sample. Thirteen commercial products (nine dairy, four plant-based; 119 slices, 357 scans) were imaged in through-transmission at 100 kHz to give two-channel amplitude and time-of-flight C-scans, and each slice was then measured by TPA. The C-scans revealed millimetre-scale acoustic domains in ripened dairy cheeses, consistent with their fused-curd microstructure, that were absent from the homogeneous starch and oil matrix of the plant-based products. TPA showed that the texture gap is one of range and failure behaviour rather than average firmness. Mean hardness was similar for the two categories, but dairy products spanned a 7.5-fold range against 1.9-fold for plant-based products, and the plant-based products failed in a brittle, strain-softening manner rather than by ductile yielding. No single ultrasonic parameter tracked texture across products. However, a compact convolutional neural network predicted full true stress-strain loading and unloading curves from the C-scan images of unseen slices across all thirteen products without product-specific calibration. Clustering the predicted curves produced a texture map on which three plant-based slices sat with the Cheddars and one with Emmental, while soft, mozzarella-like textures remained unoccupied. The approach offers a one-minute, non-contact measurement suited to formulation screening and at-line quality control of cheese.

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