محسن زندی

This research aimed to model the kinetic of ohmic heating extraction method of radish (Raphanus sativus L.) leaf extract in order to understand and optimize the extraction process .The effect of extraction parameters on extraction yield, total phenol content and DPPH scavenging activity was discussed, at a gradient voltage of 10-30 V/cm, temperature of 30-60°C, solvent ration (Ethanol to water) of 30-100% and extraction time of 10-30 min. Different empirical models such as first order, adsorption and sigmoid models presented to predict the kinetics of mass transfer without taking into account the underlying phenomena. Results indicated that the effect of gradient voltage, extraction time and solvent ratio (ethanol to water) on the extraction yield, total phenolic content and DPPH scavenging activity were significant (p˂0.05). Although increasing extraction temperature significantly resulted in a lower total phenolic content and DPPH scavenging activity of extract (p<0.05), however, this parameter no significant effect the extraction yield (p>0.05). Under optimum conditions, the experimental extraction yield and total phenol content were close to the predicted values calculated from the quadratic response surface model. Based on kinetics modeling that has been done, it can be said that the sigmoid kinetic model more can represent well the experimental results of radish leaf extract by the ohmic heating method when compared with the first-order and adsorption kinetic models.

The aim of the present study was to optimization of enriched yogurt with chia seed oil and mucilage. This purpose was done with the aim of maximizing omega-3 level and minimizing changes in physical and chemical properties (pH, titrate acidity, syneresis, dry matter, peroxide values and color changes) compared to the control sample. First, in order to increase the oxidative stability, chia seed oil was encapsulated with sodium alginate and chia seed mucilage using emulsification/ internal gelation method. The physical characteristics results showed that the enrichment caused a decrease in the syneresis, an increase in the water holding capacity and viscosity. Then, yogurt was enriched and optimized with free and encapsulated chia seed oil (0 to 4%) and chia seed mucilage (0 to 0.25%). The sample without chia seed mucilage and oil used as a control sample. The results of scanning electron microscopy images showed that the successful alginate-sodium-mucilage microfiber coating of chia seeds has been made with a uniform structure. Yogurt contained 3.41% encapsulated chia seed oil and 0.213% chia seed mucilage was selected as an optimum treatment. The results showed that the number of starter bacteria of optimum sample were not significantly different from the control (p˃0.05). The texture of the yogurts was found to be viscoelastic. The flow behavior index in all samples was less than one, which indicates the non-Newtonian (pseudoplastic) behavior of the samples. Herschel Balky model had R2 and lowest RMSE in both samples, which indicates the suitability of this model to investigate the shear flow behavior in terms of shear velocity of samples.

The aim of the present study was to investigate the physicochemical and sensory properties of yogurt fortified with chia seed mucilage and oil, as a rich source of omega-3 fatty acids. For this purpose, mucilage (0.213%), free and encapsulated oil (3.41%) were added to the yogurt formulation. The prepared sampleswere compared in terms of some physicochemical (pH, titratable acidity, syneresis, dry matter, peroxide values and color attributes) and sensory properties with control (without oil and mucilage) during storage for 30 days at refrigerator temperature (4 °C).The results showed that the addition of mucilage and chia seed oil in a free and encapsulated forms caused a significant increase in water holding capacity (decrease in syneresis), acidity (decrease in pH) and dry matter during storage (p <0.05). In addition, the sample containing mucilage and encapsulated oil showed the lowest color difference (∆E) with the control. Furthermore, this sample showed an increase in whiteness index (WI) and a decrease in yellowness index (YI) compared to the sample containing free oil. The amount of peroxide value in the samples containing free and encapsulated oil after 30 days of storage was 3.19 and 1.16 meq / kg of oil, respectively, which indicates the protective effect of encapsulation process against oxidation of samples. Evaluation of sensory attributes showed that the addition of mucilage and encapsulated chia seed oil did not have significantly effects on the sensory properties of fortified yogurt samples (p˃0.05).

Analysis of the extraction modelling for natural compounds is essential for industrial application. In the present paper, ohmic-assisted extraction was investigated for the extraction of Falcaria vulgaris extract. This study was performed in order to express the effect of some pecific parameters (as: voltage gradiant, ethanol to water ratio, extraction time and temperature) on the extraction yield and total phenolic content (TPC). Kinetics models (first-order, second's-order and pelleg models) and artificial neural network were used for modeling ohmic-assisted extraction process. Kinetic study plays a very important role in evaluating the extraction process because it allows estimation of the cost-effectiveness of the process in saving time, money and energy. The results showed that the second-order and plleg's kinetic models respectively, were successfully predicted the value of the total phenol content of the extract and extraction yield in all experiments. The correlation coefficient between experimentally obtained extraction yield and total phenolic content and values predicted by artificial neural network (4-16-2) were 0.995 for training, 0.963 for validation, and 0.979 for testing, indicating the good predictive ability of the model. The artificial neural network model had a higher prediction efficiency than the kinetic models. Artificial neural network can reliably model the process with better predictive and estimation capabilities than the kinetic’s models.

The musk willow essential oil is volatile and encapsulation can protect them from environmental factors such as, light, oxygen and temperature. In the present research, preparation of sodium alginate-whey protein microcapsule containing essential oil of musk willow was carried out by internal gelation-emulsification method with encapsulation efficiency of 87.31%. The obtained microcapsules were characterized by particle size analyzer, zeta potential analyzer and scanning electron microscope. Encapsulation efficiency, swelling ratio and in vitro release of the essential oil was also investigated in fatty and acidic food simulation conditions. At acidic and fatty food simulation conditions, the complex presented negatively charged, with potential zeta values being 42.25 and 38.11 mV, respectively. The greatest electrostatic interaction occurred near pH 3.0 where the charge approached neutrality, which represents a balance between the biopolymer charges. Microcapsules shrinking in the acidic food simulation (pH=3.0) and expanding in the fatty food simulation (pH=7.0). The release results indicated that the release of musk willow essential oil from the microcapsule in both conditions occurred with a controlled manner and exhibited a slow rate. The essential oil release was found to be best fitted by Hixson–Crowell model (R2=0.993 for the acidic food simulation condition and R2=0.995 for fatty food simulation condition) which implies that a change in diameter of the microcapsule as a function of time. Mathematical modeling of release kinetics shows that musk willow essential oil loaded microsphere release follows by classical Fickian diffusion and erosion/degradation mechanisms.

In the current study, a simple image processing method based on a RGB images using machine vision system has been designed to evaluate the color and texture features of low-calorie doughnut crumb. The effect of maltodextrin substitution with margarine at the levels of 25, 50, 75 and 100% (w/w) was evaluated on vision properties. These features were crumb color (L*, a* and b*), pore area fraction, number of cells/cm2, pore shape, Euler number, fractal dimension of pore boundaries and crumb texture (contrast, entropy, correlation, energy and homogeneity). Results revealed that the addition of maltodextrin increased L* value and decrease a* value of crumb color significantly. The contrast, entropy and Euler number of doughnut crumb containing 75 and 100% maltodextrin were considerably higher than control, 25% and 50% samples. Correlation, energy, homogeneity, mean pore area and diameter, number of cells per square centimeter and pore area fraction decreased after maltodextrin addition (75 or 100%) but no significant difference observed between control, 25% and 50% samples. The fractal dimension value of pore boundaries in doughnut containing 100% maltodextrin were the highest which indicates that the addition of maltodextrin caused more irregular and tortuosity porous boundaries.

This study attempts to identify the problems arising from the relations between ethics and economics as they represent two branches of knowledge and two styles of life. In the introduction, I will examine an important historical starting point in investigations into the relations between ethics and economics and also address some significant views of one initiator of this research who believed in the inconsistency of traditional individual ethics (as an individual lifestyle) and economics (as a social/systematic lifestyle). The article follows three steps: in the first step, the common origin of the debate between ethics and economics is explored. In the second step, the concepts used in ethics and economics as two epistemic fields and as two human styles of life (theoretically and practically) are discussed. In the third step, the relations between ethics and economics are examined in terms of moral or economic motives, beliefs, and actions by raising certain central questions. The primary goal of this study is to map out the interdisciplinary crucial issues of ethics and economics for researchers and students. Each of these issues can be a subject matter for research papers or future dissertations. Accordingly, some research topics for future works are suggested.

The aim of present research was to predict the kinetics of essential oil extraction during ohmic-assisted hydrodistillation by three different modeling (nonlinear regression techniques (mathematical), artificial neural networks (ANN), and fuzzy logic) techniques to compare the accuracy of those models. Based on the results obtained the ANN was the best technique among all implemented models in predicting of extraction yield. Four mathematical models (first order, second order, adsorption and sigmoid models) describing essential oil extraction has been fitted to the extraction yield experimental data. Results indicated that first order model could satisfactorily describe the extraction kinetics of essential oil with correlation coefficient (R2) equal 0.988 and root mean square error (RMSE) equal 0.00014. Neural network with one and two hidden layers and 4–30 neurons were randomly selected and network power was estimated for predicting the extraction yield. ANNs with Feedforward–backpropagation structure, Levenberg–Marquardt training algorithm and 3-11-11-1 topology deserved the maximum R2 (0.999) and minimum RMSE (0.0004). Fuzzy logic tool in MATLAB with Mamdani model in the form of If–Then rules along with triangular membership function was used for predict the extraction yield. Despite the fact that fuzzy logic warranted lower fitting rates (R2 = 0.997) than that of ANN, it was a powerful technique for fitting of extraction yield experimental data.

In this article, the effects of stabilized zirconia oxide by Y2O3 and silicon carbide on physical ‎stability of magnesite based refractories was evaluated. For this purpose, the simultaneous deposition was used to produce MgO-ZrO2 and Y2O3-MgO-ZrO2 precursor ceramic powders, and then these compounds were analyzed using various analytical methods, including QELS laser diffraction (quasi-elastic light scattering), XRD, BET and SEM.  Evaluate. Nanoscale powders with a specific surface area greater than 60 m2/g were created. Using the Vickers indentation method, which was done to determine the hardness of the porous ceramic that was composed of synthetic powders, the hardness of the ceramics is increased by the incorporation of Y2O3, and this process also helps to stabilize the entire cubic crystal phase.

In the present research, gelatin-sodium alginate films incorporated with various concentrations of yarrow essential oil were prepared. The release of yarrow essential oil from gelatin-sodium alginate films into watery (distillated water), acidic (acetic acid 3%), alcoholic (ethanol 10%) and fatty (ethanol 85%) food simulants was measured at 25 ºC. In each simulant, all films had similar release profiles with an initial fast release followed by a sustained slow release. Results indicated that yarrow essential oil released faster from film in watery food stimulant due to the swelling in water. Films maintained their structures in fatty food simulant after 240 h. The artificial neural network (ANN) and mathematical (first order, Kopcha, Higuchi, Korsmeyer-Peppas, Weibull function and Hixson-crowell) models were applied to describing essential oil release from film. Mathematical modeling results indicated that Weibull model could satisfactorily describe the release kinetics of essential oil for all food simulant (minimal correlation coefficient (R2) and mean square error (MSE) of 0.991 and 10.22, respectively). The ANN model with 3-17-15-1 topology accurately predicted the kinetic release of yarrow essential oil from gelatin-sodium alginate film in all food simulant with R2 = 0.999 and MSE = 0.56.

Grape is a non-climacteric fruit with a low rate of physiological activity but is subject to serious physiological and parasitic disorders after harvest and during long term storage (Ciccarese et al., 2013). Currently, Edible coatings have been studied as potential substitutes for conventional plastics in food packaging. Edible coating is a thin layer of edible material formed as a coating on a food product. Edible coating can offer several advantages to the fresh fruit and vegetable industry such as improvement in the retention of color, acids, sugar and flavor components, the maintenance of quality during shipping and storage, the reduction of storage disorders and improved consumer appeal (Antoniou et al., 2015; Cazon et al., 2017; Fakhouri et al., 2015; Galus & Kadzińska, 2015). Farsi gum as a novel source of polysaccharides has drawn much attention in a wide range of various fields such as pharmaceutics, food and cosmetics industries. Functional properties of Farsi gum are influenced by its structure and molecular weight (Hadian et al., 2016; Joukar et al., 2017). By inclusion of bioactive compounds in the Farsi gum network the aforementioned impairments could be overcome and moreover, new protective and functional valences could be added. The inclusion of lipid-based component in Farsi gum gives it excellent light and moisture barrier properties. The benefic impact on human health of hemp seed oil is worldwide recognized. A recent study demonstrated the antimicrobial properties of hemp seed oil. Due to their abundance in biologically active compounds, hemp seed oil is promising natural alternatives that may extend the shelf-life, microbiological safety and nutritional values of food (Cozmuta et al., 2015; Leizer et al., 2000; Salarnia et al., 2018). Growing awareness of the quality of fruit has necessitated increasing effort to develop rapid and non-destructive methods for evaluating fruit quality (Bhargava & Bansal, 2020; Rachmawati et al., 2017; Tao & Zhou, 2017; Wu & Sun, 2013). The aim of this study was the consideration of image processing application for grape sorting based on visual surface characterize.  Coating emulsion was prepared using (Farsi gum (0%, 1.5% and 3%), hemp seed oil (0%, 0.075% and 0.15%) and glyceride (0.3%)). grape fruit were coated by immersion in coating dispersion for 5 min. Samples were then allowed to loss the excess coating dispersion. Coatings were developed at room temperature during an hour. Samples were refrigerated at 4± 1°C for 28 days and analyses were performed at days 0 and 28. Defect identification and maturity detection of grape fruits are challenging task for the computer vision to achieve near human levels of recognition. The image acquisition was performed in a homogenously controlled lighting condition. Considering the camera lens’s focal length, the samples were placed 25 cm under the camera’s lens to be under camera’s field of view. The images of grape were segmented from the background using thresholding of the high contrast images via MATLAB software (R2019a, image processing toolbox). The optimum threshold value was obtained to be 0.35, 0.45 and 0.30 for R, G and B channel, respectively.  The proposed techniques can separate between the defected and the healthy grape fruits, and then detect and classify the actual defected area. Classification is performed in two manners which in the first one, an input grape is classified with two different algorithms (RGB and binary). The Result showed that the accuracies for detecting the surface defects on grape were 97.73% and 96.30% using RGB and binary algorithms, respectively. Proposed method can be used to detect the visible defects of coated grape, and to grade the grape in high speed and precision.  The results of this research and similar ones can provide helpful recommendations in grading fruits for fresh consumption. The simplicity and the efficiency of the proposed techniques make them appropriate for designing a low-cost hardware kit that can be used for real applications.

The aim of the present research was modeling and optimizing the ultrasound-assisted hydrodistillation (USHD) of essential oil extraction process and comparing the effects of hydrodistillation (HD) and USHD techniques on the musk willow (Salix aegyptiaca L.) essential oil. Sonication time (15-45 min), ultrasound power level (100-500 W) and water to plant ratio (3-10 (v/w)) were varied in order to maximize S. aegyptiaca L.essential oil yield, total phenolic content (TPC) and IC50. The effect of sonication time and sound power levels on the essential oil yield, TPC and IC50 was significant at the 5% level. Although increasing water to plant ratio parameter significantly resulted in a higher yield (p<0.05), however, this parameter no significant effect the TPC and IC50 (p>0.05). The optimum parameters were sonication time of 39 min, sound power levels of 189 w, and water to plant ratio of 6.8 V/W. Under optimum conditions, both USHD and HD methods were used for essential oil extraction. USHD showed a significantly higher amount of extraction yield (0.108±0.009% (v/w)) compared to the HD (0.081±0.01% (v/w)) (p<0.05). Compared to the HD extraction, the USHD resulted in a shorter extraction time, less energy consumption, higher antioxidant properties and a higher extraction yield. Physical properties of OAHD and HD essential oils were not significantly different (p>0.05). Among the four kinetics models, the sigmoid model was shown to be the best one. This model can be used for modeling the kinetics of essential oil extraction by both HD and USHD.

In the current research, the effects of different ohmic assisted hydrodistillation (OAHD) parameters including voltages gradient (5, 15 and 25 v/cm), extraction time (30, 75 and 120 min) and NaCl concentrations (0.5, 1 and 1.5%) on the extraction yield, energy consumption and total phenol content (TPC) of Salix aegyptiaca L.essential oil were investigated, and then compared with conventional hydrodistillation (HD). Finally, artificial neural network (ANN) modeling is utilized to predict kinetics of essential oil extraction. Result revealed that extraction time and voltage gradient had significant effect on extraction yield, energy consumption and TPC (p<0.05). Extraction yields of essential oil obtained by OAHD and HD were 0.119 ± 0.012 and 0.081 ± 0.01, respectively. There was no significant difference (p>0.05) between specific gravity, refractive index and TPC of essential oil of OAHD and HD methods, however IC50 of essential oil extracted by OAHD was significantly higher than essential oil obtained with HD method (p<0.05). To design the ANN model, voltages gradient, extraction time and salt concentrations and their interactions were considered as input vectors while the extraction yield of essential oil was considered as the model output. The results showed that the best prediction performance belonged to 3-9-8-1 ANN architecture (RMSE=0.036 and R2=0.99). Therefore, it can be concluded that the OAHD method is applicable for S. aegyptiaca L. essential oil extraction and ANN model is an efficient quantitative tool to predict the kinetics of essential oil extraction.

In the present study, in the first step, the effect of Aloe vera gel (75% v/v) coating containing different concentrations of hemp seed oil (1-5% v/v) on some physicochemical properties of cherry tomatoes during storage at room temperature was investigated. The results revealed the ability of hemp seed oil to improve the physicochemical properties of cherry tomatoes during storage, although no significant difference was observed between 3 and 5% levels of hemp seed oil (p> 0.05). Slope change in the ripening index trend occurred for A. vera gel (75% v/v) coated sample on day 12 and for A. vera gel containing 3% hemp seed oil coated sample on day 16. Using an image processing system, the changes of the coated samples were evaluated based on the color statistical and color texture features extracted from the images and were graded through different procedures. The results showed that the principal component analysis (PCA) and artificial neural network (ANN) methods were able to divide the cherry tomatoes into intact and blemished grades which the ANN method was graded samples using color texture features with higher accuracy (97.41%). The adaptive neuro-fuzzy inference system (ANFIS) method had higher diagnostic power than the other two methods and was able to grade the samples into three grades including intact, grade 2 and unusable with accuracy of 98.96%.

The base of intelligent methods is using hidden knowledge in the experimental data, trying to extract the inherent relationships among them and generalizing results to other situations. Artificial neural networks are one of the most essential methods used in the field of artificial intelligence was inspired by how the human brain works, training takes place first, and then the information related to the data is stored in the form of the network's weights. Fuzzy logic is an important decision-making tool that has recently found some applications in food quality. Also, it is possible to find out the reasons for low and high ranking of products evaluated by the judges. The fuzzy model can be used to determine the importance of individual factors to the overall quality of a product. The ANFIS model is a combination of the artificial neural network (ANN) and a fuzzy inference system (FIS) in such a way that the neural networks are applied to determine the parameter of the fuzzy inference system. The fuzzy logic theory effectively addresses the uncertainty problems that solve the ambiguity. Sweet lemon (Citrus limetta) fruit is a popular agricultural product cultivated in tropical countries used to treat common colds, influenza and hypertension. Sweet lemon is quite perishable with postharvest losses such as weight loss, physiological deterioration, decay, and softening texture. The objective of the present study was to investigate grading of sweet lemon fruit based on quality and visual characteristics using fuzzy logic and ANFIS.  

Ripe sweet lemon (Citrus limetta) fruits and radish (Raphanus sativus L.) leaves were purchased from the local market in Zanjan, Iran. For emulsion solution preparation, 50 ml alginate sodium solution, 1 ml glycerin and 0 or 10 g radish leaf extract were mixed, then the coating solution volume was made up to 100 ml using distilled water. Finally, the mixture was steered for 200 second. Sweet lemon fruits were dipped in coating solutions or distillate water (for control treatment) for 2 minutes at ambient temperature (25℃) and were then air-dried for 2 h using a fan. All treatments stored at 4℃ for 50 days. Firmness, pH, titratable acidity (TA), total soluble solids content (TSS), color, and shape were measured at 10-day intervals. This paper introduces an adaptive neural-fuzzy inference system (ANFIS) model to classify sweet lemon based on the quality parameters and RGB intensity values. The ANFIS with different types of input membership functions (MFs) was developed. A study was performed using fuzzy logic and adaptive neural-fuzzy inference system (ANFIS) to predict the quality parameters of sweet lemon (firmness and ripening index).  

Our results showed that ‘triangle2mf’ MF performs much better than other mentioned MFs for defect inspection. The classification accuracy of the ANFIS with ‘triangle2mf’ MF was 97.5% and 96.6% for quality input and visual input, respectively, and the total correct classification rate was 97.01%. Therefore, this study indicated the possibility of developing a potentially useful classification tool using the ANFIS technique based on quality parameters and RGB values for fruit classification during processing, storage and distribution. Comparing the results obtained from fuzzy logic with various membership function, showed that the RMSE in the fuzzy logic with ‘guss2mf’ MF was lower than other algorithms. The proposed approach focuses on three research motivations. First, to develop a fuzzy rule-based classification system that can detect all the four quality grades of the sweet lemon. Second, the system should be able to predict the quality parameters of sweet lemon. Fuzzy logic deserved high level of accuracy in classification of sweet lemon, indicating high correlation between the data obtained from Mamdani fuzzy rules and experimental ones during storage time.

In this study, the effects of Farsi gum (0, 1.5% and 3%) coating containing hemp seed oil (0, 0.075% and 0.15%) on mass and volume changes of grape were investigated during storage at 4°C for 28 days. Machine vision system with learning machine methods was used to detect coated grapes from an image and estimate their mass and volume based on the image features (length, width, height and area). Four machine learning models, including linear regression (LR), artificial neural networks (ANN), radial basis function support vector regression (RBF-SVR) and Linear basis function support vector regression (LBF-SVR) were developed to predict the mass and volume of the single grape. The estimated grape mass and volume by these methods was compared statistically with actual values. The mass and volume in all treatments showed a decreasing pattern during the cold storage. The results indicated that mass and volume change decrease with Farsi gum and hemp seed oil increasing. Furthermore, according to the model evaluation results, the prediction performance of RBF-SVR model had achieved better predictive accuracy compared with the results of LR, ANN and LBF-SVR models, with R2 of 0.998 and 0.989 for mass and volume estimation, respectively, which also showed a good agreement between actual and predicted values. These results revealed that SVR model was a promising tool for estimating the mass and volume of grape during storage.

Attention to “one’s faults” is one of the significant concepts in the traditions about which most of the commentators have suggested a merely moral understanding. There exists a close relation between the two concepts of attention to “one’s faults” in the traditions and Nichols’ “one's share” [theory] in rectifying the relations of the spouses. The main question of this study is “What are the results gained from comparing the two concepts of attention to “one's faults” in the traditions and Nichols' “one's share” [theory] in rectification and improvement of the spouses’ relations?” The findings of this study indicate that in addition to the fact that this concept has moral value, it is used as a principle in regulating the inter-personal behaviors of the spouses. On the other hand, other than explanation of this concept, there have been explained in Islamic teachings strategies to facilitate “attention to one’s share” which cause differentiation with Nichols’ viewpoint. The method of collecting information applied in this article is based on the study of library-oriented documents and the descriptive-analytical method of the texts.

In recent decades, artificial intelligence systems were employed for developing predictive models to estimate and predict many agriculture processes. Neural networks have the capability of identifying complex nonlinear systems with their own high learning ability. Artificial Neural Networks as a modern approach has successfully been used to solve an extensive variety of problems in the science and engineering, exclusively for some space where the conventional modeling procedure fail. A well-trained Artificial Neural Networks can be used as a predictive model for a special use, which is a data processing system inspired by biological neural system. The short storage life of hawthorn fruit and its high susceptibility to water loss and browning are the main factors limiting its marketability. So, it is important to evaluate parameters that affected the hawthorn quality. An adaptive neuro-fuzzy inference system or adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi-Sugeno fuzzy inference system. To estimate changes in fruit quality as a function of storage conditions, the evolution of certain quality-indicative properties such as color, firmness or weight can be used to provide related information on the quality grade of the product stored. Measurement of these parameters is an expensive and time-consuming process. Therefore, parameter prediction due to affecting factors will be more useful. In this study, the physicochemical properties of hawthorn fruit during various storage was predicted using artificial neural networks method. Hawthorn (Crataegus pinnatifida), belonging to the Rosaceae family, consists of small trees and shrubs. The color of the ripe fruit ranges from yellow, through green to red, and on to dark purple. Hawthorn is one of the most widely consumed horticultural products, either in fresh or processed form. It is also an important component of many processed food products because of its excellent flavor, attractive color and high content of many macro- and micro-nutrients.
  

The purpose of this study was a prediction of color, physical and mechanical properties of hawthorn fruit (Crataegus pinnatifida) during storage condition using artificial neural networks (ANNs) and adaptive network-based fuzzy inference system (ANFIS). Experimental data obtained from fruit storage, were used for training and testing the network. In the present research, artificial neural networks were used for modeling the relationship between physicochemical properties and color attributes with different storage time. Several criteria such as training algorithm, learning function, number of hidden layers, number of neurons in each hidden layer and activation function were given to improve the performance of the artificial neural networks. The total number of hidden layers and the number of neurons in each hidden layer were chosen by trial and error. The network’s inputs include storage time, hawthorn moisture content and storage temperature and the network’s output were the values of the physicochemical and color properties. The training rules were Momentum and Levenberg-Marquardt. The transfer functions were TanhAxon and SigmoidAxon.

To predict the weight loss and firmness multilayer perceptron network with the momentum learning algorithm, topologies of 3-15-5-1 and 3-8-5-1 with R2=0.9938 and 0.9953 were optimal arrangement, respectively. The optimal topologies for color change, hue, Chroma were 3-9-7-1 (R2=0.9421), 3-9-3-1 (R2=0.9947) and 3-7-1 (R2=0.9535) respectively, with momentum learning algorithm and TanhAxon activation function. The best network for ripening index prediction was Multilayer perceptron network with the TanhAxon activation function, Levenberg-Marquardt Levenberg-Marquardt learning algorithm, topology of 3-5-1-1 and R2=0.9956.

Three factors including firmness, total soluble solids and titratable acidity were considered for ripening index calculation during fruits storage condition. Momentum and Levenberg-Marquardt learning algorithms with SigmoidAxon and TanhAxon activation functions were used for training the patterns. Results indicated artificial neural networks to be accurate and versatile and they predicted the quality changes in hawthorn fruits. The outcomes of this study provide additional and useful information for hawthorn fruits storage conditions.

The aim of this work was to achieve of technological knowledge of production of antimicrobial nanocomposite packaging containing nanoparticles in order to increase the shelf life of apple and grape fruits. Low density polyethylene (LDPE) was chosen as base polymer and titanium dioxide (TiO2, 0, 0.5 and 1.5%) and zinc oxide (ZnO, 0, 0.75 and 1.5%) nanoparticles were added to LDPE matrix to produce nanocomposites by extrusion method. At the first step, the effect of nanoparticles addition on physicochemical properties of nanocomposites were investigated. It was revealed that the antimicrobial analysis showed that the nanocomposite films have good bactericidal activity and the effect of titanium dioxide is more than zinc oxide nanoparticles. At the second step, fabricated films were used for packaging of fresh apple and grape fruits. The qualitative parameters of fruits were investigated during this period. Results indicated that the weight loss of fruits decreases by using nanocomposites. Browning index and also, hardness of apple products in nanocomposite packaged samples were more than control ones. Nanocomposite films controlled the microbial count of apple and grape and the film containing 1.5% titanium dioxide showed the most effect on mesophilic bacteria and yeasts and molds. Generally, titanium dioxide was more effective than zinc oxide in both of improving physical properties of nanocomposites and increasing the shelf life of apple and grape fruits. Resoles of this research approved that the using of LDPE nanocomposite active film containing titanium dioxide and zinc oxide nanoparticles could be a good way for increasing the shelf life of apple and grape during production and distribution.

It is important to control the ripening stages of agricultural products during storage and their quality grading based on their ripening stage. Edible coatings can prolong the storage life of agricultural products and protect them through the handling, storage, processing and marketing. The purpose of the current study was to develop an artificial vision system for quality control and segregation of cherry tomatoes in two different conditions including with and without Aloe vera gel coating. For this purpose, physicochemical properties including titrable acidity, total soluble solids and firmness of cherry tomatoes were measured in both conditions. Based on these properties, the ripening index (RPI) was determined and the samples were classified to MS1 and MS2 according to the ripening stage. Subsequently, the samples were classified using color features, color texture features separately and their combination through principal component analysis (PCA) and back propagation neural network (BPNN). Classification results showed that the use of color and color texture features combination made the classification more accurate; PCA and BPNN methods were able to segregate the samples with high accuracy (85.72 and 98.21, respectively) using the 21 color and color texture features. The higher accuracy of the BPNN method is due to its nonlinear performance. The results of this study indicate that Aloe vera gel is promising in delaying the ripening process of cherry tomatoes and artificial vision system can be used as a non-destructive method for evaluation of cherry tomato ripening level based on the color and color texture features.

Hawthorn (Crataegus pinnatifida), belonging to the Rosaceae family, consists of small trees and shrubs. The color of the ripe fruit varied from yellow, through green to red and on to dark purple. Most of the species ripen their fruit in early to mid-autumn. Beneficial effects of hawthorn fruit extracts have been confirmed by various studies. Pharmacological data showed that hawthorn fruit and its preparations enhance myocardial contraction and conductivity, protect against ischemia. They have a sedative action, a protective effect against arrhythmia and increase of coronary vessel flow. They have also positive effects on the cardiovascular system. Hawthorn is one of the most widely consumed horticultural products, either in fresh or processed form. It is also an important component of many processed food products because of its excellent flavor, attractive color and high content of many macro- and micro-nutrients. Uncertain storage conditions lead to considerable quality loss in hawthorn fruits, which affect their consumer acceptability. Properties such as color, firmness and moisture provide valuable information for the monitoring of quality changes in postharvest fruits because of their reliability and rapid and easy measurement. These changes are consequences of many biochemical and physiological processes that occur during fruit ripening, such as respiration and transpiration. In addition, these changes depend on the external conditions to which the fruit is exposed. To estimate changes in fruit quality as a function of storage conditions, the evolution of certain quality-indicative properties such as color, firmness or weight can be used to provide related information on the quality grade of the product stored. Kinetic models can be used as tools to describe quantitative physicochemical changes in foods during processing. Kinetic models can be linear or non-linear forms of rate law equations. The rate process of food constituents is usually defined by zero, first or second order kinetics.

In this study, changes in physicochemical characteristics associated with fruit quality were investigated during various hawthorn fruit storage conditions include: cold (1ºC and RH=90%), refrigerator (10 ºC and RH=85%) and room conditions (25 ºC and RH=60%). Color quality parameters (L*, a*, b*, C*, h* and ΔE), geometrical parameters, weight loss, firmness, total soluble solid (TSS), pH, titratable acidity (TA) and ripening index (RPI) were the measured factors. Different mathematical models were successfully proposed and adjusted to represent the change in physicochemical properties as a function of storage temperature. Among various kinetics model, five models (Zero-order, First-order, Second-order, Fractional conversion and Weibull models) were fitted to experimental data and model parameters in equations were determined by multiple regression analysis.

Storage of hawthorn fruits at different temperature affected their color (L*, a*, b*, C*, h* and ΔE), geometrical, physical (weight loss, firmness) and chemical properties (TSS, pH, TA and RPI). Storage at all conditions had significant impact on the physicochemical parameters analyzed (except some geometrical characteristics). Significant alterations in hawthorn color, firmness and weight loss were observed. The firmness, titratable acidity, pH, ripening index, color characteristics (except a* and C*) decreased while weight loss, total soluble solid, a* and C* increased significantly (p<0.05). Hawthorn stored at low temperatures revealed a delay on quality reduction reactions in terms of color, firmness and weight loss. The results indicated that the First-order and Weibull kinetic models provided the best prediction of the changes in physicochemical parameters. The storage temperature effect was successfully described by the Arrhenius law. Understanding the mechanisms in which these conditions affect the quality changes processes is of great importance because it allows their appropriate modification to maintain quality and maximize storage time. The outcomes of this study provide additional and useful information for hawthorn fruits under various storage conditions.

Medlar (Mespilus germanica) is widely cultivated in northern Iran and used as an edible fruit as well as home remedies. Short storage life of medlar fruit and its high susceptibility to weight loss and browning are the main limiting factors for its marketability. The aim of current work is to study geometrical (extracted from image), physical (texture and color) and chemical (titratable acidity) properties of medlar to determine the maturity and ripening stages. These parameters are of high importance due to their relation with sensorial quality and their effects on ripening level of medlar. The results revealed that none of the factors mensioned above were stable during the experiment. Result indicated that the lightness (L*), titratable acidity, firmness, h* and ripening index (RPI) decreased during medlar cold storage; while redness (a*), yellowness (b*), and C* values, total soluble solids and pH of medlar increased during this period. The use of the RPI made it possible to characterize three ripening stages (Fresh Medlar (FM), Ripe Medlar (RM), and OverRipe Medlar (ORM)) during the storage period of medlar. The results also showed that there was a significant relationship between ripeness stage and geometrical properties of medlar.