In this study, licorice root extract was obtained using a multi-stage countercurrent extraction method at bench scale, and the content of glycyrrhizic acid was investigated. The effects of various parameters including temperature, extraction time, number of extraction stages, and solvent-to-solid ratio were evaluated. The optimal extraction conditions were determined using response surface methodology (RSM). Statistical analysis revealed that temperature, time, and number of extraction stages had significant effects on the extraction yield. Under the optimized conditions temperature of 46.17 °C, extraction time of 5.98 hours, solvent-to-solid ratio of 7.70 cc/g, and 4 stages the maximum glycyrrhizic acid extraction yield was 78.7%.
Barberry (Berberis spp.), a genus in the Berberidaceae family with 650 species, holds significant potential in the pharmaceutical and food industries. This review assesses the available information and carries out a meta-analysis of published research on bioactive compounds extracted from various Berberis species. PubMed, Web of Science, and Scopus databases were searched extensively for articles published between 2009 and 2023. This analysis included 38 relevant articles, including those that evaluated multiple extraction methods. Four extraction methods involving different techniques and equipment were identified in the included studies and comparatively evaluated in this systematic review and meta-analysis. According to our meta-analysis of the published data, the frequency of use of the methods was as follows: Press Extraction (PE) (22.72%), Maceration Extraction (ME) (20.45%), Ultrasound-Assisted Extraction (UAE) (18.18%), and Subcritical Water Extraction (SWE) (6.82%). The most common solvents used in the selected studies were water (42.86%) and methanol (22.86%). In addition, this review investigated, based on the reported data, the effects of the extraction method on antioxidant activity (DPPH), Total Phenolic Content (TPC), and Total Anthocyanin Content (TAC). The results showed that, among the reported techniques, SWE was generally associated with the highest DPPH values. Moreover, UAE was most frequently used for determining TPC and TAC on a dry-weight basis, whereas ME and SWE were more commonly applied when data were expressed on a solution basis.
In this research, an attempt was made to design and evaluate a semi-industrial tea rolling machine. The study investigated the effect of roller rotational speed and the pressure applied to the green tea leaves on the quality of the rolled tea. First, prepared and weighed tea samples were rolled using the semi-industrial rolling machine with a capacity of 10 kg. The independent parameters were chosen at three pressure levels: 170, 340, and 680 kg/m², and three rotational speeds: 20, 30, and 40 RPM. Finally, the percentage of breakage, rolling time, and sensory quality were measured. The analysis of the percentage of breakage in samples due to the roller speed and pressure showed that as both speed and pressure increased, the percentage of breakage also increased. For the rolling time, it was concluded that only the independent factor of pressure had a significant effect on the required rolling time. By increasing the pressure of the roller, the time needed for the samples to roll decreased significantly. Ultimately, it was concluded that the best levels for the effective independent factors on the percentage of breakage were a pressure factor of 170 N and a rotational speed of 20 RPM. In the sensory test, the best aroma and flavor were related to the samples under a pressure of 340 N and a rotational speed of 30 RPM.
Despite the low antioxidant activity of pomegranate seeds, pomegranate seed oil (PSO) has very high antioxidant activity but is unstable. In this study, PSO was microencapsulated with natural materials and a clean label by spray drying. For this purpose, walls with different concentrations of cordia fruit gum (CFG) and maltodextrin were used. The creaming index of the emulsions was measured and, their physicochemical and oxidative stability properties were investigated. The results showed that microcapsules prepared by spray drying with a 6% CFG ratio had the highest encapsulation efficiency (88.11%), the smallest particle size (124.1 μm), the highest bulk density (0.38 g/cm3), the highest sphericity, and the highest oxidative stability. All the microcapsules' moisture contents fell below the 4% threshold that is considered acceptable for dry powders in the food sector. This study showed that using the microencapsulation as a clean-label technology (CL) system, it is possible to add value to pomegranate processing waste, thereby providing a sustainable method for using natural and indigenous ingredients that are generally recognized as safe (GRAS) in the food industry and an effective solution to solve the problem of oxidative stability of PSO.
IntroductionVinegar is a common condiment used in many foods and beverages, which has health-promoting properties. This study aimed to determine the authenticity and comparison of distilled and fermented vinegars compared to vinegar produced with industrial acetic acid.Materials and MethodsIn this study, 15 commercial brands of distilled and fermented vinegar samples were prepared and the vinegar samples were examined and compared in terms of total acidity, oxidation index, antioxidant activity, content of phenolic compounds and organic acids, and trace elements.ResultsOur results showed that the decrease in total acidity was related to fermented samples. Also, fermented vinegars had higher oxidation value, antioxidant activity, phenolic and organic acids with a significant difference (p<0.05) compared to distilled vinegars and acetic acid. FTIR spectrum also confirmed the presence of nutritional compounds in fermented samples in addition to acetic acid. Also, in terms of heavy metals such as lead and nickel, fermented vinegars were safer than industrial acetic acid (p<0.05), while fermented vinegars had more magnesium than acetic acid and distilled samples. Industrial acetic acid had higher lead and nickel (p<0.05), due to the catalyst applied for industrial acetic acid production. ConclusionThe obtained data highlight the high quality of fermented vinegars compared to commercial vinegars. The biochemical composition of vinegars traditionally obtained from fruits and through simple recipes demonstrates their role and importance for human well-being and their potential beneficial effects on health. Overall, the results of this study showed that fermented vinegars are superior in terms of nutritional and functional properties to distilled vinegars and industrial acetic acid.
This study focused on formulating a natural dietary supplement based on a combination of freeze-dried quail egg powder and dried arugula (Eruca sativa) leaves. The integration of animal- and plant-derived components produced a nutritionally dense product enriched with essential nutrients and bioactive substances. Compositional analysis revealed that the supplement is a substantial source of high-quality protein (30 g/100 g), lipids (19 g/100 g), and carbohydrates (16 g/100 g). Furthermore, it provides appreciable levels of key minerals, including calcium (210 mg), magnesium (54 mg), and iron (5.7 mg). The presence of bioactive compounds was confirmed by the high contents of total phenolics (1500 mg GAE), flavonoids (500 mg QE), and vitamin E (160 mg/100 g), supporting its functional and antioxidant potential.A short-term human intervention was conducted in which participants consumed three capsules (1.5 g) of the supplement daily. Biochemical assessments demonstrated that serum uric acid (4.41–4.58 mg/dL) and blood glucose levels (82.59–85.59 mg/dL) remained within normal ranges throughout the study period. A modest enhancement in total antioxidant capacity (1.02–1.15 µmol TE/g) was observed, whereas malondialdehyde concentrations showed minimal variation (3.21–3.27 nmol/mL). These limited physiological changes are likely attributable to the low intake level, brief supplementation period, and inter-individual variability. In addition, chemical stability evaluation indicated favorable storage properties, as evidenced by low moisture content (3.40%), near-neutral pH (6.42), and a very low peroxide value (1.18 meq O₂/kg fat), reflecting minimal lipid oxidation. Collectively, these results suggest that the developed supplement is chemically stable, safe for consumption, and may provide moderate nutritional and antioxidant benefits in humans.
Food authenticity is a crucial aspect of consumer protection, food safety, and quality assurance. Conventional methods for meat authentication often require destructive, time-consuming, or labor-intensive processes. Hyperspectral imaging, which combines imaging and spectroscopy, has emerged as a non-destructive alternative for food classification. This study investigates the application of hyperspectral imaging for differentiating between beef, chicken, and turkey zhambons using one-dimensional convolutional neural networks and long short-term memory networks. Following preprocessing—including segmentation, noise reduction, and spatial averaging—spectral signatures were extracted and classified using deep learning models and then compared to traditional machine learning approaches. The long short-term architecture demonstrated superior performance by effectively modeling sequential spectral dependencies, achieving 99.94% accuracy in the binary classification of chicken versus beef and 98.12% accuracy in the three-class problem (beef, chicken, and turkey zhambons). The findings highlight the potential of hyperspectral imaging combined with machine learning approaches as an efficient tool for processed meat authentication.
3D food printing, as an emerging technology, not only enables the layer-by-layer fabrication of customized foods, but has also gained significant importance due to its potential to address global challenges such as malnutrition, personalized nutrition management, food waste reduction, and the improvement of sustainable production. The development of this technology is crucial because it can precisely meet the nutritional needs of sensitive population groups and pave the way for producing functional, personalized foods tailored to specific dietary requirements. This method offers wide applications in preparing diverse foods, particularly for special groups such as the elderly, children, and patients. Its major advantages include enhanced production efficiency, reduced food waste, and the creation of complex and aesthetically appealing structures. Food materials used in 3D printing are generally categorized into naturally printable, non-printable, and alternative materials. Among these, hydrocolloids play a key role in improving printability due to their desirable rheological properties and gel-forming ability. Gelatin, xanthan, carrageenan, and alginate are among the most important hydrocolloids whose effects on texture, stability, water-holding capacity, and sensory acceptance have been widely investigated. Current evidence shows that selecting and combining appropriate hydrocolloids and additives can markedly enhance the mechanical and sensory quality of printed products. In addition to formulation components, printing parameters and post-printing (post-processing) treatments also play a decisive role in determining the final product quality. This review article examines the rheological performance, challenges, and novel approaches to improving the quality of printed products, with the aim of providing a comprehensive picture of the role of hydrocolloids in 3D food printing. Ultimately, 3D food printing using hydrocolloids opens new horizons for the production of healthy foods, plant-based products, and meat alternatives, and is increasingly recognized as an innovative approach to overcoming challenges in the food industry and advancing nutritional quality.
Manual date harvesting and sorting remain labor-intensive and error-prone, particularly when distinguishing intermediate ripeness stages such as Rotab. We present an image-based classification pipeline for the Berhi cultivar that assigns fruit to three ripeness stages—Khalal, Rotab, and Tamar—using compact deep structures and training strategies suited to small datasets. Rather than relying on generative or adversarial methods, our approach emphasizes (i) careful augmentation (classical transforms, automated policies, and sample-mixing), (ii) transfer and self-supervised pre training, and (iii) embedding- and metric-learning alternatives, with ensembles and test-time augmentation used as optional accuracy/robustness boosters. On a 150-image dataset (50 images per class) evaluated with 5-fold cross-validation, a ResNet18 baseline reaches about 95% average accuracy. Automated augmentation combined with MixUp/CutMix improves accuracy to 97%, and self-supervised pre training plus advanced augmentation and ensembling attain peak performance near 98%. Improvements are most pronounced for the visually ambiguous Rotab class. We also report practical robustness measures (common corruptions, geometric stability, and calibration), which show that augmentation and pre training substantially increase stability under realistic input variability. These results indicate that, for small and visually subtle datasets, augmentation and pre training—rather than synthetic data generation—offer a pragmatic path to high accuracy and robust behavior.
This study investigated the effects of various drying methods including convective, infrared, and microwave drying on the moisture content, pH, acidity, color indices, total phenolic content (TPC), and antioxidant capacity (AC) of sprouted quinoa powder. Initially, the quinoa seeds were soaked in magnetized water for 1 h. Then the quinoa seeds were incubated in a magnetic field at 25°C for 72 h for sprouting. To increase the phenolic compounds of the powders, the sprouts were treated by ultrasound for 5 min. The sprouts were dried in three ways and the powder prepared from them was analyzed. The infrared radiation facilitated removal of moisture from the quinoa sprouts, increased the effective moisture diffusivity coefficient, and shortened the dehydration duration. The moisture content and pH of the sprouted quinoa powders were in the range of 2.61 % to 7.03 %, and 5.95 to 6.08, respectively. The acidity of convective, infrared, and microwave dried sprouted quinoa powders was 1.24 %, 1.19 %, and 0.81 %, respectively. Among the sprouted quinoa powders, the sample dried using microwave treatment exhibited the lowest lightness value (67.78) and the highest redness (9.89) and yellowness (21.09) indices. The infrared-dried powders had the maximum TPC and AC. The TPC of convective, infrared, and microwave dried powders were 916.98, 1268.48, and 1262.46 μg gallic acid/g dry, respectively. In summary, using infrared was chosen as the best way to dry quinoa sprouts because it dries them faster, keeps the right color parameters, and results in the highest levels of beneficial compounds.
The aim of this study was to investigate the feasibility of rapid prediction of physicochemical properties in honey using Raman spectroscopy and chemometrics models. In this research, 51 honey samples were collected from different regions of Iran, and each sample was analyzed by Raman spectroscopy (in the range of 100–3500 cm⁻¹). The investigated properties included reducing sugars before hydrolysis, sucrose, moisture, and hydroxymethylfurfural (HMF), which were measured using standard laboratory methods. The spectral data, after mean-centering preprocessing, were correlated with the laboratory values of the physicochemical properties, and four independent models based on Partial Least Squares (PLS) regression were developed. The models were validated using the Kennard–Stone algorithm for splitting the data into calibration and test sets, cross-validation with the Leave-One-Out method to optimize the number of latent variables, and indices such as RMSEP, %REP, and RMSECV. The results showed that the predictive models for reducing sugars before hydrolysis and moisture performed excellently with low relative errors (1.64% and 5.30%, respectively). The sucrose model, with a relative error of about 11.95%, was acceptable, while the HMF model showed lower accuracy with a relative error of 25.8%. Overall, combining Raman spectroscopy with PLS models provides a rapid, non-destructive, cost-effective, and environmentally friendly approach for honey quality control, which can play a significant role in monitoring authenticity, distribution, and economic development of this product.
Annona muricata L. (soursop) leaves are recognised as valuable sources of phenolic compounds with strong antioxidant potential, although conventional solvent extraction often provides limited selectivity and inadequate recovery of key bioactive constituents. Natural deep eutectic solvents (NADES) represent a greener extraction alternative, yet their performance in recovering phenolics, flavonoids, rutin, and antioxidant components from A. muricata leaf extract (AMLE) remains insufficiently explored. This study evaluated four NADES formulations, namely choline chloride–lactic acid (ChCl–LA), citric acid–L-proline (CA–LP), betaine–lactic acid (B–LA), and choline chloride–glycerol (ChCl–G), in comparison with water using ultrasound-assisted extraction. Physicochemical properties of the solvents, including pH and viscosity, were determined prior to extraction to elucidate their influence on solvent–solute interactions and extraction behaviour. Extraction efficiency was assessed through rutin content, total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activities measured by DPPH, ABTS, and FRAP assays. All NADES systems exhibited acidic pH (1.80–4.97) and substantially higher viscosity than water, with ChCl–LA combining strong acidity and comparatively low viscosity, favourable for mass transfer. ChCl–LA demonstrated the strongest extraction of targeted bioactive constituents, achieving 164.16 ± 2.34 mg GAE/g TPC, 16.55 ± 0.52 mg QE/g TFC, 0.796 ± 0.023 mg/g rutin, and consistently high antioxidant activities across all assays. Correlation analysis indicated that FRAP activity was strongly associated with TPC, while DPPH and ABTS activities showed stronger associations with rutin and TFC, highlighting the contribution of different phenolic subclasses to antioxidant responses. The results demonstrate that solvent physicochemical properties, particularly acidity and viscosity, play a critical role in governing bioactive selectivity and antioxidant performance. ChCl–LA was identified as the most effective green solvent for producing antioxidant-rich AMLE suitable for development of functional foods and nutraceutical products.
This study aimed to develop and evaluate a non-destructive approach for predicting peroxidase (POD) enzyme activity in two plum cultivars—‘Khormaei’ and ‘Khouni’—using Vis/NIR spectroscopy combined with machine learning techniques. After acquiring absorbance spectra, the data were modeled using Partial Least Squares Regression (PLSR) and Support Vector Machine (SVM) algorithms across both the full spectral range and reduced dimensions. Model performance was assessed based on the coefficient of determination (R²), root mean square error (RMSE), and the ratio of performance to deviation (RPD). To enhance model efficiency and reduce data dimensionality, SVM was integrated with metaheuristic algorithms for effective wavelength selection. Among these, Particle Swarm Optimization (PSO) emerged as the most effective algorithm. The results demonstrated that dimensionality reduction strategies, by eliminating noise and redundant information, significantly improved the predictive accuracy of the models. Findings indicated that the optimal model varied between cultivars. For the ‘Khormaei’ cultivar, the SVM-R model with an RBF kernel and median filter preprocessing yielded the best performance, achieving an RPD of 2.687, categorizing it as an excellent model and underscoring the relevance of nonlinear approaches for this cultivar. Conversely, for the ‘Khouni’ cultivar, the best result was obtained using the linear PLSR model with normalization preprocessing, achieving an RPD of 2.29, which is considered very good. This contrast highlights that optimal model selection is inherently dependent on the intrinsic characteristics of each product. Ultimately, the study confirms that Vis/NIR spectroscopy is a powerful tool for rapid and non-destructive postharvest quality monitoring of agricultural products.
Protein bars are among the popular, healthy, and safe products; however, they may undergo physicochemical changes during storage, leading to reduced attractiveness and consumer acceptance. The use of natural compounds with antioxidant activity, particularly plant extracts, is considered an effective approach to control and mitigate undesirable changes. In this study, aqueous and ethanolic extracts of rice bran were employed as functional ingredients due to their antioxidant properties and the presence of phenolic and flavonoid compounds, aiming to improve the quality of protein bars. The stability of the rice bran extract–enriched products was evaluated in comparison with the control sample over a 28-day storage period at ambient temperature, with assessments conducted at 7-day intervals. Moisture, water activity, acidity, peroxide value, and color changes were measured, and suitable kinetic models were determined using the coefficient of determination and other error indices. The findings indicated that the addition of rice bran extracts, particularly the ethanolic form, effectively reduced undesirable changes, enhanced stability, and preserved higher product quality during storage. These results suggest that the incorporation of safe and natural compounds can improve the functional properties of food products and facilitate the development of healthier and more sustainable products.
The process of simulating selenium nanoparticle synthesis was explored as an effective approach to reduce both the cost and duration of research. The synthesis process was modeled using COMSOL Multiphysics software based on supercritical water conditions (temperatures above 100°C). Optimal parameters, including temperature and time, were identified to achieve desirable nanoparticle characteristics. The simulation results indicated that at a temperature of 200°C and duration of 80 minutes, the minimum necessary time to reach subcritical water conditions was attained. These findings suggest that process simulation plays a vital role in predicting outcomes, minimizing expenses, and accelerating the development of selenium nanoparticles across various applications. The physical and chemical analyses of the produced nanoparticles revealed an average size of approximately 95 nanometers, a polydispersity index of 0.295, and a zeta potential of around -19.8 mV, indicating high thermodynamic stability. Furthermore, the synthesized selenium nanoparticles exhibited a 42% Antioxidant activity, demonstrating a strong potential for free radical scavenging, and a 76% Antifungal efficacy, highlighting their suitability for antifungal applications.
Waffles are a popular wheat-based baked product that can be nutritionally enhanced through partial or complete substitution with legume flours such as mung bean powder, which is rich in protein and bioactive compounds. This study investigated the effects of substituting wheat flour with mung bean powder (0%, 50%, and 100%) on the physicochemical, textural, and sensory properties of waffles. Results showed a significant decrease in batter lightness from 89.58 (0%) to 69.10 (100%), accompanied by a shift in redness from –6.35 to –10.97 and an increase in yellowness from 38.50 to 44.69. The apparent viscosity of batter exhibited shear-thinning behavior across all formulations; however, viscosity increased significantly at 100% substitution (67.34 Pa•s) compared to the control (37.93 Pa•s). The ash content of waffles increased, reaching 2.57% at full substitution. pH decreased from 7.10 to 6.43, whereas acidity rose from 0.25% to 0.48% with higher mung bean incorporation. Total phenolic content (TPC) and antioxidant capacity (AC) were significantly enhanced, increasing from 1160.9 μg GAE/g and 67.4% in control samples to 1635.1 μg GAE/g and 81.2%, respectively, at 100% substitution. Color of baked waffles also changed, with lightness decreasing from 82.81 to 65.73, while yellowness increased from 54.73 to 60.71. Hardness increased from 0.47 to 1.15 N. Sensory evaluations revealed reduced scores for appearance, aroma, and texture at higher substitution levels, while flavor acceptance remained stable. Of course, the overall acceptance score for all samples remained above 8, indicating good consumer acceptability.
Detecting fraud in the cinnamon supply chain is critical for ensuring consumer safety and maintaining product integrity. Recent advances in spectral data preprocessing techniques offer enhanced accuracy in identifying adulterants in spices like cinnamon. This study investigates the impact of different spectral preprocessing techniques on predicting adulterants—specifically soybean powder, hazelnut shell powder, and dry bread powder—mixed with cinnamon powder using spectroscopy combined with multivariate analysis. The transmittance spectra were collected across the mid-infrared range of 400–4000 cm⁻¹, and Partial Least Squares Regression (PLSR) was employed to model the adulteration levels based on these spectra. Various preprocessing methods were applied to optimize the spectral data. Among them, orthogonal signal correction (OSC) combined with detrending yielded the highest predictive accuracy, with a coefficient of prediction (R²p) ranging from 0.900 to 0.981. Conversely, Extended Multiplicative Scatter Correction (EMSC) and Savitzky-Golay second derivative (D2) were less effective, with R²p values between 0.115 and 0.931. Soybean powder was the easiest adulterant to detect, with a prediction error range of 5–10%. These findings underscore the importance of selecting appropriate preprocessing techniques to improve the accuracy of fraud detection in cinnamon powder using spectroscopic methods.
Tea is not only one of the most popular beverages in the world, but also one of the most important agricultural products due to its health benefits and wide applications in various industries. However, tea fraud is one of the main challenges in the industry. These frauds include the addition of foreign materials, colors, or falsification of geographical origin, which have negative effects on the health of consumers. At present paper, two deep learning models namely EfficientNet and Swin Transformer, were tested in detecting three types of fraud (tea waste, low-quality foreign tea, and expired tea) in Iranian black tea. The results indicate that the EfficientNet model was more successful in detecting tea waste than foreign tea and expired tea (with accuracy of 96.8% and F1-Score of 95.2%), while the Swin Transformer model performed better in detecting foreign tea and expired tea, showing an accuracy of 94.5% and an F1-Score of 93.7%, respectively. However, improved settings are suggested to reduce errors and improve the performance of the models.
Rice (Oryza sativa L.) is a staple grain vital for human nutrition, particularly in Asia. Rice bran, constituting approximately 10% of the outer layer of brown rice, is a by-product of the milling process. It contains 10–16% protein, 12–22% lipids, dietary fiber, and various bioactive compounds, including B vitamins, vitamin E, and gamma-oryzanol, which exhibit notable antioxidant and nutritional properties. The oil extracted from rice bran features a well-balanced fatty acid composition, predominantly comprising 43% oleic acid (monounsaturated), 32% linoleic acid (polyunsaturated), and 15% palmitic acid (saturated). This profile contributes significantly to cardiovascular health improvement and oxidative stress reduction. The saturated to unsaturated fatty acid ratio is approximately 20:80, with minor but important amounts of linolenic acid (~0.8%). Non-saponifiable constituents such as tocopherols, phytosterols, polyphenols, and gamma-oryzanol (approximately 1.76% in enzyme-extracted oil) further enhance the oil’s antioxidant capacity and cholesterol-lowering effects. Rice bran proteins, including albumin, globulin, glutelin, and prolamin, demonstrate digestibility rates exceeding 90% and a protein digestibility-corrected amino acid score (PDCAAS) ranging from 2.0 to 2.5, underscoring their value as a high-quality protein source. Genetic variability among rice varieties leads to differences in the amino acid profile of rice bran. The proportion of essential amino acids relative to total amino acids in various bran fractions ranges from 31.35% to 34.8%, indicating consistent protein quality throughout the bran layers. Despite its rich nutritional composition, rice bran direct consumption in human diets remains limited, with most usage directed toward animal feed, fertilizers, and biofuel production. Nonetheless, due to its balanced amino acid content, beneficial fatty acid profile, and potent antioxidant compounds, rice bran holds considerable potential as a functional ingredient in food, pharmaceutical, and cosmetic industries.
Introduction: Nutrient bars have emerged as a practical and appealing solution for individuals with busy lifestyles, offering a convenient alternative to missed meals. With optimal formulation, these products can deliver a balanced composition of essential macro- and micronutrients—especially proteins and carbohydrates—necessary for maintaining normal body functions, thereby playing a significant role in improving the nutritional status of consumers. Among the diverse categories of nutritional bars, protein bars constitute a significant segment of the market. However, ensuring their physicochemical and oxidative stability during storage, particularly under ambient conditions, remains a key challenge for the food industry. Recent research has highlighted the potential of microalgae, such as Spirulina, to enhance the technological and nutritional properties of food products. Spirulina is rich in proteins, essential fatty acids, vitamins, minerals, and various antioxidants, and is widely used as a dietary supplement in diverse nutritional regimens. One of its most important bioactive compounds is phycocyanin, a natural blue protein pigment with potent antioxidant properties. Owing to the nutritional and functional advantages of these compounds, their incorporation into food products has attracted growing interest as a means to enhance both nutritional value and product quality. The present study aims to investigate and model the kinetics of physicochemical and oxidative changes in protein bars enriched with Spirulina and phycocyanin over a 28-day storage period. The parameters examined include moisture content, water activity, titratable acidity, peroxide value, and total color difference (ΔE). Kinetic modeling of physicochemical changes in food systems is a crucial tool for understanding, predicting, and controlling quality behavior during storage. It plays a vital role in shelf life determination, packaging design, optimization of storage conditions, and the overall assessment of product stability.
