Journal of Food Process Engineering最新文献

This study examined the drying kinetics and quality of desiccated coconut through hot-air drying at varying temperatures of 80°C, 85°C, and 90°C, reducing moisture content from 48% ± 2% (w.b.) to 2% ± 1.1% (w.b.). Several physicochemical parameters were assessed, including color, water activity, energy consumption, oil characteristics, surface morphology, functional groups, and fatty acid profiles. The findings revealed that drying temperature significantly affected the color attributes of the coconut, primarily driven by the Maillard reaction and sugar concentration, with desiccated coconut at 85°C demonstrating optimal color properties. Water activity consistently decreased with increasing drying temperature. Higher drying temperatures resulted in a notable reduction of free fatty acids (FFAs); however, the peroxide value of the extracted oil displayed an inverse correlation. Microstructural observations indicated that extended drying resulted in pore formation due to moisture evaporation, with 90°C exhibiting an oily surface indicative of oil cell breakdown absent at 85°C. Additionally, lauric acid content increased with drying temperature. FTIR analysis revealed distinct O–H stretching vibrations, alongside C–H and C═O stretching bonds, confirming the presence of carbohydrates and ester groups in coconut fat. Thus, optimizing the drying process at 85°C is recommended for improving the quality of desiccated coconut, suitable for industrial applications.

Dough toughness and extensibility are key rheological properties directly linked to the quality of dough-based products. Thus, this research employed a custom-built device to inflate dough sheets into bubbles, with a 3D imaging system recording real-time point cloud data of these formations. The investigation specifically targeted point cloud imagery capturing both the frame displaying maximum spatial deformation height in dough bubbles and its subsequent frame. Four novel computational methods were developed to quantify four distinct deformation characteristics from the selected two-frame bubble point cloud images. These parameters were subsequently fed into three machine learning architectures for autonomous prediction of dough toughness and extensibility. Experimental findings revealed optimal detection accuracy when utilizing deformation feature n's immediate recovery index (E_n) with the ELM algorithm, attaining R_p coefficients of 0.882 and0.940 for extensibility and toughness assessments, respectively.

This study aims to develop a novel oil hydrogenation process using a solid polymer electrolyte (SPE) electrochemical reactor, focusing on optimizing the preparation of platinum nanowire (Pt-NWs) catalysts and their application in hydrogenation. By controlling the concentration of the reducing agent (a formic acid-to-water ratio of 7:600) and maintaining the reaction temperature at 20°C, a high-performance Pt-NWs/C catalyst was synthesized. Since the <111> crystal plane is considered the most active in the Pt catalyst, characterization showed that the Pt-NWs/C exhibited sharper x-ray diffraction peaks in that direction compared to commercial Pt/C, along with a more hydrophobic catalytic layer (contact angle of 135° ± 0.3°). In addition, the Pt-NWs catalyst also demonstrates superior performance in electrochemical stability (a 29.31% decrease). The catalyst was applied in an SPE hydrogenation reactor, and its performance in soybean oil hydrogenation was systematically evaluated. The results show that the hydrogenated oil containing Pt-NWs/C has good selectivity, while transfatty acid (TFA) content was about 13% lower than with commercial Pt/C. Electronic nose analysis confirmed that the hydrogenated oil retained favorable flavor characteristics. Polarized light microscopy showed that oil produced by Pt-NWs/C had a finer (approximately 20 nm) and more uniform crystal structure. The Pt-NWs/C catalyst exhibited excellent catalytic activity (ECSA value of 27.40 m² g⁻¹) and selectivity by the SPE reactor, offering a promising technological pathway for producing healthy hydrogenated oil with low TFA content.

In this study, carvacrol (CA)/thymol (TH) nanoemulsions (CT NEs) were first prepared by the ultrasonic emulsification method, and then the effects of various nanoemulsion concentrations on the physicochemical properties and biological activities of chitosan/pullulan (CS/PU) nanocomposite films were systematically evaluated. The CT NEs exhibited good stability with a high encapsulation efficiency of 90.29% ± 0.95%, and they were well integrated in the CS/PU matrix as confirmed by the microstructure characterization. The performances of the nanocomposite films were significantly enhanced by CT NEs, including the improvements of mechanical properties and barrier properties. The CS/PU-CT films exerted notable antioxidant and antibacterial activities. In the end, the CS/PU-3CT film (with 3.0% w/v CA and TH), with the best antioxidant and antibacterial performance, was applied to the preservation of pork, and its shelf life was extended for 10 days compared to the CK group. The film loaded with 3CT NEs exhibits outstanding performance and offers significant prospects for use in food packaging.

Engineering oxidative and structural stability in oil-enriched food systems remains a primary focus in food process engineering, particularly in polyunsaturated fatty acid (PUFA)-enriched food systems. The oxygen-vacancy-enriched cerium oxide nanoparticles (CeO₂ NPs) were synthesized by a controlled synthesis route optimizing the surface defect chemistry and antioxidant capability in the present study. Structural characteristics, including Ce³⁺/Ce⁴⁺ ratio and oxygen vacancy concentration, were established by XRD, XPS, DLS, and UV–Vis methods. Prepared nanoparticles were incorporated into edible oils (rice bran and sunflower), and storage challenges in a high-oil emulsion-based mayonnaise were performed in an effort to explore designed nanoparticles' function in stabilizing Pickering-type food systems. Oxidative stability was ascertained by peroxide and anisidine value, and characterization of emulsion by textural, interfacial, and sensory tests. Addition of the CeO₂ NPs into the edible oils significantly improved oxidative stability, as evidenced by lower peroxide and p-anisidine values after prolonged storage. The emulsions at final stage also showed improved interfacial stability, greater texture profile, and higher sensory acceptance during storage at low temperature, with retained resistance against oxidative as well as structural deterioration. Most importantly, traditional workflows on production of mayonnaise were perfectly compatible with the designed nano-process engineering methodology, involving no fundamental changes in formulations. These findings lay the foundation for oxygen-vacancy-engineered CeO₂ NPs as multifunctional process additives in the creation of next-generation emulsions with extended shelf life, improved functional efficacy, and clean label characterization. The research advances progress in the field of nano-enabled food process engineering and systematic characterization of structure–function–process relationships.

Accurate detection of hulling quality-efficiency is critical for optimizing buckwheat processing and product quality. Current reliance on manual visual inspection is inefficient and impedes industrial progress. To address this issue, an online intelligent detection system is proposed based on an improved YOLOv8 algorithm. The model incorporates three key modifications: (1) a Dual Attention mechanism within the C2f module; (2) the WIoUv3 loss function replacing CIoU; and (3) an expansion to four detection heads. This refined framework enables real-time detection of buckwheat hulling efficiency. The system consists of four core components: an image-based detection module, a 6QB-150 buckwheat huller, control actuators, and auxiliary subsystems. An image acquisition device captures real-time samples, and the improved model accurately distinguishes three hulling statuses: hulled kernels, broken kernels, and unhulled kernels. Detection results are transmitted to a host computer, which interfaces with a PLC to regulate a three-phase asynchronous motor, enabling adaptive control of grinding disc speed and equipment operation. Ablation experiments demonstrated substantial performance improvements over the baseline, with increases of 7.7% in precision, 8.1% in recall, 5.9% in mAP@50, and 4.2% in mAP@50–95. Comparative trials confirmed the model's superiority over YOLOv5, YOLOv6, YOLOv8n, and YOLOv10n, with negligible computational increase. In practical experiment, model-detected rates for broken, hulled, and unhulled kernels deviated by merely +0.13%, −0.71%, and +0.53%, respectively, from manual counts, attesting to its reliability. The deployment of this system significantly enhances operational efficiency, offering an effective solution for automated quality control in buckwheat processing and contributing to agricultural modernization.

This study established an optimized CTAB-based DNA extraction method for edible oils, which enables stable and reliable DNA recovery from commercially available edible oils for authenticity identification and adulteration detection. Real-time PCR was used to assess DNA precipitation time and organic solvent extraction, revealing that longer precipitation increased inhibitors, hindering PCR amplification, while organic solvents enhanced efficiency. The method was applied to detect adulteration in commercially available soybean and olive oils. A standard curve based on real-time PCR Ct values demonstrated the ability to detect as low as 10% soybean oil in olive oil, with recovery rates of 92% and 80% for samples spiked with 70% and 30% soybean oil, respectively. The coefficients of variation (CV) were 11% and 14%, within acceptable ranges. This reliable method provides a valuable tool for edible oil authentication and adulteration detection.

This study evaluated the storage behavior of groundnut under two experiments. In the first, grains were stored in PVC drums at 40% CO₂ concentration. In the second, hermetic, traditional polyethylene, and jute bags were used, with oxygen absorbers (iron filings, 40 g, and NaCl, 4 g) for 50 kg grains to reduce O₂ levels to ~10%. Cocoon bag was exclusively tested for groundnut storage. Hermetic bags maintained higher CO₂ levels (7.81%) compared to polyethylene (3.48%) after 180 days. Cocoon bag stabilized CO₂ at 48% and O₂ at 9.6%, minimizing free fatty acid changes (0.43% to 0.68%). Thousand-grain weight losses were minimal, with final weights of 872.20 g. The study demonstrated hermetic systems' potential to reduce post-harvest losses and maintain seed viability.

Design and field evaluation of a glass fiber-reinforced plastic (GFRP) maize cob dryer with an Arduino-based humidity-responsive airflow control system is presented. The portable, 1-t capacity dryer uses a perforated GFRP bed, a transparent polyethylene roof for passive solar heating, and an automated fan to maintain a drier chamber microclimate. Field trials during the post-monsoon Kharif period showed drying of intact cobs from ~36% to ~17% (w.b.) within 8–9 days, and no visible fungal colonies were detected in dried samples. Structural assessment demonstrated safe operation of the GFRP bed under full load. Compared with typical open-sun drying durations reported in the literature for the same season, the system provides a shorter and more controlled drying period (literature-based comparison). The dryer offers a low-energy, portable option to reduce weather-related drying risks and postharvest losses for smallholder farmers.

Golden pomfret, with its abundant production, ease of capture, tender and flavorful flesh, and rich content of unsaturated fatty acids, has gained widespread popularity among consumers. However, with the development of the society, the traditional meat freshness testing methods can't satisfy the requirements of modern fisheries for rapid, nondestructive and accurate testing of aquatic products. Gas-sensor technology can achieve rapid and nondestructive determination of freshness by detecting volatile odors in aquatic products. However, the sensor is easily influenced by the interference of environmental factors due to its high sensitivity. How to eliminate the interference factors and realize the rapid detection of aquatic product quality has become a new research direction. Therefore, we analyze the basic working mechanism of the gas sensor based on the target molecule's adsorption characteristics. After the preprocessing such as filtering and de-noising the original response curve of the sensor, we propose a feature value extraction method which is more suitable for the adsorption principle of the gas sensor. The method combines the mechanical features of the sensor adsorption process so that the feature values extracted can more accurately reflect the nature of the interaction between the target gas and the sensor surface, thereby enhancing the sensitivity of the model to freshness. We applied this feature extraction method to obtain three feature value parameters, and then we used various machine learning algorithms to build the golden pomfret freshness prediction model. The comparative analysis of these models showed that the model based on the Random Forest algorithm achieved an accuracy of 0.776 ± 0.041, a precision of 0.751 ± 0.040, a recall of 0.721 ± 0.065, and an F1-score of 0.722 ± 0.056 under five-fold cross-validation. The model implementation has been basically satisfied to predict the golden pomfret freshness. Our study provides a methodological reference for feature extraction in gas-sensing applications and supports other research on quality evaluation of golden pomfret and other aquatic products.