ABSTRACT
The reusability of by-products in the food industry is consistent with sustainable and greener production; therefore, the aim of this paper was to evaluate the applicability of multiple linear regression (MLR), piecewise linear regression (PLR) and artificial neural network models (ANN) to the prediction of grape-skin compost's physicochemical properties (moisture, dry matter, organic matter, ash content, carbon content, nitrogen content, C/N ratio, total colour change of compost samples, pH, conductivity, total dissolved solids and total colour change of compost extract samples) during in-vessel composting based on the initial composting conditions (air-flow rate, moisture content and day of sampling). Based on the coefficient of determination for prediction, the adjusted coefficient of determination for calibration, the root-mean-square error of prediction (RMSEP), the standard error of prediction (SEP), the ratio of prediction to deviation (RPD) and the ratio of the error range (RER), it can be concluded that all developed MLR and PLR models are acceptable for process screening. Furthermore, the ANN model developed for predicting moisture and dry-matter content can be used for quality control (RER >11). The obtained results show the great potential of multivariate modelling for analysis of the physicochemical properties of compost during composting, confirming the high applicability of modelling in greener production processes.
ABSTRACT
The aim of this work was to analyze and compare the adsorption and desorption processes of carbohydrates (glucose as a model molecule), polyphenols (gallic acid as a model molecule), and proteins (bovine serum albumin, BSA as a model molecule) on alginate microcapsules. The adsorption and desorption processes were described by mathematical models (pseudo-first-order, pseudo-second-order, and Weber-Morris intraparticle diffusion model for adsorption, and first-order, Korsmeyer-Peppas, and the Higuchi model for desorption) in order to determine the dominant mechanisms responsible for both processes. By comparing the values of adsorption rate (k2) and initial adsorption rate (h0) based on the pseudo-first-order model, the lowest values were recorded for BSA (k1 = 0.124 ± 0.030 min-1), followed by glucose (k1 = 0.203 ± 0.041 min-1), while the model-obtained values for gallic acid were not considered significant at p < 0.05. For glucose and gallic acid, the limiting step of the adsorption process is the chemical sorption of substances, and the rate of adsorption does not depend on the adsorbate concentration, but depends on the capacity of the hydrogel adsorbent. Based on the desorption rates determined by the Korsmeyer-Peppas model (k), the highest values were recorded for gallic acid (k = 3.66236 ± 0.20776 g beads/mg gallic acid per min), followed by glucose (k = 2.55760 ± 0.16960 g beads/mg glucose per min) and BSA (k = 0.78881 ± 0.11872 g beads/mg BSA per min). The desorption process from alginate hydrogel microcapsules is characterized by the pseudo Fickian diffusion mechanism.
ABSTRACT
Within the various approaches to organic waste handling, composting has been recognized as an acceptable method to valorize organic waste. Composting is an aerobic technique of microbial disruption of organic matter which results with compost as a final product. To guarantee the quality of the compost, key process factors (like the moisture content, temperature, pH, and carbon-to-nitrogen ratio) must be maintained. In order to optimize the process, nine composting trials using grape skins were conducted in the present study under various initial moisture content and air flow rate conditions over the course of 30 days. The processes were monitored through physicochemical variables and microbiological activity. Also, the kinetics of the organic matter degradation and microbial growth were investigated. Although the thermophile phase was only achieved in experiments 3 and 8, the important variables proved the efficiency of all nine composting processes. The organic carbon content and C/N ratio decreased after the 30 days of composting processes and a great color change was noticed too. The values for the germination index for all experiments were above 80%, which means that the final products are non-toxic for plants. Also, the greatest change in organic carbon content in was evident in experiment 3; it decreased from 71.57 to 57.31%. And consequently, the rate of degradation for that experiment was the highest, at 0.0093 1/day. Furthermore, the response surface methodology was used to identify optimal operating conditions for grape skin composting and the obtained conditions were 58.15% for the initial moisture content and 1.0625 L/min for the air flow rate.
ABSTRACT
Plant-based meat analogues are food products made from vegetarian or vegan ingredients that are intended to mimic taste, texture and appearance of meat. They are becoming increasingly popular as people look for more sustainable and healthy protein sources. Furthermore, plant-based foods are marketed as foods with a low carbon footprint and represent a contribution of the consumers and the food industry to a cleaner and a climate-change-free Earth. Production processes of plant-based meat analogues often include technologies such as 3D printing, extrusion or shear cell where the ingredients have to be carefully picked because of their influence on structural and textural properties of the final product, and, in consequence, consumer perception and acceptance of the plant-based product. This review paper gives an extensive overview of meat analogue components, which affect the texture and the structure of the final product, discusses the complex interaction of those ingredients and reflects on numerous studies that have been performed in that area, but also emphasizes the need for future research and optimization of the mixture used in plant-based meat analogue production, as well as for optimization of the production process.