An Approach for External Preference Mapping Improvement by Denoising Consumer Rating Data

In this study, denoising data was advocated in sensory analysis field to remove the existing noise in consumer rating data before processing to External Preference Mapping (EPM). This technique is a data visualization used to understand consumers’ sensory profiles by relating their preferences towards products to external information about sensory characteristics of the perceived products. The output is a perceptual map which visualizes the optimal products and aspects that maximize consumers’ likings. Hence, EPM is considered as a decision tool to support the development or improvement of products and respond to market requirements. In fact, the stability of the map is affected by the high variability of judgments that make consumer rating data very noisy. This may lead to a mismatch between products features and consumers’ preferences then distorted results and wrong decisions. To remove the existing noise, the use of some filtering methods is proposed. Regularized Principal Component Analysis (RPCA) and Stein’s Unbiased Risk Estimate (SURE), based respectively on hard and soft thresholding rules, were applied to consumer rating data to separate the signal from noise and maintain only useful information about the given liking scores. As a way to compare the EPM obtained from each strategy, a sampling process was conducted to randomly select samples from noisy and cleaned data, then perform their corresponding EPM. The stability of the obtained maps was evaluated using an indicator that computes and compares distances between them, both before and after denoising. The effectiveness of this methodology was evaluated by a simulation study and a potential application was shown on real dataset. Results show that recorded distances after denoising are lower than those before in almost all cases for both RPCA and SURE. However, RPCA outperforms SURE. The corresponding map is made more stable where level lines are seen smoothed and products are better located on liking zones. Hence, noise removal reduces variability in data and brings closer preferences which improves the quality of the visualized map. Keywords—Data denoising; Regularized Principal Component Analysis; Stein’s Unbiased Risk Estimate; sensory analysis; external preference mapping stability