1. Abstract Self-modeling curve resolution (SMCR) methods are widely acknowledged as potent tool in chemometrics, facilitating the decomposition of bilinear data matrices into chemically interpretable matrices. Nonetheless, these methods frequently yield results with ambiguities, particularly rotational ambiguity, leading to non-uniqueness of outcomes. This study investigates the influence of signal contributions of chemical components on rotational ambiguity. Utilizing simulated data from HPLC-DAD and one real excitation emission fluorescence data, the impacts of signal contributions of chemical components on spectral and concentration profiles are assessed. The findings illustrate that increasing the signal contribution of a chemical component can mitigate rotational ambiguity. Furthermore, the efficacy of employing second-order standard addition in reducing rotational ambiguity and enhancing the accuracy of quantitative analyses is examined.