Green tea consumption has been common in some countries for centuries, but in others, it is just finding popularity. The objectives of this study were to (1) determine the liking of green tea by consumers in three countries: Korea, where green tea is commonly consumed; Thailand, an Asian country where green tea is generally consumed in a cold form; and the U.S.A., a country where green tea is not commonly consumed, and (2) identify the attributes which appear to influence consumers' liking. The liking of green tea varied depending on the country and the consumer segment. Korean consumers generally liked the green tea samples with various green flavors and moderate bitterness, although a few of the Korean consumer segments liked samples with other flavor profiles. Most of the U.S. consumers liked the tea samples 6 Corresponding

A lexicon consisting of 20 flavor attributes and 11 texture attributes was developed by a highly trained descriptive panel to describe sensory characteristics of nine cultivars of ripe and green mangoes grown in Thailand. Results showed that the attributes were able to describe great variation both in flavor and texture characteristics among samples. Principal component analysis grouped the attributes into six key dimensions that explained 73.3% of total variability of all mango samples. However, all the attributes were needed to explain the complete variation among samples. Attributes such as mango identity, woody, various fruit notes, floral/perfumy, earthy, sweet, mealy, fiber amount and size, slickness, chemical, peel‐like, sour, bitter, piney and spicy differentiated among cultivars. Other attributes such as viney, green, firmness, cohesiveness of mass, astringent, particle amount and size, slimy, fermented, animalic, pulpy residue and starchy explained changes in sensory characteristics occurring during ripening of the fruit. PRACTICAL APPLICATION Mango is growing in popularity around the world. Mango cultivars vary greatly in aroma, flavor and texture, especially among the main types, ripe and green mango. This research provided information on how sensory characteristics, especially flavor and texture, differed among mango cultivars and how those characteristics changed during ripening of the fruit. This information could be useful for mango producers in selecting highly desirable traits while slowly eliminating less desirable traits of certain cultivar, and useful for mango exporters in selecting cultivars that will be successful in their target markets. The lexicon could also be used for future sensory research on mangoes.

Five lines of Virginia-type peanuts, Florigiant (FG), NC 6, NC 17921 (FG × Florunner), NC 17922 (FG × Valencia), NC 17976 (FG × Spanhoma), were selected from the advanced breeding lines and the standard variety test. They were grown at 4 county locations, 2 in North Carolina and 2 in Virginia, with 3 replications (blocks) in a randomized complete block design; adjacent plots were used for each digging. Free amino acid and free sugars were determined on sound mature kernels. Statistical analysis showed significant differences for varieties and locations. The variety effects were larger in the case of the free sugar contents. NC 6 had the largest quantity of glucose (0.12 mg/g), sucrose (37.45 mg/g), and stachyose (4.24 mg/g), while FG was highest in inositol (0.15 mg/g). In the case of free amino acids (μmoles/g), significant variety effects were observed for threonine-serine (1.55–1.92), alanine (0.95–1.53), peptide-cystine (1.28–1.50), valine (0.42–0.62), and histidine (0.58–0.66). Peanuts grown at Northampton County (NC) had the highest quantities of inositol (0.16 mg/g), sucrose (32.39 mg/g), stachyose (4.63 mg/g), aspartic acid (1.48 μmoles/g), threonine-serine (2.55), glutamic acid (7.49), alanine (1.89), valine (0.76), isoleucine (0.23) unknown 4-tyrosine (0.37), and histidine (0.78). The arginine maturity index value, calcium content, % sound mature kernels, % extra large kernels, and yield data are discussed.

Rose apple, an economic fruit in Southeast Asia, has potential as an export to western countries where its special attributes are better known to consumers. This research studied sensory characteristics of 8 rose apple varieties using highly trained panels and a defined sensory lexicon. A total of 9 sensory attributes including rose apple identity (ID), fruity, overall sweet, overall sour, bitter, firmness, cohesiveness of mass, astringent, and pulpy residue were present in all rose apple samples, but at different intensities for each variety. Only 5 attributes (floral/perfumy, green, spongy, initial crispness, and sustained crispness) were found in most samples, and were absent in a few samples; and "characteristic" attributes (rose, peel like, spicy, woody, earthy, starchy, and mealy) were detected in only 1 or a few samples. Based on their sensory characteristics, the rose apples being examined were categorized into 3 clusters. Rose apples in this study that are popularly cultivated and consumed including Pet-Num-Ping, Toon-Klao, Tub-Tim-Jun, Pet-Sai-Rung, and Pet-Sam-Pran, all of which were grouped together in cluster 3. They all had a crispy texture, but were very bland in flavor. Ma-Meow (cluster 1) and Num-Dok-Mai and Sa-Rak (cluster 2), which are not as popular, did not have a crispy texture; however, they did have the characteristic flavors of floral/perfumy and rose. The rose apples in cluster 3 might become better accepted if they had the flavor notes similar to those in cluster 1 and 2.

Prediction of sensory scores of roasted peanuts through analysis of the chemical components of raw peanuts is a goal of current research. Thirty samples of Virginia-type peanuts selected for wide variation in flavor precursors (amino acids and sugars) were roasted and evaluated by the Critical Laboratory Evaluation Roast (CLER) technique and by a flavor profile panel. Stepwise MAXR procedure was used to develop mathematical models for predicting both CLER scores and roasted peanut flavor ratings for the flavor profile. The best 10 variable model for predicting CLER scores had an R* of 0.969. A good fit (R' = 0.762) was obtained for predicting CLER score from only raw peanut data. The best 10 variable model for predicting roasted peanut flavor gave an R' of 0.928.