A colorimetric sensor array was developed to characterize and quantify the taste of white wines. A charge-coupled device (CCD) camera captured images of the sensor array from 23 different white wine samples, and the change in the R, G, B color components from the control were analyzed by principal component analysis. Additionally, high performance liquid chromatography (HPLC) was used to analyze the chemical components of each wine sample responsible for its taste. A two-dimensional score plot was created with 23 data points. It revealed clusters created from the same type of grape, and trends of sweetness, sourness, and astringency were mapped. An artificial neural network model was developed to predict the degree of sweetness, sourness, and astringency of the white wines. The coefficients of determination (R2) for the HPLC results and the sweetness, sourness, and astringency were 0.96, 0.95, and 0.83, respectively. This research could provide a simple and low-cost but sensitive taste prediction system, and, by helping consumer selection, will be able to have a positive effect on the wine industry.

Fresh fruit bruising caused by mechanical impact is the most problematic effect of mechanical postharvest processing of tomatoes. The conventional postharvest process of tomato fruit (Solanum lycopersicum L.) handling was surveyed from harvest to shipping preparation at a farm in Toechon in the Republic of Korea. Acceleration sensors located on the harvest boxes and fruit skin showed that the most severe impact forces (>10 g) occurred while fruit was moved in and out of the harvest box. Next, multivariate analysis was used to evaluate nondestructively the susceptibility of tomatoes to bruising by developing five estimation models incorporating quality factors and the presence of bruising resulting from peak contact forces. Linear regression models, artificial neural network (ANN) regression models, and a logistic regression model were built; the primary dependent variables were the rates of weight loss and firmness loss, and impact-induced bruising. The impact force was controlled by using a pendulum, which produced four levels of impact force on tomato fruit. Increasing the relative humidity (RH) and firmness, and decreasing the temperature and degree of weight loss decreased tomato fruit bruise damage. In addition, the proposed drop–impact measurement technique, with multivariate analysis, can be used to evaluate tomato quality nondestructively.

Purpose: This study was carried out to predict the growth period and fresh weight of sprouts grown in a cultivator designed to grow sprouts under optimal conditions. Methods: The temperature, light intensity, and amount of irrigation were controlled, and images of seed sprouts were acquired to predict the days of growth and weight from pixel counts of leaf area. Broccoli, clover, and radish sprouts were selected, and each sprout was cultivated in a 90-mm-diameter Petri dish under the same cultivating conditions. An image of each sprout was taken every 24 hours from the 4th day, and the whole cultivating period was 6 days, including 3 days in the dark. Images were processed by histogram inspection, binary images, image erosion, image dilation, and the overlay image process. The RGB range and ratio of leaves were adjusted to calculate the pixel counts for leaf area. Results: The correlation coefficients between the pixel count of leaf area and the growth period of sprouts were 0.91, 0.98, and 0.97 for broccoli, clover, and radish, respectively. Further, the correlation coefficients between the pixel count of leaf area and fresh weight were 0.90 for broccoli, 0.87 for clover, and 0.95 for radish. Conclusions: On the basis of these results, we suggest that the simple image acquisition system and processing algorithm can feasibly estimate the growth period and fresh weight of seed sprouts.

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