面向大量消费者参与的巧克力曲奇投射映射研究的几何与统计技术
Geometric and statistical techniques for projective mapping of chocolate chip cookies with a large number of consumers
AI总结：
本研究提出融合统计与图论的投射映射分析新方法，首次用349名无经验消费者评估9款巧克力曲奇，验证了大样本下MFA与SensoGraph方法结果的高稳定性。
摘要：
所谓的快速感官分析方法已被证实可用于由不同类型评价小组开展的食品感官研究，评价人员范围从经过培训的评估员到无经验的消费者均有覆盖。这类方法产生的数据传统上采用统计技术进行分析，近期已有部分研究提出可运用几何技术与图论方法。本研究旨在深化这一研究方向，提出一种融合统计学与图论工具的新方法，用于分析投射映射（Projective Mapping）实验数据。此外，本研究首次在投射映射研究中纳入数量达n=349名的无经验消费者，对9款市售巧克力曲奇进行评估，其中包含某跨国畅销品牌的盲样重复样品，以及7款自有品牌产品。研究分别采用标准统计技术多因素分析（Multiple Factor Analysis, MFA）、近期提出的结合Gabriel聚类的几何方法SensoGraph，以及本文提出的基于样本间成对距离的新型变体方法对所得数据进行处理。三种方法均得到了相同的样本分组结果，且盲样重复样品在结果中位置相近。最后，研究通过自助法（bootstrapping）以及RV系数和Mantel系数对结果的稳定性进行了分析。结果表明，即便评估对象为无经验消费者，当评估员数量足够多时，MFA和SensoGraph均可获得高度稳定的结果——对于MFA的共识图或SensoGraph的全局相似矩阵而言，所需评估员数量约为200名。
英文摘要：
The so-called rapid sensory methods have proved to be useful for the sensory study of foods by different types of panels, from trained assessors to unexperienced consumers. Data from these methods have been traditionally analyzed using statistical techniques, with some recent works proposing the use of geometric techniques and graph theory. The present work aims to deepen this line of research introducing a new method, mixing tools from statistics and graph theory, for the analysis of data from Projective Mapping. In addition, a large number of n=349 unexperienced consumers is considered for the first time in Projective Mapping, evaluating nine commercial chocolate chips cookies which include a blind duplicate of a multinational best-selling brand and seven private labels. The data obtained are processed using the standard statistical technique Multiple Factor Analysis (MFA), the recently appeared geometric method SensoGraph using Gabriel clustering, and the novel variant introduced here which is based on the pairwise distances between samples. All methods provide the same groups of samples, with the blind duplicates appearing close together. Finally, the stability of the results is studied using bootstrapping and the RV and Mantel coefficients. The results suggest that, even for unexperienced consumers, highly stable results can be achieved for MFA and SensoGraph when considering a large enough number of assessors, around 200 for the consensus map of MFA or the global similarity matrix of SensoGraph.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
