A novel application of check‐all‐that‐apply with semi‐trained assessors for tea sensory characterization and preference: Using Longjing tea as a case study

Abstract

AbstractThe consumer‐centric tea‐sensory evaluation is the premise and guarantee for product optimization and consumer guidance. However, there is no effective consumer‐centric tea sensory characterization tool till now. The goal of this study was to explore a novel application of check‐all‐that‐apply with semi‐trained assessors (ST‐CATA) on tea sensory characterization and hedonic driver investigation, using Longjing teas with complex flavor characteristics as a case study. In this study, a 17 attributes lexicon and corresponding references were developed as attributes marker for sensory training. 17 Longjing teas with different sensory characteristics were analyzed by ST‐CATA (N = 40). Tea characterization by ST‐CATA based on correspondence analysis (CA) and multidimensional alignment (MDA) showed that fresh‐type, roasty‐type and defect‐type Longjing teas were distinguished successfully. Penalty analysis based on hedonic scale from ST‐CATA indicated that fresh‐type and roasty‐type Longjing tea were two directions for consumer preferences. Attributes “tender flavor”, “umami”, “sweetness”, “Orchid‐like aroma”, “roasty”, and “fresh” were the key drivers for overall liking, while “grassy flavor”, “strong”, “bitterness”, and “astringency” were negatively associated with overall liking. Overall results verified ST‐CATA is a valuable consumer‐centric tool for tea sensory characterization, which have powerful potential for identifying liking drivers and directions for consumption preference.Practical ApplicationTea sensory characteristics are complex and variable. This study developed a novel consumer‐centric method for tea sensory evaluation based on check‐all‐that‐apply with semi‐trained assessors (ST‐CATA). An effective tea characterization was performed by ST‐CATA and used to hedonic analysis with acceptable sensitivity. The novel application of ST‐CATA was verified to be suitable for tea sensory analysis and consumer preference with the advantage of easy‐to‐understand and intuitive consumer experience, which provided effective information for tea product optimization and consumer guidance.