In this study, a coupled model integrating flow, temperature, phase separation, fibre alignment, and wall-slip has been developed to elucidate the complex behaviour observed during high moisture extrusion (HME) fibre formation. By departing from previous high-resolution approaches, the model uses a mean-field simplification to conveniently address wall-slip, thus avoiding the numerical intractability associated with resolving microscopic phases through solving the full Cahn-Hilliard equations. The critical simulation parameters are justified through prior studies and microscopy data and may to a certain extent be quantifiable from dead-stop experiments. The model can capture key qualitative features of HME, including the spatial distribution of fibres in the cooling die and their orientation, as observed in microscopy. Moreover, the model explains a potential delicate interplay between die cooling, phase separation/syneresis and protein melt flow characteristics. The study identifies extensional and pre-cooling die orientation of fibres as promising avenues for future model refinement

Understanding the rheological properties of protein melts is critical in the design of meat analogues and the formation of texturised products from plant-based proteins. This study investigated the influence of temperature, moisture content (MC) and protein concentration on the rheological properties of pea protein isolate and pea fibre blends. The blends were chosen as an experimental space where it is possible to extrude fibrous meat analogues using high-moisture extrusion. Mechanical spectra by small amplitude oscillatory shear were determined using conventional rheometry and were compared to closed cavity rheometry (CCR) to extend the available temperature range. All blends behaved as polymer melts in the rubbery region with moduli increasing with frequency, and storage modulus larger than loss modulus for temperature 40-90°C, MC 54-63%, protein concentration 75-85%. Complex viscosity was strongly shear thinning. The relative influence of the parameters from additive and linear mixed models showed an influence of temperature > MC > concentration. The increase of modulus with concentration was quite weak and not statistically significant. The behaviour of the complex modulus was explained well with an Arrhenius-type log-linear mixed model. Conventional rheometry agreed well with CCR, showing an exponential decrease of moduli between 40 and 130°C. 

The present paper is concerned with the development of a custom pulsed ultrasound velocity profiling (PUV) methodology to non-invasively measure, analyze and control protein melt flow and power-law flow indices in the cooling die during high moisture (HME) extrusion processing. The methodology is first validated on glycerol and a carboxymethyl cellulose (CMC) solution as Newtonian and shear-thinning reference fluids, respectively, at different flow rates followed by application to two pea protein melts at different moisture contents (MCs) in the low flow rate regime typical of a pilot-scale extruder and characterized by a poor signal to noise ratio (SNR) close to the extruder die wall. The flow indices were compared with those obtained from conventional rheometry, showing good agreement for the reference fluids and semi-quantitative agreement for the protein melts. The study confirms that PUV can be used for in-line application in a cooling die by measuring the local flow conditions, as well as contribute to the understanding of protein melt fibre formation. On the other hand, the SNR close to the die wall need to be improved e.g. by using an ultrasound transducer operating at higher frequency and modifying the die to enable quantitative agreement with flow simulations to properly extract local rheometric data. Thus, it is concluded that further refinement of the methodology is both possible and needed to improve the accuracy of the measurements in future work for in-line application during HME extrusion. 

A finite element model of spinodal decomposition in a power-law fluid in the extruder cooling die has been developed to investigate the effects of different parameters on fibre formation and alignment. The model makes use of the Cahn-Hilliard equations with a thermodynamic potential and numerical approximations to simulate local compositions in the separated state. The constitutive model is calibrated towards extrusion-relevant strain rates and temperatures by using a combination of rheometry techniques. The simulations show that the effect of decreased wall cooling has a limited effect on fibre development. Instead, decreasing the die width or increasing the die length can be used somewhat interchangeably to achieve fibre formation at the die exit. Viscosity also seemed to influence fibre formation in the outer viscous regions of the die by yielding comparably finer lamellar structures. The local composition of fibres also varied across the die, which may indicate differences in fibre consistency. 

lant proteins such as soy, pea and wheat gluten are known to form a fibrous structures resembling chicken meat when extruded at elevated temperature with subsequent active cooling. The current hypothesis on the mechanisms responsible for the fibre formation contribute to understanding but are not sufficient to describe the full picture and cannot be used to predict fibre formation ability of protein melts thus hampering the use of more sustainable protein ... interactions between protein chains or polymer crystallites. The aim of the present study to use rheological data of protein melts combined with simulation to elucidate the fibre formation mechanisms and this paper will show an example.

There is growing interest in consumer health related to food, behaviourand lifestyle determinants. At the same time, digitalisation of societies creates newsets of data on consumers. A major driving force in the paradigm shift towards adigital consumer society is the continuous ICT developments that enable a future datasharing economy, where consumers are donating their data, thus making it availablefor food business analysts, marketing researchers and scientists. Business intelligence& analytics (BI&A) has gone through an evolution process and huge amounts ofconsumer data is now collected and analysed. The methodologies for data collectionand analysis are based on the different smart IC technologies that have transformedthe way goods are purchased, extending beyond just transactions. Similarly, from ascientific perspective, it is reasonable to assume that similar ICT solutions may play animportant role in the understanding of public health issues.The study was carried out as part of the RICHFIELDS project aimed at designing afuture European research infrastructure (RI) for innovative research on healthy foodchoices, preparation and consumption of EU-citizens and their respective relationshipswith socio- economic factors. The project, funded by the H2020 program is seeking todevelop state-of- the–art RI that combines knowledge of consumer behaviour and foodintake in one data platform.

This paper presents a new approach combining experimental methodology and modelling, developed to evaluate the effective diffusivity of water in skim milk during drying over a full range of water contents and temperatures. This parameter is important to support modelling of spray-drying processes and designing of equipment. The effective diffusion coefficient is evaluated using a combination of nuclear magnetic resonance (NMR) and parameter estimation. NMR is used to determine the temperature dependence and parameter estimation is used to estimate the water concentration dependence of the effective diffusivity of water in skim milk (0.90 on total weight basis) during drying by comparing the experimental data obtained using a suspended-drop method, which allows the recording of weight and temperature changes during drying, with the results of a distributed heat and mass transport model. The results indicate that the free-volume theory best predicts the dependence of the effective diffusion coefficient of water in skim milk. A mathematical correlation of effective diffusivity over a full range of water contents and temperatures (from 50 to 90 °C) was obtained and experimentally successfully validated for concentrated skim milk (0.70 on total weight basis).

This paper presents a new approach combining experimental methodology and modelling, developed to evaluate the effective diffusivity of water in skim milk during drying over a full range of water contents and temperatures. This parameter is important to support modelling of spray-drying processes and designing of equipment. The effective diffusion coefficient is evaluated using a combination of nuclear magnetic resonance (NMR) and parameter estimation. NMR is used to determine the temperature dependence and parameter estimation is used to estimate the water concentration dependence of the effective diffusivity of water in skim milk (0.90 on total weight basis) during drying by comparing the experimental data obtained using a suspended-drop method, which allows the recording of weight and temperature changes during drying, with the results of a distributed heat and mass transport model. The results indicate that the free-volume theory best predicts the dependence of the effective diffusion coefficient of water in skim milk. A mathematical correlation of effective diffusivity over a full range of water contents and temperatures (from 50 to 90°C) was obtained and experimentally successfully validated for concentrated skim milk (0.70 on total weight basis).