فهرست مطالب hasan safiyari

In this research, the ability of the integrated non-destructive system including ultrasound, electrical resistance measurements and colorimetric assays to predict the textural properties (hardness, brittleness, viscosity, elasticity, guminess and chewiness) of trout fillets were evaluated. For this purpose, fillets were stored for 12 days at refrigerator temperature and then examined at interval times. At the same time physical, mechanical, chemical and sensory tests were performed on the fillets. The performance of neural network and support vector machine methods for predicting and modeling tissue properties were compared. In each model, physical properties were considered as inputs and textural properties as outputs and modeling was performed. The results showed that in the prediction of hardness, fragility and elasticity of the support vector machine and in the viscous, resin and chewability indices, the neural network method had more capability to model the texture properties, so that the root mean square error (RMSE) Hardness, fragility, viscosity, elasticity, gum and chewability indices were equal to 0.144, 0.025, 0.015, 0.015, 0.044 and 0.171 respectively and their correlation coefficients were equal 0.993, 0.985, 0.992, 0.961, 0.995 and 0.995. Therefore, the proposed non-destructive system in combination with artificial intelligence methods showed promises as a non-destructive and efficient tool for monitoring and quality control during trout fillet storage.

In breeding environments, in order to know the fate of the fish given to the fish, the biomass and control of fish growth should also be monitored during the breeding period to determine the growth rate and feed conversion ratio (FCR). For this purpose, 20 rainbow trout (31.42±3.81) were randomly selected. They were then divided into groups A and B. Group A fish fed 1.5% body weight and group B fish fed 2.4% body weight. The length and weight of each fish were measured. Samples were taken in the photographic chamber to calculate the environment, area, height and diameter equivalent to fish in the bioassay. Based on the results of the present study, the interval between the two biometric of final weight, final average length and final height were 39.26 ± 4.91 g, 15.05±0.7 cm and 3.45±0.45 cm respectively and in group A 36.01 ±2.59 g, 14.77±0.62 cm and 3.06±0.26 cm, respectively, which had increased with increasing percentage of feeding. Also final area, final environment and final diameter increased with increasing percentage of nutrition relative to body weight (p

Persimmon fruits are subjected to different environmental conditions from harvest to consumption. Foods, those are exhibiting characteristics of both liquid and solid, are described as viscoelastic where stress relaxation and strength properties are time dependant. Knowledge of viscoelastic properties of foods and agricultural materials are important when considering harvesting, handling, transportation, processing, and storage. Also the data on viscoelastic properties are required as an input for mathematical models, which describe and predict internal stress and cracking during different handling and processing procedures. The mechanical properties are considered one of the most important four parameters, which reflect the quality of food material. Those parameters include texture, firmness and chewability. Viscoelastic material exhibit stress relaxation phenomena, which is one of the most important factors in characterizing agricultural materials. The objectives of the current study were: (1) to obtain data to describe the stress relaxation characteristic of Persimmon fruits during Storage, (2) to investigate the effect of storage time on stress relaxation properties, and (3) to find the best model to describe the obtained stress relaxation data.

The persimmon fruits were manually picked up at maturity stage from persimmon trees in a garden, in Fars province, Iran. They were handled to Biophysics laboratory in Agricultural Engineering department, at Shiraz University, on the foam sheets in one layer array for minimizing any likely compaction. Initial moisture content was determined by gravimetric method in an oven at 70C till obtaining constant successive weight loss of 0.001 g. The initial moisture content was 44.26% (d.b). Since the rheological characteristics of fruits are important for any processing operation, viscoelastic behavior of persimmon fruits during storage in environmental conditions was investigated using estimating its relaxation parameters from experimental stress relaxation data. The rheological behavior of persimmon fruits was evaluated to uniaxial compression test for relaxing the stress during 480 s. Texture Profile Analyser (TPA) (TA.XT2 plus model, Stable Microsystems, England) with Exponent Lite (Version 4,0,8,0, UK) software was used to compress whole date samples and export data into Excel worksheets. TPA was equipped with a 30 kgf load cell. The cross head speed was adjusted on 1 mm/s, and samples compressed with 30% strain. The cross head then kept at 30% of strain for relaxing the stress in 480 s duration. A chamber with temperature control circuit was fabricated around the TPA cross head to prepare constant temperature identical to sample temperature with an accuracy of ±0.1C during the stress relaxation tests. All tests were performed with ten replications. Three popular stress relaxation models, namely the generalized Maxwell with three elements, Nussinovitch, and Peleg were fitted to experimental data and was used from R-square, standard error of estimation (SEE) and percent of average relative error (ARE%) indexes for evalution of models.

The force–time curves were converted to stress–time curves by dividing the true compression force values to the corresponding true values of contact surface area. Analysis of variance showed that storage time had significant effect on initial stress relaxation. The results showed that the magnitude of initial stress (stress at time zero) required to create a fixed strain on whole fruit was significantly decreased up to 80% during storage in environmental conditions, but the relaxation time was increased at the range of 312.10 to 353.90 s. Based on the results, elastic behavior of persimmon fruit was enhanced and its firmness decreased during fruit storage. Persimmon is sugar rich fruit that its behavior is principally partly controlled by physico-chemical properties of sugar. Changes in sugar type as a result of enzymatic action are taken place during the storage process. However, in the present study all samples were in the time of maturation, and therefore the physical state of sugar was influenced by free water which is resulted in increasing in fruit moisture content. Stress relaxation curves were plotted at different storage time. Some variations were apparently observed among the curves. Sucrose decreased initial stress decay rate and increased proportion of un-relaxed force. Variations in stress decay rate might refer to the amount of initial stress which was affected by physic-chemical properties of the sugar. Recall that nonlinear regression method with non-negative constraint was used to derive the models constant.

All three models were valid for quantifying the viscoelastic behavior of persimmons; however the generalized Maxwell with three elements (with R2=0.999, SEE=0.017 and ARE=0.19%) and Nussinovitch (with R2=0.996, SEE=0.057, ARE=0.36%) were the best models in predicting experimental data.

Grading science and grading equipment for many kinds of sea products are growing rapidly in developed communities and variety of grading equipment can be found in most of the large fishery units. Computer vision has the potential to be used as a precise method for recognition and assessment of apparent characteristics. In this study، machine vision technology was used to sort fish based on species، size and weight. Tiger-toothed croaker and Silver pomfret fishes were selected for this study. In the first stage، each sample fish was weighted and put in the illumination chamber and images were captured. Matlab environment was used for segmentation and image processing tasks. Linear and non linear regressions were used to estimate fish weight. Seven variables extracted from each image (length، height، area، perimeter، equal diameter، major axis length and minor axis length) in four models of mathematical approach (linear، logarithmic، binomial and exponential) were considered for developing each weight prediction equation. Results indicated that fish weight can be estimated with R2 values of 95. 4% and 94% for Tiger-toothed croaker and Silver pomfret، respectively. Model validation was investigated with new data. Results showed that there is not a significant difference between the actual and estimated weight at 5% significance level for all fish species in this study. It was also concluded that the system can accurately measure the length of the fishes using machine vision technology envisaged in this study. The algorithm was also able to sort two species of fishes including Tiger-toothed croaker and Silver pomfret with an accuracy of 100%.

The chemical-morphological relationships in rainbow trout, Oncorhynchus mykiss, was assessed through photography in light room. The image data were transferred to a laptop and analyzed by MATLAB (Matrix Laboratory) version R2009a. The designated algorithm of 7 features, including length, height, area, perimeter, equivalent diameter, major and minor axis length were extracted from the images. Afterward, chemical compositions of the fish (including crude protein, crude fat, moisture content, pH value and leakage), were determined. Pearson's correlation coefficient was used to correlate visual and chemical properties. Factors were assayed with linear, logarithmic, exponential and power method. Results showed that pH, leakage, protein and fat had a good correlation with fish length, and moisture with fish perimeter, in Quadratic equation (R2= 0.993, 0.991, 0.991, 0.989 and 0.992, respectively). In conclusion, this methodology can be applied for automated chemical analysis and on-line monitoring in fish farms.