Radio frequency (RF) heating is a promising novel food enzyme inactivation technology featuring rapid heating, deep penetration, and minimal quality degradation. However, continuous-flow treatment of viscous liquids remains limited by nonuniform electric-field heating, restricted effective residence volume within the electrode region, and difficulty in directly observing the coupled electric, flow, and temperature fields. This study used carboxymethyl cellulose (CMC) as a high-viscosity non-Newtonian model fluid to develop a continuous RF inactivation system using S-shaped tubes and a Multiphysics model coupling electromagnetic, flow, temperature, and polyphenol oxidase (PPO) inactivation kinetics. Compared with a straight tube, the S-shaped tube increases the effective heating length within a limited electrode area and promotes radial mixing through bend-induced secondary flow. The model was assessed by comparing simulated and experimental temperature profiles and residence-time distributions (RTDs); the maximum RMSE was 2.73 °C and the maximum residence-time error was 2.4%. PPO inactivation followed first-order kinetics, with the rate constant k1 increasing from 0.0110 to 0.3221 as temperature rose from 60 to 75 °C, while D-values decreased from 208.57 min to 7.15 min over the same range. Higher CMC concentrations resulted in higher RF-heating temperatures, greater PPO inactivation, and higher suitable flow rates. Using 20% residual PPO activity as the target threshold, the optimal volumetric flow rates for 0.5%, 1.0%, 1.5%, and 2.0% CMC solutions were 1660.26, 1799.43, 1909.90, and 1966.00 mL/min, respectively. Red Fuji apple juice treated at 1900 mL/min, whose dielectric properties at a frequency of 27.12 MHz were similar to those of the 1.5% CMC solution, achieved 18.9 ± 3.6% residual PPO activity, providing preliminary application evidence under the tested condition. This study may provide valuable information for the industrial application of RF inactivation.
This study investigated the covalent binding of 6-gingerol at different concentrations (10, 20, 50 μmol/g protein) with myofibrillar protein (MP) under radical-induced conditions, and its effects on the structure and gel properties of MP. The results demonstrated covalent bonding between MP and 6-gingerol through analyses of total phenolic content, free amino acids, and total thiol groups, corroborated by SDS-PAGE. Spectral analysis indicated that covalent modification of polyphenol alters protein secondary structure. With increasing 6-gingerol concentration, the α-helix content in the adduct decreased from 35.07% to 24.90%, while the β-sheet content increased from 17.58% to 22.57% and random coil content increased from 27.82% to 29.47%. Particle size distribution indicated that 6-gingerol binding reduced particle size (from 304.70 nm to 205.55 nm). Scanning electron microscopy revealed that the polyphenol-treated Z10, Z20, and Z50 groups exhibited covalent adduct particles with spherical morphology, smooth surfaces, and distinct cross-linked structures. Mass spectrometry indicated that the MP–6-gingerol covalent adduct formed via an ‘amino-quinone’ addition reaction, with covalent modification occurring at threonine (T), lysine (K), tyrosine (Y), and serine (S) sites. Furthermore, MP gel treated with 50 μmol/g protein 6-gingerol exhibited enhanced gel strength (from 18.68 g to 48.53 g), water-holding capacity (from 58.74% to 76.3%), and storage modulus. These findings highlighted a potential effect 6-gingerol binding in favoring MP gelation.
Bag bursting during microwave thermal processing remains a major challenge for maintaining product quality and safety in packaged sauerkraut. To address this issue, a pressure-assisted microwave pasteurization system was integrated with a rule-based adaptive fuzzy pressure controller grounded in process physics. The controller coordinates temperature and pressure in real time to limit internal vapor imbalance and improve processing performance. Physicochemical and microbial changes, including lactic acid content, vitamin C, color values (L*, a*, b*), texture hardness, bag-bursting rate, and microbial load, were systematically investigated under different pressure-assisted heating conditions. The pasteurization process consisted of a heating stage and a holding stage, followed by a cooling stage. Results showed that during the heating stage, increasing pressure reduced the rate of water phase transition and enhanced heating uniformity, while in the holding stage, excessive pressure effectively decreased bag bursting but elevated the boiling point of water, leading to partial quality degradation. To address this trade-off, a fuzzy pressure-control algorithm was developed for the holding stage. Its inputs were the temperature differences associated with the pressure-dependent boiling point and the selected internal high- and low-temperature sensing locations. The proposed adaptive control strategy enabled dynamic coordination between thermal treatment intensity and pressure regulation, achieving substantial microbial reduction while maintaining product quality and package integrity under the tested conditions. Note that the conditions investigated correspond to pasteurization rather than commercial sterilization. Overall, this study presents a data-driven and intelligent control framework for the microwave pasteurization of high-moisture packaged foods, providing a laboratory-scale basis for understanding pressure regulation and adaptive control in microwave pasteurization of packaged sauerkraut.
Although collagen peptide-enriched functional foods have been extensively developed, the incorporation of collagen peptides into chocolate matrices for 3D printing applications has not yet been explored. Therefore, this study aimed to develop 3D-printed functional chocolate enriched with collagen peptides. The collagen peptide microcapsules were prepared using porous starch/β-cyclodextrin as composite materials via spray drying. The effects of processing parameters (including core-to-wall ratio, solid content, and inlet temperature) on encapsulation efficiency were first evaluated, and the resulting microcapsules were characterized using FTIR, XRD, and SEM. Subsequently, the effects of microcapsule addition levels on the rheological properties, textural characteristics, and printing accuracy of chocolate were investigated. The results showed that the optimized microcapsules achieved an encapsulation efficiency of 79.53% under the conditions of a core-to-wall ratio of 1:2, a solid content of 13%, and an inlet air temperature of 180 °C. Chocolate containing 12% microcapsules exhibited the highest printing accuracy (96.5%) under the optimized printing conditions of a printing speed of 25 mm/s, nozzle diameter of 0.8 mm, and printing temperature of 38 °C. Mass spectrometric analysis confirmed an L-hydroxyproline retention rate of 92.69% after printing, as well as the presence of collagen peptides in the 3D-printed products. Overall, this study provides a promising approach for the development of 3D-printed chocolate products fortified with collagen peptides.
The poor redissolubility of freeze-dried Spirulina protein extract (SPE) powder severely limits its application in functional foods. Based on mild and efficient SPE extraction (93% yield while preserving macromolecular subunit integrity), this study elucidated the molecular mechanism of freeze-drying-induced redissolubility loss by analyzing particle-size distribution, molecular-weight/subunit distribution, secondary structure, and intermolecular forces. After freeze-drying, SPE showed approximately 65% protein solubility, a polydisperse particle-size distribution, and a predominantly ordered secondary structure (α-helix + β-sheet > 60%). Hydrophobic interactions (25.2 mg L−1) and hydrogen bonds (48.5 mg L−1) mainly maintained the compact aggregates. Optimizing aqueous dissolution conditions alone was insufficient; even at pH 12.0, solubility reached only 87%. Intermolecular-force-targeted analysis confirmed hydrophobic interactions as the key factor limiting SPE redissolubility. Unlike high-concentration SDS (0.5 mol L−1), which caused non-specific strong disruption, SDS (0.005 mol L−1), DTT (0.005 mol L−1), and urea (2 mol L−1) disrupted aggregates through a cascade synergistic mechanism: urea broke down the hydrogen-bond network and relaxed the rigid framework, whereas DTT reduced disulfide bonds and opened molecular crosslinks. These changes exposed buried hydrophobic regions, enabling low-concentration SDS to precisely disrupt dominant hydrophobic interactions. Consequently, protein conformation shifted toward random coils (63.4%), uniformly sized soluble complexes formed, and protein dissolution rate increased to 96%. These findings provide a mechanistic basis for improving highly hydrophobic freeze-dried protein powders.
New technologies are increasingly emerging as alternatives to conventional treatments. This review summarizes recent advances in packaging solutions that enable in-package applications of pulsed light, UV-C radiation, high-pressure processing (HPP), ohmic heating, ultrasound, microwave-assisted thermal sterilization (MATS) and microwave-assisted pasteurization (MAPS). Emphasis is given to polymer packaging materials, as these are the main structures currently employed in such applications. The prerequisites for each operation are mapped, such as high transmittance and optical homogeneity for pulsed light and UV-C radiation, isostatic compressibility, robust sealing, and dimensional recovery for HPP, electro-thermal stability and field uniformity for ohmic heating, and mechanical resilience to cavitation in ultrasound. Material choices and their characteristics are detailed, including thickness, crystallinity, additives, coatings, and properties related to barrier, mechanical, and optical performance. Adjustments involving barrier properties, sealing force, and transparency are discussed, along with polymer-dependent effects on overall or specific migration. A validation process flow and headspace guidelines are outlined to prevent shadowing, particularly for pulsed light and UV-C radiation, and defects in HPP. Studies also indicate that optimized processing can extend shelf life for products such as meats, dairy, fruits and vegetables, and fish. The conclusion highlights that enabling materials and customized process parameters make emerging routes scalable while maintaining food safety, quality, and sustainability.
Oleogels are popular for low saturated fatty acid contents and absence of trans fatty acid as the replacement of traditional solid fats to be used for baked foods. However, oil separation and brittleness have been the issues for oleogels. To address this issue, the effects of one combined gelling agents on the properties of solid oleogels were investigated. Results showed that among all tested gelling agents, the oleogels were formed by the combination of the glyceryl monopalmitate and glyceryl monostearate as gelling agents, which formed the dense interconnected needle-like crystals. Therefore, the sufficient molecular chain relaxation and continuous nucleation were enabled in the oleogels by the combined gelling agent with an extremely low kcorr (1.87×10−6-1.23 × 10−5) and high Avrami n value (1.504-1.794). A stable polymorphic structure of β and β′ was preferentially generated, and hydrogen bonding together with hydrophobic interactions further strengthened the three-dimensional network. The optimized oleogels presented excellent viscoelasticity, shear resistance and thermal stability (63–67 °C), as well as outstanding oil retention capacity. When the oleogels were applied in toasts, the compact network of needle-like crystal was acted as a physical barrier, which impeded molecular rearrangement of starch and homogenized the gluten matrix. Consequently, the internal crumb pores of the toast were refined. Baking loss, water migration, and staling of the toast were effectively suppressed. This work developed a novel structured fat alternative for bakery products, which possessed the superior health benefits relative to the traditional hydrogenated shortening by eliminating trans fatty acids and lowering saturated fatty acids.
Emulsion proved to be an effective system stabilized by pectin from steam explosion-treated dried citrus peel (PDP) for nobiletin delivery to overcome its low solubility and poor bioavailability in vitro in our previous study. Bulk density and water content of citrus peel were key factors during steam explosion processing. However, effect of steam explosion (SE) on nobiletin delivery fabricated by pectin from fresh (PFP) or dried citrus peel was unclear. So, emulsion stabilized by PFP or PDP was fabricated, characterized and applied to deliver nobiletin in vivo. Results showed that SE lowed the Mw (29.93 kDa) and DM (64.41 ± 0.37%) of PFP but enhanced its extraction rate and nobiletin embedding rate by 2.68 times and 1.91 times, respectively. PFP with low Mw and loose conformation decreased the creaming index from 49.54 ± 5.95%, 22.68 ± 0.80% to 12.00 ± 1.64% compared with untreated pectin and PDP. Nobiletin loss was reduced significantly by 44.69% in PDP group and 97.61% in PFP group in vivo. Nobiletin loss was significantly reduced by 44.69% in the PDP group and 97.61% in the PFP group in vivo. The PFP-stabilized emulsion improved intestinal retention of nobiletin and reduced its loss during digestion.
Ensuring stable product concentration in industrial evaporation systems remains challenging due to complex flow behaviour and the limited observability of key internal phenomena using conventional instrumentation. This work introduces a generalisable Computational Fluid Dynamics (CFD)-informed methodology for diagnosing process inefficiencies and developing control-oriented surrogate models. The approach links otherwise unobservable heat and mass transfer dynamics to measurable process variables and production targets, with particular attention to non-Newtonian product behaviour. The simulation methodology was first demonstrated and validated on an industrial-scale tomato evaporation system. The simulations were used to reconstruct internal transport dynamics and quantify the effect of non-Newtonian product rheology on recirculation and concentration development. The actual recirculation flow rate was found to be approximately 17–25% higher than the nominal design value, explaining why the evaporator configuration was unable to reach the target product concentration. These results reveal limitations of traditional constant-viscosity design assumptions and demonstrate the diagnostic usefulness of CFD for addressing performance deviations. Based on a dedicated simulation campaign, a computationally efficient surrogate model was developed to provide rapid predictions based on process measurements and production targets, allowing to estimate total temperature increase and water evaporated with a low-cost multivariate regression model. The model can support hybrid feedback-feedforward control by enabling predictive adjustment of operating conditions and early detection of performance degradation. The proposed methodology supports a transition from reactive operation to predictive, model-based management of industrial evaporation processes, with potential reductions in material and energy losses caused by process deviations.
This study presents the development of a magnesium-based in situ hydrogen-producing system (H2-P-Mg) designed to control oxidoreduction potential (ORP) in a beverage matrix. H2-P-Mg was incorporated into pasteurized orange juice (OJ) to induce controlled hydrogen release via an acid–metal reaction, and its performance was compared with that of direct hydrogen infusion (H2-OJ). Hydrogen generation behavior, dissolved hydrogen retention, ORP evolution, and associated physicochemical and microbial changes were monitored during 28 days of refrigerated storage. The results demonstrated that H2-P-Mg exhibited a sustained-release profile, maintaining a stable negative ORP environment throughout storage. Reaction-driven hydrogen production effectively modulated oxidative conditions without adversely affecting physical or sensory attributes. Kinetic trends in ORP evolution and hydrogen depletion revealed enhanced redox stability and reduced markers of oxidative degradation in the treated samples. H2-P-Mg OJ exhibited the highest TPC values throughout storage. Antioxidant activity on the 0th day was highest in the H2-OJ, while it was low on the remaining days for both H2-P-Mg OJ and H2-OJ. In the phenolic profile, no significant differences were observed among the groups at the end of storage. The sugar and organic acid contents were slightly affected by treatment throughout storage. The two treatments did not alter the color parameter of orange juice during storage. H2-P-Mg treatment imparted an OJ-like syrupy flavor to OJ. Principal component analysis showed that fortifying H2-P-Mg OJ or H2-OJ preserved the quality parameters. This novel method of fortifying orange juice with H2-P-Mg can confer health benefits to consumers and technological advantages for the product.
This study addresses the gap between algorithmic performance and manufacturing decision needs in bakery quality control by developing a decision-oriented framework for AI-based visual inspection model selection. Three YOLO object-detection architectures, namely YOLOv5, YOLOv8 and YOLOv11, were benchmarked on a bakery-defect dataset under nominal and controlled synthetic perturbation conditions, with Faster R-CNN included as a non-YOLO baseline. Standard detection metrics, including precision, recall, mAP@50 and mAP@50–95, together with inference latency, were translated into three managerial indicators: Risk-Weighted Total Cost of Quality (TCQ), Throughput Efficiency (TE) and Waste Reduction Index (WRI). These indicators enabled model comparison across high-volume commodity, brand-protection and resource-constrained SME production scenarios. Results show that model suitability is scenario-dependent rather than determined by aggregate detection accuracy alone. YOLOv11 achieved the most favourable profile in high-speed commodity production because its lower latency avoided downtime costs. YOLOv8 provided the lowest TCQ in brand-protection and SME scenarios, where weighted missed-defect costs were more influential than throughput losses. YOLOv5 did not yield the lowest TCQ under the adopted assumptions, but remained relevant as a lower-complexity alternative when false-positive control, waste avoidance or legacy deployment are prioritised. Faster R-CNN was consistently dominated in the investigated case. The proposed cost-quality-throughput decision matrix supports small and medium-sized enterprises (SMEs) in selecting “good-enough” architectures based on constraints and risk appetite rather than technical accuracy alone.
Final wine microfiltration is a critical bottling-line operation, because membrane fouling limits the effective filtration capacity available for each production lot. Filterability is usually assessed through laboratory tests performed shortly before bottling, too late to anticipate capacity constraints or adjust production plans. This work proposes a soft sensing framework that incorporates prediction uncertainty to infer the specific filtration capacity of wine before final filtration, using 22 industrial variables on wine origin, technological interventions, and routine physicochemical control. The estimated capacity is combined with the effective membrane area of the assigned bottling line and the lot volume to compute an operational slack indicator. Using 1342 industrial records, an observation-specific error model quantified prediction uncertainty and was propagated to estimate the probability of insufficient filtration capacity. With Extra Trees as the final estimator, the soft sensor reconstructed the specific filtration capacity with R2=0.470 on the test set (cross-validated R2=0.585±0.076), MAE=415.4 L m−2 and RMSE=976.8 L m−2, and the prediction intervals contained 92.6% of the measured values. A decision rule based on the lower bound of the predicted capacity eliminated false-safe classifications (precision of 100% on the test set), versus about 10% false-safe classifications under point-prediction rules. The proposed measurement prioritization index concentrated verification on a small subset of batches: 4.3% high priority, 2.3% medium, and 93.4% routine. Rather than replacing laboratory filterability tests, the approach identifies batches near the filtration capacity limit and provides a quantitative tool for early filtration planning and risk-based measurement allocation in industrial bottling lines.
Chocolate quality and processability are strongly influenced by the particle size distribution (PSD) together with fat content and the emulsifier system, through their combined effect on rheology, texture, and sensory perception. Roller refining is a dominant industrial route to achieve target PSDs, yet practical “recipe-to-PSD” inverse design remains challenging because the process response is nonlinear, multi-stage, and depends on both operating conditions and effective material breakage behavior. This study presents an engineering-oriented PBM-driven inverse-design framework for roller refining in which a staged population balance model (PBM) is embedded into a constrained optimization loop to compute feasible roll-speed and throughput setpoints that match both PSD percentile metrics and the cumulative PSD curve. The Bees Algorithm (BA) is used here as a representative derivative-free solver choice for the resulting nonconvex, constraint-driven optimization problem; the contribution is the inverse-design formulation and validation rather than the selection of a specific metaheuristic. A feasibility scan is introduced to quickly verify whether a target PSD lies inside a coarse reachable envelope under operating bounds, and selected effective breakage parameters are tuned under explicit regularization to mitigate model mismatch without overfitting. In the demonstrated case study, the optimized settings achieved close agreement with target metrics (d10, d50, d90, span) and a low cumulative-curve error (curveRMSE ≈ 0.0118). Experimental validation is provided by overlaying measured PSD data against both the target and model-optimized predictions in differential (volume %) and cumulative forms, showing strong agreement in the target size range. The proposed framework enables fast inverse tuning for roller refiner operation planning and provides a practical bridge between PBM modeling and industrial setpoint optimization.
Research purpose Within food processing, there is a need for sustainable and energy-efficient processing technologies for high-quality, nutritious foods. Microwave technology as emerging, direct heating method fulfills these goals. Fast and gentle heating is combined with precise process control, and the use of green energy sources. Extrusion is a well-established, efficient, and continuous food processing method for e.g. snacks and texturates like meat alternatives. However, its energy consumption is high, and its scalability is limited. Combining the advantages of both technologies results in microwave-assisted extrusion, presenting an innovative food processing tool, offering new opportunities for scaling. Principal results This review highlights the basic principles of extrusion technology and microwave heating involving their applications fields and technical implementations within food processing. Furthermore, a novel approach of combining both processes to a microwave-assisted food extruder is presented. Challenges involving this technology are the choice of extruder wall and screw material and temperature measurement on the microwave heating section. Possible options for scale-up and food applications as well as limitations are discussed. Major conclusion This review presents insights into a novel set-up of a microwave-assisted extruder for food processing applications. Besides technological challenges like equipment material, beneficial points are process scalability, process control, and the possibility of heating high-viscosity foods. Finally, this process bears high potential towards sustainable and energy-efficient food processing.
