Detection Of Soluble Solids Content And Firmness Of Tomato Using Hyperspectral Imaging

Nondestructive quality inspection of fruits and vegetables as well as automatic grading is a hot topic in the agro-processing research area. It is of great importance to satisfy consumers" increasing demands for food quality and safety, to ensure the quality of fruits and vegetables and enhance their market competitiveness and to improve farmers’income. Although there are a great amount of fruits and vegetables in our country, the market competition capability is still left behind in the international market because of insufficient and low-tech processing of post-harvest. Internal ingredients and characters, along with external quality indicators including shape, size, color, and defects are important indexes to determine the quality of products. However, it is a difficult task to assess the indicators, because they cannot be visualized directly. Tomatoes from different regions were chosen as the study objects of this thesis. Soluble solid content (SSC) and firmness including peel firmness Fl, flesh firmness F2and corrected finnness Fxz were detected to represent internal ingredients and characters of tomatoes. Hyperspectral imaging systems in reflectance, scattering and diffuse transmittance modes were constructed and used as platforms to obtain spatial spectra and texture parameters (or fitting parameters). Then quantitative prediction models of SSC and firmness were established by using partial least square regression (PLSR). With the help of hyperspectral imaging systems in reflectance, scattering and diffuse transmittance modes, potential applications for detecting SSC and firmness were investigated to establish a theoretical basis for online detection of ingredients and characters simultaneously. The main results are as follows:(1) A background segmentation method based on principle component score images was proposed, and was utilized to obtain mean spectra and texture information of target objects. This method is more effective in wiping out background compared with single-band segmentation.(2) The methods for spectral correcting of pixels were applied in tomato spectral images under hyperspectral reflectance and diffuse transmittance modes. Results showed that these two correction methods could improve the detection of SSC, and the pixel spectral normalization was better (RPD=2.315) under reflectance mode; and could also enhance results which were obtained after spectral nonnalization of pixels under diffuse transmittance mode (RPD=1.983), while the robustness was slightly worse after correction using pixel spectral standard norm*variate. Overall, the pixel spectral normalization can advance the detection of SSC unde hyperspectral reflectance and diffuse transmittance modes.(3) Comparative analysis of prediction results based on hyperspectral reflectance imaginj and near-infrared diffuse transmission spectroscopy showed that hyperspectral reflectanc imaging performed much better in predicting SSC and firmness of tomatoes by obtaining overal spatial spectra information in detecting SSC, F1, F2and Fxz, with RPD=2.071,1.150,1.1501.345, respectively.(4) Hyperspectral diffuse transmittance imaging was proposed as a new hyperspectra imaging mode, and SSC and firmness values were measureed by three hyperspectral imaging modes. Results showed as following:1) Mean spectra performed better than textural parameters (or fitting parameters) in detectioi of SSC and firmness under the three hyperspectral imaging modes, in which the detection o SSC was the best with RPD being close to2or larger than2. The detection of Fxz was the bes in firmness detection, however, compared with SSC detection resultes, spectral imaging foi firmness testing is not very satisfactory with the RPD<1.5.2) For single hyperspectral imaging modes, the hyperspectral diffuse transmittance imaging has certain advantages in predicting SSC compared with reflectance and scattering imaging because of its accessibility to spectral information in deeper tissue of tomatoes. Hyperspectra reflectance imaging was superiority in detecting Fxz compared with hyperspectral scattering anc diffuse transmittance imaging mode, because spectral information in bigger pixel areas was utilized. Overall, mean spectra of tomato obtained by combined positions was more suitable foi detecting SSC and Fxz than part pixels areas of tomato.3) The results of predicting SSC and firmness using combined spectra information, which obtained by different imaging modes, showed no obvious advantage compared with the single imaging modes. The Fxz performed best in detecting firmness, while F1and F2detection showed poor results.Therefore, Fxz was considered as the best parameter to characterize the firmness of tomatoes And hyperspectral diffuse transmittance imaging is more suitable for detecting SSC, while hyperspectral reflectance imaging is more suitable for Fxz detection.