Multivariate curve resolution (MCR) methods are powerful chemometric approaches that have been significantly involved to study the complex chemical systems. The accuracy of MCR results is directly related to the applied constraints which determine the properties of the resolved profiles. Constraints have been, and still are, an active field of research in MCR studies. Different constraints have different impacts on the range of feasible solutions, so it is important to study the effect of each constraint, to examine its compliance with physico-chemical principles, and to find sufficient conditions to ensure unique solutions.In this study, we focus on known-value constraint and the requirements for reducing the range of feasible solutions under this constraint. In addition, several theoretical rules are presented to determine the minimum number of required known-values in order to get a unique solution. The theory of known-values was accessed using several simulated and experimental datasets. As shown previously, known-value information can be applied in quantitative analysis of first-order data. The prediction performance of MCR-ALS method under known-value constraint was compared to the results of the well stablished partial least squares (PLS) method in the presence of minimum number of required calibration samples. The comparison shows the advantage of using MCR-ALS method under known-value constraint over the PLS results in such experiments.