This　paper　proposes　a　subset　selection　method,　named　sample　set　partitioning　based　on　joint　x-y-z　distances　(SPXYZ)　algorithm,　for　multivariate　modelling.　The　proposed　method　is　a　modified　version　of　the　original　sample　set　partitioning　based　on　joint　x-y　distances　(SPXY)　algorithm.　The　contributions　from　the　dependent　variable　(z)　space　for　parameters　that　cause　the　systematic　error　in　measured　spectra,　including　external　factors　and　inherent　characteristics,　are　added　to　the　original　SPXY　algorithm.　Here,　the　z　differences　denotes　the　variability　in　the　dimension　of　external　disturbances　and　inherent　characteristics.　Based　on　two　real　world　datasets,　SPXYZ　is　employed　with　partial　least-squares　(PLS)　to　demonstrate　the　advantages　of　subset　selection　by　adding　the　contributions　from　external　factor,　i.e.,　temperature　and　inherent　characteristic,　i.e.,　background　components.　We　compare　the　prediction　performance　of　SPXYZ-PLS　model　with　other　three　PLS　models　using　random　sampling　(RS),　Kennard-Stone　(KS)　and　SPXY.　The　prediction　performance　results　from　experimental　studies　showed　that　the　prediction　performance　of　SPXYZ-PLS　is　significantly　better　than　the　other　models.　Therefore,　the　proposed　method　is　an　alternative　method　of　subset　selection　for　calibration　modeling.