Comparison of the Adsorption and Desorption Dynamics of Biological Molecules on Alginate Hydrogel Microcapsules-The Case of Sugars, Polyphenols, and Proteins

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.

Application of Multivariate Regression and Artificial Neural Network Modelling for Prediction of Physicochemical Properties of Grape-Skin Compost

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.

A Predictive Assessment of Ochratoxin A's Effects on Oxidative Stress Parameters and the Fermentation Ability of Yeasts Using Neural Networks

The aim of this paper was to examine the effect of different OTA concentrations on the parameters of oxidative stress (glutathione (GSH) and malondialdehyde (MDA) concentrations) and glucose utilization in ethanol production by wine yeasts. In addition to the above, artificial neural networks (ANN) were used to predict the effects of different OTA concentrations on the fermentation ability of yeasts and oxidative stress parameters. The obtained results indicate a negative influence of OTA (4 µg mL-1) on ethanol production after 12 h. For example, K. marxianus produced 1.320 mg mL-1 of ethanol, while in the control sample 1.603 µg mL-1 of ethanol was detected. However, after 24 h, OTA had no negative effect on ethanol production, since it was higher (7.490 and 3.845 mg mL-1) in comparison to control samples. Even low concentrations of OTA affect GSH concentrations, with the highest being detected after 12 and 24 h (up to 16.54 µM), while MDA concentrations are affected by higher OTA concentrations, with the highest being detected at 24 h (1.19 µM). The obtained results with the use of ANNs showed their potential for quantification purposes based on experimental data, while the results of ANN prediction models have shown to be useful for predictions of what outcomes different concentrations of OTA that were not part of experiment will have on the fermentation capacity and oxidative stress parameters of yeasts.

An Overview of Ingredients Used for Plant-Based Meat Analogue Production and Their Influence on Structural and Textural Properties of the Final Product

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.

Sustainable Isolation of Bioactive Compounds and Proteins from Plant-Based Food (and Byproducts)

Plant-based food produces significantly less greenhouse gases, and due to its wealth of bioactive components and/or plant-based protein, it becomes an alternative in a sustainable food system. However, the processing and production of products from plant sources creates byproducts, which can be waste or a source of useful substances that can be reused. The waste produced during the production and processing of food is essentially nutrient- and energy-rich, and it is recognized as an excellent source of secondary raw materials that could be repurposed in the process of manufacturing and preparing food, or as feed for livestock. This review offers an overview of the sources and techniques of the sustainable isolation of bioactive substances and proteins from various sources that might represent waste in the preparation or production of food of plant origin. The aim is to uncover novel approaches to use waste and byproducts from the process of making food to provide this waste food an additional benefit, not forgetting the expectations of the end user, the consumer. For the successful isolation of bioactive ingredients and proteins from food of plant origin, it is crucial to develop more eco-friendly and efficient extraction techniques with a low CO2 footprint while considering the economic aspects.

Application of Spectroscopy Techniques for Monitoring (Bio)Catalytic Processes in Continuously Operated Microreactor Systems

In the last twenty years, the application of microreactors in chemical and biochemical industrial processes has increased significantly. The use of microreactor systems ensures efficient process intensification due to the excellent heat and mass transfer within the microchannels. Monitoring the concentrations in the microchannels is critical for a better understanding of the physical and chemical processes occurring in micromixers and microreactors. Therefore, there is a growing interest in performing in-line and on-line analyses of chemical and/or biochemical processes. This creates tremendous opportunities for the incorporation of spectroscopic detection techniques into production and processing lines in various industries. In this work, an overview of current applications of ultraviolet–visible, infrared, Raman spectroscopy, NMR, MALDI-TOF-MS, and ESI-MS for monitoring (bio)catalytic processes in continuously operated microreactor systems is presented. The manuscript includes a description of the advantages and disadvantages of the analytical methods listed, with particular emphasis on the chemometric methods used for spectroscopic data analysis.

Emulsification of Rosemary and Oregano Aqueous Extracts and Their In Vitro Bioavailability

Due to their richness in phenolic compounds, Mediterranean plants such as rosemary and oregano are increasingly recommended for consumption for their numerous health benefits. The pH shift and the presence of digestive enzymes significantly reduce the bioavailability of these biochemicals as they pass through the gastrointestinal tract. To prevent this degradation of phenolic compounds, methods such as emulsification of plant aqueous extracts are used. The aim of this study was to investigate the effects of emulsification conditions on the chemical properties (total polyphenolic content and antioxidant activity) of emulsified rosemary and oregano extracts. Response surface methodology was applied to optimize sunflower oil concentration, rotational speed, and emulsifier concentration (commercial pea protein). The emulsions prepared under optimal conditions were then used in bioavailability studies (in vitro digestion). The antioxidant activity of the emulsified rosemary/oregano extracts, measured by the DPPH method, remained largely stable when simulating in vitro digestion. Analysis of antioxidant activity after in vitro simulation of the gastrointestinal system revealed a higher degree of maintenance (up to 76%) for emulsified plant extracts compared to aqueous plant extracts. This article contributes to our understanding of how plant extracts are prepared to preserve their biological activity and their application in the food industry.

Analysis of Hepatic Lipid Metabolism Model: Simulation and Non-Stationary Global Sensitivity Analysis

Lipid metabolism is a complex process and it is extremely helpful to simulate its performance with different models that explain all the biological processes that comprise it, which then enables its better understanding as well as understanding the kinetics of the process itself. Typically, kinetic parameters are obtained from a number of sources under specific experimental conditions, and they are a source of uncertainty. Sensitivity analysis is a useful technique for controlling the uncertainty of model parameters. It evaluates a model's dependence on its input variables. In this work, hepatic lipid metabolism was mathematically simulated and analyzed. Simulations of the model were performed using different initial plasma glucose (G_B) and plasma triacylglyceride (TAG) concentrations according to proposed menus for different meals (breakfast, lunch, snack and dinner). A non-stationary Fourier amplitude sensitivity test (FAST) was applied to analyze the effect of 78 kinetic parameters on 24 metabolite concentrations and 45 reaction rates of the biological part of the hepatic lipid metabolism model at five time points (t_f = 10, 50, 100, 250 and 500 min). This study examined the total influence of input parameter uncertainty on the variance of metabolic model predictions. The majority of the propagated uncertainty is due to the interactions of numerous factors rather than being linear from one parameter to one result. Obtained results showed differences in the model control regarding the different initial concentrations and also the changes in the model control over time. The aforementioned knowledge enables dietitians and physicians, working with patients who need to regulate fat metabolism due to illness and/or excessive body mass, to better understand the problem.

Near-Infrared Spectroscopy Coupled with Chemometrics and Artificial Neural Network Modeling for Prediction of Emulsion Droplet Diameters

There is increased interest in the food industry for emulsions as delivery systems to preserve the stability of sensitive biocompounds with the aim of improving their bioavailability, solubility, and stability; maintaining their texture; and controlling their release. Emulsification in continuously operated microscale devices enables the production of emulsions of controllable droplet sizes and reduces the amount of emulsifier and time consumption, while NIR, as a nondestructive, noninvasive, fast, and efficient technique, represents an interesting aspect for emulsion investigation. The aim of this work was to predict the average Feret droplet diameter of oil-in-water and oil-in-aqueous mint extract emulsions prepared in a continuously operated microfluidic device with different emulsifiers (PEG 1500, PEG 6000, and PEG 20,000) based on the combination of near-infrared (NIR) spectra with chemometrics (principal component analysis (PCA) and partial least-squares (PLS) regression) and artificial neural network (ANN) modeling. PCA score plots for average preprocessed NIR spectra show the specific grouping of the samples into three groups according to the emulsifier used, while the PCA analysis of the emulsion samples with different emulsifiers showed the specific grouping of the samples based on the amount of emulsifier used. The developed PLS models had higher R² values for oil-in-water emulsions, ranging from 0.6863 to 0.9692 for calibration, 0.5617 to 0.8740 for validation, and 0.4618 to 0.8692 for prediction, than oil-in-aqueous mint extract emulsions, with R² values that were in range of 0.8109-0.8934 for calibration, 0.5017-0.6620, for validation and 0.5587-0.7234 for prediction. Better results were obtained for the developed nonlinear ANN models, which showed R² values in the range of 0.9428-0.9917 for training, 0.8515-0.9294 for testing, and 0.7377-0.8533 for the validation of oil-in-water emulsions, while for oil-in-aqueous mint extract emulsions R² values were higher, in the range of 0.9516-0.9996 for training, 0.9311-0.9994 for testing, and 0.8113-0.9995 for validation.

Development of NIR-Based ANN Models for On-Line Monitoring of Glycerol Concentration during Biodiesel Production in a Microreactor

During the production process, a whole range of analytical methods must be developed to monitor the quality of production and the desired product(s). Most of those methods belong to the group of off-line monitoring methods and are usually recognized as costly and long-term. In contrast, on-line monitoring methods are fast, reliable, simple, and repeatable. The main objective of this study was to compare different methods for monitoring total glycerol concentration as one of the indicators of process efficiency during biodiesel production in a batch reactor and in a microreactor. During the biodiesel production process, the glycerol concentration was measured off-line using standard methods based on UV-VIS spectrophotometry and gas chromatography. Neither method provided satisfactory results, namely, both analyses showed significant deviations from the theoretical value of glycerol concentration. Therefore, near infrared spectroscopy (NIR) analysis was performed as an alternative analytical method. The analysis using NIR spectroscopy was performed in two ways: off-line, using a sample collected during the transesterification process, and on-line by the continuous measurement of glycerol concentration in a rector. Obtained results showed a great NIR application potential not only for off-line but also for on-line monitoring of the biodiesel production process.

Application of NIRs coupled with PLS and ANN modelling to predict average droplet size in oil-in-water emulsions prepared with different microfluidic devices

In this study, the potential of microfluidic systems with different microchannel geometries (microchannel with teardrop micromixers and microchannel with swirl micromixers) for the preparation of oil-in-water (O/W) emulsions using two different emulsifiers (2 % and 4 % Tween 20 and 2% and 4 % PEG 2000) at total flow rates of 20-280 μL/min was investigated. The results showed that droplets with a smaller average Feret diameter were obtained when a microfluidic device with tear drop micromixers was used. To predict the average Feret diameter of O/W emulsion droplets, near-infrared (NIR) spectra of all prepared emulsions were collected and coupled with partial least squares (PLS) regression and artificial neural network modelling (ANN). The results showed that PLS models based on NIR spectra can ensure acceptable qualitative prediction, while highly non-linear ANN models are more suitable for predicting the average Feret diameter of O/W droplets. High R² values (R²_validation greater than 0.8) confirm that ANNs can be used to monitor the emulsification process.

Evaluation of the Adsorption and Desorption Dynamics of Beet Juice Red Dye on Alginate Microbeads

The use of alginate microcapsules has often been mentioned as one of the ways to remove dyes from waste solvents, water and materials from the food industry. In addition, alginate can be used as a wall material for the microencapsulation of food dyes and their further application in the food industry. The aims of this study were to: (i) determine the effect of the alginate concentration (1, 2, 3 and 4%) on the ability of the adsorption and desorption of natural beetroot red dye and (ii) evaluate the kinetic parameters of the adsorption and desorption process, as well as the factors affecting and limiting those processes. According to the obtained results, the viscosity of alginate solutions increased with an increase in the alginate concentration. Based on k₂ values (the pseudo-second order kinetic rate constant), when a more concentrated solution of alginate was used in the adsorption process, the beads adsorbed a smaller amount of dye. Furthermore, based on the values for n derived from the Korsmeyer-Peppas model, the dye release rates (k) were higher for beads made with lower alginate concentrations, and this release was governed by a pseudo-Fickian diffusion mechanism (n values ranged from 0.2709 to 0.3053).

Rapid quantification of dissolved solids and bioactives in dried root vegetable extracts using near infrared spectroscopy

Artificial neural networks (ANN) were developed for prediction of total dissolved solids, polyphenol content and antioxidant capacity of root vegetables (celery, fennel, carrot, yellow carrot, purple carrot and parsley) extracts prepared from the (i) fresh vegetables, (ii) vegetables dried conventionally at 50 °C and 70 °C, and (iii) the lyophilised vegetables. Two types of solvents were used: organic solvents (acetone mixtures and methanol mixtures) and water. Near-infrared (NIR) spectra were recorded for all samples. Principal Component Analysis (PCA) of the pre-treated spectra using Savitzky-Golay smoothing showed specific grouping of samples in two clusters (1st: extracts prepared using methanol mixtures and water as the solvents; 2nd: extracts prepared using acetone mixtures as the solvents) for all four types of extracts. Furthermore, obtained results showed that the developed ANN models can reliably be used for prediction of total dissolved solids, polyphenol content and antioxidant capacity of dried root vegetable extracts in relation to the recorded NIR spectra.

Use of near-infrared spectroscopy on predicting wastewater constituents to facilitate the operation of a membrane bioreactor

The use of near-infrared (NIR) spectroscopy in wastewater treatment has continuously expanded. As an alternative to conventional analytical methods for monitoring constituents in wastewater treatment processes, the use of NIR spectroscopy is considered to be cost-effective and less time-consuming. NIR spectroscopy does not distort the measured sample in any way as no prior treatment is required, making it a waste-free technique. On the negative side, one has to be very well versed with chemometric techniques to interpret the results. In this study, filtered and centrifuged wastewater and sludge samples from a lab-scale membrane bioreactor (MBR) were analysed. Two analytical methods (conventional and NIR spectroscopy) were used to determine and compare major wastewater constituents. Particular attention was paid to soluble microbial products (SMPs) and extracellular polymeric substances (EPSs) known to promote membrane fouling. The parameters measured by NIR spectroscopy were analysed and processed with partial least squares regression (PLSR) and artificial neural networks (ANN) models to assess whether the evaluated wastewater constituents can be monitored by NIR spectroscopy. Very good results were obtained with PLSR models, except for the determination of SMP, making the model qualitative rather than quantitative for their monitoring. ANN showed better performance in terms of correlation of NIR spectra with all measured parameters, resulting in correlation coefficients higher than 0.97 for training, testing, and validation in most cases. Based on the results of this research, the combination of NIR spectra and chemometric modelling offers advantages over conventional analytical methods.

Development of ANN models based on combined UV-vis-NIR spectra for rapid quantification of physical and chemical properties of industrial hemp extracts

OBJECTIVES

The aim of this study was to develop artificial neural network (ANNs) models for prediction of physical (total dissolved solids, extraction yield) and chemical (total polyphenolic content, antioxidant activity) properties of industrial hemp extracts, prepared by two different extraction methods (solid-liquid extraction and microwave-assisted extraction) based on combined UV-VIS-NIR spectra. Spectral data were gathered for 46 samples per extraction method.

RESULTS

The PCA analysis ensured efficient separation of the samples based on the amount of ethanol in extraction solvent using NIR spectra for both conventional and microwave-assisted extraction.

CONCLUSIONS

Results showed that reliable ANN models (R² >0.7000) for describing physical, chemical, and simultaneously physical and chemical characteristics can be developed based on combined UV-VIS-NIR spectra of industrial hemp extracts without spectra pre-processing.

In-vitro digestion of the bioactives originating from the Lamiaceae family herbal teas: A kinetic and PLS modeling study

The stability of lavender, lemon balm, mint, sage, and thyme teas was investigated using in-vitro simulation of the digestive system. Kinetics of changes in the total polyphenolic content (TPC) and the antioxidant activity during the in-vitro trials were also evaluated. Results showed that TPC of mint teas decreases the fastest. Mathematical models for prediction of the TPC and the antioxidant activity of prepared teas based on UV-VIS and NIR spectra collected before, during, and after simulation showed that the best prediction was obtained for the wavelength ranges from 410 to 900 nm, 904 to 928 nm, and 1,399 to 1699 nm. It was concluded that the NIR can be used for calibration, validation, and classification of teas prepared from Lamiaceae plants. PRACTICAL APPLICATIONS: The bioactives' in-vitro digestion process can successfully be characterized by chemical, spectroscopic, and mathematical analysis. Application of NIR spectroscopy, in combination with multivariate analysis, leads to a reduction of time, costs, and chemical consumption and gives reliable results that pharmaceutical, food, and chemical industries can benefit from.

Effects of high power ultrasound treatments on the phenolic, chromatic and aroma composition of young and aged red wine

In this study, the effects of both ultrasonic bath and probe treatments on the phenolic, chromatic and aroma composition of young red wine Cabernet Sauvignon were studied and modeled by artificial neural networks (ANNs). Moreover, the effect of high power ultrasound (HPU) along with antioxidants addition (sulfur dioxide and glutathione) was investigated during 6 months of aging in bottles. Lower amplitude and temperature, shorter treatment duration and particularly lower frequency showed a more favorable and milder effect on the chemical composition of wine. In the case of the ultrasonic probe treatment, similar effect was achieved primarily by a larger probe diameter as well as lower amplitude and treatment duration. Selected ANN models showed the best predictions for the chromatic characteristics followed by total phenolics and anthocyanins. The changes induced by HPU treatment after 6 months of aging were mainly detected in the composition of phenolic compounds (both total and individual), where higher concentration of antioxidants (sulfur dioxide and glutathione) slowed down the decrease rate of these compounds during aging. However, HPU treatment did not influence most of the chromatic characteristics and aroma compounds, except lightness and fatty acids. The obtained results indicated that suitable ultrasound treatment might accelerate some aging reactions and shorten the period of wine aging.

Applicability of Foam Mat Drying Process for Production of Instant Cocoa Powder Enriched with Lavender Extract§

The foam mat drying process is a simple and economical alternative to spray and freeze drying. It is considerably cheaper due to rapid drying at low temperatures, preservation of nutritional quality and easy reconstitution. Basic principle of the process is conversion of a liquid or a semi-liquid foodstuff to foam and thin layer drying of the foam. This study explores the possibility of the foam mat drying for production of instant cocoa powder enriched with lavender (Lavandula × hybrida L.) extracts. The aqueous extraction of lavender was optimized using Taguchi orthogonal array design. Extracts obtained under optimal conditions were added to a mixture of egg white, cocoa powder, sugar and gelatine. Mixtures were blended for 4 min to obtain stable foam which was dried at three different temperatures (t=50, 60 and 70 °C) and milled into a powdered product. Drying rates were obtained from the experimental data using nonlinear model estimation. Flow properties, bulk density, particle size distribution, reconstitution and sensory properties of the final product were also assessed. Based on the obtained data, the drying process was best described by Page's drying model. Samples dried at lower temperature (t=50 °C) exhibited the best powder flow and reconstitution properties. Sensory analysis resulted in similar findings: powder samples dried at lower temperatures had better appearance, colour and taste. The obtained data confirm the suitability of the foam mat drying for the production of instant cocoa powder enriched with lavender extract.