The maturity of avocado fruit is usually assessed by measuring its dry matter content (DM), which is a destructive and time consuming process. The aim of this study is to introduce a non-destructive and quick technique that can estimate the DM content of an avocado fruit. 'Hass' avocado fruits at different maturity stages and varying skin color were analyzed by hyperspectral imaging in reflectance and absorbance modes. The DM ranged from 19.8% to 42.5%. The hyperspectral data consist of mean spectra of avocados in the VIS/NIR region, from 400nm to lOOOnm, for a total of 163 different spectral bands. Relationship between spectral wavelengths and DM content were carried out using a chemometric partial least squares (PLS) regression technique. Calibration and validation statistics, such as correlation coefficient (R2) and prediction error (RMSEP) were used as means of comparing the predictive accuracies of the different models. The results of PLS modeling, over several different randomizations of the database, with full cross validation methods using the entire spectral range, resulted in a mean R2 of 0.86 with a mean RMSEP of 2.45 in reflectance mode, and a mean R2 of 0.94 with a mean RMSEP of 1.59 for the absorbance mode. This indicates that reasonably accurate models (R2>0.8) could be obtained for DM content with the entire spectral range. Also this study shows that wavelengths reduction can be applied to the problem. Starting with 163 spectral bands, the DM could be predicted with identical performances using 10% of the initial wavelengths (16 spectral bands). Thus the study demonstrates the feasibility of using VIS/NIR hyperspectral imaging in absorbance mode in order to determine a physicochemical property, namely DM, of 'Hass' avocados in a non-destructive way. Furthermore it gives some clues about which spectral bands could be useful for that purpose.