Conference Agenda

Drying is a critical energy-intensive process for preserving the quality and shelf life of medicinal and aromatic plants (MAP). This study investigated the potential for enhancing the energy efficiency and performance of conventional hot-air dryers through controlled recirculation of exhaust air. Experiments were conducted in eastern Croatia using a 27 m² chamomile flatbed dryer retrofitted with a tailored automated recirculation system. Four strategies were compared, including operation with fresh ambient air (FA), two threshold-based strategies switching to full recirculation at 40% (RH_out-40) and 60% (RH_out-60) of exhaust relative humidity, and a dynamic strategy maintaining a 40% inlet humidity setpoint (RH_in-40). On average, 21.1 h was required to reduce moisture content from 77.8% to 6.7%, with throughput ranging from 18.8 to 22.5 kg_DM∙h^-1. Air recirculation significantly reduced thermal energy demand and fuel consumption without compromising product quality. The RH_in-40 strategy proved most efficient, lowering fuel consumption from 11.1 kg·h⁻¹ of FA to 7.6 kg·h⁻¹, while specific energy utilization dropped from 7.6 to 5.5 MJ·kg⁻¹_H₂O. Additionally, the estimated CO_2​ footprint per drying batch decreased from 917.8 to 545.1 kg CO₂-eq. These findings highlight controlled air recirculation as a practical and low-complexity retrofit for enhancing efficiency and sustainability in conventional MAP drying systems.

Microwave-vacuum drying (MVD) is not widely adopted at large industrial scale for producing high-quality dried fruits due to the low economic, technical, and process-related sustainability. The study describes a model suitable to design a small and medium-capacity drying plant intended for small fruit and vegetable producing companies. To develop the model were investigated the effects of microwave power (2, 4, 6 and 8 kW/kg) on the drying kinetics, energy consumption, and functional quality of kiwifruit slices. The drying behavior was evaluated through drying time, drying rate, and effective moisture diffusivity, while process performance was assessed using specific energy consumption and energy efficiency. Quality attributes included color variation, total phenolic content, total flavonoid content, antioxidant activity, and vitamin C retention. Increasing microwave power significantly reduced drying time from 91.5 to 26.5 min and increased the effective moisture diffusivity up to 49.81 × 10⁻¹⁰ m²/s. Specific energy consumption increased slightly. Higher power levels led to reductions in vitamin C and antioxidant activity. Overall, the model allowed to estimate the best compromise between drying efficiency and quality retention, demonstrating the potential of optimized MVD as a scalable and product-efficient dehydration technology.

This study is one of the first studies performed in Costa Rica on white pepper (Piper nigrum L.) which is one of the most valued spices in the world because of the aroma, flavor, and properties. Two drying methods were evaluated, solar radiation and convection (40 °C) for white pepper from the Rocío Agricultural Farm. The focus was on drying kinetics and hygroscopic equilibrium curves to determine the optimal time to finish the drying process. In addition, physicochemical properties were evaluated (bulk density, pH, color, moisture content, water activity, and dimensions). Finally, storage conditions were evaluated in three environments 91.42; 73.24; and 29.58 % relative humidity; and three packaging material ‘doypack’, high-density polyethylene (HDPE) bags, and polyethylene terephthalate containers. The overall fit of the data was achieved with Peleg and Yanniotis & Blahovec models, both were suitable for representing the sorption isotherm (25 °C). The Midilli model was appropriate to represent the effective time drying for both methods. HDPE and doypack bags reported less mass, color, and moisture content change during 2.56 months. As a conclusion, the hygroscopic equilibrium curve shows a similar trend for both drying method, and white pepper was classified as Grade I, which enables international marketing.

Non-destructive technologies combined with machine learning algorithms emerge as promising alternatives to overcome the limitation the method traditional for grain classification. This study aimed to investigate the use of near-infrared (NIR) spectroscopy and hyperspectral sensors as fast and accurate tools to analyze the physicochemical properties of grains and identify machine learning (ML) models with higher classification accuracy. Samples of hundred grains each type were used for evaluation moisture, crude protein, starch, lipid, crude fiber, and ash contents, using a FOSS NIR DS2500 spectrometer. Hyperspectral reflectance data were acquired with an ASD FieldSpec 4 Jr sensor. The instrument covers the 350 to 2500 nm spectral range. The acquired spectra were processed and averaged into representative intervals prior to model development. Six ML models were tested: Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machine (SVM), Zero-R (SL), J48 Decision Tree (J48), and REPTree, evaluated using the metrics correct classification percentage, Kappa, and F-score. The best performances were obtained by the SVM. The integration of NIR, hyperspectral sensors, and ML enabled the characterization of the grain quality, demonstrating classification efficiency and outperforming conventional methods. The results offer a practical contribution to industrial classification systems by enabling more efficient monitoring and decision-making.

The aim of this study was to evaluate the distribution of intergranular aeration air in different grain mass stored in aerator silos and dryers at reduced and real scale, using computational fluid dynamics and mathematical modeling. The governing equations were solved using the quick numerical scheme, incorporating experimentally determined resistance coefficients and the true velocity approach. The findings highlight that both silo geometry and grain properties strongly determined airflow behavior. Silos with internal ductwork consistently showed higher airflow resistance due to their structural complexity, while silos with fully perforated floors demonstrated more uniform airflow with lower pressure drops. CFD simulations validated experimental results, effectively mapping turbulence patterns, velocity distribution, and pressure gradients across different silo-grain combinations. The models confirmed that restricted airflow in duct-based silos creates high-pressure zones and turbulence, while silos with uniform aeration maintain steadier flow. Thermal analysis demonstrated that well-aerated silos maintained more stable temperature distributions, reducing risks of hot spots and moisture accumulation. Grain-specific analysis indicated that wheat exhibited the highest airflow resistance, attributable to its smaller kernel size and denser packing, whereas corn and soybean allowed for easier airflow. This integrated experimental and computational methodology offers a robust framework for optimizing grain storage systems.

Acrylamide, a food processing contaminant formed during frying, is classified as a probable human carcinogen. Fried potatoes, particularly chips and French fries, represent a major dietary source of this compound. The references methods for acrylamide quantification are costly, time-consuming, and require extensive sample preparation. This study evaluates the potential of two near-infrared spectrometers for classifying potato chips according to their acrylamide content. A total of 300 potato tubers were fried, homogenized, and scanned using two portable instruments: a Brimrose Luminar 5030 (1200–2200 nm range) and a Büchi ProxiScout™ (1350–2550 nm range). Acrylamide content was subsequently determined by liquid chromatography- electrospray ionisation- tandem mass spectrometry (LC-MS/MS) as the reference method. For chemometric analysis, the samples were divided into two classes using the threshold of 750 µg/kg of acrylamide as a reference, which is the reference level for potato chips recommended by the European Commission. Random Forest classification models were developed using various spectral pretreatments. The ProxiScout™ instrument yielded superior classification performance, achieving accuracy rates exceeding 85% in Test. These results demonstrate the feasibility of NIR spectroscopy combined with chemometrics for identifying high-risk potato batches prior to processing, potentially enabling the food industry to mitigate acrylamide formation more efficiently.

Forced-air cooling (FAC) is common practice in the South African citrus export supply chain to rapidly remove field heat post-harvest. Studies have investigated the effect of FAC on multiple aspects of fruit quality. However, con-cerns have been raised about the commercial representativeness of the conditions reported in the literature compared to those applied in commercial practice. In this study, four commercial FAC facilities across South Africa were investigated to establish current operating conditions. The study found that facilities operate in the superficial airspeed range of 0.14–0.27 m s⁻¹, rather than the 0.30–1.30 m s⁻¹ range suggested in literature. Commercially, citrus is thus cooled at much lower rates than those suggested in literature. Yet, even these slower cooling rates are suspected to be too high with respect to the risk of inducing chilling injury. This research highlights the importance of benchmarking citrus research against up-to-date commercial standards.

Berry ripening heterogeneity, both inter- and intra-cluster, represents a major constraint in the selection of appropriate winemaking practices and significantly affects final wine quality. Numerous factors influence ripening dynamics, many of which are not controllable under field conditions. Consequently, accurate berry selection is pivotal, as even small proportions of defective or lower-quality fruit can compromise overall product quality and negatively impact market performance. Efficient sorting technologies capable of processing substantial grape volumes within limited timeframes are therefore essential.

Densimetric sorting systems classify berries into fractions of differing “quality” based primarily on sugar content. Considering the intrinsic variability among clusters and individual berries, this thesis investigates the temporal evolution of detachment force, °Brix, and density to optimize densimetric selection systems. The analyzed parameters exhibit distinct trends. Detachment force shows statistically significant differences across sampling times but exhibits high variability and no direct relationship with ripening progression. °Brix displays a dual pattern: mean values increase consistently during ripening, while standard deviation and coefficient of variation remain relatively constant. Density, particularly its variability indices, shows the most defined evolution: initial variability (24–18%) decreases linearly and stabilizes at low levels (~6%). Thus, density emerges as the most reliable parameter for optimizing selective sorting processes.

This study evaluated the performance of a prototype garlic clove separator on Ilocos White and Taiwan Garlic varieties under a single-pass and two-pass operations. The machine was tested using a Box-Behnken experimental design on a Response Surface Methodology to analyze the effects of roller speed, speed ratio, and roller clearance. The performance was assessed using key performance metrics such as singulation efficiency, separation efficiency, damaged cloves, separation capacity, cleaning efficiency and specific energy consumption. The prototype successfully separated garlic bulbs, but its overall performance lagged behind existing designs. Implementing a second pass significantly improves separation and singulation efficiencies but increased damage and specific energy consumption while reducing capacity. Response surface methodology revealed a general trend for reliably modeled responses. Tighter clearance increased singulation and damage, while higher roller speeds generally increased capacity and reduced specific energy consumption. Speed ratio often displayed complex, non-linear effects and significantly interacted with other factors. Findings of this study suggests a need for design improvements, potentially involving different roller materials or surface texture and other mechanisms. Comprehensive multi-objective optimization is currently not feasible due to insufficient factorial understanding on several non-significant models and lacking sufficient predictive capabilities.

This study presents the methodological framework and optimization approach developed to design efficient biomass logistics for grass-based biorefineries. Four alternative supply chain configurations such as centralized, decentralized wet fractionation, on-farm processing, and hybrid models, were examined through a Smart Biomass Supply Chain (SBSC) tool. The core of the method is a multi-objective optimization model integrated into a digital twin of the entire value chain. This model uses geospatial data, real-time sensor inputs, and biological models of protein evolution and degradation to simulate thousands of feasible logistics and processing combinations.

Key objectives include minimizing total cost and processing time while maximizing protein yield and resource efficiency. The multi-objective evolutionary algorithm identifies Pareto-optimal configurations balancing transport distance, facility utilization, and product quality. Constraints such as facility capacity, vehicle availability, and harvest time windows are rigorously applied to ensure operational feasibility.

Expected outcomes include up to 30% reduction in greenhouse gas emissions, 20–40% decrease in logistics costs, and significant improvement in protein preservation across the supply chain. The hybrid scenario, supported by adaptive optimization, demonstrates the potential to dynamically combine centralized efficiency with decentralized flexibility and on-farm circularity. Overall, this framework offers a method for designing sustainable and region-specific green biorefinery networks.

The control of dissolved gases in wine represents a significant technological challenge in modern oenological engineering, as oxygen and carbon dioxide play a decisive role in product stability and sensory properties. In this context, the present study aimed to evaluate an innovative device designed for the continuous regulation of dissolved gases in wine. The system is based on a physical separation principle using a hydrophobic membrane combined with the application of negative relative pressure, enabling controlled modulation of gas concentrations.The experimental trial involved the application of three levels of negative relative pressure (−0.3; −0.6; −0.9 bar), each performed in triplicate (n = 3), on three different wine types. Samples were analyzed for dissolved oxygen and carbon dioxide, volatile organic compounds, and sensory attributes through a panel test. Statistical analysis was conducted using two-way ANOVA in R (v. 4.5.1) to evaluate treatment effectiveness.The results highlighted a relevant effect of the treatment, with a reduction in dissolved O₂ and CO₂ concentrations. No differences were observed in the content of the analyzed volatile organic compounds; however, sensory analysis revealed significant variations in selected attributes.

This paper presents a novel approach to computational fluid dynamics (CFD) modelling of geometric variations in pallet stacking that exist across shipments of fully loaded refrigerated citrus fruit containers. Inconsistency in pallet stacking can influence temperature distribution within the container, leading to unique patterns of fruit quality preservation. By exploring the relationship between packing configuration and resulting temperatures, we aim to design improved container loading strategies and packaging to improve overall fruit quality by addressing the flow challenges they pose. Geometric variability (pallet lean percentage) in pallet stacking is visually identified through observation and imagery to ensure accurate representation within the model. The pallet lean percentage is implemented using a multinomial factorial to generate 100 different geometric configurations of a fully loaded container and their probabilities, for CFD simulation using STAR-CCM+. Monte Carlo is used to determine whether the sample size is representative of the bulk of container configurations. Results show that the sample size is representative of 85,5% of all possible configurations. Early simulations are consistent with validation experiments. However, meshing quality remains a challenge due to geometric gaps in the model. This is a known problem in CFD modelling, and the effectiveness of several solutions is reported.

Factors affecting the performance of corrugated citrus export cartons from South Africa throughout the full logistics chain are poorly understood and are often compensated for by applying safety factors to box compression test data. These safety factors are generally not scientifically based and do not necessarily capture the effect of individual factors unique to a specific logistics route or destination. This study thus investigated creep testing, which is an alternative performance-based technique. As relevant literature is limited, this project initially focused on designing a low-cost (commercial) test rig and establishing the most effective testing methodology. A dead-weight creep fixture was designed to withstand a load of 1000 kg. Different configurations were analysed, and double or triple-stack tests with dead-weight-loaded upper cartons proved most representative, ensuring failure occurred on the bottom carton. A major shortcoming is the difficulty of isolating bottom carton deflection from total stack deflection, making this impractical for predictive modelling. Additionally, cartons survived significantly longer under commercially relevant loads without failure, making this impractical for performance testing. Accelerated testing was therefore investigated and showed significant promise for further study.

Moringa oleifera Lam. is an emerging nutraceutical crop increasingly cultivated in Mediterranean environments due to its high nutritional value and adaptability to semi-arid conditions. Within precision agriculture systems, advanced monitoring technologies can support the production of high-quality plant materials for innovative food applications. This study evaluated the use of Moringa oleifera leaves cultivated in Sicily through precision agriculture techniques as a functional ingredient in sourdough bread. Crop monitoring was performed using unmanned aerial vehicle (UAV) multispectral imaging and vegetation index analysis to determine the optimal harvesting stage and ensure leaf collection at peak vegetative vigor. The harvested material was processed into powdered M. oleifera leaves (PMOL) and incorporated into sourdough bread formulations by partially replacing durum wheat semolina at 5% and 10% (w/w). Microbiological analyses based on culture-dependent methods assessed the dynamics of sourdough lactic acid bacteria during fermentation, while sensory evaluation determined consumer acceptability. PMOL supplementation did not affect bacterial growth, with populations reaching approximately 10⁸ CFU g⁻¹ at the end of fermentation. Breads enriched with 5% PMOL achieved the highest sensory scores. These results highlight the potential of precision-grown M. oleifera as a sustainable ingredient for nutritionally enhanced bakery products.

Postharvest maize shelling in small-scale systems commonly relies on manual feeding, exposing operators to safety risks, repetitive strain, and inconsistent processing rates. Automation offers potential to improve productivity, safety, and reliability while reducing losses. This study evaluated the performance of a photovoltaic-powered maize sheller equipped with an automated feeding system. A robotic arm was integrated with the shelling unit and controlled via a customized graphical user interface (GUI) using Wi-Fi for real-time control. Shelling experiments on maize of variety LG 30.258 were conducted at varying rotational speeds under controlled conditions to assess the shelling quality and the throughput. For the automated feeding system, experiments focused on the optimal cob gripping position, orientation, and insertion trajectory to minimize jamming. The optimized system successfully automated the feeding process, reducing operator intervention and improving operational consistency. The percentage of broken grains was 22.6 ± 5 %, 29.0 ± 6%, and 27.1 ± 4 % for low, medium and high moisture content maize, respectively. No significant differences in energy consumption were observed across different rotational speeds Overall, the system demonstrates the potential of integrating renewable energy and robotic automation into small-scale maize shelling operations, reducing grain damage while improving operator safety and working conditions.

Industrial pasta drying is a complex process in which environmental gradients may influence product quality and uniformity. This study aimed to investigate the effect of spatial variability within a static dryer and to evaluate its impact on the final moisture and physical stability of different pasta formats. Temperature and relative humidity were monitored using six data loggers positioned at different locations inside the drying cabinet. During the drying cycle, temperature ranged between approximately 30 and 57 °C, while relative humidity varied between 22% and 96%. Moisture analysis revealed significant variability among samples, mainly associated with horizontal carriage positions. Physical measurements indicated that variability during drying depended on the pasta shape: Paccheri samples exhibited nearly uniform thickness and diameter, Fusilloni showed moderate variability in thickness, while Fusilli al Bronzo displayed the highest sensitivity to drying position. The results indicate that thermo-hygrometric gradients established within the static dryer lead to non-uniform moisture distribution and variations in product quality attributes, depending on pasta geometry and position. Therefore, optimizing the spatial distribution of the carriage and the drying parameters (temperature, humidity and air velocity) is essential to achieve uniform drying conditions and standard product quality within a production batch.