Journal of Food Process Engineering最新文献

This study has introduced a novel methodology for detecting brick powder (BP) adulteration in red chili powder (RCP; Variety: Bullet Lanka-5) by strengthening advanced digital image processing techniques. Specifically, this approach integrated color space histogram and texture features, subsequently refined through Z-score normalization and followed by the infinite latent feature selection (InLFS) method. By combining these innovative image-based techniques with machine learning (ML) algorithms, this research sets a standard for ensuring the safety and authenticity of RCP. The digital image-based dataset consisting of images of pure and adulterated RCP with BP at various concentrations, is used to extract the features for the evaluation of models. Three histograms (i.e., YCbCr, RGB, and Lab) and texture feature models (i.e., GLCM, GLDM, and GLRM) are extracted from each image. Subsequently, the InLFS model is employed to identify the most desirable features for the extracted histogram and texture features, which are further trained on the ML models to evaluate the existence and extent of BP adulteration in RCP. The regression model has given a higher coefficient of determination (R²) of 0.99 when using exponential Gaussian Process Regression (GPR) trained on Lab color space histogram features, with corresponding RMSE, MSE, and MAE values of 2.14, 12.21, and 1.08, respectively. Meanwhile, the subspace KNN classifier, with SF-C-Texture-Lab-hist, has achieved an accuracy of 99.31%. Therefore, the findings of this study underscore the potential applications of digital image-based feature extraction in combination with ML models to ensure the safety and authenticity of RCP.

Edible insects have gained increased attention as the important and sustainable source of nutrients for both human and animal consumption due to their rich content of protein, oil, fiber (as chitin), vitamins, and minerals. This study focuses on examining the effects of advanced pretreatments and oil extraction methods from prominent edible insect species commercially cultivated in Thailand, namely the house cricket (Acheta domesticus), black soldier fly larvae (Hermetia illucens), and silkworm (Bombxy mori). The raw insect samples underwent pretreatment using high pressure processing (HPP) at 200–600 MPa for 5 min or ultrasonication (UL) at 37 kHz for 5–15 min, followed by dehydration and extraction using supercritical carbon dioxide (SC-CO₂) or solvent extraction. The study revealed that HPP significantly reduced the initial microbial load of all edible insects by 6–8 log cycles, while UL showed a slight effect on microbial reduction. Additionally, pretreatments notably improved the oil extraction yield of most samples by disrupting the integrity of the cell membrane, facilitating the release of oils. The extracted edible insect oils were rich in unsaturated fatty acids, omega 3, 6, and 9, making them suitable for various applications such as cosmetics, food, and feed additives. Furthermore, the protein meals obtained as a residue after oil removal were identified as potential meat alternatives or replacements in food or feed formulations. This study provides valuable insights into the potential of edible insects as a sustainable source of oil and protein, highlighting the significance of pretreatments and extraction methods in maximizing their utility for various applications in the food, cosmetic, and feed industries.

Freezing pretreatment is one of the novel pretreatments for fruit and vegetable drying, and its treatment intensity has a direct impact on the effectiveness of color protection and drying promotion. However, there are major limitations on transformations of dynamic changes of and water status of material, which is crucial for judging processing intensity. Here a mathematical model was presented to characterize heat transfer in freezing pretreatment of carrot crisps. The model accounts for freezing phase change heat transfer. And a further analysis on the effect of freezing pretreatment on product quality was made according to experimental results. Results showed that the heat transfer process of freezing pretreatment is divided into four stages based on the temperature changes, namely fast cooling segment, phase transition segment, secondary cooling segment, constant temperature holding segment. In the phase transition segment, the presence of latent heat is the core element that the temperature of the material can be maintained constant. In the constant temperature holding segment, the effect of processing time on the material was significant. The optimal pre-freezing conditions for carrots was a temperature of −15°C for the duration of 45 min plus an additional 10 h, resulting in dried products with minimal color difference, maximum rehydration ratio, and optimal puffing degree.

Most research publications on the application of ozone for the extension of shelf life of food and crop products suffer from, firstly, not clearly defining the dose of ozone used relative to the sample sizes of produce and, secondly, not quantifying the cost implication. The aim of this article is to clearly calculate the ozone dosage for some 30 different food storage applications. Then, a rationale for the cost estimation of ozonation on an industrial scale was established. The cost of ozonation was analyzed by separating the capital, operating and maintenance costs, and then expressing each of these as the cost per kg of applied ozone. This was done both for the use of ozone dissolved in water and for dry applications, where ozonated oxygen or air is applied to food products in a treatment chamber or directly in the storage area. Using the cost approaches as developed, it was shown that the most economical approach to generating ozone for all but small installations is to generate oxygen on-site, thus using a smaller ozone generator and saving on power cost, ozone transfer, and maintenance. The estimated costs were applied to 30 research case studies on food preservation with ozone published in various journals. This covers seven on vegetable storage, eight on fruits, four on juices, five on meat and seafood, and five on stored grain. In summary, the cost of ozonation of food articles is less than $0.01/kg of fruit, vegetable, or meat product, and about $0.002/kg grain. Fruit juices require more ozone, and the cost could be $0.01-$0.04 per liter.

The internal meshing screw mixer, driven by extensional rheology, demonstrates excellent mixing efficiency for high-viscosity materials while minimizing fiber damage. This study developed a computational model for the mixing process of high-viscosity fluids based on the motion equations of the internal meshing screw. The model was validated through experiments involving the mixing of corn syrup, flour and water. The results reveal the flow field's cross-sectional streamlines and confirm the presence of chaotic mixing states at the system interface through horseshoe mapping and the existence of hyperbolic points. By comparing the extensional and shear rates, as well as analyzing the distribution of mixing indices, this paper establishes that the primary mixing mechanism within the cross-section is extensional force, highlighting the role of the internal meshing screw as a mixer dominated by extensional flow fields. We investigate the variations in fluid velocity, velocity uniformity index, extensional rate, shear rate, and mixing index over time under different conditions of eccentricity, rotor radius, and rotational speed. The findings indicate that while eccentricity has a limited impact on average velocity, it significantly enhances velocity disturbances and increases the ratio of extensional effects within the fluid domain. In contrast, rotor radius and speed lead to a linear increase in average velocity but have little effect on velocity disturbances and the extensional effects in the fluid domain. This study provides valuable insights into how various cross-sectional parameters influence the flow field of the internal meshing screw mixing process, offering crucial support for its application in mixing high-viscosity non-Newtonian fluids within the food industry.

Natural konjac glucomannan is an ideal candidate for hydrogels due to its excellent gel-forming abilities, though its limited nutritional value somewhat restricts its applications. This project primarily explored the thermal and rheological properties of composite hydrogels formed by combining konjac glucomannan with Euglena. The hydrogels were created using a Euglena suspension processed through hydration, high-speed shearing, and ultrasonic homogenization. This is the first report to document the creation of composite gels using konjac glucomannan and Euglena. This novel combination enhanced the thermal stability of the gels to 258.7°C, improved frost resistance to −19.9°C, increased the maximum activation energy to 24,179.6 J/mol, and raised maximum creep compliance to 0.6259 1/Pa. These characteristics significantly expand their potential for food preservation and active nutrient delivery while also offering new strategy for developing nutritionally rich gel foods and new resource food matrices.

The temperature data obtained from cables monitoring stored grain are crucial for real-time surveillance of grain storage and serve as a reference for processes such as ventilation. This study focuses on optimizing the arrangement of temperature cables within the warehouse and employing numerical simulations to analyze the variation in grain temperature during a 7-day cooling ventilation period. By comparing the predicted average temperature of bulk grain with the temperatures recorded by cable sensors, the study evaluates the potential benefits and cost implications of various cable layouts. The maximum difference in the average temperature between the monitoring temperature by cables and the predicted temperature in bulk grain was 0.70°C, and the root mean square error (RMSE) was 25.83% when the cables were arranged according to the national standard (National Standard of P.R. China, GB/T 26882.1-2011). The study further examines the impact of optimizing cable layout, including variations in horizontal spacing, on the RMSE. It was found that reducing cable spacing decreased the RMSE but necessitated an increased number of cables. Conversely, increasing cable spacing led to a decrease in the number of cables but resulted in a higher RMSE. Based on an optimized non-uniform layout with the less cables, which were inserted with dense cables near the wall and sparse cables in bulk grain, it was found that the RMSE was reduced to 22.23%, and the number of the cables was reduced by four cables compared to the national standard layout. The non-uniform layout was also verified for applicability in large-scale warehouses, showing a reduction of 26 cables compared to the national standard. The results demonstrated that the current cable layout of the national standard needs optimization, and that the optimization direction of the uniform layout does not guarantee economy and accuracy at the same time during the monitoring temperature in bulk grain. The non-uniform cable layout, with fewer cables and improved monitoring accuracy, presents a promising approach for practical application.

This study emphasized the need for postharvest technology in Nigeria's vegetable production to reduce postharvest losses ranging from 5% to 50%, focusing on enhancing processes of automated packaging unit of vegetable processing plant through the use of artificial neural networks (ANN). The experiment was conducted on a vegetable leaf processing plant with the objective of improving the reliability and performance of the automated packaging unit. Operating parameters such as moisture contents, leave particle size, time taken, throughput capacity, and specific mechanical energy consumption were varied to determine the optimum condition for each parameter. Statistical analysis was performed using R software. The appropriate model was chosen based on selection of the highest coefficient of prediction where the additional terms are significant and the model was not aliased, insignificant lack of fit and the maximization of the “Adjusted R² value” and the “Predicted R² value.” An optimum packaging condition was obtained at 15% moisture content, and 104.4 particle sizes which gave an optimum packaging time of 0.02 h, optimum packaging capacity of 57.31 kg/h, optimum SMEC value of 0.008 kw/h/kg, optimum repeatability value of 0.128 kg, optimum linearity value of 4.713 cm, optimum accuracy value of 5.2 cm (±0.45). The performance of the ANN model was evaluated using various measures such as mean squared error (MSE), the coefficient of determination (R²), mean absolute error (MAE), and the adjusted R-squared (Adj. R²) for packaging machine. The results of this study suggest that ANN can be used to effectively optimize packaging units of the vegetable leaf processing plant.