The relationship between ultra-small-angle X-ray scattering and viscosity measurements of casein micelles in skim milk concentrates

Rheology of liquid foods under shear flow conditions: Recently used models

Proper modeling of flow or viscosity curves as a function of shear rate is a useful tool in any engineering activity. The rheology of foods depends on the composition, processing to which they have been subjected and the state of dispersion in which they are found. Liquid foods are complex biosystems, that show non-Newtonian behavior under flow conditions. This review presents models used in recent decades to describe the experimental rheological behavior of various liquid foods, ranging from Newtonian fluids to the most complex. Some non-Newtonian parameters such as those of the Ostwald-de Waele, Bingham, Herschel-Bulkley, Casson, Cross, and Carreau models are summarized. Examples of thixotropic behavior described by the Weltman and Abu-Jdayil models are also presented. In each model, explanations based on the composition and dispersion state of the food are made. This is useful in innovative processing technologies and for scientists new to the field of food rheology. An attempt is made to exemplify and group the expected behavior for most fluid foods, including some for a dysphagia diet, depending on their composition or the dispersed system formed, which will be useful for professionals who wish to compare reported rheological parameters with those obtained experimentally.

Effect of Thermal Pretreatment on the Physiochemical Properties and Stability of Pumpkin Seed Milk

During the production of plant-based milk, thermal treatment of raw materials is an important processing method to improve the physicochemical and nutritional quality of the final products. The objective of this study was to examine the impact of thermal processing on the physiochemical properties and stability of pumpkin seed (Cucurbita pepo L.) milk. Raw pumpkin seeds were roasted at different temperatures (120 °C, 160 °C, and 200 °C), and then processed into milk using a high-pressure homogenizer. The study analyzed the microstructure, viscosity, particle size, physical stability, centrifugal stability, salt concentration, heat treatment, freeze-thaw cycle, and environment stress stability of the resulting pumpkin seed milk (PSM120, PSM160, PSM200). Our results showed that the microstructure of pumpkin seeds was loose and porous, forming a network structure because of roasting. As the roasting temperature increased, the particle size of pumpkin seed milk decreased, with PSM200 showing the smallest at 210.99 nm, while the viscosity and physical stability improved. No stratification was observed for PSM200 within 30 days. The centrifugal precipitation rate decreased, with PSM200 showing the lowest rate at 2.29%. At the same time, roasting enhanced the stability of the pumpkin seed milk in the changes in ion concentration, freeze-thaw, and heating treatment. The results of this study suggested that thermal processing was an important factor in improving the quality of pumpkin seed milk.

A comparison of deep-learning-based inpainting techniques for experimental X-ray scattering

The implementation is proposed of image inpainting techniques for the reconstruction of gaps in experimental X-ray scattering data. The proposed methods use deep learning neural network architectures, such as convolutional autoencoders, tunable U-Nets, partial convolution neural networks and mixed-scale dense networks, to reconstruct the missing information in experimental scattering images. In particular, the recovered pixel intensities are evaluated against their corresponding ground-truth values using the mean absolute error and the correlation coefficient metrics. The results demonstrate that the proposed methods achieve better performance than traditional inpainting algorithms such as biharmonic functions. Overall, tunable U-Net and mixed-scale dense network architectures achieved the best reconstruction performance among all the tested algorithms, with correlation coefficient scores greater than 0.9980.