One of the problems in near infrared quantitative analysis is that the calibration model built on one instrument can not be directly used on other instruments because there are many differences between a master instrument and a slave instrument.A quantitative model built on the master instrument may result in a great deviation when it was used on the slave instrument.There are many ways to solve this problem,which can be divided into two categories,eg.standard sample methods and non standard sample methods.Direct standardization（DS） algorithm is one of standard sample methods.An improvement for the direct standardization algorithm was made by a principal analysis on the data matrix generated in a slave instrument,determining the principal component number by root mean square error of prediction （RMSEP） and calculating the transfer matrix by the principal number.This number was determined by the relationship between RMSEP and the principal component number.When the RMSEP reaches a minimum,the corresponding principal component number can be used to calculate the transfer matrix.The improved method was validated by using both corn data and tobacco data.The corn data has two components,water and protein and the tobacco data has four components which are sugar,total sugar,total nitrogen and total plant akaloid.The results showed that the prediction accuracy of the corn data was improved greatly when the improved DS algorithm was used to compare with direct forecast and standard DS algorithm.The prediction accuracy of the tobacco data was relatively robust too.