Red ginseng is a steamed and dried ginseng that has more functional properties and a longer shelf-life. Red ginseng is graded by appearance and inner quality. However, this conventional process has a high cost in terms of time and human resources, and has the disadvantage of subjective assessment results. Therefore, the convolutional neural network (CNN) method was proposed to automate the grading process of red ginseng and optimize the preprocessing method, select an accurate and efficient deep learning model, and to explore the feasibility of rating discrimination solely based on external quality information, without considering internal quality characteristics. In this study, the effect of five distinct preprocessing methods, including RGB, binary, gray, contrast-limited adaptive histogram equalization (CLAHE), and Gaussian blur, on the rating accuracy of red ginseng images was investigated. Furthermore, a comparative analysis was conducted on the performance of four different models, consisting of one CNN model and three transfer learning models, which were VGG19, MobileNet, and DenseNet121. Among them, DenseNet121 with CLAHE preprocessing reported the best performance; its accuracy in the Dataset 2 test set was 95.11%. This finding suggests that deep learning techniques can provide an objective and efficient solution for the grading process of red ginseng without an inner quality inspection.

Recently, there has been a growing trend to replace animal ingredients used in food with plant-based alternatives for health, environment, and animal welfare. In this study, chickpea aquafaba was used as emulsifier for mayonnaise, and the effect of ultrasound on aquafaba and plant-based mayonnaise was investigated. Aquafaba with chickpea [1:2 (w/w)] was treated by ultrasound in 40 kHz and 200 W for 0, 30, 60, and 90 min. The emulsion property and quality of aquafaba with ultrasonic treatment time (UTT) and plant-based mayonnaise (UTT0, UTT30, UTT60, and UTT90) were analyzed, and compared with mayonnaise using egg yolk (CONT). As the UTT of aquafaba, the protein solubility increased, but emulsion activity index was highest in sample treated for 30 min (p<0.05). These results affected plant-based mayonnaise. The microstructure was confirmed that oil droplet of plant-based mayonnaises were larger than CONT, and size of oil droplet decreased by UTT. The emulsion stability of plant-based mayonnaises were about 7-11% lower than CONT, and among plant-based mayonnaises, UTT30 was the highest at 81.30%, and UTT60 and UTT90 were the lowest. Plant-based mayonnaise had higher viscosity and overall acceptability than CONT, and viscosity decreased by UTT. Optimal UTT had a beneficial effect on the emulsion property of aquafaba and mayonnaise, and a positive role can be expected as plant-based alternative emulsifier.

In the present study, the application of suitable thermal and nonthermal pretreatment methods was explored to develop a dried products comprising onion and garlic slices. Pretreatment was performed via blanching for 30 sec, steaming for 1 min, dipping in warm water (45°C) for 30 min, and dipping in cold water (8°C) for 1 hr. Furthermore, physicochemical and microbiological analyses were performed in conjunction with sensory evaluations. The group subjected to pretreatment via cold-water dipping exhibited the lowest moisture content and water activity with a negligible redness, yellowness, and browning index. Furthermore, the results of sensory evaluation revealed that this group exhibited relatively high color and overall acceptability. The group subjected to pretreatment via blanching presented severe browning and shape distortion. The samples in the group subjected to pretreatment via cold-water dipping did not exhibit a significant difference in pH; furthermore, the number of microorganisms during storage was lower than that in the control group. It was concluded that the hot-air drying of onion and garlic slices after cold-water dipping resulted in an increase in drying efficiency and the suppression of browning, without inducing major component changes. The facile technique demonstrated in the present study is expected to be fast and cost-effective, thereby facilitating practical application in farms.

Ice cream consumption has increased over the years. In this study, we investigated the potential of using rice varieties with varying amylose contents for ice cream production. We analyzed the physical and chemical properties and sensory quality characteristics (appearance, taste, texture, chewiness, aroma, and rice flavor) of rice-based ice cream made from five varieties with low and high amylose levels. To make the ice cream, we ground rice into a fine powder and combined it with skim milk powder, butter, sugar, glycerin esters of fatty acids, locust bean gum, and water to form a gelatinized mixture. This mixture was then aged, frozen, and hardened. The ice cream’s key quality characteristics, such as viscosity (2170–25,030 cP), hardness (4.27–49.55 N cm⁻²), and overrun (17.95–46.99%), showed a wide range. Ice cream made from Saemimyeon (high amylose content rice variety) exhibited the highest hardness value (49.55 N cm⁻²) among the varieties tested, but had relatively low viscosity (4030 cP), overrun (17.95%), and drip-through (0.75 g/min) values. These findings suggest that rice varieties with different amylose contents are suitable for making ice cream and have the potential to expand the rice processing market and increase its value.

Aronia (Aronia melanocarpa) berries are rich in polyphenols. However, they are extremely astringent, which reduces their commercial viability. This study suggests a treatment method to produce more commercial aronia products. In this method, the berries were pretreated with osmotic dehydration and then dried with hot air or lyophilized to reduce their astringency. Sucrose, glucose, and xylitol were used as the pretreatment solutions. The products were prepared and grouped as follows: raw aronia (CON), freeze-dried (FD), osmo-dehydrated using sucrose solution and freeze-dried (FDS), osmo-dehydrated using glucose solution and freeze-dried (FDG), osmo-dehydrated using xylitol solution and freeze-dried (FDX), hot-air-dried (HD), osmo-dehydrated using sucrose solution and hot-air-dried (HDS), osmo-dehydrated using glucose solution and hot-air-dried (HDG), and osmo-dehydrated using xylitol solution and hot-air-dried (HDX). Water loss was highest in aronia berries that were osmo-dehydrated using sucrose solution (16.36%), followed by berries that were dehydrated using glucose solution (14.26%), and those that were osmo-dehydrated using xylitol solution (12.83%). Moisture contents and total soluble solid contents of the FD products were 3.83-7.45% and 6,908.47 °Brix, respectively. FDS and FDX showed relatively high phenolic compound contents and antioxidant activities. FDG and FDS showed better texture and redness. FDS received the highest score for sweetness (5.07) and overall preference (5.13). Hence, FDS proved to be the most appropriate pretreatment, because it allows a better control of the moisture content and maintenance of their appearance and texture, with a relatively low reduction of their phenolic contents. Hence, it affords better preservation efficiency to aronia products.