Journal of Food Process Engineering最新文献

Mango, as a highly perishable climacteric fruit, incurs significant postharvest losses due to rapid ripening and spoilage. To address this challenge, environmentally friendly bioactive coating technology has emerged as a promising preservation strategy. This study developed a novel coating based on fish gelatin (FG), enhanced by a metal-phenolic network (MPN) formed through the coordination between tannic acid (TA) and Fe³⁺ ions. By systematically optimizing the molar ratio of TA to Fe³⁺, the formulation with a 0.2% Fe³⁺ concentration (FG/MPN₂) was identified as optimal, exhibiting the best system stability (zeta potential: +16.32 ± 1.21 mV) and micro-uniformity (PDI < 0.3). Structural analyses via Fourier-transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy confirmed the formation of a stable composite structure within the FG matrix. Benefiting from this, the FG/MPN₂ coating demonstrated significantly enhanced barrier properties, interfacial adhesion, and excellent antioxidant activity. Finally, the coating was applied to mango preservation (25°C, 60% RH). The preservation results indicated that the coating could significantly inhibit weight loss, maintain higher fruit firmness (17.92 ± 0.50 N) on the 12th day, effectively delay color yellowing, and reduce lipid peroxidation damage, thereby extending the shelf life.

The adoption of Artificial Intelligence (AI) in the meat industry signifies a transformative shift toward intelligent, secure, and sustainable food production systems. This review paper explores how AI is enhancing operations across the entire meat supply chain—including processing, quality assessment, spoilage identification, regulatory compliance, and logistics optimization. Conventional methods, often limited by human bias, workforce shortages, and inefficiencies, are being replaced by AI-powered solutions such as machine learning, computer vision, hyperspectral imaging, smart sensors, and edge computing, enabling real-time, non-invasive, and data-centric decision-making. Beyond process automation, AI facilitates predictive safety controls, smart packaging technologies, and customized consumer engagement. Applications like robotic meat processing, cloud-based traceability platforms, and smartphone-integrated spoilage sensors highlight AI's contribution to precision meat processing. In addition, AI promotes clean-label formulation, operational energy savings, and automated hygiene management via intelligent Cleaning-in-Place (CIP) systems. Global implementations—ranging from carcass grading systems in Australia and blockchain traceability in Europe to biosensor deployment in Asian markets—demonstrate the adaptability and impact of AI-based solutions. Although barriers such as high implementation costs, lack of algorithmic transparency, and regulatory conservatism remain, the integration of AI with the Internet of Things (IoT), blockchain technology, and explainable AI (XAI) offers a promising pathway. This technological convergence is set to enhance food safety, boost consumer confidence, expand export potential, and redefine industrial competitiveness in the evolving post-pandemic marketplace.

Desiccant drying technologies have gained attention for their ability to efficiently remove moisture from air, which is crucial in various applications, like in agriculture and post-harvest management. The integration of advanced systems, such as hybrid solar air collectors and rotary solid desiccant dehumidifiers, has been explored to enhance dehumidification performance and energy efficiency. This work reviews the evolution of desiccant drying technologies over the past decade. The approach includes a bibliometric analysis of the data that quantitatively and qualitatively assesses the research landscape, mapping key trends, research clusters, and emerging topics within the field. This paper allows for a structured overview of advancements and challenges in desiccant drying systems. Desiccant-based systems outperform conventional drying methods by reducing energy use and preserving product quality. However, the existing technologies have limitations and require further optimization. The study concludes that while progress has been made, challenges remain, urging future research into innovative materials and designs for better efficiency and sustainability. The novelty of this work lies in its bibliometric approach, offering a structured overview of the field and highlighting the potential of desiccant technologies over traditional methods.

This study evaluates the ultrasonic treatment on the physicochemical, structural, and color characteristics of corn and khesari pea starch blends. Increasing khesari pea starch proportion increases amylose content (28.27%–36.84%) and water-holding capacity, with the highest values in blends containing 75% khesari pea starch. Ultrasonication further enhanced swelling power and solubility. Moisture (8.76%–9.12%) and ash content (0.14%–0.17%) remained largely unaffected. Native starch granules exhibited polygonal, oval, and irregular shapes, while ultrasonication induced cavitation effects surface roughness with cracks, pores, and fissures. Fourier transform infrared (FTIR) spectroscopy and x-ray diffraction (XRD) analyses confirmed no formation of new chemical bonds and retention of A-type crystallinity. Color evaluation showed reduced lightness with increasing khesari pea starch content, with ultrasonication intensifying the darkening effect. Rheological analysis revealed shear-thinning, non-Newtonian behavior in all samples, with G′ consistently exceeding G″, indicating dominant elastic properties. Blending and ultrasonication enhanced viscoelastic strength while maintaining structural stability. Overall, the combined approach of blending khesari pea starch with ultrasonic processing improved functional performance without compromising structural integrity, suggesting potential applications in starch-based food and industrial formulations.

Cold-pressed sesame oil is increasingly popular; however, it exhibits relatively poor oxidative stability. Yet it suffers from poor oxidative stability. In this study, two eco-friendly modified pretreatment methods, short-term germination (12–24 h) and low-temperature isothermal microwave (70°C–110°C, 10 min), were first combined and applied to prepare cold-pressed sesame oil with improved oxidative stability. The results showed that the combined treatment increased the levels of total phenolics, tocopherols, and phytosterols in sesame oil by up to 210.5, 249.3, and 551.2 mg/kg, respectively. Moreover, the oxidative stability index of sesame oil increased from 4.59 to 9.91 h, and its DPPH and ABTS radical scavenging activities also increased by up to 3.5% and 4.8%, respectively. Furthermore, the undesirable flavor of sesame oil was decreased at moderate treatment parameters. Therefore, the novel pretreatment method was effective in improving the quality of sesame oil, with potential benefits for the development of the cold-pressed oil industry.

The chocolate is a complex fluid where its rheological parameters are highly dependent on the components and quality parameters, such as the particle size distribution and purity. In principle, the friction factor indicates several quality aspects of chocolate during the consumer experience. This study presents a theoretical approach for computing the friction factor for three types of chocolate for a commercial coating equipment similar to a concentric rotating process. The rheological data came from the literature and followed the Bingham, Herschel-Bulkley, and Casson rheological models. The strategy solved the angular momentum equation by applying the fourth-order Runge–Kutta method with a shooting approach; the numerical calculation of the friction factor relates the wall shear stress to the kinetic energy of the averaged angular velocity. The results revealed that the velocity profile is directly proportional to the temperature and inversely proportional to the outer angular velocity and the size of the displacement zone. The flow curves validated the results, where the Casson model reached a good representation of the experimental data from the literature. The friction factor values are inversely proportional to the cocoa composition and directly proportional to the content of non-components of coca. Thus, the purity of chocolate contrasts with the prediction of the friction factor through this approach. Based on these results, this approach may be extended to other industrial equipment in order to relate the quality of chocolate and the flow behavior.

Quality estimation of agro-food products is a top priority for producers, suppliers, and consumers. Non-destructive techniques, particularly acoustic vibration methodology, have emerged as promising tools for the pre-harvest and post-harvest quality evaluation of agricultural products. This technique is effective in assessing the stiffness or firmness of fruits based on the mass and resonance frequency of the sample. This review explores the principles and applications of acoustic vibration methods in evaluating the quality of agro-food products. Impact and forced excitation methods induce sample vibration, while contact and non-contact sensors detect the resulting acoustic signals. These signals are processed to extract features that determine quality parameters. The review highlights the integration of artificial neural networks (ANN) and machine learning algorithms to enhance these assessments' accuracy, reliability, and efficiency. The acoustic vibration technique has the capability to evaluate several internal quality parameters such as texture, firmness, crunchiness, and crispiness. It is also useful for defect detection, sorting, grading, and assessing the ripeness of fruits and vegetables. Incorporating ANN and machine learning algorithms reduces human error and improves system reliability and speed. Additionally, this technique can indirectly measure soluble solid content, titratable acidity, and water activity in various commodities. Acoustic vibration technology holds tremendous potential for online quality detection of food products in an industrial context. However, further research is needed to develop cost-effective and accurate devices to make this technology accessible to the agro-food industry.

To optimize the extraction of chiltepin pepper (Capsicum annum var. glabriusculum), microwave extraction and meat quality evaluations were conducted guided by a Box–Behnken design. Fixed (microwave power) and variable (extraction time, solvent volume, and sieve size) parameters were factored to generate solutions for possible future preparations of optimized extracts before extract evaluation via meat quality (pH, refrigeration, and cooking loss percentages, 2-thiobarbituric acid reactive substance value, and total bacteria count) assessment in a Box–Behnken design. Outcomes were analyzed using response surface methodology, followed by preparation of the optimized extract of the best desirability following the suggested extraction criteria. The prepared extract was compositionally (GC–MS) analyzed for biocompounds and antioxidant capacity. A total of 54 solutions were achieved post-optimization, with the suggested desirability level of 0.862 recommended adopting chiltepin screen size, irradiation time and distilled water combination of 0.536 mm, 77.836 min and 360.00 mL respectively, while microwave power is held constant. Utilizing this combination, the prepared extract had 31 biocompounds, comprising nitrogen-, sulfur, and chlorine-based biocompounds. Esters identified comprised two phthalic acid esters, one carboxylic acid ester, and two methoxyacetic and carbonic acid esters. Also, one elemental-based nitrogen, seven nitrogen-oxygen, and two nitrogen-sulfur-based compounds were identified alongside one chlorine-based compound. Also, acetamide, N-n-heptyl-, N-octyl-acetamide, and acetamide, N-tetradecyl- were harvested in significant quantities. Also, the determined antioxidant capacity revealed phenolic and vitamin C contents of 504.26 mg GAe/100 g and 3.48 mg/g respectively. A Box–Behnken approach to microwave extraction of chiltepin is promising for future biological and pharmaceutical applications.

This study addresses the critical challenge of early detection for Alternaria alternata infection in postharvest cherries by developing a hyperspectral imaging (HSI) system integrated with machine learning (ML) algorithms. After acquiring HSI data from cherries at different infection stages, principal component analysis (PCA) was first employed to demonstrate the superior discriminative capability of HSI-based multivariate analysis over conventional color imaging. We then systematically evaluated the effects of preprocessing methods (raw data [RAW] vs. first-derivative [FD] transformation) and wavelength selection algorithms (Successive Projections Algorithm [SPA] and Competitive Adaptive Reweighted Sampling [CARS]) on model performance. Through comprehensive comparison of multiple ML classifiers—including Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbors (KNN), and Partial Least Squares Discriminant Analysis (PLS-DA)—combined with soft voting ensemble learning, we developed an optimized FD-CARS-Voting model that achieved exceptional classification accuracy (99.06% ACC, 98.59% Kappa). This approach provides an efficient, non-destructive solution for real-time quality monitoring in commercial supply chains, effectively balancing detection accuracy, processing speed, and cost-effectiveness. The proposed framework demonstrates strong potential for extension to other perishable agricultural products requiring rapid quality assessment.

The particle-fluid convective heat transfer coefficient (h_fp) is a critical parameter in cooking processes, determining heat and mass transfer rates between food particles and heating media, which has a significant impact on the final food quality. In this study, pork loin was used as the test sample. The particle-fluid relative velocity was measured and a linear relationship was established between control conditions and particle-fluid relative velocity during boiling, steaming, and stir-frying processes. The h_fp was determined by a hypothetical trial algorithm, which revealed its relationship with the cooking operating conditions. Additionally, the dimensional analysis method was employed to construct h_fp dimensionless models during cooking processes. Our results demonstrated the existence of a linear relationship between the heating power during boiling and steaming, the stirring speed during stir-frying, and the particle-fluid relative velocity (Equations 11–13). The h_fp values for boiling, steaming, and stir-frying ranged from 544 to 1321, 211 to 404, and 410 to 1706 W/m²K, respectively. The h_fp was influenced by various factors, including particle size, the particle-fluid relative velocity, medium temperature, and thermophysical properties of the heat transfer medium. Specifically, smaller pork loin feature size, higher heating power during boiling and steaming, faster stirring speed, and higher oil temperature during stir-frying resulted in a greater h_fp. We constructed h_fp dimensionless models during boiling, steaming, and stir-frying based on dimensional analysis, where h_fp was expressed as a function of the Nusselt number (Nu). The validation experiments showed that the average relative errors of h_fp dimensionless models for boiling, steaming, and stir-frying processes are 0.82%, 0.90%, and 1.89%, respectively, which confirmed the accuracy and reliability of h_fp dimensionless models.