| Popis: |
Currently, destructive methods are often used to measure the quality parameters of agricultural products. These methods are often complex, time consuming and costly. Recently, studying to find a solution to the disadvantages of destructive methods has become a major challenge for researchers. Non-destructive methods can be useful for the rapid detection of the quality parameters of agricultural products. In this study, hyperspectral imaging was used to evaluate the non-destructive quality parameters of Red Delicious (Red Delicious) and Golden Delicious (Golden Delicious) apples, including pH, soluble solids content (SSC), titratable acid (TA) and total phenol (TP). In order to predict the quality characteristics of apples, the partial least squares (PLS) method with different pre-processing was used. The developed models were evaluated using the root mean square parameters of RMSECV validation error, correlation coefficient (Rcv) and standard deviation ratio (SDR). The results showed that in Red Delicious, for pH, TA, SSC and TP the best forecasting methods were SNV, SNV, MSC and normalized pre-processing with the regression coefficient values of 0.9919, 0.9939, 0.9909 and 0.9899, respectively. In Golden Delicious (Golden Delicious), for pH, TA, SSC and TP, the first derivative, (smoothing and second derivative), normalize (and SNV and normalize) preprocessors were selected as the best prediction models, with values of 0.9989, 0.9989, 0.9999 and 0.9989, respectively. The results related to an artificial neural network also showed that in hyperspectral imaging, the best state of the feed-forward network structure with the LM training algorithm was R = 0.93, Performance = 0.005 and RMSE = 0.03 in 325 inputs, 5 outputs and 2 hidden layers. The results showed that hyperspectral imaging has different predictive capabilities for the qualitative characteristics studied in this study with high accuracy. |
