Journal of Food Process Engineering最新文献

Tagatose is a low-calorie alternative sweetener with several health benefits. This study is the first demonstration of continuous-flow microwave-assisted isomerization of galactose using arginine as an alkaline catalyst, which enabled precise residence time control under pressurized conditions. With a microwave power of 800 W and a residence time of 3.18 min (flow rate of 0.5 mL/min), a tagatose yield of 10.9% was obtained using 0.2 M galactose and 10 mol% arginine, together with a small amount of talose and sorbose. Increasing arginine concentration from 2.5 to 15 mol% significantly increased the yield to the highest value of 14.9%, but the yield decreased at 20 mol%. Initial galactose concentration from 0.1 to 0.6 M with 15 mol% arginine did not enhance tagatose yield, but productivity increased to the highest value of 254.4 g/L h. Acid formation and browning increased with the galactose concentration. Lower galactose concentration (0.2 M) provided lower productivity (101.4 g/L h), but also much lower browning, which is an advantage for further downstream purification. Overall, this study shows the potential of this novel continuous process using microwave heating, which is more energy-efficient than the conventional batch method and provides a novel and sustainable rare sugar production approach.

The objective of this study was to investigate the influence of ultra-high pressure homogenization [UHPH, 0 MPa (Control without UHPH), 100, and 150 MPa] and different enzyme treatments (non-enzyme, pectinase and cellulase) on some quality and color changes of fruit juices produced from dried apple pomace powder (DAPP). DAPP was a valuable waste product due to its high total phenolic content (198.42 ± 2.60 mg GAE/100 g dw) as well as low moisture (2.78 ± 0.17%) and pH (3.20 ± 0.03). The pH, total soluble solids (TSS), color, total color difference (ΔE), and National Bureau of Standards (NBS) parameters of fruit juices (DAPPJ) produced from DAPP were affected by both enzyme (p < 0.01) and UHPH treatments (p < 0.01). Enzyme and UHPH treatments increased the TSS value (p < 0.05). Pectinase treatment of DAPPJ resulted in lower pH values and color coordinates (L*, a*, and b*) compared to cellulase and non-enzyme groups (p < 0.05). Regardless of enzyme treatment, UHPH processing increased lightness and yellowness, while decreasing redness in DAPPJ. The combination of pectinase enzyme and 100 MPa UHPH offered a ‘distinct’ color difference with the lowest ΔE, while ΔE was ‘very distinct’ in all treatment groups processed with 150 MPa UHPH (p < 0.05). The color difference based on NBS classification was “noticeable” for enzyme treatments and “appreciable” for UHPH processing. Considering a lighter yellowish and less reddish fruit juice for consumer acceptance, the combination of cellulase enzyme and 150 MPa UHPH can be recommended for industrial applications of DAPPJ. On the other hand, to minimize the visible color difference, a combination of pectinase enzyme and 100 MPa UHPH is a more suitable option for DAPPJ production.

The orange affected by freeze damage will experience the loss of partial nutrients, decline in taste quality, and acceleration of deterioration. According to the early signs of freeze damage, it becomes challenging for the naked eye to differentiate between oranges with freeze damage and those that are intact. This study aimed to investigate the feasibility of using hyperspectral imaging for identification of oranges with different degrees of early freeze damage. Firstly, the fundamental mechanism of hyperspectral imaging for identifying early freeze damage of orange was investigated by weight change before and after freeze damage, soluble solids content, and fluorescence images of the oranges with different degrees of early freeze damage. Subsequently, a variance inflation factor modified competitive adaptive reweighted sampling (VIF-CARS) algorithm and a reptile search algorithm optimized support vector machine (RSA-SVM) were proposed to extract effective wavelengths associated with freeze damage information of oranges and identify oranges with different degrees of early freeze damage. The results demonstrated that VIF-CARS exhibited promising outcomes in dimensionality reduction, effective information extraction, and wavelength interpretability compared with traditional methods. Moreover, when combined with the RSA-SVM model, this combination showcased reliable discrimination capabilities for identifying oranges with different degrees of early freeze damage, achieving the optimal classification accuracy of 94.44% and the F1-score of 0.9484. Therefore, hyperspectral imaging offers a feasible approach for identification of oranges with different degrees of early freeze damage, which is a crucial step toward improving the detection system for the fruit supply chains.

Under high temperature and humidity environments, the pollution characteristics of rice exhibit instability and high complexity, which makes traditional prediction models face challenges of insufficient stability and robustness in feature screening and outlier processing. Therefore, this paper proposes a rice safety risk prediction model of OREDRVFL (Outlier-robust Ensemble Deep Random Vector Functional Link Network) improved by the FAOA (Fitness-Distance-Balance-Arithmetic Optimization Algorithm). First, the MI (Mutual Information) method is employed to screen a series of key risk factors. Second, the AOA (Arithmetic Optimization Algorithm) is improved by the FDB (Fitness-Distance-Balance) strategy. Then, regularization constraints and sparsity modeling are used to construct the OREDRVFL network, and FAOA is employed to optimize its number of hidden layers and regularization parameters. Finally, experimental verification is carried out using the detection data of a major rice-producing province from 2022 to 2023. The results show that the improved MI-FAOA-OREDRVFL model significantly outperforms traditional models in terms of indicators such as root mean square error (RMSE = 0.4300), mean absolute error (MAE = 0.2900), and correlation coefficient (R² = 0.8800). Under noise interference, its RMSE remains stable at 0.1700, verifying the high precision and strong robustness of the model, and providing technical support for the prediction of rice safety risks under high temperature and humidity environments.

In this study, bigel systems were developed as potential alternatives to palm oil in cocoa spreads. This study developed citrus fiber (CF)-based bigel systems as alternatives to palm oil in cocoa spreads. Bigels were formulated by combining hydrogels (HG) containing CF (5% and 7.5%) with olive oil oleogels (OG) structured using 10% beeswax at different HG:OG ratios (80:20, 50:50, and 20:80). All bigels exhibited higher storage (G′) and loss (G″) moduli than palm oil, indicating enhanced viscoelastic behavior. Increasing the oleogel fraction improved the structural integrity of the bigel network. Bigels containing 7.5% CF were incorporated into cocoa spreads, where higher oleogel ratios increased the solid character (G′). All bigel-enriched cocoa spreads showed thixotropic behavior with lower deformation and higher structural recovery compared to palm oil-based spreads. Additionally, bigel addition improved temperature-dependent stability. These results demonstrate that CF-based bigels provide a compact, viscoelastic, and stable fat system, highlighting their potential as palm oil replacers in cocoa spread formulations.

The ultrasound assisted aqueous two-phase extraction (UATPE) process of crude polysaccharides from Taraxacum mongolicum Hand.-Mazz. (TMHPs) was optimized by an orthogonal test. A major homogeneous polysaccharide fraction (TMHPs-1-SG) was purified from the crude TMHPs via DEAE-52 and Sephadex-G150 column chromatography, and the structure of TMHPs-1-SG was characterized by using ultraviolet–visible spectroscopy (UV–vis), ion chromatography, high-performance gel permeation chromatography (HPGPC), Fourier transform infrared spectroscopy (FT–IR), Congo red test, thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and transmission electron microscopy (TEM), and then evaluated their antioxidant activity. The results show that the optimal extraction process was obtained as follows: the ammonium sulfate content of 24%, ethanol concentration of 45%, ultrasound power of 400 W, and solid-to-liquid ratio of 1:20 g/mL, and the yield of TMHPs was 12.06% ± 0.16%. TMHPs-1-SG with Mw of 11.30 kDa consisted of arabinose (Ara, 8.59%), glucose (Glc, 72.16%), and mannose (Man, 13.27%). TMHPs-1-SG showed the typical absorption patterns of polysaccharides. Moreover, TMHPs-1-SG did not have a triple helix structure and presented an irregular structure, and the surface of TMHPs-1-SG was smooth and attached with many spherical particles. Furthermore, the IC₅₀ values of TMHPs-1-SG for DPPH, ABTS⁺, OH, and O₂⁻ radicals scavenging rates were 0.57, 0.75, 0.74, and 0.58 mg/mL, respectively. The results provide a green and feasible strategy for the efficient extraction of natural polysaccharides.

The process optimization of corn drying is difficult due to the strong coupling of multidimensional parameters and time-varying lag characteristics of hot air material interaction. In view of this, this study proposes a drying process optimization system that combines dynamic feature weighted long and short-term memory networks with elite oriented multi-objective evolutionary algorithms. It achieves accurate prediction of moisture content and real-time generation of Pareto near optimal set of process parameters through adaptive feature weighting and dynamic guidance mechanism of archives. The experiment showed that the prediction model of the proposed system significantly reduced the prediction deviation of key indicators such as moisture content and crack rate, and remained within 0.03% and 0.06% throughout the process. In terms of optimizing performance, the Pareto solution set obtained by the system had higher quality, with a key evaluation index of 0.98, which was more than 23% higher than the existing optimal methods. Under actual testing and application, the system has achieved a drying energy consumption as low as 0.72 MJ/kg, with an average energy saving of over 40% compared to existing optimization schemes. At the same time, the crack suppression effect was stable, the energy consumption of key process nodes was reduced by 48%, and the computational resource requirements were dynamically adapted to changing working conditions. The constructed system can effectively improve the stability of drying quality and the synergy of energy efficiency, providing reliable technical solutions for postharvest loss reduction of grain and intelligent upgrading of drying processes.