Pérez Aparicio Jesús, Toledano Medina M Angeles, Lafuente Rosales Victoria
IFAPA, Centro de Palma del Río (Area de Tecnología Postcosecha e Industrias Agroalimentarias), Avda Rodríguez de la Fuente s/n, 14700 Palma del Río (Córdoba), Spain.
Anal Chim Acta. 2007 Jul 9;595(1-2):238-47. doi: 10.1016/j.aca.2007.02.054. Epub 2007 Feb 25.
Free-choice profile (FCP), developed in the 1980s, is a sensory analysis method that can be carried out by untrained panels. The participants need only to be able to use a scale and be consumers of the product under evaluation. The data are analysed by sophisticated statistical methodologies like Generalized Procrustean Analysis (GPA) or STATIS. To facilitate a wider use of the free-choice profiling procedure, different authors have advocated simpler methods based on principal components analysis (PCA) of merged data sets. The purpose of this work was to apply another easy procedure to this type of data by means of a robust PCA. The most important characteristic of the proposed method is that quality responsible managers could use this methodology without any scale evaluation. Only the free terms generated by the assessors are necessary to apply the script, thus avoiding the error associated with scale utilization by inexpert assessors. Also, it is possible to use the application with missing data and with differences in the assessors' attendance at sessions. An example was performed to generate the descriptors from different orange juice types. The results were compared with the STATIS method and with the PCA on the merged data sets. The samples evaluated were fresh orange juices with differences in storage days and pasteurized, concentrated and orange nectar drinks from different brands. Eighteen assessors with a low-level training program were used in a six-session free-choice profile framework. The results proved that this script could be of use in marketing decisions and product quality program development.
自由选择剖面法（FCP）于20世纪80年代开发，是一种可由未经训练的小组进行的感官分析方法。参与者只需能够使用量表并成为所评估产品的消费者即可。数据通过复杂的统计方法进行分析，如广义普罗克汝斯忒斯分析（GPA）或主成分分析（STATIS）。为了促进自由选择剖面程序的更广泛应用，不同的作者主张基于合并数据集的主成分分析（PCA）采用更简单的方法。这项工作的目的是通过稳健的主成分分析将另一种简单程序应用于这类数据。所提出方法的最重要特征是质量负责人可以在不进行任何量表评估的情况下使用这种方法。应用该脚本只需要评估人员生成的自由术语，从而避免了非专业评估人员使用量表时产生的误差。此外，该应用程序还可以处理缺失数据以及评估人员出席会议情况的差异。通过一个示例从不同类型的橙汁中生成描述符。将结果与STATIS方法以及合并数据集上的主成分分析进行比较。所评估的样品包括储存天数不同的新鲜橙汁以及来自不同品牌的巴氏杀菌、浓缩和橙汁饮料。在一个六阶段的自由选择剖面框架中使用了18名接受过低水平培训的评估人员。结果证明，该脚本可用于营销决策和产品质量计划的制定。
