Bootstrap Resampling of Temporal Dominance of Sensations Curves to Compute Uncertainties

Synergy Analysis Between the Temporal Dominance of Sensations and Temporal Liking Curves of Strawberries

The Temporal Dominance of Sensations (TDS) method allows for the real-time tracking of changes in multiple sensory attributes, such as taste, aroma, and texture, during food tasting. Over the past decade, it has become an essential tool in sensory evaluation, offering novel insights into temporal sensory perception. When combined with the Temporal Liking (TL) method, TDS enables the investigation of how sensory changes influence instantaneous liking. Existing methods in time-series sensory evaluation have not simultaneously achieved the following two key objectives: (1) predicting TL curves from TDS curves and (2) identifying shared sensory-liking synergies across samples. In this study, we address this gap by applying supervised non-negative matrix factorization, which enables both precise prediction and interpretable synergy extraction. This novel approach has the potential to extend the applicability of TDS analysis to broader sensory evaluation contexts. We validated the method using the data for strawberries recorded in an earlier study. Our model, utilizing three latent synergy components accounting for 94% of the data variation, accurately predicted the TL curves from TDS curves with a median RMSE of 0.36 in cross-validation, approximately 1/16 of the maximum TL score. Moreover, these synergy components were highly interpretable, suggesting some key factors that explain individual variations in sensory perception. These findings highlight the effectiveness of synergy analysis in time-series sensory evaluation, leading to deeper understanding of the connections between temporal sensory and liking responses.

Prediction of Temporal Liking from Temporal Dominance of Sensations by Using Reservoir Computing and Its Sensitivity Analysis

The temporal dominance of sensations (TDS) method has received particular attention in the food science industry due to its ability to capture the time-series evolution of multiple sensations during food tasting. Similarly, the temporal liking method is used to record changes in consumer preferences over time. The conjunctive use of these methods provides an effective framework for analyzing food taste and preference, making them valuable tools for product development, quality control, and consumer research. We employed the TDS and temporal liking data of strawberries that were recorded in our earlier study to estimate the temporal liking values from sensory changes. For this purpose, we used a reservoir network, a type of recurrent neural network suitable for time-series data. The trained models exhibited prediction accuracy of the determination coefficient as high as 0.676-0.993, with the median being 0.951. Further, we proposed two types of sensitivities of each sensory attribute toward the change in the temporal liking value. Elemental sensitivity indicates the degree that each sensory attribute influences the temporal liking. In the case of strawberries, the sweet attribute was the greatest contributor, followed by the attribute of fruity. The two least-contributing attributes were light and green. Interactive sensitivity indicates how each attribute affects the temporal liking in conjunction with other attributes. This sensitivity analysis revealed that the sweet attribute positively influenced the liking, whereas the green and light attributes impacted it negatively. The proposed methods offer a new approach to comprehensively analyze how the results of TDS are linked to those of the temporal liking method, serving as a step toward developing an alternative system to human panels.

Multi-attribute temporal descriptive methods in sensory analysis applied in food science: A systematic scoping review

Among descriptive sensory evaluation methods, temporal methods have a wide audience in food science because they make it possible to follow perception as close as possible to the moment when sensations are perceived. The aim of this work was to describe 30 years of research involving temporal methods by mapping the scientific literature using a systematic scoping review. Thus, 363 research articles found from a search in Scopus and Web of Science from 1991 to 2022 were included. The extracted data included information on the implementation of studies referring to the use of temporal methods (details related to subjects, products, descriptors, research design, data analysis, etc.), reasons why they were used and the conclusions they allowed to be drawn. Metadata analysis and critical appraisal were also carried out. A quantitative and qualitative synthesis of the results allowed the identification of trends in the way in which the methods were developed, refined, and disseminated. Overall, a large heterogeneity was noted in the way in which the temporal measurements were carried out and the results presented. Some critical research gaps in establishing the validity and reliability of temporal methods have also been identified. They were mostly related to the details of implementation of the methods (e.g., almost no justification for the number of consumers included in the studies, absence of report on panel repeatability) and data analysis (e.g., prevalence of use of exploratory data analysis, only 20% of studies using confirmatory analyses considering the dynamic nature of the data). These results suggest the need for general guidelines on how to implement the method, analyze and interpret data, and report the results. Thus, a template and checklist for reporting data and results were proposed to help increase the quality of future research.

TDS Similarity: Outlier Analysis Using a Similarity Index to Compare Time-Series Responses of Temporal Dominance of Sensations Tasks

Temporal dominance of sensations (TDS) methods are used to record temporally developing sensations while eating food samples. Results of TDS tasks are typically discussed using averages across multiple trials and panels, and few methods have been developed to analyze differences between individual trials. We defined a similarity index between two time-series responses of TDS tasks. This index adopts a dynamic level to determine the importance of the timing of attribute selection. With a small dynamic level, the index focuses on the duration for attributes to be selected rather than on the timing of the attribute selection. With a large dynamic level, the index focuses on the temporal similarity between two TDS tasks. We performed an outlier analysis based on the developed similarity index using the results of TDS tasks performed in an earlier study. Certain samples were categorized as outliers irrespective of the dynamic level, whereas the categorization of a few samples depended on the level. The similarity index developed in this study achieved individual analyses of TDS tasks, including outlier detection, and adds new analysis techniques to TDS methods.

Linking Temporal Dominance of Sensations for Primary-Sensory and Multi-Sensory Attributes Using Canonical Correlation Analysis

Sensory responses dynamically change while eating foods. Temporal dominance of sensations (TDS) methods record temporal evolution and have attracted attention in the last decade. ISO 13299 recommends that different levels of attributes are investigated in separate TDS trials. However, only a few studies have attempted to link the dynamics of two different levels of sensory attributes. We propose a method to link the concurrent values of dominance proportions for primary- and multi-sensory attributes using canonical correlation analysis. First, panels categorized several attributes into primary- and multi-sensory attributes. Primary-sensory attributes included sweet, sour, fruity, green, watery, juicy, aromatic, and light. Multi-sensory attributes included refreshing, fresh, pleasurable, rich/deep, ripe, and mild. We applied the TDS methods to strawberries using these two categories of attributes. The obtained canonical correlation model reasonably represented the relationship between the sensations in a reductive manner using five latent variables. The latent variables couple multiple primary- and multi-sensory responses that covary. Hence, the latent variables suggest key components to comprehend food intake experiences. We further compared the model based on the dominance proportions and the time-derivatives of the dominance proportions. We found that the former model was better in terms of the ease of interpreting the canonical variables and the degree to which the canonical variables explain the dominance proportions. Thus, these models help understand and leverage the sensory values of food products.