Integrated sensory profiling of fresh lettuce varieties: Combining affect magnitude profiling with generalized descriptive analysis

Understanding sensory characteristics is essential for predicting consumer preferences and guiding product innovation. However, profiling fresh produce poses challenges due to its inherent variability and the lack of standardized category benchmarks. This study applied and evaluated Affect Magnitude Profiling (AMP), a consumer-centered sensory profiling method designed to address these challenges. AMP combines familiarization sessions, allowing consumers to generate intuitive and consumer-relevant attribute terms, with the Double-Faced Applicability (DFA) test, a two-step, Signal Detection Theory (SDT)-based procedure using bipolar semantic descriptors to quantify both the applicability and perceived strength of attributes with minimal training. Ten lettuce varieties were profiled using both AMP and generalized Descriptive Analysis (gDA). gDA, conducted with a trained panel, provided detailed, analytically derived sensory attributes, while AMP, conducted with a small consumer panel, generated holistic, affective, and consumer-relevant descriptors. Across 18 paired descriptors, d′_A (affect-magnitude d-prime) values derived from AMP demonstrated great sample discriminability and identified key consumer-driven attributes driving liking, including ‘not bitter’, ‘taste good’, and ‘crinkled’. These findings highlight the complementary value of AMP and gDA: AMP captures consumer-relevant, affective perceptions and enables rapid, resource-efficient profiling, while gDA delivers analytical precision for interpreting consumer insights. Together, these approaches provide a robust framework for sensory characterization, early-stage product development, and the study of complex or variable products. AMP's integration of consumer-derived language, familiarization, and SDT-based quantification offers actionable insights for both research and industry, enhancing the ability to align fresh product design with consumer expectations.