M Mousavi Nasab, Gh Mesbahi, L Maghsodi,
Volume 12, Issue 46 (fall 2009)
Abstract
Pectin is a hydrocolloid with different characteristics and applications. In this study, the cryoprotective effect of pectin on frozen surimi was investigated. In this research, Kapoor surimi was made for the first time in Iran. Surimi was mixed with 1% pectin solution with the ratio of 1 to 3 (w/v). Then, pectin-containing surimi samples and control samples were packaged, frozen and stored at -20oC. Water binding capacity (WBC), salt extractable protein and drip loss of samples were measured at after 0, 2 and 4 months of storage. The results showed that the loss of WBC in pectin-containing surimi and control samples was 20% and 58%, respectively, after 4 months storage at -20oC. It means pectin could improve the WBC up to 38% in the frozen product. The loss of SEP in pectin containing surimi samples was 21% and in control samples was 25% after 4 month frozen storage, indicating pectin was again effective in maintaining the quality of frozen products. Furthermore, the increase in drip loss in pectin containing surimi was about 7% and in control samples 37%. In this case pectin also helped to decrease the loss of water soluble nutrients. Overall, the results indicate that pectin as a cryoprotectant can improve the quality of frozen surimi.
Z. Maghsodi, M. Rostaminia, M. Faramarzi, A Keshavarzi, A. Rahmani, S. R. Mousavi,
Volume 24, Issue 2 (Summer 2020)
Abstract
Digital soil mapping plays an important role in upgrading the knowledge of soil survey in line with the advances in the spatial data of infrastructure development. The main aim of this study was to provide a digital map of the soil family classes using the random forest (RF) models and boosting regression tree (BRT) in a semi-arid region of Ilam province. Environmental covariates were extracted from a digital elevation model with 30 m spatial resolution, using the SAGAGIS7.3 software. In this study area, 46 soil profiles were dug and sampled; after physico-chemical analysis, the soils were classified based on key to soil taxonomy (2014). In the studied area, three orders were recognized: Mollisols, Inceptisols, and Entisols. Based on the results of the environmental covariate data mining with variance inflation factor (VIF), some parameters including DEM, standard height and terrain ruggedness index were the most important variables. The best spatial prediction of soil classes belonged to Fine, carbonatic, thermic, Typic Haploxerolls. Also, the results showed that RF and BRT models had an overall accuracy and of 0.80, 0.64 and Kappa index 0.70, 0.55, respectively. Therefore, the RF method could serve as a reliable and accurate method to provide a reasonable prediction with a low sampling density.
