Journal of Food Process Engineering最新文献

The cooling effect of slurry ice as cooling medium on large yellow croaker (Pseudosciaena crocea) was studied by considering different fish/ice ratio, salinity and initial ice mass fraction (40%, 60%, and 80%). Results obtained with optical microscope clarified that the ice particle size and cooling time of slurry ice were decreased at increasing salinity. Best precooling efficiency was obtained with specific values of fish/ice ratio 1:2, salinity 5.0%, and initial ice mass fraction 60%. Then, the effects of precooling time (0, 3, 12, and 24 h) in slurry ice on the quality of large yellow croaker stored at 4°C were investigated. The results showed that slurry ice precooling could significantly (p < 0.05) inhibit the microbial reproduction and retard the quality deterioration of large yellow croaker. Therefore, slurry ice can be a promising precooling method for the quality maintenance of marine fish.

Practical applications

Large yellow croaker is extremely perishable due to its nutrient abundance. The spoilage process of fish is vitally influenced by the changes of internal environment and external environment after harvest. Timely precooling can prominently improve the shelf-life of perishable foods. Slurry ice, a two-phase system consisting of seawater (or artificial seawater) and spherical ice microcrystals, is regarded as an alternative precooling medium. Compared with conventional ice, slurry ice has faster cooling rate, little physical damage to soft tissue such as aquatic products and has better fluidity allows it to comprehensively cover the fish surface, thereby protecting fish from oxidation.

Parameter selection in ultrasonic pretreatment is closely related to the quality of dried fruits and vegetables, so it is essential to investigate the impact of various parameters of ultrasonic pretreatment on the product quality. In this paper, data from 67 relevant literatures were collected to evaluate the effects of ultrasonic pretreatment on dried fruits and vegetables through meta-analysis. Overall, the findings indicated that ultrasonic pretreatment could shorten drying time, reduce hardness and improve color appearance of dried products. Subgroup analysis showed that high power ultrasound (P ≥ 300 W) could increase total phenol content and antioxidant activity of dried fruits and vegetables. Additionally, applying short-duration ultrasound (t < 15 min) increased antioxidant activity, while medium-duration ultrasound (15 min < t < 30 min) simultaneously enhanced flavonoid content and antioxidant activity. In summary, the application of ultrasonic pretreatment has a positive impact on processing high quality dried fruits and vegetables and obtains better-quality products in a shorter time compared to direct drying technology.

Practical applications

Obtaining higher quality dried fruit and vegetable products in a shorter time has always been a research hotspot in the food industry. This study emphasized the positive effects of ultrasonic pretreatment on fruit and vegetable drying, and provided a theoretical basis for the selection of ultrasonic pretreatment parameters.

The present study aims to investigate how the geometric shapes of semi-rigid plastic trays impact the quality characteristics of a retort processed food simulant. Initially, oval, triangular, rectangular, and round-shaped plastic trays with a tomato-based food simulant were subjected to retorting at rotational speeds of 6 and 11 revolutions per minute (RPM), and various food quality attributes were assessed. At 6 RPM, triangle-shaped and round-shaped trays exhibited a significantly greater reduction (p < 0.05) in ascorbic acid (AA) content compared to oval or rectangular-shaped trays. Furthermore, there were no significant differences (p > 0.05) in lycopene and hydroxymethylfurfural (HMF) content change from control among the different tray shapes at 6 RPM. However, hexanal content change was found to be greater in rectangular-shaped trays than in oval-shaped trays, while no significant difference was observed between triangular and round-shaped trays. Both triangular and rectangular-shaped trays showed a substantially greater (p < 0.05) total color difference (ΔE) compared to other shapes. This data suggests the presence of a temperature gradient in the food simulant influenced by the tray's shape. At 11 RPM, oval and round-shaped trays showed a significant reduction in AA content, while rectangular-shaped trays exhibited higher lycopene levels compared to oval and round trays. There was an increase in HMF response from control across all tray shapes; however, differentiation between shapes was not significant (p > 0.05), while both triangle and round-shaped trays had elevated hexanal content as compared to oval-shaped trays (p < 0.05). Oval, round, and triangle-shaped trays all exhibited a greater ΔE than rectangular-shaped trays. In summary, oval and round-shaped trays exhibited a more pronounced change in AA content at 11 RPM, indicating a faster temperature equilibration. In contrast, triangle and rectangular-shaped trays displayed elevated total color differences at 6 RPM, which may suggest the presence of a temperature gradient leading to uneven heating due to their respective shapes.

Practical applications

This information would be applicable to new food products designed for thermal treatment in which the packaging shape could be manipulated to not only fit infrastructure requirements (packaging equipment, processing equipment, distribution devices) but also help support heat penetration at an optimal rate. Depending on product type and makeup, this approach may hold some value to the product developer whose product will be packaged in semi-rigid plastic containers.

Today, diets rich in fruits and vegetables are highly recommended since they contribute to health. The high deterioration rate of fruits distinguishes them from other crops. This study aimed to evaluate the effects of various storage conditions on some quality attributes of kiwifruit through machine learning (ML) using support vector machine (SVM) models. Kiwifruits were subjected to quasi-static loading and coated with different coatings, such as grape, date, and mulberry syrups. Then, the coated kiwifruits were stored at humidity levels of 90% and 95% in completely dark and bright environments with a Compact fluorescent lamp (CFL) bulb for 5, 10, and 15 days. Once the storage period had been completed, quality attributes of the kiwifruit, including antioxidant content, phenolic content, total soluble solids (TSS), pH, and firmness were measured. Each test was performed three times. The numerical results were analyzed through an ML approach using an SVM model on MATLAB. To predict the physical properties of kiwifruit using storage conditions and vice versa, it was found that the most accurate SVM model with a linear kernel predicted the weight loss of kiwifruit based on storage conditions, with the coefficient of determination (R²) being 0.54. To predict the biochemical properties using the storage conditions and vice versa, it was found that kiwifruit firmness was most accurately predicted by the SVM model with the Gaussian kernel, with an R² of 0.70. Moreover, humidity and storage duration were modeled by SVMs with linear kernels, calculating the coefficients of determination to be 0.39 and 0.90, respectively. To predict biochemical properties using physical properties and vice versa, it was observed that the weight loss was more accurately predicted by an SVM with a linear kernel, with an R²-value of 0.76. Reliable results were not obtained for further research for the other modeled parameters using an SVM approach.

Practical Applications

It was found that light was most accurately predicted by the linear SVM. Storage conditions revealed SVM (linear kernel) accurately predicting kiwifruit weight loss. Linear and Gaussian SVMs accurately modeled phenolic and antioxidant content, R²-values: 0.73 and 0.34, respectively.

This study proposed using color components as artificial intelligence (AI) input to predict milk moisture and fat contents. In this sense, an adaptive neuro-fuzzy inference system (ANFIS) was applied to milk processed by moderate electrical field-based non-thermal (NP) and conventional pasteurization (CP). The differences between predicted and experimental data were not significant (p > 0.05) for lightness (L*), redness-greenness (a*), yellowness-blueness (b*), total color differences (∆E), hue angle (h), chroma (C), whiteness (WI), yellowness (YI), and browning index (BI). ANFIS well-predicted milk fat and moisture content using quadratic and two-factor interaction models with mean errors of .00858–.01260 and correlation coefficient of .8051–.8205. Stability tests showed L* and WI reduced while a*, b*, ΔE, h, C, YI, and BI increased during the storage. NP milk had 77.21% higher half-life than CP, as predicted by ANFIS modeling. Findings indicated milk quality characteristics could be estimated based on physical parameters (e.g., color components), contributing to sustainable food production.

Practical applications

The findings offer practical applications of artificial intelligence (AI) as an innovative monitoring and prediction technique to enhance food quality and sustainability. The proposed methodology makes the real-time prediction of milk quality feasible by leveraging AI and physical parameters. An adaptive neuro-fuzzy inference system (ANFIS) accurately predicts moisture and fat contents according to color values, facilitating quality assessment. Stability tests during cold storage provide insights into milk quality changes over time, aiding in determining key parameters in predictive modeling. The proposed approach was found to be applicable to both conventional and non-thermal pasteurized milk. This study also provides a step-by-step protocol, facilitating the implementation of emerging technologies in the food industry.

Cracked carrots account for a large proportion of defective carrots, which are usually discarded or used as feed, resulting in resource waste and economic benefit loss. Cracked carrots can be used as raw materials for further processed carrot products after being trimmed. So, an auxiliary device was developed combining PLC (Programmable Logic Controller) and machine vision to achieve clamping and crack defect localization for intelligent trimming of crack defect. The carrot was orientated based on the area method, rotated by flexible jaws and a rotating cylinder to make the carrot head and tail aligned. The clamping device could non-destructively clamp carrot in axial and radial direction with a force threshold of 10 and 22 N respectively, and rotate carrot to make the crack defect upwards. The results showed that the recognition accuracy of carrot head and tail orientation was 98%, the average deviation of carrot centroid clamping position was less than 9 mm at a speed of 300 mm/s, the rotational positioning time was less than 3 s and the number of adjustments were no more than 2 at a motor speed of 180 rpm, which could meet the subsequent trimming requirements.

Practical applications

Trimming the cracked area of carrots can reduce bacterial growth and decay, which is beneficial for subsequent processing and reduces resource waste. This article designs a carrot auxiliary trimming device, which uses image processing technology to recognize the orientation of the head and tail of carrots and the cracked area, and uses automated equipment to achieve stable clamping of carrots and make the cracked area face the trimming tool. This study is beneficial for the refined management of agricultural products.