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This study aimed to provide novel information on the impact of indigenous non-Saccharomyces yeasts, including Metschnikowia chrysoperlae, Metschnikowia sinensis/shanxiensis, Metschnikowia pulcherrima, Lachancea thermotolerans, Hanseniaspora uvarum, Hanseniaspora guilliermondii, and Pichia kluyveri, on metabolites related to the metabolism of tryptophan, phenylalanine, and tyrosine. The experiment included two fermentation practices: monoculture and sequential fermentation with commercial Saccharomyces cerevisiae, using sterile Maraština grape juice. A targeted approach through ultrahigh-resolution liquid chromatography associated with mass spectrometry was used to quantify 38 metabolites. All the indigenous yeasts demonstrated better consumption of tryptophan in monoculture than in interaction with S. cerevisiae. M. sinensis/shanxiensis was the only producer of indole-3-carboxylic acid, while its ethyl ester was detected in monoculture fermentation with H. guilliermondii. H. guilliermondii consumed the most phenylalanine among the other isolates. 5-hydroxy-L-tryptophan was detected in fermentations with M. pulcherrima and M. sinensis/shanxiensis. M. pulcherrima significantly increased tryptophol content and utilised tyrosine in monoculture fermentations. Sequential fermentation with M. sinensis/shanxiensis and S. cerevisiae produced higher amounts of N-acetyl derivatives of tryptophan and phenylalanine, while H. guilliermondii-S. cerevisiae fermentation resulted in wines with the highest concentrations of L-kynurenine and 3-hydroxyanthranilic acid. P. kluyveri produced the highest concentration of N-acetyl-L-tyrosine in monoculture fermentations. These findings highlight the different yeast metabolic pathways.

The surging popularity of plant-based diets and the growing emphasis on clean-label products have intensified interest in Spirulina within the food industry. As more people adopt vegetarian, vegan, or flexitarian lifestyles, demand for plant-based protein sources has escalated. Spirulina’s high protein content and complete amino acid profile make it an ideal candidate to meet this demand. However, incorporating Spirulina into food products is not without its challenges. Its strong, earthy, or fishy taste can be off-putting to consumers and difficult to mask in food formulations. Furthermore, isolating Spirulina’s bioactive compounds while preserving their integrity is complex, especially considering the heat sensitivity of many of these components. Traditional extraction methods often employ high temperatures, which can degrade these valuable compounds. Consequently, there is a growing preference for non-thermal extraction techniques. This paper provides an overview of recent advancements in Spirulina cultivation, bioactive extraction, and their application in food products.

Food adulteration which is economically motivated (i.e., food fraud) is an incentive for the development and application of new and fast detection methods/instruments. An example of a fast method that is extremely environmentally friendly is near-infrared spectroscopy (NIRS). Therefore, the goal of this research was to examine the potential of its application in monitoring the adulteration of blended sunflower/olive oils and to compare two types of NIRS instruments, one of which is a portable micro-device, which could be used to assess the purity of olive oil anywhere and would be extremely useful to inspection services. Both NIR devices (benchtop and portable) enable absorbance monitoring in the wavelength range from 900 to 1700 nm. Extra virgin oils (EVOOs) and “ordinary” olive oils (OOs) from large and small producers were investigated, which were diluted with sunflower oil in proportions of 1–15%. However, with the appearance of different salad oils that have a defined share of EVOO stated on the label (usually 10%), the possibilities of the recognition and manipulation in these proportions were tested; therefore, EVOO was also added to sunflower oil in proportions of 1–15%. The composition of fatty acids, color parameters, and total dissolved substances and conductivity for pure and “adulterated” oils were monitored. Standard tools of multivariate analysis were applied, such as (i) analysis of main components for the qualitative classification of oil and (ii) partial regression using the least square method for quantitative prediction of the proportion of impurities and fatty acids. Qualitative models proved successful in classifying (100%) the investigated oils, regardless of whether the added thinner was olive or sunflower oil. Developed quantitative models relating measured parameters with the NIR scans, resulted in values of R2 ≥ 0.95 and was reliable (RPD > 8) for fatty acid composition prediction and for predicting the percentage of the added share of impurity oils, while color attributes were less successfully predicted with the portable NIR device (RPD in the range of 2–4.2). Although with the portable device, the prediction potentials remained at a qualitative level (e.g., color parameters), it is important to emphasize that both devices were tested not only with EVOO but also with OO and regardless of whether proportions of 1–15% sunflower oil were added to EVOO and OO or EVOO and OO in the same proportions to sunflower oil.

In recent years, the increasing need for energy conservation and environmental protection has driven industries to explore more efficient and sustainable processes. Liquid–liquid extraction (LLE) is a common method used in various sectors for separating components of liquid mixtures. However, the traditional use of toxic solvents poses significant health and environmental risks, prompting the shift toward green solvents. This review deals with the principles, applications, and advantages of aqueous two-phase systems (ATPS) as an alternative to conventional LLE. ATPS, which typically utilize water and nontoxic components, offer significant benefits such as high purity and single-step biomolecule extraction. This paper explores the thermodynamic principles of ATPS, factors influencing enzyme partitioning, and recent advancements in the field. Specific emphasis is placed on the use of ATPS for enzyme extraction, showcasing its potential in improving yields and purity while minimizing environmental impact. The review also highlights the role of ionic liquids and deep eutectic solvents in enhancing the efficiency of ATPS, making them viable for industrial applications. The discussion extends to the challenges of integrating ATPS into biotransformation processes, including enzyme stability and process optimization. Through comprehensive analysis, this paper aims to provide insights into the future prospects of ATPS in sustainable industrial practices and biotechnological applications.

Background: Childhood obesity is a global public health concern. Development of effective public health interventions represents the only viable option for decreasing the prevalence of childhood obesity. The objective of this study was to assess the effectiveness of a school-based “PETICA—Play for Health” obesity prevention program in urban and rural areas in Croatia. Methods: This before-and-after study included 28 elementary schools in Croatia focused on pupils (n = 753, 2nd grade) and their guardians (n = 753) during the school year 2022/2023. “PETICA” multicomponent lessons and workshops (10) were implemented in the school curriculum and home settings (parents) as part of the intervention. Knowledge and lifestyle habit changes were evaluated via the questionnaire on knowledge and habits regarding nutrition and physical activity pre-/post-education. The Wilcoxon test for paired samples, Student’s t-test (dependent and independent samples), multiple Mann Whitney U tests (p < 0.05), Spearman’s correlation, and heatmaps were used. Results: The analysis showed a significant increase in acquired knowledge among pupils (both settings) and habits in the rural setting (increase in sports activities participation, breakfast, and vegetables consumption (p < 0.001, p = 0.003, p = 0.004)), decrease of sweets and salty snacks intake (p = 0.027, p = 0.011), and also, the link between parents’ and children’s physical activity levels (p = 0.028 vs. p = 0.022), emphasizing the importance of parents as healthy lifestyle role-models. Conclusion: The study shows that “PETICA” is an efficient school-based educational model that contributes to positive changes in children’s knowledge and lifestyle habits that are a prerequisite for childhood obesity prevention.

Spirulina platensis microalga has become recognized as a promising source of highly nutritious food components to feed the growing global population. Because of its high protein content, abundance of essential amino acids, and excellent digestion, it is employed in human nutrition and there is growing interest in analyzing bioactive compound present in Spirulina platensis microalga. In this work, a portable near-infrared (NIR) spectrometer was used for the monitoring of physicochemical properties (extraction yield (EY), total polyphenols concentration (TPC), total proteins concentration (TP), antioxidant activity measured by (i) the DPPH method (DPPH) and (ii) FRAP method (FRAP)) of Spirulina platensis aqueous extracts. The ultrasound-assisted aqueous extraction (ultrasonic bath with an ultrasound frequency of 35 kHz) of bioactive molecules from Spirulina platensis was performed throughout 30 independent experiments. NIR spectra were recorded in the wavelength range of 900–1700 nm. Raw NIR spectra were subjected to the physicochemical properties applying Principal Component Analysis (PCA), partial least square (PLS), and artificial neural network (ANN) modeling. Results show that ANN models developed for the prediction of TPC and DPPH can be utilized for extraction process control (RER > 10), while the other three models can be employed for screening (RER > 4). Generally, the obtained results indicate significant potential for using portable NIR spectroscopy for the analysis of Spirulina platensis aqueous extracts.

Theobroma cacao L. seeds, commonly known as cocoa beans, are the foundation for cocoa and chocolate production. Following harvest, these beans undergo a multi-step processing chain including fermentation, drying, roasting, and grinding. This process yields cocoa solids, cocoa butter, and cocoa powder—all fundamental ingredients in the food and beverage industry. Beyond its sensory appeal (flavor, aroma, and texture), cocoa has garnered significant interest for its potential health benefits attributed to a rich profile of bioactive compounds. Cocoa is a well-documented source of polyphenolics, specifically flavanols, alongside methylxanthines, phytosterols, and dietary fibers. These constituents have been associated with a diverse range of bioactivities, including antioxidant, anti-carcinogenic, anti-diabetic, anti-inflammatory, anti-obesity, and anti-allergenic properties, potentially contributing to overall health maintenance. Efficient extraction techniques are crucial for maximizing the recovery of these valuable bioactive components from cocoa plant material. Modern methods are continuously being explored to optimize this process. This review focuses on the established health benefits associated with the bioactive compounds present in cocoa. Additionally, it will explore and discuss contemporary approaches for the extraction of these bioactive compounds from this plant source.

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.

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.

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.

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.

Obesity stands out as an ongoing pandemic today, and it is crucial to recognize the basic factors that influence it in the observed group and to intervene through lifestyle changes. Therefore, in this work, the k ketogenic diet (E = 6280 ± 210 kJ) was used in a weight loss program for two regionally different groups (including 200 participants) from southeastern European countries (Republic of North Macedonia (n = 100) and Kosovo (n = 100)). The applied data analysis revealed similarities and differences in (ii) the consumption of certain food groups (e.g., 0.5–1 kg Nuts/week; in region 1 is consumed by 11.3% of participants while in region 2 by 37.8%, respectively) and (ii) anthropometric indicators of excess body mass (body mass index and waist-to-hip ratio). Nutritional intervention with a ketogenic diet also reduces the intake of sweet and salty snacks that are rich in carbohydrates. The average expected time to reach the target body mass was 112 days, and the results of the progress of all participants were presented after 120 days. The results show regional differences, especially in women; in group 1, 73.91% achieved a body mass index in the healthy range (2), while in group 2, the success rate was 81.69%. Understanding the different eating habits in the mentioned regions is key here, and it was shown that in region 2, over 40% of the participants consume 500–1000 g of seeds per week. The above indicates that the results of this study and regional differences can be considered when designing strategies and intervention programs in the lifestyle of overweight and obese people in similar environments. The study also shows that the ketogenic diet is one of the useful dietary intervention approaches used to change eating habits that will show results relatively quickly.

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.

In this work, the solid–liquid extraction of bioactive molecules from grape skin was performed using water as the extraction solvent. The effects of extraction time (t = 60, 75, and 90 min), extraction temperature (T = 40, 60, and 80 °C), solid–liquid phase ratio (S/L = 10, 20, and 30 g/L), and mixing speed (rpm = 250, 500, and 750 1/min) on the total dissolved solids, extraction yield, concentration of total polyphenols, and antioxidant activity were determined using the 1,1-diphenyl-2-picrylhydrazyl (DPPH) and ferric reducing antioxidant power (FRAP) methods. According to response surface modeling, the optimal extraction conditions were t = 75 min, T = 80 °C, S/L = 30 g/L, and rpm = 750 1/min, and under optimal process conditions, 8.38 mgGAE/gd.m. was obtained. Furthermore, the potential of near-infrared (NIR) spectroscopy coupled with artificial neural network (ANN) modeling for prediction of the physical and chemical properties of prepared extracts was also analyzed. The use of ANN modeling demonstrated highly favorable correlations between the NIR spectra and all the variables tested, particularly the total dissolved solids (TDS) and antioxidant activity measured using the FRAP method. As a result, ANN modeling proved to be a valuable tool for predicting the concentration of total polyphenols, the antioxidant activity, and the extraction yield of a plant extract based on its NIR spectra.

The use of mathematical modeling and optimization in nutrition with the help of artificial intelligence is indeed a trendy and promising approach to data processing. With the ever-increasing amount of data being generated in the field of nutrition, it has become necessary to develop new tools and techniques to help process and analyze these data. The paper presents a study on the development of a neural-networks-based model to investigate parameters related to obesity and predict participants’ health outcomes. Improvement techniques of model performances are made (classification performance by reducing overfitting, capturing non-linear relationships, and optimizing the learning process). Predictions are also made with the random forest model to compare the performance of accuracy and prediction scores of two different models. The dataset contains data relating to the obesity of 200 participants in a weight loss program. Information is collected on their basic anthropometric data, as well as biochemical data, which are significant parameters closely related to obesity. It is important to note that weight loss is not always linear and can vary based on individual factors; so, a prediction is made on supervised learning based on patient data (before the diet regime, during the regime, and reaching the desired weight). The dataset is trained on individuals features such as age; gender; body mass index; and biochemical attributes such as MCHC (Mean Corpuscular Hemoglobin Concentration), cholesterol, glucose, platelets, leukocytes, ALT (alanine aminotransferase), triglycerides, TSH (thyroid stimulating hormone), and magnesium. The results of the developed neural network model show high accuracy, low loss in training, high-precision predictions during evaluation of the model, and improved performance over other machine learning models. Calculations are conducted in Anaconda/Python. Overall, the combination of mathematical modeling, optimization, and AI offers a powerful set of tools for analyzing and processing nutrition data. As our understanding of the relationship between diet and health continues to evolve, these techniques will become increasingly important for developing personalized dietary recommendations and optimizing population-level dietary guidelines.

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.

The implementation of a suitable and ecologically friendly solid waste management plan is accepted as an essential need. Given that organic matter constitutes the majority of solid waste, composting has gained popularity as an alternative way of organic refuse recycling. Compost quality is defined by its stability and maturity, both of which must be assessed by measuring a large number of physical–chemical parameters, microbiological variables, and enzymatic activities. These procedures are complex and time-consuming, making it difficult to assess compost quality correctly. Spectroscopy methods could be used as an efficient alternative. In this work, general information about composting processes and near-infrared spectroscopy (NIRS) is given. A discussion and comparison of the different approaches of coupling NIRS and chemometric tools for the monitoring and/or control of composting processes are presented in this work.

The potential of applying Artificial Neural Network (ANN) models based on near-infrared (NIR) spectra for the characterization of physical and chemical features of oil-in-aqueous oregano/rosemary extract emulsions was explored in this work. Emulsions were prepared using a batch emulsification process, with pea protein as the emulsifier. NIR spectral data were connected to the results of the analysis of physical and chemical properties of the emulsions (zeta potential, Feret droplet diameter, total polyphenolic content, and antioxidant capacity) with the final aim of quantitative prediction of the physical and chemical features. For that purpose, robust non-linear multivariate analysis (Artificial Neural Network modeling) was applied. The spectra themselves were preprocessed using several approaches (raw spectra, Savitzky–Golay smoothing, standard normal variate, and multiplicative scatter corrections) after which the impact of NIR spectral preprocessing on the ANN model’s efficiency was evaluated. The results show that NIR spectroscopy integrated with ANN computation can be employed to quantitatively predict the physical and chemical properties of oil-in-plant extract emulsions (R2 > 0.9).

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.

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.

Honey adulteration with cheap sweeteners such as corn syrup or invert syrup results in honey of lesser quality that can harm the objectives of both manufacturers and consumers. Therefore, there is a growing interest for the development of a fast and simple method for adulteration detection. In this work, near-infrared spectroscopy (NIR) was used for the detection of honey adulteration and changes in the physical and chemical properties of the prepared adulterations. Fifteen (15) acacia honey samples were adulterated with glucose syrup in a range from 10% to 90%. Raw and pre-processed NIR spectra of pure honey samples and prepared adulterations were subjected to Principal Component Analysis (PCA), Partial Least Squares (PLS) regression, and Artificial Neural Network (ANN) modeling. The results showed that PCA ensures distinct grouping of samples in pure honey samples, honey adulterations, and pure adulteration using NIR spectra after the Multiplicative Scatter Correction (MSC) method. Furthermore, PLS models developed for the prediction of the added adulterant amount, moisture content, and conductivity can be considered sufficient for screening based on RPD and RER values (1.7401 < RPD < 2.7601; 7.7128 < RER < 8.7157) (RPD of 2.7601; RER of 8.7157) and can be moderately used in practice. The R2validation of the developed ANN models was greater than 0.86 for all outputs examined. Based on the obtained results, it can be concluded that NIR coupled with ANN modeling can be considered an efficient tool for honey adulteration quantification.

This study investigates the colour and standard chemical composition of must and wines produced from the grapes from Vitis vinifera L., ‘Maraština’, harvested from 10 vineyards located in two different viticultural subregions of the Adriatic region of Croatia: Northern Dalmatia and Central and Southern Dalmatia. The aim was to explore the use of NIR spectroscopy combined with chemometrics to determine the characteristics of Maraština wines and to develop calibration models relating NIR spectra and physicochemical/colour data. Differences in the colour parameters (L*, a*, hue) of wines related to the subregions were confirmed. Colour difference (ΔE) of must vs. wine significantly differed for the samples from the Maraština grapes grown in both subregions. Principal component regression was used to construct the calibration models based on NIR spectra and standard physicochemical and colour data showing high prediction ability of the 13 studied parameters of must and/or wine (average R2 of 0.98 and RPD value of 6.8). Principal component analysis revealed qualitative differences of must and wines produced from the same grape variety but grown in different subregions.

Olive leaves are a rich source of polyphenols that have beneficial antihypertensive, hypocholesterolemic, cardioprotective, and anti-inflammatory effects. The aim of this study was to compare the efficiency of conventional extraction (CE), microwave-assisted extraction (MWE), and microwave–ultrasound-assisted extraction (MWUE) for the extraction of bioactive molecules from olive leaves using water as a solvent and to define the optimal extraction conditions for all three methods used. CE conditions (temperature, time, magnetic stirrer rotational rate and particle diameter) and MWE extraction and MWUE conditions (microwave power, time, particle diameter, and temperature) were optimized using response surface methodology (RSM) based on the Box–Behnken experimental design. The total polyphenol content and antioxidant activity of all prepared extracts was analyzed and compared. The results showed that MWUE provided the highest amount of total polyphenols (Total Polyphenolic Content (TPC) = 273.779 ± 4.968 mgGAE gd.m.−1) and the highest antioxidant activity, which was about 3.1 times higher than CE. Optimal extraction conditions were determined to be 80 °C, 15 min, 200 μm, and 750 min-1 for CE, 700 W, 7.5 min, 300 μm, and 80 °C for MWE, and 800 W, 5 min, 100 μm, and 60 °C for MWUE. Considering the maximum amount of total polyphenols extracted, the results suggest that MWUE is the most effective green extraction process that extracted the highest amount of polyphenols and could be used by the food industry for commercial exploitation of currently unprofitable plant bioactive sources.

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).

Chemical characteristics of raw and processed Istarski pršut (PDO) produced from two different pig genotypes were studied with special emphasis on amino and fatty acid composition and factors of lipid stability. Raw hams of Large White (LW)xLandrace (L), and (LWxL)xDuroc (D) pig genotypes were used in the study (20 hams of each genotype). All left raw hams from each carcass were processed in accordance with the PDO specification of Istarski pršut, and other half (the right ones) of LWxL)xD genotype were used for analyses of raw hams (fresh muscles). Istarski pršut was evaluated on the basis of the chemical parameters of the raw and matured lean ham. The process of dry curing significantly influenced the chemical properties of Istarski pršut. Despite the higher content of intramuscular fat and polyunsaturated fatty acids, the fat of (LWxL)xD ham was much more resistant to hydrolysis and oxidation, suggesting that fatty acid profile and other factors, also play a significant role. Significant differences between pig genotypes in the amino acid and fatty acid profiles were found. The analyzed Istarski pršut may be distinguished by prints of multivariate chemometric statistical analysis, based on their amino acid and fatty acid compositions.

Histamine fish poisoning is a foodborne illness caused by the consumption of fish products with high histamine content. Although intoxication mechanisms and control strategies are well known, it remains by far the most common cause of seafood-related health problems. Since conventional methods for histamine testing are difficult to implement in high-throughput quality control laboratories, simple and rapid methods for histamine testing are needed to ensure the safety of seafood products in global trade. In this work, the previously developed SERS method for the determination of histamine was tested to determine the influence of matrix effect on the performance of the method and to investigate the ability of different chemometric tools to overcome matrix effect issues. Experiments were performed on bluefin tuna (Thunnus thynnus) and bonito (Sarda sarda) samples exposed to varying levels of microbial activity. Spectral analysis confirmed the significant effect of sample matrix, related to different fish species, as well as the extent of microbial activity on the predictive ability of PLSR models with R2 of best model ranging from 0.722–0.945. Models obtained by ANN processing of factors derived by PCA from the raw spectra of the samples showed excellent prediction of histamine, regardless of fish species and extent of microbial activity (R2 of validation > 0.99).

Dehydration or drying of grapes is one of the most important steps in the production of Croatian traditional dessert wine Prošek. The natural sun drying of grapes is the traditionally used method in Prošek production. Alternative methods, such as dehydration under controlled conditions, have been studied as safer and faster methods than the traditional sun drying but without precise knowledge of the effect on volatile compounds. The objective of this work was to study how dehydration of grapes carried out in a greenhouse and an environmentally controlled chamber impacts on the free and glycosidically bound volatile compounds of native grape cv. ‘Maraština’. The 36 volatile compounds were identified and quantified using headspace solid-phase micro extraction coupled with gas chromatography-mass spectrophotometry (HS-SPME-GC/MS). The results showed that the aroma profile of dehydrated grapes was significantly different from that of fresh grapes. Regarding free forms, significant increases in the concentration of 2-methyl-1-propanol, 1-butanol, 2-hexen-1-ol, 1-hexanol, ethyl hexanoate, hexyl acetate, o-cymene, linalool oxide, and terpinen-4-ol and geraniol were found in greenhouse-dried grapes, whereas increases in cis-limonene-epoxide, trans-limonene epoxide, and γ-hexalactone were higher in chamber-dried grapes compared to greenhouse-dried grapes. Glycosidically bound forms of o-cymene, linalool oxide, linalool, and terpinen-4-ol were increased in both types of drying, whereas β-damascenone was increased only in greenhouse-dried grapes.

Consumer demand for food of high quality has driven research for alternative methods of food preservation on the one hand, and the development of new and rapid quality assessment techniques on the other hand. Recently, there has been a growing need and interest in healthier food products, which has led to an increased interest in natural preservatives, such as essential oils, plant extracts, and edible films and coatings. Several studies have shown the potential of using biopreservation, natural antimicrobials, and antioxidant agents in place of other processing and preservation techniques (e.g., thermal and non-thermal treatments, freezing, or synthetic chemicals). Changes in food quality induced by the application of natural preservatives have been commonly evaluated using a range of traditional methods, including microbiology, sensory, and physicochemical measurements. Several spectroscopic techniques have been proposed as promising alternatives to the traditional time-consuming and destructive methods. This review will provide an overview of recent studies and highlight the potential of spectroscopic techniques to evaluate quality changes in food products following the application of natural preservatives.

Rosemary (Rosmarinus officinalis L.) is a Mediterranean medicinal and aromatic plant widely used due to valuable bioactive compounds (BACs) and aromas. The aim of the study was to evaluate the extraction of intracellular compounds from rosemary combining experimental procedure by means of high voltage electrical discharge (HVED), with a theoretical approach using two computational simulation methods: conductor-like screening model for real solvents and Hansen solubility parameters. The optimal HVED parameters were as follows: frequency 100 Hz, pulse width 400 ns, gap between electrodes 15 mm, liquid to solid ratio 50 mL/g, voltage 15 and 20 kV for argon, and 20 and 25 kV for nitrogen gas. Green solvents were used, water and ethanol (25% and 50%). The comparison was done with modified conventional extraction (CE) extracted by magnetic stirring and physicochemical analyses of obtained extracts were done. Results showed that HVED extracts in average 2.13-times higher total phenol content compared to CE. Furthermore, nitrogen, longer treatment time and higher voltage enhanced higher yields in HVED extraction. HVED was confirmed to have a high potential for extraction of BACs from rosemary. The computational stimulation methods were confirmed by experimental study, ethanol had higher potential of solubility of BACs and aromas from rosemary compared to water.

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 β-sitosterol and Δ7,22-stigmastadienol in the analysed oils.