. 2021 Jan 21;46:bjab003. doi: 10.1093/chemse/bjab003

Table 3.

Model equations corresponding to each primary hypothesis

Model name (model type)	Equation
Sweetness1 (LMM)	$S w e e t n e s s = β_{0} + β_{1} \times S e s s i o n + β_{2} \times C o n d i t i o n + β_{3} \times S e s s i o n \times C o n d i t i o n + μ_{0} + μ_{1} \times$ $S e s s i o n \times C o n d i t i o n + ε$
Sweetness2 (LMM)	$S w e e t n e s s = β_{0} + β_{1} \times S e s s i o n + β_{2} \times C o n d i t i o n + μ_{0} + μ_{1} \times S e s s i o n + μ_{2} \times C o n d i t i o n + ε$
Pleasantness1 (LMM)	$P l e a s a n t n e s s = β_{0} + β_{1} \times S e s s i o n + β_{2} \times C o n d i t i o n + μ_{0} + μ_{1} \times S e s s i o n + μ_{2} \times C o n d i t i o n + μ_{3} \times$ $S e s s i o n \times C o n d i t i o n + ε$
Pleasantness2 (LMM)	$P l e a s a n t n e s s = β_{0} + β_{1} \times S e s s i o n + β_{2} \times C o n d i t i o n + β_{3} \times S e s s i o n \times C o n d i t i o n + μ_{0} + μ_{1} \times S e s s i o n + μ_{2} \times C o n d i t i o n + μ_{3} \times S e s s i o n \times$ $S e s s i o n \times C o n d i t i o n + ε$
Pleasantness3 (LMM)	$P l e a s a n t n e s s = β_{0} + β_{1} \times S e s s i o n + β_{2} \times C o n d i t i o n + β_{3} \times S w e e t P r e f e r e n c e + β_{4} \times S e s s i o n \times$ $C o n d i t i o n + β_{5} \times S e s s i o n \times S w e e t P r e f e r e n c e + β_{6} \times C o n d i t i o n \times S w e e t P r e f e r e n c e + β_{7} \times S e s s i o n \times$ $C o n d i t i o n \times S w e e t P r e f e r e n c e + μ_{0} + μ_{1} \times S e s s i o n + μ_{2} \times C o n d i t i o n + μ_{3} \times S e s s i o n \times C o n d i t i o n + ε$
Intensity1 (LMM)	$I n t e n s i t y E n h a n c e m e n t = β_{0} + β_{1} \times S e s s i o n + β_{2} \times C o n d i t i o n + β_{3} \times S e s s i o n \times C o n d i t i o n +$ $μ_{0} + μ_{1} \times S e s s i o n \times C o n d i t i o n + ε$
Intensity2 (LMM)	$I n t e n s i t y E n h a n c e m e n t = β_{0} + β_{1} \times S e s s i o n + β_{2} \times C o n d i t i o n + μ_{0} + μ_{1} \times S e s s i o n + μ_{2} \times C o n d i t i o n + ε$
OdorReferral_OC1 (GLMM)	$O r a l C a v i t y, L o g i t (O d d s) = β_{0} + β_{1} \times S e s s i o n + β_{2} \times C o n d i t i o n + β_{3} \times S e s s i o n \times C o n d i t i o n +$ $μ_{0} + μ_{1} \times S e s s i o n \times C o n d i t i o n + ε$
OdorReferral_T1 (GLMM)	$T o n g u e, L o g i t (O d d s) = β_{0} + β_{1} \times S e s s i o n + β_{2} \times C o n d i t i o n + β_{3} \times S e s s i o n \times C o n d i t i o n +$ $μ_{0} + μ_{1} \times S e s s i o n \times C o n d i t i o n + ε$
OdorReferral_OC2 (GLMM)	$O r a l C a v i t y, L o g i t (O d d s) = β_{0} + β_{1} \times S e s s i o n + β_{2} \times C o n d i t i o n + μ_{0} + μ_{1} \times S e s s i o n + ε$
OdorReferral_OC3 (GLMM)	$O r a l C a v i t y, L o g i t (O d d s) = β_{0} + β_{1} \times S e s s i o n + β_{2} \times C o n d i t i o n + μ_{0} + μ_{1} \times C o n d i t i o n + ε$
OdorReferral_T2 (GLMM)	$T o n g u e, L o g i t (O d d s) = β_{0} + β_{1} \times S e s s i o n + β_{2} \times C o n d i t i o n + μ_{0} + μ_{1} \times S e s s i o n + μ_{2} \times C o n d i t i o n + ε$

LMM, linear mixed-effects model; GLMM, generalized mixed-effects model with binomial error distribution and a logit link function, $β$ , fixed effects; $μ$ , random effects, $ε$ , residuals.