Near-infrared spectroscopy (NIRS) detection technology has been widely used in the field of edible oil quality detection due to its features of simple operation, no damage, fast detection and analysis. However, due to differences in manufacturing and testing environment, instrument aging, replacement of accessories and other factors, the spectral response of the same sample on different instruments was not completely consistent, resulting in the failure to share calibration models established by different instruments. To solve the problem of edible oil quality detection of acid value and peroxide value model transfer problem, transfer model was established by using the spectral space transformation method, which realized the calibration transfer of acid value and peroxide value between different instruments, and the slope/bias algorithm, direct standardized algorithm and piecewise direct standardization, extreme learning machine auto-encoder algorithm were compared. The VERTEX-70 Fourier near infrared spectrometer and MATRIX-F on-line process analysis Fourier spectrometer were used. Totally 125 edible oil samples were used in the experiment, VERTEX-70 was used as master instrument and MATRIX-F as slave instrument to establish the multivariate calibration model of acid value and peroxide value. The results showed that the prediction root-mean-square error of the validation set of acid value and peroxide value was reduced from 0.5836mg/g and 15.8010mmol/kg to 0.1670mg/g and 9.9893mmol/kg by using spectral space transformation method for model transfer. Obviously, spectral space transformation can be effectively applied to model transfer between edible oleic acid value and peroxidation value, so as to realize model sharing between different instruments. The problem of time and energy consumption caused by re-modeling can be avoided, which was of great significance for the application of NIRS in the rapid detection of edible oil quality.