جستجوی مقالات مرتبط با کلیدواژه « انبارداری » در نشریات گروه « مهندسی شیمی، نفت و پلیمر »

Regarding this fact that peroxidase (POD) activity is considered as one of the important qualitative parameters of apple fruits, in this study, the effect of cold storage on POD activity of Red Delicious apples were investigated during 60 days. Hyperspectral reflecting imaging in range of 400-1000 nm has been applied while POD of samples were measured according to standard methods. After discarding noises using principal component analysis (PCA), to improve spectrum, different primary pre-processing had been applied and their effects were investigated. The suitable model was obtained via Partial Least Square method (PLS). Important wavelengths were selected based on regression coefficient of the best model includes large absolute values of weighted regression coefficients (RC) and sequential predictions algorithm (SPA) and using various techniques were modeled. Concerning the PLS analysis, the best results were obtained through smoothing Savitzky-Golay pre-processing with mean square root error (RMSE) of 0.475 and 0.518 and coefficient of determination (R2) of 0.948 and 0.940 for calibration and validation data, respectively. According to RC and SPA, 9 wavelengths were determined as the best. In modeling by efficient wavelength, artificial neural network (ANN) and SPA Combined Model gave the best result. The results indicated that hyperspectral imaging could be considered as a valuable tool for POD activity prediction and the selected wavelengths could be potential resources for instrument development.

Quality assessment and fruits’ sorting are important activities in postharvest process that are attracting notices interestingly because of increasing demand for healthy products with better quality. In the recent decades, various non-invasive and non-destructive techniques for fruits and vegetables evaluations have been employed. Among these methods, hyperspectral imaging as a non-destructive, fast and ecofriendly technique is getting researchers’ attraction increasingly in order to crops properties assessment. Regarding this fact that consumption of organic acids and consequent changes pH is considered as one of the important qualitative parameters of apple fruits, this study aimed to investigate the effect of shelf life time on pH value of Red Delicious apples during 60 days. Hyperspectral reflecting imaging in range of 400-1000 nm has been applied while the pH of samples was measured destructively. After discarding noises using principal component analysis (PCA) analysis, to improve spectrum, different primary pre-processing had been applied and their effects were investigated. The suitable model was obtained via Partial Least Square method (PLS). Important wavelengths were selected based on regression coefficient of the best model includes large absolute values of weighted regression coefficients (BW) and using various techniques were modeled. Concerning the PLS analysis, the best results were obtained through smoothing Savitzky-Golay pre-processing with mean square root error (RMSE) of 0.02 and 0.018 and coefficient of determination (R2) both 0.980 for calibration and validation data, respectively. According to regression coefficient of the best model, 9 wavelengths were determined as the best. In modeling by efficient wavelength, artificial neural network (ANN) gave the best result. Hence, it was obvious that non-destructive method of hyperspectral imaging was capable to predict pH values of apple fruits with high accuracy during the shelf life.