Understanding consumer requirements with respect to the sensory attributes of food and the sentimental consequences is critical for enhancing consumer satisfaction and achieving market success. A recent innovation, the signal detection expectation profiling method, introduced by , utilizes the two-step rating-based ‘double-faced applicability (DFA)’ test to create expectation profiles for product attributes, quantified by output measures. This study aimed to demonstrate and test the usage and efficacy of the expectation profiling method to provide insight for product development. The efficiency of this approach for using a two-step rating-based DFA was examined first and the information guided by the expectation profiling output measures was compared to satisfaction drivers identified through partial least square (PLS) regression and landscape segmentation analysis (LSA), commonly used to link consumer satisfaction/liking and sensory perception. Consumer expectations and satisfaction/sensory evaluations of six different mayonnaise products formed the dataset. Overall, the expectation profiles effectively identified the key attributes significantly impacting overall satisfaction, aligning with the results of the PLS regression and LSA. The advantage of expectation profiles lies in their quantitative representation of the degree of expected sensory attributes, extending beyond the scope of actual evaluated products and offering actionable insights for product optimization. Furthermore, by incorporating custom attributes (descriptor pairs) based on the hedonic valence of target consumers, the expectation profile showcases the potential for effectively addressing consumer-relevant attributes tailored to specific target consumer groups.

中文翻译：

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具有两步评级的信号检测期望分析方法，用于指导产品优化

了解消费者对食品感官属性和情感后果的要求对于提高消费者满意度和取得市场成功至关重要。最近的一项创新是由 引入的信号检测期望分析方法，利用基于两步评级的“双面适用性 (DFA)”测试来创建产品属性的期望配置文件，并通过输出测量进行量化。本研究旨在演示和测试期望分析方法的用法和功效，为产品开发提供见解。首先检查了这种使用基于两步评级的 DFA 的方法的效率，并将期望分析输出测量指导的信息与通过偏最小二乘 (PLS) 回归和景观分割分析 (LSA) 确定的满意度驱动因素进行了比较，通常用于将消费者满意度/喜好与感官知觉联系起来。消费者对六种不同蛋黄酱产品的期望和满意度/感官评估构成了该数据集。总体而言，期望概况有效地识别了显着影响整体满意度的关键属性，与 PLS 回归和 LSA 的结果保持一致。期望概况的优势在于其对预期感官属性程度的定量表示，超出了实际评估产品的范围，并为产品优化提供了可行的见解。此外，通过结合基于目标消费者的享乐效价的自定义属性（描述符对），期望概况展示了有效解决针对特定目标消费者群体定制的消费者相关属性的潜力。