Choi Eun-Sil, Lee Ha-Lim, Kwon Bo-Kyoung, Kim Min-A, Lee Hye-Seong
Department of Food Science and Biotechnology, College of Engineering, Ewha Womans University, Seoul 03760, South Korea.
Department of Food Science and Biotechnology, College of Engineering, Ewha Womans University, Seoul 03760, South Korea; Division of Food and Nutrition, Chonnam National University, Gwangju 61186, South Korea.
Food Res Int. 2023 May;167:112592. doi: 10.1016/j.foodres.2023.112592. Epub 2023 Feb 16.
Signal detection theory (SDT) sensory discrimination analysis using A-Not A with a two-step rating is an efficient approach to in-house sensory quality management in the food industry. For such sensory analysis using an internal panel, the panels' ability to use stable decision criteria and provide a consistent response distribution responding to "A" vs "Not A" is critical for guaranteeing the data quality. This study examined the effects of the familiarization procedure (FP) and reference presentation probability (RPP) in the SDT A-Not A rating protocol on the panels' sensory learning of samples and stability of decision criteria using SDT parameters, recognition d' (d'),criteria location (c), and discrimination d' indices. Three different protocols were compared using ice-tea samples with small differences: Control, 0.25 RPP with repeated reference tasting (FP); Modified-1, 0.25 RPP with reference categorization (FP); Modified-2, 0.5 RPP with reference categorization (FP). An independent sample design with three groups having equal sensitivity was used to identify the differences among the protocols. For each protocol, two sub-groups with similar decision criteria (response bias) were formed according to the results obtained from the pre-test and used for the main-test analysis. SDT analysis results indicated that the Modified-2 protocol with a higher RPP (0.5) induced the most efficient sensory learning of the reference. The protocol improved the subjects' recognition of the reference and test samples, better differentiating from the reference and stabilizing the decision criterion, resulting in higher discrimination performance (larger d'). The results showed that d' analysis, together with d' analysis using a sensory panel, is a useful tool for monitoring the panel performance and checking for the sensory data quality of the sensory difference tests. In the present paper, a detailed illustration of the A-Not A sensory test procedure and examples of how to apply the SDT indices for different business decision-making is also introduced using the design and results of the present experiment.
信号检测理论（SDT）使用“是 - 非A”两步评分法进行感官辨别分析，是食品行业内部感官质量管理的一种有效方法。对于使用内部评估小组进行的此类感官分析，评估小组使用稳定决策标准并针对“是A”与“非A”提供一致响应分布的能力对于保证数据质量至关重要。本研究使用SDT参数、识别d'（d'）、标准位置（c）和辨别d'指数，考察了SDT“是 - 非A”评分方案中的熟悉程序（FP）和参考呈现概率（RPP）对评估小组样本感官学习及决策标准稳定性的影响。使用差异较小的冰茶样本比较了三种不同方案：对照组，0.25的RPP并重复参考品尝（FP）；改良 - 1组，0.25的RPP并进行参考分类（FP）；改良 - 2组，0.5的RPP并进行参考分类（FP）。采用具有同等敏感性的三组独立样本设计来识别各方案之间的差异。对于每个方案，根据预测试结果形成两个具有相似决策标准（响应偏差）的子组，并用于主测试分析。SDT分析结果表明，具有较高RPP（0.5）的改良 - 2方案能诱导出最有效的参考物感官学习。该方案提高了受试者对参考物和测试样本的识别能力，能更好地区分参考物并稳定决策标准，从而产生更高的辨别性能（更大的d'）。结果表明，d'分析以及使用感官评估小组进行的d'分析，是监测评估小组表现和检查感官差异测试感官数据质量的有用工具。在本文中，还利用本实验的设计和结果，详细说明了“是 - 非A”感官测试程序以及如何将SDT指数应用于不同业务决策的示例。
