In　this　paper,　we　propose　a　maximum　likelihood　estimation　based　regression　(MLER)　model　for　multivariate　calibration.　The　proposed　MLER　method　seeks　for　the　maximum　likelihood　estimation　(MLE)　solution　of　the　least-squares　problem,　and　it　is　much　more　robust　to　noise　or　outliers　and　accurate　than　the　traditional　least-squares　method.　An　efficient　iteratively　reweighted　least　squares　technique　is　proposed　to　solve　the　MLER　model.　As　a　result,　our　model　can　obtain　accurate　spectra-concentrate　relations.　Experimental　results　on　three　real　near-infrared　(NIR)　spectra　data　sets　demonstrate　that　the　proposed　MLER　model　is　much　more　efficacious　and　effective　than　state-of-the-art　partial　least　squares　(PIS)　methods.　(C)　2017　Elsevier　B.V.　All　rights　reserved.