Sweet cherry: composition, postharvest preservation, processing and trends for its future use

Background Sweet cherries (Prunus avium L.) are a nutritious fruit which are rich in polyphenols and have high antioxidant potential. Most sweet cherries are consumed fresh and a small proportion of the total sweet cherries production is value added to make processed food products. Sweet cherries are highly perishable fruit with a short harvest season, therefore extensive preservation and processing methods have been developed for the extension of their shelf-life and distribution of their products. Scope and Approach In this review, the main physicochemical properties of sweet cherries, as well as bioactive components and their determination methods are described. The study emphasises the recent progress of postharvest technology, such as controlled/modified atmosphere storage, edible coatings, irradiation, and biological control agents, to maintain sweet cherries for the fresh market. Valorisations of second-grade sweet cherries, as well as trends for the diversification of cherry products for future studies are also discussed. Key Findings and Conclusions Sweet cherry fruit have a short harvest period and marketing window. The major loss in quality after harvest include moisture loss, softening, decay and stem browning. Without compromising their eating quality, the extension in fruit quality and shelf-life for sweet cherries is feasible by means of combination of good handling practice and applications of appropriate postharvest technology. With the drive of health-food sector, the potential of using second class cherries including cherry stems as a source of bioactive compound extraction is high, as cherry fruit is well-known for being rich in health-promoting components.

Distributional effects and individual differences in L2 morphology learning

Second language (L2) learning outcomes may depend on the structure of the input and learners’ cognitive abilities. This study tested whether less predictable input might facilitate learning and generalization of L2 morphology while evaluating contributions of statistical learning ability, nonverbal intelligence, phonological short-term memory, and verbal working memory. Over three sessions, 54 adults were exposed to a Russian case-marking paradigm with a balanced or skewed item distribution in the input. Whereas statistical learning ability and nonverbal intelligence predicted learning of trained items, only nonverbal intelligence also predicted generalization of case-marking inflections to new vocabulary. Neither measure of temporary storage capacity predicted learning. Balanced, less predictable input was associated with higher accuracy in generalization but only in the initial test session. These results suggest that individual differences in pattern extraction play a more sustained role in L2 acquisition than instructional manipulations that vary the predictability of lexical items in the input.