The exposure of tomato fruits to unfavorable environments during the postharvest could result in severe losses along the supply chain. In this research, four tomato cultivars were stored in a controlled chamber under three levels of postharvest conditions to investigate the effects of delayed cooling on selected physicochemical quality parameters of the tomatoes. The tomato cultivars were subjected to three postharvest treatments: Immediate storage (IS) at harvest day; delayed storage (DS), leaving tomatoes without cover for one day; and under cover (DSC), separately in a greenhouse, and then stored in a controlled chamber at a temperature of 10 ± 1 °C and relative humidity of 90% ± 3%. Fresh weight, firmness, total soluble solids (TSS), and hue angle (h°) were examined over 15 days, every 5 days. Among the tomato cultivars, Cherry (7160), treated under DS showed the highest weight loss (13.01%) and firmness loss (42.14%) after 15 days of storage. Dabol (large) tomatoes treated with DS showed higher changes in TSS (°Bx) values (4.79 to 5.76). Low changes in hue angle values were found in IS-treated Cherry (7160) tomatoes at the end of the storage period. Overall quality changes were slower for all tomato cultivars treated with IS than with other treatments throughout the storage period. This study indicated the importance of reducing cooling delays to maintain the quality during the postharvest and prolong storage of harvested tomatoes.

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.

Beyond the use of 2D images, the analysis of 3D images is also necessary for analyzing the phenomics of crop plants. In this study, we configured a system and implemented an algorithm for the 3D image reconstruction of red pepper plant (Capsicum annuum L.), as well as its automatic analysis. A Kinect v2 with a depth sensor and a high-resolution RGB camera were used to obtain more accurate reconstructed 3D images. The reconstructed 3D images were compared with conventional reconstructed images, and the data of the reconstructed images were analyzed with respect to their directly measured features and accuracy, such as leaf number, width, and plant height. Several algorithms for image extraction and segmentation were applied for automatic analysis. The results showed that the proposed method showed an error of about 5 mm or less when reconstructing and analyzing 3D images, and was suitable for phenotypic analysis. The images and analysis algorithms obtained by the 3D reconstruction method are expected to be applied to various image processing studies.