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The reflectance spectrum of green leaves is considerably affected by their biochemical and biophysical properties. It is possible to extract biochemical information from a continuous vegetation spectrum produced using hyperspectral sensors. The numerous absorption features present in the vegetation spectrum carry a considerable amount of information related to the content and the structure of the leaves and stems. In the present study, we tried to introduce a method for relative quantification of vegetation leaves protein contents using EO-1 Hyperion datasets through an innovative index named PALI (Protein Absorption Lines Index). The results of applying PALI to AVIRIS data also showed its robustness. However, applying PALI index for Hyperion images can only show the vegetation leaves protein contents of a pixel relative to its neighboring pixels and not absolute values. Nonetheless, it is assumed that absolute measurements will be possible if one can calibrate this index with field data.

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Changing the thermos-mechanical properties, variety of formulation and storage conditions, 36 samples of low-fat mozzarella cheese were produced and their hardness, adhesiveness, cohesiveness, springiness, cohesiveness, gumminess and chewiness were evaluated by TPA followed by analyzing data using completely randomized factorial design with univariate analysis through IBM SPSS Statistics. 26. Then, Imaging of the same samples with a Hyperspectral camera in the range of 400-1000 nm as well as pre-processing the spectra and preferring the important wavelengths by feature selection algorithms to developed the calibration models including multiple linear regression algorithms, partial least squares regression, support vector machine with a linear kernel, multilayer perceptron neural network, random forests and majority voting algorithm was performed in Python software followed by the performance of models were evaluated. Results showed that the more increased the stretching time in hot water from 2 to 8 minutes, the more the hardness, springiness, gumminess and chewiness and cohesiveness increased, but adhesiveness was decreased. The majority vote algorithm (VOTING) revealed the highest performance in hardness prediction (R²p=0.878, RMSEp=2606.52 and RPD=2.12) and was able to predict the cohesiveness of mozzarella with higher accuracy more than other algorithms. Multiple linear regression couldn’t predict the adhesiveness properly, but random forest method with high performance predicted this feature (R²p=0.808, RMSE=56.49, RPD=1.90). The multi-layer perceptron neural network with the least error, predicted springiness (R²p = 0.848, RMSEp = 0.094, RPD = 2.12) and chewiness (R²p = 0.84, RMSEp = 1117.21, RPD = 1.96) with high accuracy. All methods except random forest were able to predict the gumminess of mozzarella with high efficiency. In this study, it was cleared that the process conditions had significant effects on the textural characteristics and the Hyperspectral imaging was found to be a suitable alternative method for estimating the textural characteristics of mozzarella cheese.