In the development of sensory and consumer science, data are often collected in several blocks responding to different aspects of consumer experience. Sometimes the task of organizing the data and explaining their relation is non-trivial, especially when considering structural (casual) relationship between data sets. In this sense, PLS path modelling (PLS-PM) has been found as a good tool to model such relations, but this approach faces some issues regarding the assumption of uni-dimensionality of consumers’ data blocks. Sequential Orthogonalised PLS path modelling (SO-PLS-PM) has been proposed as an alternative approach to handle the multi-dimensionality and to explain the relations between the original data blocks without any preprocessing of the data. This study aims at comparing the efficacy of SO-PLS-PM and PLS-PM (together with splitting blocks into uni-dimensional sub-blocks) for handling multi-dimensionality. Data sets from two satiety perception studies (yoghurt, biscuit) have been used as illustrations.

The main novelty of this paper lies in underlining and solving a major, but little studied problem, related to the assumption of one-dimensional blocks in PLS-PM. The findings from the comparisons indicated that the two approaches (PLS-PM and SO-PLS-PM) highlighted the same main trends for the less complex samples (yoghurt samples): liking was the essential driver of satiation perception and portion size selection; while satiation mainly predicted satiety perception. For the more complex data set - from a sensory perspective - (biscuit samples), the relations between data blocks in PLS-PM model was difficult to interpret, whereas they were well explained by SO-PLS-PM. This underlines the ability of SO-PLS-PM to model multi-dimensional data sets without requiring any preprocessing steps.

在感官科学和消费者科学的发展中，通常会按照对消费者体验不同方面的响应，将数据收集为几个区块。有时，组织数据并解释它们之间的关系的任务并非易事，特别是在考虑数据集之间的结构（偶然）关系时。从这个意义上说，已经发现PLS路径建模（PLS-PM）是建立这种关系的一个很好的工具，但是这种方法面临着一些有关消费者数据块一维假设的问题。已经提出了顺序正交PLS路径建模（SO-PLS-PM）作为处理多维和解释原始数据块之间关系的替代方法，而无需对数据进行任何预处理。这项研究旨在比较SO-PLS-PM和PLS-PM（连同将块拆分为一维子块）处理多维的功效。来自两个饱腹感研究（酸奶，饼干）的数据集已用作例证。

本文的主要新颖之处在于强调和解决与PLS-PM中一维块的假设有关的主要但尚未研究的问题。比较的结果表明，两种方法（PLS-PM和SO-PLS-PM）强调了不太复杂的样本（酸奶样本）的相同主要趋势：喜好是饱足感和份量选择的基本驱动力；饱食主要预测饱腹感。对于更复杂的数据集（从感官角度而言）（饼干样本），PLS-PM模型中数据块之间的关系难以解释，而SO-PLS-PM可以很好地解释它们。这强调了SO-PLS-PM无需任何预处理步骤即可对多维数据集建模的能力。