Journal of Food Process Engineering最新文献

Escherichia coli O157:H7 contamination in poultry poses significant public health risks, with outbreaks linked to undercooked chicken causing severe gastrointestinal illnesses. This study provides the first comprehensive comparison of five kinetic models (First-Order, Weibull, Biphasic, Log-Logistic, and Gompertz) to predict E. coli O157:H7 inactivation in ground chicken under varying time–temperature matrices (60°C, 65°C, 70°C, and 75°C) and treatment durations (0, 5, 10, 15, 20, 25, and 30 min). By integrating microbial lethality with physicochemical quality analysis, we identified optimal thermal processing conditions that ensured both food safety and minimal quality degradation, addressing a critical gap in poultry processing guidelines. The findings demonstrated that thermal treatment at 75°C for 15 min achieved a > 6-log reduction, meeting food safety standards. However, extending the treatment to 30 min at 75°C better preserved physicochemical properties, yielding minimal texture loss. Among evaluated models, the First-Order kinetics provided the best fit (R² ≥ 0.99) across all temperatures, while extended treatment (30 min at 75°C) preserved optimal physicochemical properties, yielding minimal texture loss and acceptable color changes (ΔE < 5). The rate constants, k, (between 0.008 and 0.095) across all treatment conditions for the First-Order model defined the inactivation process at a low pace. Physicochemical analysis, which included color, pH, and texture profile analysis (TPA) was likewise conducted. Thermal treatment at 75°C for 30 min ensured optimal physicochemical properties. This study aims to advance public health by improving thermal food processing techniques that ensure food safety while maintaining product quality.

Sugarcane quality assessment is vital for optimizing yield and processing. However, traditional laboratory methods are slow, costly, and unsustainable. Moreover, they cannot capture spatial and temporal variability across large cultivation areas, often resulting in inconsistent and less representative evaluations. To overcome these limitations, this manuscript proposes the Enhanced Sugarcane Quality prediction using near-infrared spectroscopy and Rotation-Invariant Coordinate Convolutional Neural Network (ESQ-NIRS-RICCNN). At first, the spatial variability of the geo-referenced sampling data were gathered from a commercial field using NIRS. The input data are pre-processed using an Implicit Unscented Particle Filter (IUPF) to remove outliers, then fed into a Rotation-Invariant Coordinate Convolutional Neural Network (RICCNN) to forecast sugarcane quality attributes such as fiber, brix, and Pol. In general, RICCNN does not inherently include an optimization strategy for tuning its parameters for predicting sugarcane quality. Hence, the weighted leader search algorithm (WLSA) is used to optimize RICCNN, which accurately predicts sugarcane quality. The proposed model achieved RMSE values of 0.70% for brix, 0.52% for Pol, and 0.61% for fiber, with R² values of 0.81, 0.82, and 0.84 respectively. Compared to existing methods, PSLN-NIRS-SVMR, SSBP-NIRS-ANN, and NIRS-DCPS-SVR, the approach demonstrated superior performance with a 0.99 Determination Coefficient (R²) and a 0.245% root mean squared error (RMSE). These findings establish ESQ-NIRS-RICCNN as a robust solution for sugarcane quality monitoring, offering improved pricing accuracy and processing efficiency while supporting the integration of NIRS-based systems into commercial harvesting equipment.

The formation of stable virgin coconut oil (VCO) emulsions is critical for effective spray drying, as it directly affects encapsulation efficiency and powder quality. This study aims to evaluate the effects of homogenization duration (5, 10, 15, 20, and 25 min) and speed (5000, 10,000, 15,000, 20,000, and 25,000 rpm) on the physicochemical properties and stability of VCO emulsions. Emulsions were assessed for interfacial tension using the DU Noüy ring method, droplet size and distribution via light scattering (Mastersizer 3000), rheological behavior using a rheometer, and microstructure through transmission electron microscopy (TEM). The results showed that interfacial tension remained largely unaffected by variations in homogenization conditions, with values ranging consistently between 3 and 4 mN/m. However, rheological analysis revealed significant changes, where the condition of 10 min at 15,000 rpm yielded the highest viscosity (μ₀ = 4820.70 Pa·s) and consistency index (K = 40.671 Pa·s). This condition also correlated with the smallest mean droplet size (D4,3 = 1108.00 nm), indicating improved emulsion stability. Microscopy images confirmed that the droplets under optimal conditions were predominantly spherical or oval with smooth surfaces, exhibiting uniform dispersion in the aqueous phase. These findings suggest that while interfacial tension is not significantly influenced by mechanical shear, homogenization plays a crucial role in reducing droplet size and enhancing rheological stability. This insight underscores the importance of optimizing homogenization parameters to formulate stable VCO emulsions, especially for applications like spray drying, where particle size and viscosity significantly impact the final product quality.

Arecanut is a major crop in India, holding economic and cultural significance. However, significant losses occur due to fruits falling and manual collection of these fallen nuts is exhausting and time-consuming. Existing defect detection methods often lack adaptability and accuracy for diverse object types, complicating quality control. This study aims to address these challenges by proposing an automated system to identify and collect good quality fallen arecanuts, while discarding the defective ones. The system utilizes a vision-guided robotic arm and deep learning techniques. A real-time dataset was built by collecting and preprocessing arecanut images. Convolutional neural networks (CNN) and traditional machine learning methods such as support vector machine (SVM), random forest (RF) were evaluated and compared for defect detection and classification. The system integrates force-based classification with real-time object handling. A PicArecanut (PA) algorithm using RAPID programming was developed to control the robotic pick-and-place operation. CNN outperformed SVM and RF, achieving a classification accuracy of 86.44%, compared to 84% and 81%, respectively. The robotic system, using PickMaster, demonstrated high precision (0.94) and recall (0.93) in classifying arecanuts. This demonstrates CNN's superior ability to generalize across defect types. The deep learning-powered robotic system offers a scalable, efficient, and cost-effective solution for arecanut collection, reducing manual labor and minimizing losses.

Cookies are widely consumed baked products around the world; therefore, enriching them with health-promoting ingredients can enhance their nutritional value. Potato peel and potato flour are promising ingredients for gluten-free cookie production. This study evaluates the functional properties of freeze-dried waste potato peel flour (PPF), commercial potato flour (PF), and wheat flour (WF), and investigates the physicochemical, textural, and sensory attributes of cookies fortified with PPF and PF. Cookies were formulated by substituting WF with PF and PPF at 0%, 50%, and 100% ratios. Results indicated statistically differences (p < 0.05) in swelling, bulk density, solubility, oil, and water absorption capacities among the flours. PPF demonstrated advantageous functional qualities for baking applications. Cookies with PPF and PF exhibited increased moisture and water activity, altered color profiles, decreased hardness, and increased fracturability. Sensory evaluation showed higher preference for PF cookies, though PPF cookies also received acceptable scores. This study demonstrates the potential of waste PPF as a sustainable, health-oriented ingredient in gluten-free bakery products.

The world's ever-increasing population has expanded the need to study sustainable technologies for green synthesis. Scientists and researchers have focused on exploring natural bioactives in the food and pharmaceutical industries in the last few years. Betanin is a naturally occurring bioactive compound with exceptional antioxidant properties found in beetroots in significant amounts. However, due to its environmental sensitivity, the extraction and concentration of this compound from beetroot juice is difficult. This study investigates a cleaner, more energy-efficient hybrid system combining ultrafiltration (UF) and nanofiltration (NF) to concentrate betanin while preserving its stability and bioactivity. UF was employed as a pre-treatment step to remove high-molecular-weight impurities, while the NF step enabled selective concentration of betanin. The hybrid process significantly improved betanin retention, with an observed percentage rejection in the 87%–94% range. Additionally, a maximum flux decline of 65% was observed at high pressure and low flow rates, indicating fouling. The fouling mechanism analysis showed that the complete blocking model is the best-fit model for experimental values. These findings contribute to the development of eco-friendly and cost-effective membrane-based techniques for the recovery of natural compounds.

Meat is a vital component of the human diet, yet its vulnerability to microbial contamination during production, processing, and storage presents serious public health concerns and economic losses. Conventional decontamination methods, including chemical sanitizers and heat treatments, may degrade meat quality, leave harmful residues, and require high energy input. This review explores emerging non-ionizing technologies—ultrasonication, ultraviolet (UV) radiation, microwave irradiation, and pulsed light (PL)—as sustainable alternatives for improving meat safety and quality. Ultrasonication uses acoustic cavitation to disrupt microbial cells, while UV and PL inactivate pathogens by damaging microbial DNA and proteins. Microwave irradiation offers effective volumetric heating while retaining nutritional and sensory attributes. The review presents a comparative analysis of these techniques, discussing their benefits, limitations, and potential roles in the meat industry. Integration into multi-hurdle strategies, alongside packaging and antimicrobial agents, is also considered. Finally, it highlights the challenges of scalability, cost, and regulatory approval for broader adoption.

This study optimized ultrasound-assisted extraction (UAE) for faba bean protein (FBP) and evaluated its gelation potential. Three types of protein gels (acid-, salt-and enzyme-induced) were prepared, and the physicochemical properties, structures and gel characteristics of ultrasound-assisted extracted faba bean protein (UAE-FBP) were discussed. The results showed that the optimal extraction parameters were a material-liquid ratio of 1:10, and an ultrasound power of 500 W for 30 min. Compared to the alkaline-extracted faba bean protein (AE-FBP), the protein purity, ash content and fat content of UAE-FBP showed no significant differences, while the protein particle size and turbidity were significantly reduced. Furthermore, UAE-FBP showed a significant increase in β-sheet content and surface hydrophobicity by 4.82% and 53.87%, respectively, while particle size, turbidity, and β-turn content were significantly decreased. Compared to AE-FBP gel, the enzyme-induced and acid-induced protein gels of UAE-FBP exhibit significantly increased gel strength, water-holding capacity, and gel quality, while presenting higher apparent viscosity, storage modulus (G′), and loss modulus (G″). In conclusion, the ultrasonic extraction process has a relatively high protein extraction rate, and UAE-FBP is more suitable for processing into transglutaminase (TG)-induced gels, providing a novel strategy and valuable insights for the utilization of FBP.

Drying techniques play a crucial role in food preservation, ensuring quality, safety, and extended shelf life for agricultural products. This study reviews refractance window drying (RWD) as an energy-efficient and sustainable approach for drying heat-sensitive agricultural products while evaluating its potential integration with traditional drying methods to improve efficiency and reduce energy consumption. RWD, in comparison to conventional techniques, like drum drying, emphasizes advantages in energy efficiency, nutritional preservation, and drying time. RWD reduces energy consumption by 40%–50% and preserves more vitamins and antioxidants than traditional methods. It reduces drying time by 30%–40% compared to hot-air oven drying. The study examines the impact of RWD on the product's quality parameters, including texture and color, which are crucial for both industrial and commercial products. The findings indicate that RWD is a reliable alternative to conventional drying methods in both laboratory and industrial environments. A comprehensive bibliometric analysis of a total of 110 peer-reviewed research studies showed that RWD improved energy efficiency by requiring 30%–50% less energy than conventional methods. RWD retained a much higher percentage of quality, with vitamins and antioxidants preserving over 90% of their integrity, compared to 60%–80% for other methods. Furthermore, RWD markedly reduced microbial load, achieving a 3–4 log reduction that exceeds the effectiveness of the majority of different drying methods. Artificial intelligence and machine learning in practical data operations led to an efficiency improvement of 15%–20% in the drying process while enhancing product quality and reducing operational expenses. RWD is an efficient method for drying heat-sensitive crops, providing significant advantages in energy efficiency, quality retention, and microbiological safety.

Ohmic heating (OH) is an innovative method of converting electrical energy into heat. This study determined the effect of temperature and time on total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activity (AA) in clear apple juice that was heat-treated using conventional heating in a water bath and OH at the voltage gradients of 10 and 20 V/cm. Additionally, the degradation kinetics for the reduction rates of these properties over time were assessed. The TPC, TFC and AA values of fresh apple juices prepared from concentrate were 19.38 ± 0.53 mg GAE/100 mL, 2.96 ± 0.10 mg CE/100 mL and 12.05 ± 0.17 μmol TE/100 mL, respectively. OH at 10 V/cm for 30 min at 80°C reduced the TPC, TFC and AA values of apple juice to 16.75 ± 0.90, 2.48 ± 0.36 and 4.66 ± 0.17, respectively, while these values decreased to 15.14 ± 1.18, 2.55 ± 0.07 and 4.67 ± 0.07 for OH at 20 V/cm, respectively. In conventional heating, the TPC, TFC and AA values of juices were 16.94 ± 0.19 mg GAE/100 mL, 1.98 ± 0.03 mg CE/100 mL and 10.80 ± 0.97 μmol TE/100 mL, respectively. Therefore, OH treatments were more effective in protecting TFC, and a greater decrease in AA values was observed. Ohmic heating caused a 9.4% increase in the hydroxymethyl furaldehyde (HMF) content while this increase was 57.1% in conventional heating. OH, especially at 20 V/cm, preserved the original color of clear apple juice better than conventional heating, as indicated by the lower values of total color changes. OH was found superior for the heat treatment of apple juices, minimizing browning, HMF formation, and quality loss, while following first-order degradation kinetics for the reduction rates in the TPC, TFC, and AA values.