Optimizing a determination of chewing efficiency using a solid test food

A test using a solid food is relevant to measure chewing ability (CA) as (a) it includes an integrated functioning of all oral structures involved, (b) an impairment of chewing a solid food causes inevitably diet restrictions, and (c) chewing efficiency (CE) can easily be defined. CE is the number of chewing cycles, N(1/2-Xo), needed to attain a particular chewing outcome (a median particle size, X ₅₀ , which is half the initial particle size Xo) whereas chewing performance (CP) is a state of chewing outcome (X ₅₀ ) at an arbitrary number of chewing cycles. The use of CE is preferable for CA because inter-subject ratios are constant regardless of the initial conditions of the test food. Furthermore, the inter-subject variation is two times larger for CE values than for CP ones, yielding a better inter-subject differentiation of CA. However, a determination of CP needs only one N-value, and that of CE at least two N-values for enabling an interpolation of N(1/2-Xo). Using samples of only two half-cubes (9.6 x 9.6 x 4.8 mm; limiting test load) of Optosil (an artificial test food), and detailed previous information on log(X ₅₀ )-log(N) relationships (Liu et al., Archives of Oral Biology, 2018, 91, 63-77) as a "gold standard," a short procedure has been developed for a priori choosing two appropriate N-numbers, and the subsequent determination of a subject's CE. This procedure has been developed using results from 20 young adults (23.7 years, SD 1.1) and was validated in 10 middle-aged and older adults (52.3 years, SD 10.1), where impairments in the dentition were reflected in the CE-values. Our short procedure to determine CE will improve studies on relationships between CA and food preference, or between CA and dental factors and/or physiological factors. The first type of relationship may be of interest for food industry whereas the second type may be of interest for population studies in rapidly aging societies and for clinical studies in dentistry. Results can be compared between subjects and studies without bias by using CE rather than CP as a measure of CA.