Development and quality assessment of low-sodium functional chevon patties

PurposeThe study aims to develop low-sodium chevon patties from low-fat emulsion using various salt substitutes (KCl, CaCl2 and mushroom extract) in different combination without affecting the quality of the products.Design/methodology/approachEfficacy of salt substitutes was assessed for the development of low-sodium chevon patties. The developed products were assessed for various physicochemical properties, instrumental texture and color profile, estimation of mineral and sensory attributes.FindingsSignificant difference (p < 0.05) in mineral content was observed among the patties developed with different salt blends. Sodium content was reduced to the level of 38.07% in salt-substituted chevon patties. Calcium content was significantly (p < 0.05) higher in LS2, LS3 and LS4 salt-substituted chevon patties. Hardness, gumminess and chewiness values were significantly (p < 0.05) higher, and the springiness value was significantly (p < 0.05) lower in control chevon patties compared to treatments. Flavor and saltiness were significantly (p < 0.05) lower in LS1, LS3 and LS4 compared to control. However, flavor, juiciness and saltiness as well overall acceptability scores for LS2 were superior and widely accepted among the sensory panelists.Practical implicationsBased on sensory attributes and physicochemical properties, it is concluded that LS2 salt blend may suitably be used as excellent salt replacer to develop low-sodium chevon patties.Originality/valueInnovative findings of the experiment strengthen the current literature available on functional animal food products. Further, it provides one of the important natural alternatives to develop low-sodium meat products with special reference to chevon. Looking to increase attention toward health of the consumer and increase in the risk of cardiovascular diseases, the demand of low-sodium products is very high. Therefore, this may be the excellent choice without affecting the quality and sensory attribute of the products.

Model-based electron density profile estimation and control, applied to ITER

In contemporary magnetic confinement devices, the density distribution is sensed with interferometers and actuated with feedback controlled gas injection and open-loop pellet injection. This is at variance with the density control for ITER and DEMO, that will depend mainly on pellet injection as an actuator in feed-back control. This paper presents recent developments in state estimation and control of the electron density profile for ITER using relevant sensors and actuators. As a first step, Thomson scattering is included in an existing dynamic state observer. Second, model predictive control is developed as a strategy to regulate the density profile while avoiding limits associated with the total density (Greenwald limit) or gradients in the density distribution (e.g. neo-classical impurity transport). Simulations show that high quality density profile estimation can be achieved with Thomson Scattering and that the controller is capable of regulating the distribution as desired.

Gradient Profile Estimation Using Exponential Cubic Spline Smoothing in a Bayesian Framework

Attaining reliable gradient profiles is of utmost relevance for many physical systems. In many situations, the estimation of the gradient is inaccurate due to noise. It is common practice to first estimate the underlying system and then compute the gradient profile by taking the subsequent analytic derivative of the estimated system. The underlying system is often estimated by fitting or smoothing the data using other techniques. Taking the subsequent analytic derivative of an estimated function can be ill-posed. This becomes worse as the noise in the system increases. As a result, the uncertainty generated in the gradient estimate increases. In this paper, a theoretical framework for a method to estimate the gradient profile of discrete noisy data is presented. The method was developed within a Bayesian framework. Comprehensive numerical experiments were conducted on synthetic data at different levels of noise. The accuracy of the proposed method was quantified. Our findings suggest that the proposed gradient profile estimation method outperforms the state-of-the-art methods.