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An algorithm has been developed for finding the global minimum of amultidimensional error function by fitting model spectral maps into observedones. Principal component analysis is applied to reduce the dimensionality ofthe model and the coupling degree between the parameters, and to determine theregion of the minimum. The k-nearest neighbors method is used to calculate theoptimal parameter values. The algorithm is used to estimate the physicalparameters of the contracting dense star-forming core of L1287. Maps in theHCO+(1-0), H13CO+(1-0), HCN(1-0), and H13CN(1-0) lines, calculated within a 1Dmicroturbulent model, are fitted into the observed ones. Estimates are obtainedfor the physical parameters of the core, including the radial profiles ofdensity ($\propto r^{-1.7}$), turbulent velocity ($\propto r^{-0.4}$), andcontraction velocity ($\propto r^{-0.1}$). Confidence intervals are calculatedfor the parameter values. The power-law index of the contraction-velocityradial profile, considering the determination error, is lower in absolute termsthan the expected one in the case of gas collapse onto the protostar in freefall. This result can serve as an argument in favor of a global contractionmodel for the L1287 core.

Principal component analysis for estimating parameters of the L1287 dense core by fitting model spectral maps into observed ones