Distribution of Volatile Compounds in Different Fruit Structures in Four Tomato Cultivars

Distribution of volatile compounds in different fruit structures were analyzed in four tomato cultivars by headspace-solid-phase microextraction (SPME)-gas chromatography-mass spectrometry (GC-MS). A total of 36 volatile compounds were identified in fruit samples, which were primarily aldehydes, hydrocarbons, alcohols, ketones, furans, esters, nitrogen compounds, and sulfur and nitrogen-containing heterocyclic compounds. The volatile compositions in pericarp (PE), septa and columella (SC), locular gel and seeds (LS), and stem end (SE) tissues showed different profiles. The PE tissue showed the highest total volatile concentration due to a high abundance of aldehydes, especially cis-3-hexenal and benzaldehyde. Meanwhile, it showed higher aromatic proportion and herbaceous series intensity than other tissues. Floral and fruity series showed higher intensity in SC and LS tissues. The concentration of alcohols in the LS was higher than that in other tissues in association with the higher abundances of 2-methyl propanol, 3-methyl butanol, and 2-methyl butanol. However, the numbers and concentrations of volatile compounds, especially cis-3-hexenal, benzaldehyde, and geranyl acetone were lower in SE than in the other tissues, indicating less tomato aromas in SE. SE tissues were also lacking in floral and fruity characteristic compounds, such as geranyl acetone, 1-nitro-pentane, and 1-nitro-2-phenylethane. “FL 47” contained more volatile compounds than the other three, and the contents of aldehydes, ketones and oxygen-containing heterocyclic compounds in the “Tygress” fruit were higher than the other cultivars.

Kinneret watershed analysis tool: a cell-based decision tree model for watershed flowand pollutants predictions

This paper presents the methodology and application underlying the Kinneret Watershed Analysis Tool (KWAT), developed for flow and contaminant predictions for Lake Kinneret (the Sea of Galilee) watershed located in northern Israel. Lake Kinneret watershed is about 2,730 km2 (2,070 in Israel, the rest in Lebanon), inhabited by about 200,000 people organized in 25 municipalities, and three cities (the Israeli part). The model aims to predict flow and contaminant transports within the watershed, down to its outlet – Lake Kinneret, the most important surface water resource in Israel. The model is comprised of two sections: quantity and quality. The objective of the quantity section is to tune the values of a vector of coefficients α that multiply the average rainfall time series intensity I(t) (the input) imposed on given sub-sets (i.e., cells) of the basin so as to calibrate their outlet flows Q(t); the quality section then uses these optimal flows Q(t) and the effective optimal rainfall intensities to adjust the values of a vector of coefficients β so as to calibrate the sub-watersheds outlet concentrations C(t). The model uses decision trees coupled with a genetic algorithm for optimally tuning the KWAT coefficients for each of the watershed cells, which taken together comprise the flow and contamination amounts measured at the watershed outlet.