Abstract: Aiming at the characteristics of high dimensionality of near-infrared spectroscopy data and serious multi-collinearity between features， a method of calibration transfer via correcting distributions difference(CT-CDD) was proposed without transfer standards. CT-CDD firstly establishes PLS(partial least square) model of the master instrument， and then latent variables of both the master instrument and the slave instrument are extracted by the PLS model. Next， the latent variables of the two instruments are clustered. The method is based on the assumption that the characteristic spectra of each part of the master and slave instruments after clustering is a single Gaussian distribution. Finally， the nearest sub-distribution of the two instruments is found， and the differences in data distribution is corrected by correcting mean and variance. The experimental results show that CT-CDD is generally more robust and can also achieve the lowest RMSEP (root mean squared error on prediction).

Key words: near-infrared spectroscopy; transfer standard; calibration transfer; partial least square (PLS) model; distributions difference