Rotated Fourier transform (RFT) enables the quantification of anisotropic structure in high-moisture plant-protein extrudates

When producing plant-protein-based meat analogues via high moisture extrusion (HME), the structure of extrudates is determined by complex interactions between ingredient composition and processing conditions. Insights into the structuring process can be gained by imaging samples using MRI or confocal microscopy. However, existing software for analysing these images provide limited options for quantitatively analysing both structure and anisotropy. Here, we present a novel image processing method, Rotated Fourier Transform (RFT), that enables the quantification of anisotropic structures of extrudates. RFT provides a single, spatial dependent measure of structural anisotropy, namely the weighted order parameter (WOP). RFT uses Fourier transforms to obtain the dominant angles representing the structural orientation detected within the image. By calculating an amplitude per angle as the weighing factor, noise is effectively filtered and improved signal-to-noise ratios can be obtained. In particular, we applied RFT to quantify structural anisotropy of soy protein concentrate HME samples. We employed magnetic resonance imaging (MRI) at micrometer resolution to show that samples prepared at neutral pH feature higher structural anisotropy than samples prepared at acidic pH. Using confocal laser scanning microscopy (CLSM) at sub-micrometer resolution, we imaged samples from the skin to the core region along the cooling die and show that the anisotropy increases towards the skin. We note that RFT is a generic method applicable to any image displaying anisotropic features. Thus, RFT is a powerful tool for the comprehensive quantification of food structures and beyond.