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.

한국식품저장유통학회지(Korean Journal of Food Preservation)

한국식품저장유통학회지(Korean Journal of Food Preservation)

The purpose of this study was to analyze the correlation between the external, internal and chemical quality characteristics of 6-year-old red ginseng produced in punggi area. Red ginseng samples are graded as cheon (1st grade), ji (2nd grade), yang (3rd grade), gi (out of grade) by trained inspectors. External characteristics of red ginseng depend on weight, volume, density, length and color. Internal characteristics were expressed by length and occurrence ratio of inner white and inner cavity. Chemical characteristics were evaluated by crude saponin, ginsenoside (Rg1, Rb1, Rc), total sugar, acidic polysaccharide, and 50% ethanol extracts contents. Overall, this study showed that quality of external and internal characteristics was matched with grade, but chemical quality was not matched.