Abstract ComDim (Common Dimensions) analysis was initially introduced within the context of sensometrics to analyze conventional and free choice sensory profiling data, and more generally multiblock datasets. Thereafter, it has gained some popularity in chemometrics and has been extended in different ways to meet specific needs. Recently, this strategy of analysis has been adapted to the supervised case, under the name of P-ComDim. Going further, we propose herein to extend ComDim to Path-ComDim where the datasets at hand are assumed to have a specific pattern of directed relations among them reflecting, for instance, a chain of influence. The aim of Path-ComDim is to analyze these datasets taking into account the structural connections among them. After a brief review of alternative path modeling approaches, Path-ComDim is detailed encompassing both methodological and algorithmic aspects. In the particular case of a single block to be predicted, it is shown that Path-ComDim is equivalent to P-ComDim analysis. Path-ComDim analysis is illustrated on the basis of a case study involving instrumental, sensory and preference data. Finally, the outcomes are compared to those obtained from alternative path modeling methods.

中文翻译：

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ComDim：从多块数据分析到路径建模

摘要 ComDim（通用维度）分析最初是在传感测量学的背景下引入的，用于分析传统和自由选择的感官分析数据，以及更普遍的多块数据集。此后，它在化学计量学中获得了一定的普及，并以不同的方式扩展以满足特定需求。最近，这种分析策略以 P-ComDim 的名义适用于监督案例。更进一步，我们在此建议将 ComDim 扩展到 Path-ComDim，其中假设手头的数据集在它们之间具有特定的定向关系模式，反映了例如影响链。Path-ComDim 的目的是分析这些数据集，同时考虑它们之间的结构连接。在简要回顾了替代路径建模方法之后，Path-ComDim 详细介绍了方法和算法方面。在要预测的单个块的特定情况下，表明 Path-ComDim 等效于 P-ComDim 分析。Path-ComDim 分析以涉及仪器、感官和偏好数据的案例研究为基础进行说明。最后，将结果与从替代路径建模方法获得的结果进行比较。