| The maturity of avocado fruit is usually assessed by measuring its dry matter content (DM), a destructive and time consuming process. The aim of this study is to introduce a quick and non-destructive technique that can estimate the dry matter content of an avocado fruit.;'Hass' avocado fruits at different maturity stages and varying skin fruit color were content analyzed by hyperspectral imaging in reflectance and absorbance modes. The dry matter ranged from 19.8% to 42.5%. The hyperspectral data consist of mean spectra of avocados in the visible and near infrared regions, from 400nm to 1000nm, for a total of 163 different spectral bands.;Relationship between spectral wavelengths and dry matter content were carried out using a chemometric partial least squares (PLS) regression technique. Calibration and validation statistics, such as correlation coefficient (R 2) 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 R 2 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 dry matter could be predicted with identical performances using 10% of the initial wavelengths (16 spectral bands).;Thus the study demonstrates the feasibility of using visible, near infrared region hyperspectral imaging in absorbance mode in order to determine a physicochemical property, namely dry matter content, of 'Hass' avocados in a non-destructive way. Furthermore it gives some clues about which spectral bands could be useful to this end. |
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