“…However, like the sampling variation in YP flour and the batch effect of sample loading, the heteroskedasticity cannot be ignored and must be checked when applying regression analysis to establish calibration curves. 11 Under such conditions when heteroskedasticity is detected, 9 the assumption for OLSR is no longer validated since the error variances throughout the same concentrations of beany odor standards (replicated independent variables) are unequal. 12 Alternatively, common data pretreatment such as power transformation (the Box–Cox transformation, variance-stabilizing transformation), randomization tests, non-parametric models (the Kruskal–Wallis test), and weighted least squares regression (WLSR) can be introduced to minimize or correct the heteroskedasticity.…”