The cider industry has experienced recent growth within the USA and Virginia in 12 particular. However, the sensory characteristics and drivers of consumer acceptance of ciders 13 are largely uncharacterized. Therefore, this work describes the sensory profiles of commercial 14Virginia ciders and links these to consumer acceptance. In study 1, a descriptive analysis (DA)

The growing popularity of hard cider in the United States has been accompanied by an inconsistent understanding of the nature and importance of consumers' perception of dryness and sweetness in the product. In 2018, the New York Cider Association proposed the Merlyn Dryness Scale as a tool to predict cider dryness using basic cider chemistry, but this approach has yet to be validated in sensory experiments. In the current study, panelists (N = 48) evaluated three different commercial ciders served at two different temperatures (2 °C and 22 °C) in three parts: by rating the dryness of the sample on a line scale equivalent to the range of the Merlyn Dryness Scale, by using a simple check-all-that-apply (CATA) tool that included dryness, and by rating their overall liking on a 9-point hedonic scale. The results indicated that the Merlyn Dryness Scale may not achieve its goal of predicting perceived dryness in cider, as consumers perceived cider samples to be more dry than was suggested using Merlyn Scale chemical procedures. Contrary to expectations, the serving temperature of the cider samples did not significantly impact perceived dryness rating but did influence overall liking. This study suggests that predicting sensory dryness from cider-chemistry parameters requires further study.

Rapid methods of text analysis are increasingly important tools for efficiently extracting and understanding communication within the food and beverage space. This study aimed to use frequency‐based text mining and biterm topic modeling (BTM) as tools for analyzing how cider products are communicated and marketed on cider‐producer websites for products made in Virginia, Vermont, and New York. BTM has been previously used to explore topics in small corpora of text data, and frequency‐based text mining is efficient for exploring patterns of text across different documents or filters. The present dataset comprised 1115 cider products and their website descriptions extracted from 124 total cider‐producer websites during 2020 and 2021. Results of the text mining analyses suggest that cider website descriptions emphasize food‐pairing, production, and sensory quality information. Altogether, this research presents the text mining approaches for exploring food and beverage communication.Practical applicationsThis research will be valuable to stakeholders in the United States' cider industry by providing relevant insight as to how cider marketing and sensory communication varies based on extrinsic product factors, such as geography and packaging. This research also demonstrates the efficiency and potential of text mining tools for exploring language and communication related to foods, beverages, and sensory quality. Further, this research provides a framework for extracting sensory‐specific language from a large corpus of data, which may be adopted by other researchers wishing to apply rapid descriptive methods in the sensory, quality, and consumer research fields.