This study was carried out to determine the descriptive sensory profile of oriental melon. The sensory profile of oriental melons (cultivated in Seonju, Kyungsangbuk-do) were used quantitative descriptive analyses and twelve trained panel developed the total forty sensory attributes related appearance, aroma, texture etc. Six appearances attributes and two aromas attributes were derived from whole oriental melon with skin. Five aromas, six tastes and six textures were derived from the mesocarp of oriental melon. And also, oriental melons were analyzed for pH, titratable acidity, solid soluble contents and weight. The weight, titratable acidity and soluble solid content of oriental melons showed significant differences according to samples. And also, in the sensory attributes of these samples by trained panel and consumer test, there were significant differences between organic cultivated samples and conventional cultivated samples. Based on principal component analysis of sensory attributes data by trained panel and consumer, oriental melons were primary separated along the first principal component, which accounted for 27. 73% (trained panel test) and 19.82% (consumer test) of total variance (trained panel test; 58.36%, consumer test; 46.18%) between the samples farm, cultivation method, total acidity, sweet aroma, bitterness, etc. Generally, whole oriental melon with skin showed significant differences in the sensory attributes according to cultivation method and farms. But, the mesocarp and endocarp part showed not certainly differences in the sensory attributes between organic cultivated samples except conventional cultivated samples. Key words：oriental melon, sensory profile, quantitative descriptive analysis, cultivation method서 론 1) 참외(Cucumis melo var. makuwa)는 박과류에 속하는 1년 생 식물로 분류학적으로는 멜론(Cucumis melo)의 한 변종 이고 원산지는 아프리카 사하라 남부, 중국, 이란, 터키, 인도 등이라고 알려져 있다. 한국에는 삼국시대 또는 그 이전 중국의 화북으로부터 들어 온 것으로 추측되는데 외

In this study, the spectral image of red pepper powder, which had been prepared in accordance with the standard particle size distribution ratio, was acquired in the short-wave infrared region using a hyperspectral camera. Spectral information was analyzed using multivariate statistical analyses including principal component analysis (PCA) and least partial squares (PLS) analysis. PCA revealed that powders were grouped according to their pungency level, regardless of their particle size distribution (PC1=97%, PC2=2%). The regression coefficient derived in PLS discriminant analysis indicated that 1,201-1,226 nm, 1,387-1,411 nm, and 1,508-1,529 nm are key wavelengths that are affected by the vibration of C-H, O-H, and N-H bonds present in capsaicinoid molecules. Pungency grade was successfully determined, and capsaicinoid content was predicted with high accuracy using PLS analysis of raw data at key wavelength (Rc2=0.9389, Rp2= 0.9261). It was possible to reduce the time required for data calculation and analysis by reducing the amount of spectral data utilized to predict spiciness from 256 to 21 bands. Finally, the distribution of capsaicinoids was mapped visually according to particle size. In conclusion, hyperspectral imaging is a suitable technology for real time, non-destructive monitoring of red pepper powder quality relative to the standard method used during the manufacturing process.

We analyzed the major quality characteristics of red pepper powders from various regions and predicted these characteristics nondestructively using shortwave infrared hyperspectral imaging (HSI) technology. We conducted partial least squares regression analysis on 70% (n=71) of the acquired hyperspectral data of the red pepper powders to examine the major quality characteristics. Rc2 values of >0.8 were obtained for the ASTA color value (0.9263) and capsaicinoid content (0.8310). The developed quality prediction model was validated using the remaining 30% (n=35) of the hyperspectral data; the highest accuracy was achieved for the ASTA color value (Rp2=0.8488), and similar validity levels were achieved for the capsaicinoid and moisture contents. To increase the accuracy of the quality prediction model, we conducted spectrum preprocessing using SNV, MSC, SG-1, and SG-2, and the model’s accuracy was verified. The results indicated that the accuracy of the model was most significantly improved by the MSC method, and the prediction accuracy for the ASTA color value was the highest for all the spectrum preprocessing methods. Our findings suggest that the quality characteristics of red pepper powders, even powders that do not conform to specific variables such as particle size and moisture content, can be predicted via HSI.

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