Lipid oxidation is one of the main factors contributing to the deterioration of the quality of bakery products, negatively affecting their flavor, texture, appearance and nutritional value. This reduces their quality and shortens their shelf life, thus reducing consumer acceptance. Lime by-products, rich in phenolic compounds with antioxidant potential, offer a sustainable strategy to both reduce food waste and improve product stability.

This study investigated the effect of increasing concentrations (0%, 1%, 2%, and 3%) of lime by-product extract on lipid oxidation in muffins during storage. Lime by-product consists of lime pomace, the residue remaining after juice extraction. The Thiobarbituric Acid Reactive Substances (TBARS) assay, which quantifies malondialdehyde (MDA) as a marker of lipid oxidation, was used to monitor oxidative stability. Muffins were stored at 4 °C in sealed plastic containers, and TBARS measurements were taken on days 1, 2, 4, 7, 10, and 14.

Results showed that all extract-enriched muffins (M1, M2, and M3) exhibited significantly lower MDA levels than the control (M0) from day 0. M2 initially had the lowest MDA concentration (0.41 mg Eq. MDA/kg on day 0), followed by M1 and M3 (both 0.45 mg Eq. MDA/kg on day 0). However, over time, M1 consistently showed superior oxidative stability and ended with the lowest MDA value on day 14 (0.56 mg Eq. MDA/kg, p < 0.05). Interestingly, the highest extract concentration (M3) showed higher MDA levels, suggesting a pro-oxidant effect, since on day 10, M0 had 0.65 mg Eq. MDA/kg, while M3 showed 0.71 mg Eq. MDA/kg.

These findings demonstrate that moderate enrichment with lime by-product extract (1%) can effectively delay lipid oxidation in muffins, thus extending shelf life up to 14 days, while enhancing sustainability. Furthermore, this approach adds flavor to food while contributing to food waste reduction through upcycling of citrus processing residues. Importantly, higher extract concentrations may not provide added benefits and may accelerate oxidation due to pro-oxidant effects.

Sustainability and food security are critical challenges that require innovative approaches to utilize alternative raw materials effectively. Project BlueGreenFeed explores using land-based raw materials, such as grass and feathers, to produce sustainable protein for aquaculture through mealworm bioconversion. This study aimed to evaluate the acceptance, survival, and growth of mealworms fed on grass and feather-based feed components.

Mealworm cultivation was carried out using experimental feeds derived from grass and feathers alongside standard feed composed of wheat and barley (69% carbohydrates, 14% protein, 4% lipids). The growing cycle of mealworms was 12 weeks, during which mealworm survival rates, weight gain, and feed consumption were monitored. Feed textures and nutritional compositions were analyzed to assess their influence on mealworm acceptance, growth, and composition.

The results demonstrated that while grass and feather-based feeds were effective protein sources, they failed to provide lipids. The texture differences between standard feed and experimental feeds significantly impacted feed acceptance, with the light, powdery texture of grass and feather feeds proving less appealing to mealworms. This led to reduced survival rates and lower weight gain compared to mealworms fed on standard feed. Grass and feathers feed mix, as well as feathers alone, were identified as promising protein sources but lacked sufficient water content for optimal mealworm development.

The study concluded that while grass and feather-based feed components hold promise as sustainable protein sources, further optimization is required to improve feed texture and lipid content to enhance mealworm growth and survival. Additional research is recommended to refine feed formulations and explore complementary lipid sources.

The study was done in collaboration with scientists from SINTEF in Norway, Aarhus University in Denmark, the University of Iceland and Matis in Iceland, and Tallinn University of Technology in Estonia, with the support of Blue Bioeconomy ERA-NET Cofund and its national agencies.

Streptococcus gallolyticus subsp. gallolyticus (Sgg) is an emerging zoonotic pathogen linked to infective endocarditis and colorectal cancer. It is naturally found in the intestinal microbiota of both animals and humans. While Sgg is less frequently isolated from foods, other Streptococcus bovis/Streptococcus equinus complex (SBSEC) members are more common in dairy products such as raw milk cheeses and fermented drinks. All members exhibit pathogenic potential, raising food safety concerns.

This study evaluated the growth dynamics and acid production/resistance of two human-derived Sgg isolates in sterile milk. Strains were inoculated at 10⁵ CFU/mL and incubated for 72 hours. Microbial enumeration and pH tracking revealed rapid growth, reaching 11.2 log₁₀ CFU/mL and causing a pH drop to 5.09. A biphasic growth pattern suggested metabolic adaptability and utilization of secondary substrates. In simulated gastric juice (pH 2.1–2.5), both isolates were completely inactivated within 5 minutes, suggesting limited acid tolerance. However, factors such as proton pump inhibitor (PPI) use, age-related hypochlorhydria, or buffering by cheese and milk may allow Sgg to survive gastric transit.

To explore biocontrol strategies, over 80 lactic acid bacteria (LAB) strains from our collection were screened using spot inhibition assays. Effective Inhibition Ratios (EIRs) were calculated. Thirty-eight LAB strains including Lacticaseibacillus casei, L. delbrueckii, and L. rhamnosus exhibited strong inhibitory activity (EIR > 1.5) against Sgg.

These findings suggest that Sgg can thrive in milk environments and lower pH significantly, posing a potential risk if present in dairy products. However, selected LAB strains demonstrated promising inhibition and may serve as protective cultures. Further studies are planned to test selected LAB strains in cheeses to assess their protective efficacy under realistic production conditions.

The increasing focus on sustainable agricultural practices and the circular bioeconomy has driven interest in the valorization of agro-industrial residues. Olive leaves (OLs), a by-product of pruning operations, represent a rich source of bioactive compounds such as polyphenols, as well as structural materials like cellulose. This study aims to assess the environmental sustainability of a novel biorefinery process developed to extract polyphenols and cellulose from Olea europaea L. cv. Caiazzana leaves using a Life Cycle Assessment (LCA) approach. The LCA was conducted in accordance with international standards, adopting a cradle-to-gate system boundary. The functional unit was defined as 1 kg of OLs processed. Primary data were collected from laboratory-scale experiments, while secondary data were sourced from the Ecoinvent database. The process flow included mechanical pre-treatment, polyphenol extraction, separation and purification steps, and cellulose recovery from the residual biomass. The environmental impacts were assessed using the ReCiPe 2016 Midpoint and Endpoint method. The LCA revealed that the main environmental hotspots were energy consumption during the drying and extraction phases and the use of chemical reagents. The most affected impact categories included climate change, freshwater eutrophication, and human toxicity. However, the co-extraction of high-value polyphenols and cellulose significantly improved the process's eco-efficiency. A sensitivity analysis indicated that replacing fossil-based electricity with renewable sources could reduce the total GHG emissions by over 40%. The valorization of OL pruning residues into bioactive compounds and cellulose presents a promising strategy to reduce agro-industrial waste and promote resource efficiency. Integrating LCA in early-stage process development highlights potential environmental trade-offs and improvement opportunities, supporting the transition toward sustainable and circular agricultural systems.

The probiotic yeast Saccharomyces boulardii exhibits remarkable tolerance to gastrointestinal stress conditions, yet the specific metabolic adaptations enabling this resilience remain incompletely characterized. In the present study, we employed comprehensive LC-MS-based metabolomic profiling to investigate S. boulardii's dynamic metabolic responses to simulated gastric fluid (SGF), intestinal fluid (SIF), and sequential SGF-SIF exposure. Our systematic analysis revealed that organic acids, lipids, and organoheterocyclic compounds constituted the predominant metabolic classes affected by gastrointestinal stress, with carboxylic acids and their derivatives representing the most significantly altered subclass. Notably, intestinal fluid exposure induced the most profound metabolic perturbations, resulting in quantitative changes to 2,029 distinct metabolites, including 1,008 upregulated and 1,021 downregulated species. A detailed pathway analysis (KEGG) demonstrated significant activation of purine/pyrimidine metabolism and amino sugar/nucleotide sugar biosynthesis pathways, which are fundamentally important for maintaining cellular energy status and cell wall integrity during stress exposure. Conversely, tyrosine metabolism and taurine-related pathways showed marked downregulation, suggesting potential metabolic trade-offs during stress adaptation. Through an advanced correlation network analysis, we identified Parisvanioside A and specific glyceryl phosphatides as key metabolites positively associated with stress tolerance, while valorphin and D-glucosamine exhibited substantial depletion under stress conditions. These findings provide compelling evidence for S. boulardii's metabolic flexibility during gastrointestinal transit and reveal specific molecular targets for probiotic optimization. The identified metabolic signatures suggest promising strategies for enhancing probiotic viability, including targeted metabolite stabilization or nutritional supplementation approaches during gastrointestinal passage.

Industry 5.0, as proposed by the European Commission, marks a paradigm shift from profit-oriented production towards a model that prioritizes human well-being and environmental sustainability. In contrast to Industry 4.0, which emphasized automation and efficiency, Industry 5.0 integrates advanced technologies with a human-centric and eco-conscious approach. The agrifood sector—particularly the winemaking industry—is a strategic application area, given its socio-economic relevance and environmental impact, especially in countries such as Italy. In response, we developed CANTINA 5.0, a research initiative aimed to operationalize Industry 5.0 principles in Italian wineries through a multidimensional approach centered on sustainability, human factors, and product quality. The research structure is based on four interconnected pillars: (i) quality of life monitoring via wearable sensors and structured questionnaires assessing workers’ physical and psychological conditions; (ii) environmental monitoring of pollutants within wine cellars using both IoT-based devices and conventional GC-MS analysis; (iii) sustainability assessment through standardized questionnaires addressing environmental practices and social responsibility indicators, with a focus on workforce well-being; and (iv) wine quality evaluation, combining chemical profiling, expert sensory panels, and emotional response tracking of consumers using wearable devices. To date, 20 wineries (12 in Friuli-Venezia Giulia, 8 in Tuscany) have participated. All completed sustainability and well-being surveys; selected facilities underwent environmental monitoring, and workers in some wineries wore smartwatches during harvest and non-harvest periods. Wine samples were analyzed chemically and sensorially, while emotional responses to wine tasting—alone and paired with music—were collected during five public events. Preliminary results show high stakeholder engagement and public interest. Future work will extend data collection and develop targeted dissemination strategies to foster the adoption of Industry 5.0 in national and European R&D frameworks.

This study investigates the predictive modeling of total Polycyclic Aromatic Hydrocarbon (PAH) concentrations in smoked fish products based on various smoking parameters using machine learning techniques in the WEKA software environment. Key input variables included fish fat content, smoking temperature, and wood type, all of which were statistically significant predictors of PAH levels (p <0.05). A multiple linear regression analysis conducted in SPSS revealed a strong correlation between predictors and PAH concentration (r = 0.801), with an explained variance of 64.1% (R² = 0.641) and a standard error of 3.52. Among the evaluated machine learning algorithms—Linear Regression, SMOreg, Multilayer Perceptron, M5P, Random Forest, and IBk—performance was assessed using five criteria: Correlation Coefficient, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). All models were validated using 10-fold cross-validation. For classification tasks based on fish species, Logistic Regression outperformed the Random Forest and J48 algorithms, indicating superior predictive capability. This integrated analytical framework demonstrates the effectiveness of machine learning in food safety monitoring and provides a scientific basis for optimizing smoking processes to mitigate PAH contamination.Overall, the findings underscore the practical value of machine learning tools in the predictive modeling of PAH contamination in smoked fish. The approach not only offers high predictive accuracy but also serves as a scientific framework for improving food safety by optimizing smoking conditions to minimize PAH formation. This integrated model can aid food technologists and manufacturers in establishing safer processing parameters while maintaining product quality.

Buttermilk, a co-product generated during butter production, has high nutritional value; however, it is often underutilized by small- and medium-sized dairy industries in developing countries. This study aimed to evaluate the use of different proportions of whole milk and buttermilk (100:0; 80:20; 60:40; 40:60; 20:80; and 0:100) for kefir production, with the goal of adding value to this co-product and promoting bioeconomy principles in the dairy sector. A 100% milk formulation was used as the control. The proximate composition, kefir grain biomass growth over 21 days, syneresis, and water retention capacity (WRC) were evaluated. The results showed that increasing the proportion of buttermilk led to a linear increase in moisture content (from 89.70% to 96.57%) and a significant decrease in fat (from 3.05% to 0.55%), protein (from 3.43% to 1.89%), and carbohydrate content (from 3.34% to 0.63%). Regarding kefir grain growth, formulations with higher milk content (100%, 80%, and 60%) showed the greatest increases in biomass (150.00 g, 105.50 g, and 107.60 g), respectively. Conversely, kefir produced exclusively with buttermilk (100%) reached only 16.62 g, indicating that whole milk provides more favorable nutritional conditions for grain development. WRC decreased with increasing amounts of buttermilk: 100% milk (52.42%), 80% (42.42%), 60% (33.91%), 40% (29.55%), 20% (23.01%), and 0% milk (18.58%). This is likely due to buttermilk's lower protein content (1.76%) compared to milk (3.00%). On the other hand, syneresis increased proportionally with the buttermilk content, ranging from 31.92% (100% milk) to 73.92% (100% buttermilk), suggesting weaker and less stable gel structures favoring greater phase separation. Therefore, partial replacement of milk by buttermilk in kefir production proved viable up to the 40% level, maintaining acceptable physicochemical and technological characteristics. The inclusion of buttermilk contributes to the valorization of an underutilized co-product, promoting sustainable alternatives aligned with bioeconomy principles in the dairy industry.

Red grape pomace, a by-product generated during red wine manufactureingand rich in phenolic compounds, particularly anthocyanins, can be used to obtain natural food additives with antioxidant properties, thus improving their functional characteristics. As polyphenols, and anthocyanins specifically, tend to oxidize, which results in a loss of bioactivity, the microencapsulation of these compounds is proposed to prolong their stability during processing and storage. From a mixture of red grape pomace obtained as residues from Vitis vinifera L. wine production, microencapsules (maltodextrin, whey proteins, and chitosan) rich in pomace polyphenols were developed by means of spray drying. These microcapsules were then added to yogurt (skim, natural, and sweetened with Stevia), a dairy matrix for mass consumption, with low cost and variable storage periods. The resulting yogurts (YM) were subjected to in vitro gastrointestinal and colonic digestion. Then, polyphenol extracts were obtained from the different stages of digestion. These extracts were purified using SPE columns, and their polyphenol content was evaluated via FOLIN CT. The impact of these processes on bioactivity was assessed by analyzing the effect on antioxidant and antiproliferative responses in vitro in human intestinal cells via flow cytometry. The results obtained contribute to our understanding of how the modifications that occurred during the digestion process impact the final biological response achieved.

This study explores the application of powder X-ray diffraction (p-XRD) combined with chemometric analysis as an innovative approach for the quality assessment and classification of cheese varieties. A total of 64 cheese samples, including Cheddar, Kefalotyri, and Halloumi, were analyzed to investigate differences in their crystalline structures. p-XRD, a non-destructive analytical technique traditionally used in material science, was employed to obtain detailed information on the texture, composition, and internal structure of the samples. Chemometric modeling was then applied to the spectral data to classify the cheeses according to type, highlighting distinct patterns in their crystalline fingerprints. The crystalline fingerprint of each cheese type was investigated, revealing that Cheddar exhibited a low degree of crystallinity, Kefalotyri a medium level, and Halloumi a high degree of crystallinity. These differences were associated with variations in casein structure, calcium salt content, residual lactose, and other inorganic salts present in the cheeses.

The study is particularly novel in its focus on Halloumi, a traditional Cypriot cheese with Protected Designation of Origin (PDO) status. The use of p-XRD in dairy research is limited, and its application to Halloumi is unexplored. The results demonstrate that this technique, when combined with multivariate data analysis, can effectively differentiate cheese types based on subtle structural variations, thereby offering potential for quality control, authenticity verification, and process optimization.

This research paves the way for future studies into the structural characterization of dairy products, especially those produced with emerging ingredients such as milk powder. Although cheese ripening (aging) was not studied in the present work, future research may explore how maturation affects crystalline patterns, further expanding the application of this technique. The methodology developed could serve as foundation for the design of new analytical workflows in food science, and contribute to innovation in the dairy industry through enhanced quality assurance and product traceability.