chewR : An Open‐Source R Package for Quantifying Masticatory Performance With Applications in Oral Processing Research

Abstract

Masticatory performance is a measure of oral physiology that affects food breakdown and eating behavior. Existing methods to quantify masticatory performance rely on labor-intensive procedures (e.g., sieving expectorated samples), or proprietary software (e.g., ViewGum), limiting adoption and reproducibility. In this cross-sectional methods validation study, we: (1) examine the convergent validity of chewR, a user-friendly R package that quantifies masticatory performance in a 2-color gum mixing task via automated image analysis, and (2) examine how chewR based masticatory performance measures relate to other oral processing behaviors. Adults (n = 47, 601% female) completed a laboratory visit with a 2-colored gum mixing task, stimulated salivary flow, and eating rate objectively measured using Tang's carrot test. Chewed gum samples were flattened with a standardized method, scanned, and resulting images were analyzed using ViewGum and chewR. ViewGum generates a score based on the gum color mixing, with lower scores indicating greater masticatory performance. chewR produces a raw score based on gum color mixing; from this, a transformed chewR Masticatory Performance Score was calculated, with higher values indicating greater masticatory performance. chewR measures showed strong convergent validity with ViewGum Score: the raw chewR score and transformed chewR Masticatory Performance Score were each strongly correlated with ViewGum Score (r's = ±0.81, p < 0.00001), with signs in expected directions. Regarding predictive validity, the transformed chewR Masticatory Performance Score was weakly associated with carrot eating rate (r = 0.12), self-reported eating rate (r = 0.20), and stimulated salivary flow (r = 0.21); the same associations were similarly weak for ViewGum Score. These findings support chewR as an open-source alternative to ViewGum to quantify masticatory performance in a gum mixing task.

The authors' abstract, as published at the source. Journal of Texture Studies, 2026 · DOI ↗