The goal of this study was to develop prediction models to estimate the storage days of tomato. The transmittance spectral data measured on tomato were preprocessed through normalization, SNV, Savitzky-Golay, and Norris Gap and then were used to build the prediction models using partial least square (PLS) method. For the experiments, the tomato samples of different varieties were collected at different harvest time. The samples were taken right after harvest from the field and then were stored in a low-temperature storage room in which room temperature was maintained at $10^{circ}C$. The transmittance spectral data of the tomato samples were measured at three-day intervals for 16 days. The performance of the prediction models was affected by the preprocessing techniques as well as the varieties and harvest time of the tomato. The best model was found when SNV was applied. The accuracy of the best model was 90.2%. It can be concluded that the transmittance spectra are useful information for predicting the period of storage of tomato.