Super-resolution microscopy and empirically validated autocorrelation image analysis discriminates microstructures of dairy derived gels

Perspectives on the yogurt rheology

The oral processing of yogurt is a dynamic process involving a series of deformation processes. Rheological knowledge is essential to understand the structure and flow properties of yogurt in the mouth and to explore its relationship with sensory perception. Yogurt is rheologically characterized as a non-Newtonian viscoelastic material. The rheological properties of yogurt are affected by many factors, from production to consumption. Therefore, rheological measurements are widely used to predict and control the final quality and structure of yogurts. Recent studies focus on the elucidation of the effects of cultures and processes used in production, as well as the design of different formulations to improve the rheological properties of yogurts. Moreover, the science of tribology, which dominates the surface properties of interacting substances in relative motion to evaluate the structural sensation in the later stages of eating in addition to the rheological properties that give the feeling of structure in the early stages of eating, has also become the focus of recent studies. For a detailed comprehension of the rheological properties of yogurt, this review deals with the factors affecting the rheology of yogurt, analytical methods used to determine rheological properties, microstructural and rheological characterization of yogurt, and tribological evaluations.

Mesoscopic Characterization of the Early Stage of the Glucono-δ-Lactone-Induced Gelation of Milk via Image Analysis Techniques

We provide a method for quantifying the kinetics of gelation in milk acidified with glucono-δ-lactone (GDL) using image analysis techniques, particle image velocimetry (PIV), differential variance analysis (DVA) and differential dynamic microscopy (DDM). The gelation of the milk acidified with GDL occurs through the aggregation and subsequent coagulation of the casein micelles as the pH approaches the isoelectric point of the caseins. The gelation of the acidified milk with GDL is an important step in the production of fermented dairy products. PIV qualitatively monitors the average mobility of fat globules during gelation. The gel point estimated by PIV is in good agreement with that obtained by rheological measurement. DVA and DDM methods reveal the relaxation behavior of fat globules during gelation. These two methods make it possible to calculate microscopic viscosity. We also extracted the mean square displacement (MSD) of the fat globules, without following their movement, using the DDM method. The MSD of fat globules shifts to sub-diffusive behavior as gelation progresses. The fat globules used as probes show the change in matrix viscoelasticity caused by the gelling of the casein micelles. Image analysis and rheology can be used complementarily to study the mesoscale dynamics of the milk gel.

Extending resolution within a single imaging frame

The resolution of fluorescence microscopy images is limited by the physical properties of light. In the last decade, numerous super-resolution microscopy (SRM) approaches have been proposed to deal with such hindrance. Here we present Mean-Shift Super Resolution (MSSR), a new SRM algorithm based on the Mean Shift theory, which extends spatial resolution of single fluorescence images beyond the diffraction limit of light. MSSR works on low and high fluorophore densities, is not limited by the architecture of the optical setup and is applicable to single images as well as temporal series. The theoretical limit of spatial resolution, based on optimized real-world imaging conditions and analysis of temporal image stacks, has been measured to be 40 nm. Furthermore, MSSR has denoising capabilities that outperform other SRM approaches. Along with its wide accessibility, MSSR is a powerful, flexible, and generic tool for multidimensional and live cell imaging applications.

ArGSLab: a tool for analyzing experimental or simulated particle networks

Microscopy and particle-based simulations are both powerful techniques to study aggregated particulate matter such as colloidal gels. The data provided by these techniques often contains information on a wide array of length scales, but structural analysis methods typically focus on the local particle arrangement, even though the data also contains information about the particle network on the mesoscopic length scale. In this paper, we present a MATLAB software package for quantifying mesoscopic network structures in colloidal samples. ArGSLab (Arrested and Gelated Structures Laboratory) extracts a network backbone from the input data, which is in turn transformed into a set of nodes and links for graph theory-based analysis. The routines can process both image stacks from microscopy as well as explicit coordinate data, and thus allows quantitative comparison between simulations and experiments. ArGSLab furthermore enables the accurate analysis of microscopy data where, e.g., an extended point spread function prohibits the resolution of individual particles. We demonstrate the resulting output for example datasets from both microscopy and simulation of colloidal gels, in order to showcase the capability of ArGSLab to quantitatively analyze data from various sources. The freely available software package can be used either with a provided graphical user interface or directly as a MATLAB script.

Latest advances in imaging techniques for characterizing soft, multiphasic food materials

Over the last two decades, the development and production of innovative, customer-tailored food products with enhanced health benefits have seen major advances. However, the manufacture of edible materials with tuned physical and organoleptic properties requires a good knowledge of food microstructure and its relationship to the macroscopic properties of the final food product. Food products are complex materials, often consisting of multiple phases. Furthermore, each phase usually contains a variety of biological macromolecules, such as carbohydrates, proteins and lipids, as well as water droplets and gas bubbles. Micronutrients, such as vitamins and minerals, might also play an important role in determining and engineering food microstructure. Considering this complexity, highly advanced physio-chemical techniques are required for characterizing the microstructure of food systems prior to, during and after processing. Fast, in situ techniques are also essential for industrial applications. Due to the wide variety of instruments and methods, the scope of this paper is focused only on the latest advances of selected food characterization techniques, with emphasis on soft, multi-phasic food materials.