Although human perception of food flavors involves integration of multiple sensory inputs, the most salient sensations are taste and olfaction. Ortho- and retronasal olfaction are particularly crucial to flavor because they provide the qualitative diversity so important to identify safe versus dangerous foods. Historically, flavor research has prioritized aroma volatiles present at levels exceeding the orthonasally measured odor threshold, ignoring the variation in the rate at which odor intensities grow above threshold. Furthermore, the chemical composition of a food in itself tells us very little about whether or not that food will be liked. Clearly, alternative approaches are needed to elucidate flavor chemistry. Here we use targeted metabolomics and natural variation in flavor-associated sugars, acids, and aroma volatiles to evaluate the chemistry of tomato fruits, creating a predictive and testable model of liking. This nontraditional approach provides novel insights into flavor chemistry, the interactions between taste and retronasal olfaction, and a paradigm for enhancing liking of natural products. Some of the most abundant volatiles do not contribute to consumer liking, whereas other less abundant ones do. Aroma volatiles make contributions to perceived sweetness independent of sugar concentration, suggesting a novel way to increase perception of sweetness without adding sugar.

Phenotypic diversity within cultivated tomato (Solanum lycopersicum) is particularly evident for fruit shape and size. Four genes that control tomato fruit shape have been cloned. SUN and OVATE control elongated shape whereas FASCIATED (FAS) and LOCULE NUMBER (LC) control fruit locule number and flat shape. We investigated the distribution of the fruit shape alleles in the tomato germplasm and evaluated their contribution to morphology in a diverse collection of 368 predominantly tomato and tomato var. cerasiforme accessions. Fruits were visually classified into eight shape categories that were supported by objective measurements obtained from image analysis using the Tomato Analyzer software. The allele distribution of SUN, OVATE, LC, and FAS in all accessions was strongly associated with fruit shape classification. We also genotyped 116 representative accessions with additional 25 markers distributed evenly across the genome. Through a model-based clustering we demonstrated that shape categories, germplasm classes, and the shape genes were nonrandomly distributed among five genetic clusters (P , 0.001), implying that selection for fruit shape genes was critical to subpopulation differentiation within cultivated tomato. Our data suggested that the LC, FAS, and SUN mutations arose in the same ancestral population while the OVATE mutation arose in a separate lineage. Furthermore, LC, OVATE, and FAS mutations may have arisen prior to domestication or early during the selection of cultivated tomato whereas the SUN mutation appeared to be a postdomestication event arising in Europe.

Shapes of edible plant organs vary dramatically among and within crop plants. To explain and ultimately employ this variation towards crop improvement, we determined the genetic, molecular and cellular bases of fruit shape diversity in tomato. Through positional cloning, protein interaction studies, and genome editing, we report that OVATE Family Proteins and TONNEAU1 Recruiting Motif proteins regulate cell division patterns in ovary development to alter final fruit shape. The physical interactions between the members of these two families are necessary for dynamic relocalization of the protein complexes to different cellular compartments when expressed in tobacco leaf cells. Together with data from other domesticated crops and model plant species, the protein interaction studies provide possible mechanistic insights into the regulation of morphological variation in plants and a framework that may apply to organ growth in all plant species.

Measuring fruit morphology and color traits of vegetable and fruit crops in an objective and reproducible way is important for detailed phenotypic analyses of these traits. Tomato Analyzer (TA) is a software program that measures 37 attributes related to two-dimensional shape in a semi-automatic and reproducible manner 1,2 . Many of these attributes, such as angles at the distal and proximal ends of the fruit and areas of indentation, are difficult to quantify manually. The attributes are organized in ten categories within the software: Basic Measurement, Fruit Shape Index, Blockiness, Homogeneity, Proximal Fruit End Shape, Distal Fruit End Shape, Asymmetry, Internal Eccentricity, Latitudinal Section and Morphometrics. The last category requires neither prior knowledge nor predetermined notions of the shape attributes, so morphometric analysis offers an unbiased option that may be better adapted to high-throughput analyses than attribute analysis. TA also offers the Color Test application that was designed to collect color measurements from scanned images and allow scanning devices to be calibrated using color standards 3 .TA provides several options to export and analyze shape attribute, morphometric, and color data. The data may be exported to an excel file in batch mode (more than 100 images at one time) or exported as individual images. The user can choose between output that displays the average for each attribute for the objects in each image (including standard deviation), or an output that displays the attribute values for each object on the image. TA has been a valuable and effective tool for indentifying and confirming tomato fruit shape Quantitative Trait Loci (QTL), as well as performing in-depth analyses of the effect of key fruit shape genes on plant morphology. Also, TA can be used to objectively classify fruit into various shape categories. Lastly, fruit shape and color traits in other plant species as well as other plant organs such as leaves and seeds can be evaluated with TA. Video LinkThe video component of this article can be found at http://www.jove.com/video/1856/ ProtocolTomato Analyzer (TA) software is designed to recognize objects of a certain size and image resolution, measured in dots (pixels) per inch (dpi). The software automatically determines the boundaries of fruit in a scanned image. The object boundary is determined through contour tracing, which results in a list of adjacent points describing the border of an object in an image. All fruit shape measurements are calculated based on the boundaries. The color test module "Tomato Analyzer Color Test" is designed to quantify the color parameters inside the boundaries recognized by the software. The color measurements are based on the RGB color space: R (red), G (green), and B (Blue). The average RGB values for each pixel is taken by Color test module and then translated to the CIELAB color space which uses L*, a*, b* to describe color in a way that approximates human visual perception. The Color test module calculates Hu...