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.

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.

Grape Skin Composting Process to Recycle Food Waste: Kinetics and Optimization.

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.

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.

Diet Quality and Its Association with Lifestyle and Dietary Behaviors among Croatian Students during Two COVID-19 Lockdowns.

The present study aims to assess the diet quality, the relationship between diet quality and lifestyle, and the association of diet quality with body mass index and students' field of study during COVID-19 lockdown periods (spring and winter) in 2020. Datasets were collected via an anonymous online self-reported questionnaire distributed during two time periods using social media. A total of 1939 Croatian students (82.4% women and 17.6% men) completed the questionnaire. Diet quality was assessed using the pro-healthy diet index (pHDI) and non-healthy diet index (nHDI). An increase in diet quality was noted during both lockdown periods but was lower during the winter lockdown. Cooking for oneself was associated with a high level of pHDI, while ordering or buying ready-to-eat food was linked to a low level of pHDI. Additionally, a decrease in screen time and increased physical activity was associated with high pHDI values. Furthermore, during both lockdown periods, students with a BMI above 30 kg/m2 had the highest nHDI values compared to other students. Although positive changes were found during both lockdown periods, they were less pronounced throughout the winter lockdown. Further studies are needed to elucidate the real impact of these changes in the post-COVID period.

Assessment of Nutrient Intake and Diet Quality in Adolescent Dietary Supplement Users vs. Non-Users: The CRO-PALS Longitudinal Study.

Dietary supplement users tend to have a better diet quality and overall prudent lifestyle. The main goals of this research were to report the prevalence and type of dietary supplements among Croatian adolescents and to examine the differences in the diet quality among dietary supplement users vs. non-users at the beginning (15/16 y) and at the end of high school education (18/19 y). This research is based on results of the longitudinal CRO-PALS study in which 607 adolescents participated, who had complete dietary, anthropometric, and physical activity data at the beginning (15/16 y) and at the end of their high school education (18/19 y). The dietary assessment method used was a single multi-pass 24 h recall. Dietary supplement users were divided into two groups for the purposes of statistical analysis-users of vitamin and multivitamin preparations (VMV) and users of mineral and multivitamin preparations (MMV). As they aged, there was an increase in the consumption of dietary supplements, and the most frequently used preparation in both age groups was vitamin C (23.7% of users). Dietary supplement users had a higher intake of non-carbonated sweetened drinks and a lower intake of fruits and vegetables in both genders and both age groups. Fast food intake was higher among dietary supplement girl users and boys who were not dietary supplements users in both age groups. Dietary supplement users had a higher achieved average intake of most micronutrients (values obtained only from food) in both genders and both age groups (with exceptions for certain vitamins and minerals). By observing other parameters for assessing the diet quality in this research, we can conclude that girls who do not use dietary supplements have a better diet quality in both age groups.

Changes in diet quality and its association with students' mental state during two COVID-19 lockdowns in Croatia.

Background: The COVID-19 pandemic has left substantial consequences on the mental health of the student population, primarily through an increase in the symptoms of acute stress and anxiety. Aim: This study evaluated diet quality and its association with mental state among Croatian students during the spring and winter COVID-19 lockdowns in 2020. Methods: Data were collected using an anonymous online self-reported questionnaire taken on two occasions. During the spring lockdown (from May 18 to June 7, 2020) and the winter lockdown (from December 14-22, 2020), 751 and 1188 students completed the questionnaire, respectively. Diet quality was determined using the pro-healthy diet index (pHDI) and non-healthy diet index (nHDI). Results: Although to the greatest extent associated with increased white meat, fish, and legumes intake, an increase in pHDI was also linked to increased consumption of water and sweets and decreased intake of processed meat products and nuts. Increased nHDI was mainly due to increased intake of processed meat products, alcohol, and sweets. Moreover, diet quality was higher during the lockdowns than in the periods before. However, diet quality increased to a lesser extent during the winter lockdown and was associated with a poorer mental state, shown as type D (Distressed) personality. Type D personality positively correlated with nHDI and sweets intake but negatively with pHDI, fruit, and vegetable consumption. Conclusion: These results indicate the need to alleviate further students' diet quality and mental state impairment. Also, further investigation of the association between diet quality and mental state should be provided.

Croatian white grape variety Marastina: First taste of its indigenous mycobiota.

The indigenous vineyard mycobiota contribute both to wine quality and vineyard sanitary status. Wines made from same grape variety but from different geographical locations are appreciated for their diversity. Because no information on indigenous mycobiota of Croatian grapevines is available, the aim of the present study was to start filling this knowledge gap by characterizing the indigenous mycobiota of Marastina variety. The use of metataxonomic approach has enabled the identification of 25 different fungal genera present on Marastina grape berries collected from 11 vineyards located within the Croatian coastal winegrowing region of Dalmatia (Northern Dalmatia, Dalmatian hinterland, Central and Southern Dalmatia). The substantial regional and local scale differences in their distribution were observed. Overall, Aureobasidium was the dominant genus followed by Cladosporium and Metschnikowia. Botrytis and Plenodomus were associated with the vineyards located in Central and Southern Dalmatia, whereas Pichia was associated with Northern Dalmatia vineyards. The largest abundance of Buckleyzyma, Cladosporium, Eremothecium, Fusarium, Papiliotrema, and Rhodotorula was observed in Dalmatian hinterland. Moreover, data suggested that climate conditions and soil type partially influenced the distribution of fungal communities. The local-scale differences emerged also for the physicochemical characteristics of fresh musts. The high malic acid content supported the development of Metschnikowia, and inhibited Fusarium growth, whereas a positive correlation between Erysiphe and pH values was observed. Sporobolomyces and Cystobasidium were negatively associated with high glucose concentration. The revealing of Marastina indigenous mycobiota provided information on the members of fungal community negatively influencing the grapevine sanitary status as well as those which could be employed in disease biocontrol.

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 (GB) 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 (tf = 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.

Assessment of Pumpkin Seed Oil Adulteration Supported by Multivariate Analysis: Comparison of GC-MS, Colourimetry and NIR Spectroscopy Data.

Because of its high market value, pumpkin seed oil is occasionally adulterated by cheaper refined oils, usually sunflower oil. The standard method for detecting its authenticity is based on expensive and laborious determination of the sterol composition. Therefore, the objective of this study was to determine the sterol content and authenticity of retail oils labelled as pumpkin seed oil and also to investigate the potential of near-infrared spectroscopy (NIR) and colourimetry in detecting adulteration. The results show that due to the significant decrease in Δ7-sterols and increase in Δ5-sterols, 48% of the analysed oils can be declared as adulterated blends of pumpkin seed and sunflower oil. Significant differences in NIR spectroscopy data, in the range of 904-922 nm and 1675-1699 nm, and colourimetric data were found between the control pumpkin seed oil and sunflower oil, but only the NIR method had the potential to detect the authenticity of pumpkin seed oil, which was confirmed by principal component analysis. Orthogonal projection on latent structures (OPLS) discriminant analysis, resulted in working classification models that were able to discriminate pure and adulterated oil. OPLS models based on NIR spectra were also able to successfully predict the content of ß-sitosterol and Δ7,22-stigmastadienol in the analysed oils.

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 (R2validation greater than 0.8) confirm that ANNs can be used to monitor the emulsification process.

Properties of Milk Treated with High-Power Ultrasound and Bactofugation.

RESEARCH BACKGROUND: Two methods of milk treatment were used, ultrasound (innovative method) and bactofugation, after which the physicochemical and sensory properties of the milk were examined, with the primary aim of achieving the quality and consistency of the pasteurized milk. EXPERIMENTAL APPROACH: Ultrasound power of 200 and 400 W and frequency of 24 kHz with constant wave cycle were used. Milk was treated for 2.5, 5, 7.5 and 10 min with sonification at 20 °C (room temperature) and thermosonification (ultrasound at temperature higher than room temperature) at 55 °C. The purpose of this study is to investigate the effect of high-power ultrasound combined with a slightly increased temperature on whole, skimmed and skimmed cow's milk pretreated with bactofugation. RESULTS AND CONCLUSIONS: The best sensory quality was achieved when milk was treated with ultrasound power of 200 W at 20 °C for max. 7.5 min. This research shows the potential of the applications of high-power ultrasound in dairy industry combined with bactofugation as a pre-treatment of milk at a slightly increased temperature (up to 55 °C). NOVELTY AND SCIENTIFIC CONTRIBUTION: The application of these two treatments requires milder processing conditions than pasteurization, it is economical and more environmentally friendly technological process that preserves better nutritional values of milk, which is preferred by consumers.

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.

Cooking at Home and Adherence to the Mediterranean Diet During the COVID-19 Confinement: The Experience From the Croatian COVIDiet Study.

Introduction: The primary aims of this study were to evaluate the changes in dietary behavior among the Croatian adult population during the COVID-19 outbreak and to explore the impact of confinement on cooking habits. Methods: The study was based on results from COVIDiet_Int cross-sectional study-a part of COVIDiet project (NCT04449731). A self-administered online questionnaire was used to assess the frequency of food consumption, eating habits, and sociodemographic information. A total number of 4,281 participants (80.5% females and 19.4% males) completed the questionnaire. Results: The Mediterranean Diet Adherence Screener (MEDAS) score before the confinement was 5.02 ± 1.97, while during the confinement, the MEDAS score increased to 5.85 ± 2.04. Participants who had higher adherence to the Mediterranean diet (MedDiet) during the confinement were mostly females (88.8%), aged between 20 and 50 years, with the highest level of education (66.3%) and normal BMI (70.6%). The majority of participants maintained their dietary behavior as it was before COVID-19 confinement, while 36.9% decreased their physical activity. Participants with higher MEDAS score were more eager to increase their physical activity. Additionally, higher median values of MEDAS score were noted for participants with body mass index values below 24.9 kg/m2 (6.0 vs. 5.0 for participants with BMI above 25 kg/m2). Participants in all residence places increased their cooking frequency during the confinement (53.8%), which was associated with an increase in vegetables, legumes, as well as fish and seafood consumption. Conclusions: According to our findings, Croatian adults exhibited medium adherence to the MedDiet during the COVID-19 confinement. The results suggest that cooking frequency could be positively associated with overall dietary quality, which is of utmost importance in these demanding times.

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.

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).

The Influence of High-Power Ultrasound and Bactofugation on Microbiological Quality of Milk.

RESEARCH BACKGROUND: The application of high power ultrasound combined with a slightly increased temperature on raw cow's milk, skimmed cow's milk and skimmed cow's milk that passed the bactofugation process was analysed. We combined ultrasound with bactofugation of milk to achieve the microbiological accuracy that is equivalent to pasteurization. EXPERIMENTAL APPROACH: The milk samples (200 mL) were treated for 2.5, 5, 7.5 and 10 min with high-power ultrasound (200 and 400 W) with a frequency of 24 kHz. The treatments were conducted with a constant duty cycle of 100%. Temperatures during the treatments were 20 and 55 °C. The somatic cell count of the aerobic mesophilic bacteria, as well the number of Enterobacteriaceae, Escherichia coli and Staphylococcus aureus cells were analysed. RESULTS AND CONCLUSIONS: From the perspective of the reduction of the total count of bacteria, the best result was achieved by high-power ultrasound at 400 W treated for 10 min. High reduction of Enterobacteriaceae, E. coli and S. aureus cells was achieved with ultrasound treatment of raw, skimmed and skimmed cow's milk that passed the bactofugation with a power of 200 and 400 W regardless of the treatment time. NOVELTY AND SCIENTIFIC CONTRIBUTION: This work combines bactofugation and high-power ultrasound for the inactivation of microoganisms. This combination was used at a slightly increased temperature (up to 55 °C), which is much more economical than pasteurization, while it preserves the sensory and physicochemical properties of milk.

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.