Identification of mushrooms that have been physically damaged and the measurement of time elapsed from harvest are very important quality issues in industry. The purpose of this study was to assess whether the chemical changes induced by physical damage and the aging of mushrooms can: (a) be detected in the visible and near infrared absorption spectrum and (b) be modeled using multivariate data analysis. The effect of pre-treatment and the use of different spectral ranges to build PLS models were studied. A model that can identify damaged mushrooms with high sensitivity (0.98) and specificity (1.00), and models that allow estimation of the age (1.0-1.4 days root mean square error of cross-validation) were developed. Changes in water matrix and alterations caused by enzymatic browning were the factors that most influenced the models. The results reveal the possibility of developing an automated system for grading mushrooms based on reflectance in the visible and near infrared wavelength ranges.