نشریه فناوری های جدید در صنعت غذا
سال ششم شماره 2 (پیاپی 22، زمستان 1397)

In present study, the thin- layer drying of orange slices in a laboratory scale hot-air dryer has been modeled. Drying experiments were conducted at three different temperatures of 50, 60 and 70°C, and two air velocities of 1.0 and 2.0 m/s. The statistical results of data showed the change of drying temperature and air velocity had significant effects on moisture ratio (p<0.05) but interaction effect of air velocity and temperature had insignificant effect on moisture ratio. Based on the results, the minimum moisture ratio of dried orange slices was obtained 5.3% when the dryer air temperature and velocity were 70°C and 2.0 m/s, respectively. After the end of experiments, the experimental data were fitted to the 7 well-known drying models. According to fitting results, Page’s model with determination coefficient R2-3 showed better performance to predict the moisture ratio. Also, this study used a feed forward back propagation neural network in order to estimate orange slices moisture ratio, based on the temperature, air velocity and time as input variables. In order to design this model, two main activation functions called tanh and purlin, widely used in engineering calculations, were applied in hidden and output layer, respectively. The artificial neural network with 3-20-1 topology and Levenberg-Marquardt training algorithm provided best results with the maximum determination coefficient (0.9994) and minimum Root of Mean Square Error (1.009×10-3) values. The results indicated the artificial neural network model was more accurate than Page’s model for prediction of moisture ratio of orange slices during drying process.

In this research infrared radiation was used for drying of quince slices. For this reason, the influence of drying temperature of 50, 60, 70 and 80 °C acquired by 51, 73, 98 and 12 W infrared lamp was investigated. Drying results showed that the drying rate increased with increasing temperature. The drying decreased up to 60% when temperature was increased from 50 to 80 °C. Affected by the lamp power from 51 to 125 W, the moisture content diminished from 453% (d.b.) to 16% (d.b.). Modeling of drying process using genetic algorithm-artificial neural networks (GA-ANNs) with 3 inputs (drying time, drying temperature and the slice enter temperature) and one output (the amount of moisture ratio (MR)) was done. The modeling results demonstrated a network with 7 neurons in hidden layer and tangent hyperbolic transport function could precisely predict the moisture content of slices during drying (R2 = 0.9997 and RMSE = 0.0044). This precision for optimized GA-ANNs was even higher than that of Midilli model -the best empirical model- (R2 = 0.9987-0.9994 and RMSE = 0.0068-0.0098) at all the temperatures tested. The results obtained from the sensitivity analysis by the optimized neural networks revealed that the center temperature of slices was the most pronounced factor (0.0044) to control the MR. Increase in temperature resulted in an increase in the effective diffusivity coefficient, so that this coefficient reached to 26.1×10-9 m2/s at 80 °C from the initial value of 10.8×10-9 m2/s at 50 °C. The activation energy (Ea) calculated for the quince slices were 28.68 kJ/mol.

Resistant starch (RS) is part of starch, which remains unchanged and not digested after 120 minutes of incubation against digestive enzymes such as α-amylase. Among the various types of resistant starch, resistant starch type 3 has been given more attention due to its thermal stability during the heat treatment process of foods. In order to produce resistant starch type 3, the granular structure of starch should be destroyed by heating in the presence of sufficient water, followed by amylose chains re-association upon cooling. Autoclaving of starch-based products leads to retrogradation and, as a result, increases the amount of resistant starch. The purpose of this study was to investigate the effect of various autoclaving temperatures (108, 121 and 134°C) and autoclave-cooling cycles (1 to 3 cycles) on the formation of resistant starch and its physicochemical and functional properties. The results showed that with increasing the autoclaving temperature as well as temperature cycles, the amount of resistant starch formation increased. The solubility of produced resistant starch was significantly higher than natural corn starch, while it did not change in term of apparent amylose. Also, the autoclaving-retrogradation process led to changes in starch crystalline type from A to B and V, and the degree of starch crystallinity also increased. Scanning electron micrographs showed significant changes in starch granules and formation of a sponge like texture which was due to the autoclaving process. Also, all textural properties (hardness, cohesiveness, springiness and gumminess) of resistant starch were reduced compared to natural corn starch.

In this study Co-immobilization of α-amylase and maltogenic amylase by using of lysine functionalized magnetic nanoparticles cross-linked enzyme aggregates method was carried out for high maltose syrup production from corn starch. Nanomanetite (Fe3O4) was prepared by co-precipitation method and then, surface functionalization by lysine amino acid was achieved. Enzyme aggregation was performed by acetonitrile, acetone, tert-butanol, isopropanol, ethanol, and saturated ammonium sulfate that tert-butanol implied high enzyme activity for immobilization. Enzyme ratio of 1:9 (α-amylase: maltogenic amylase) optimum pH 6 and optimum temperature 65º C, 2 mM glutaraldehyde concentration, and enzyme to lysine ratio of 1:0.75 were the best conditions for enzyme immobilization. DLS results indicated that, mean particle size of nanomagnetites was in the range of 81.9-88.9 nm with PDI= 0.242 and zeta potential of -21 and mean particle size of immobilized enzyme was in the range of 99.60-110.5 nm with PDI= 0.088 and zeta potential of -32 respectively. The appearance of new NH2 and NH spectra in FTIR spectroscopy confirmed the enzyme''''s immobilization on magnetic nanoparticles. Moreover, immobilized enzyme preserved 80.36% of its activity after 10 consecutive cycles. Comparative study on kinetic parameters of immobilized enzyme versus free one revealed that, Vmax had not any significant change while, Km was reduced 1.5 time. The enzyme half-life of immobilized enzyme increased 2.5 times at 95º C compared to free one. Enzyme loading of immobilized enzyme on magnetic nanoparticles were determined 82%.

Spices in addition to effect on the taste and quality of food, it also increases the shelf-life of foods because of its antimicrobial or antioxidant properties. Different types of spices have various quality and economic value based on their geographical origin. Therefore, classification and separation based on geographic origin are of great interest to consumers and sellers and is particular importance. In this research, the ability of an electronic nose based on metal oxide semiconductor sensors as a non-destructive tool for detecting the geographical origin (India, China and Pakistan) of three spices of black pepper, cinnamon and turmeric was studied. Temperature modulation was performed as a sinusoidal voltage pattern and transient responses of sensors were analyzed for data analysis. Principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM) and artificial neural network (ANN) were the methods used to achieve this goal. The results showed that the data visualization using the PCA method, created a completely distinct cluster on the PC's deformed space. By using the LDA, SVM and ANN methods, the classification accuracy was 100% based on the geographical origin for all three spices. Also, verification of these models was carried out and accuracy of 100% was achieved. Therefore, we can conclude that the electronic nose based on metal oxide semiconductor sensors under temperature modulation and in combination with the chemometrics methods as an effective and efficient tool can be used for fast and non-destructive classification of black pepper, cinnamon and turmeric samples based on geographical origin.

The purpose of this research is to obtain the thermodynamic properties of lemon drying under the influence of ultrasonic pre-treatment in a hot air dryer. Experiments were performed using a convection dryer with ultrasound pretreatment in 40, 55 and 70 °C air temperature, 1 m/s air velocity and duration of ultrasonic pre-treatment of 0 min (for control sample), 10, 20 and 40 min. The drying kinetic of the lemon was estimated by 14 mathematical models. The results showed that the drying time decreased with increasing the air temperature and the time of applying ultrasound. The best model to predict the drying of lemons was selected by Midilli et al. The use of ultrasonic pre-treatment at different temperatures resulted in a significant increase in the effective moisture diffusivity ( ) from 5.04×10-11 to 2.00×10-10 m2/s. Activation energy ( ) of the lemon was obtained between 34.93 and 42.97 kJ/mol. The values of specific energy consumption ( ) were 47.39 to 240.46 kWh/kg.

In this research, at first the carrots were cut into equal pieces and pre-treated with consideration of the desired level. Voltage levels were in pre-treatment of ohmic 40, 60 and 80 volts and time 2, 4 and 6 minutes. The time was a parameter that investigated in pre-treatment of blanching, with different levels of 2, 4 and 6 minutes. Immediately after pretreatment, samples were dried in microwaves at 360, 600 and 900 W. Finally, microwave dried and pre-treated samples and various levels of the mill were ground and the desired physiological properties including antioxidants, flavonoids and phenol were measured. According to the results, In most cases the lowest amount of antioxidant among the levels and parameters was 21.051% at pre-treatment of ohmic and 6 minutes. The lowest amount of flavonoids, 52.637 mg / 100 g, was related to the time of 6 minutes at pre-treatment of ohmic, and the lowest phenolic content at the blanching pre-treatment and 360 W microwave power was measured at 11.816 mg / 100 g. The results of the experiments showed that the relationship between microwave power and physiological properties measured in most cases was direct, and the relationship between the voltage and pre-treatment time with the physiological properties was inverse. Also, in most cases, it can be said that blanching pre-treatment in drying has more amounts of physiological properties.

Fish quality is affected by terms of handling, maintenance and storage time. These terms make chemical changes in fish and accelerate the deterioration its of tissue and make it dangerous for human body. There are several methods use for assessment of fish freshness, most of them are costly and destructive., Therefore, in this paper a non-destructive machine vision system based on gill and eye color and textural features is proposed. . Accordingly, after segmentation of region of interest in the images (eyes and gills), the color and textural properties of the images were extracted and the most suitable ones were selected using Fisher's selection algorithm and QDA and LDA classification methods were applied. For the QDA classifier , the V_HSV (extracted from the gills), the energy and the contrast (extracted from the fish's eye) and for the LDA classifier, the energy (extracted from the eye), the contrast )extraction from the eye) ,V_HSV (extracted from the gills) ,homogeneity (extracted from the eye) and H_HSV (extracted from the gills) were extracted. The classification accuracy for QDA and LDA were 93% and 96%, respectively.

The lack of uniform distribution of temperature in the product is one of the main problems of thermal processes. The modeling of microwave pistachio (Ahmadaghaee cv.) thermal process was studied using response surface method (RSM). The optimum conditions were determined in order to achieve the highest uniformity of temperature distribution in the product. Independent quantitative variables included the heating time (20-50 s) and the microwave power (630-900 w). In addition, an Independent qualitative variable included the position of the thermometer in six positions in the container. The dependent variable (response) is the temperature of the product under microwave heating. These experiments were performed separately for two cubic and cylindrical containers. The results of analysis of variance showed that all the main effects and the interaction are significant (P< 0.01). According to the results, the uniformity of temperature distribution in the product was more cubic than the cylindrical container. The uniformity of temperature distribution in the minimum power (630 w) was greater than the maximum power (900 w). The optimum conditions were determined in order to maximize the uniformity of temperature distribution for cylindrical containers with a heating time of 44 to 45 seconds and power of 720 to 750 watts. These conditions for the cube containers were obtained with a heating time of 44 to 47 seconds and power of 630 to 670 watts. The accuracy of the model was evaluated by calculating the root mean square error (RMSE). The low value of RMES in all six positions of the product indicates the proximity of the experimental and predicted values and confirms the model's accuracy.

In this study, the amount of total phenolic compounds in propolis extract obtained from extraction by two methods of maceration and ultrasound and also the effect of extraction method on the type and amount of each of phenolic compounds using chromatography method was investigated. Extraction by maceration method was carried out in different ratios (10, 30, 50% w/v) of propolis to ethanol solvent 70% at different time intervals (2, 8, 24 h). The ultrasonic extraction method was performed at different times (7, 14, 21 min) and vibrational amplitudes (60, 80, 100%). The maximum percentage of phenolic compounds in propolis extract was obtained from the maceration method applying the extraction time of 24 h and when the ratio of 10% w/v of propolis to solvent was 44.82%. However, with the ultrasonic method the maximum percentage obtained was at 21 min with a vibrational amplitude of 80%, where most of the phenolic compounds were obtained with 41.19%. Identification of phenolic compounds of extracts using high performance liquid chromatography showed that 15 phenolic compounds in the extract of propolis were obtained by maceration method, while 13 extracts were obtained by sonication. The highest recovery and identification components in propolis extract determined by both of the extraction methods was Pinocemberin, with 15.1% of extracts in the ultrasound method and 11.7% in the maceration method. The results of chromatography showed that the percentage of each of the compounds identified in the propolis extract which were extracted using ultrasound method, was higher than the ones extracted applying maceration method.