Assessment of masticatory performance by geometric measurement of the mixing ability with 2-color chewing gum.

STATEMENT OF PROBLEM

The 2-color mixing ability test has been recently introduced for objective assessment of masticatory performance. However, the ideal bicolor specimens have not yet been identified, and the color analysis of digital images requires improvement.

PURPOSE

The purpose of this clinical study was to formulate a custom-made, 2-color chewing gum for the mixing ability test and to develop an image-processing method for color mixing analysis.

MATERIAL AND METHODS

Specimens of red-green (RG) chewing gum were prepared as a test food. Twenty dentate participants (10 men, 10 women; mean age 21 years) took part in this study. Each participant masticated 1 piece of RG gum for 3, 6, 9, 15, and 25 cycles, and this task was repeated 3 times consecutively (total n=15 for each participant). The boluses were retrieved and flattened to 1-mm-thick wafers and scanned with a flatbed scanner. The digital images were analyzed using ImageJ software equipped with a custom-built plug-in to measure the geometric dispersion (GD) of baseline red segment. The predictive criterion validity of this method was determined by correlating GD to the number of mastication cycles. The hardness and mass of RG chewing gum were measured before and after mastication. Hardness loss (%) and mass loss (%) were then calculated and compared with those of a commercially available chewing gum.

RESULTS

The 2-way repeated-measures ANOVA with post hoc Bonferroni test showed that GD was able to discriminate among the groups of different numbers of mastication cycles (P<.001). Pearson correlation coefficient confirmed the significant correlation between GD and the number of mastication cycles (r=0.90, P<.001). The hardness loss and mass loss of RG chewing gum were significantly lower than those of commercial chewing gum (P<.001).

CONCLUSIONS

The newly formulated chewing gum provides an appropriate test food material for masticatory performance assessment. The new image-processing method discriminated among the different levels of color mixture and quantified the mixing ability.