جستجوی مقالات مرتبط با کلیدواژه "منطق فازی" در نشریات گروه "صنایع غذایی"

The increase in the production and consumption of grapes in the world has increased the need for research in the field of creating conditions for further preservation of this fruit, and the lack of sensors that can provide accurate information from the production and storage process to control systems for decision-making is obvious and evident. Therefore, this study aimed to determine some characteristics of grape fruit (taste index, pH, firmness, ion leakage, ∆E, number of mold and general acceptance) stored in cold storage in the range of storage time from 0 to 60 days was measured and according to the importance Achieving these characteristics during fruit storage, a model using fuzzy logic was created as an expert system with accuracy (with a correlation coefficient greater than 0.96) and very high speed capable of predicting and determining these product characteristics only by using It was time to save it. This system was able to obtain the value of each of these studied characteristics at any desired storage time in the range of 0 to 60 days with very high accuracy and in a fraction of a second. On the other hand, it was found that with increasing storage time, the amount of taste index, pH, number of molds, ∆E and ionic leakage of the samples increased, but the degree of hardness and overall acceptance decreased.

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 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.

Mulberry is a fruit that has a considerable acceptance among consumers. The short harvest season and low persistence of this fruit have led to the use of methods such as freezing that have the least effect on phenolic compounds and antioxidant properties and sensory properties of the mulberry. One of the problems of sensory evaluation is the non-parametric results of the answers and the low accuracy of their analysis. Today, methods such as fuzzy logic allow inaccurate parameters to be analyzed by mathematical methods. In the present study, mulberry was stored in a freezer at -18 °C for six months and removed from the freezer at 0, 72 h, one month, two months and six months. After juice making, the sensory evaluation was performed using five-point hedonic method and the data were analyzed using fuzzy logic methodology, while attributes were color, aroma, taste, and feel mouth. Also, pH, acidity, antioxidant activity and total phenol content were evaluated. Sensory evaluation of the samples showed that the freezing of the mulberry increased the overall suitability of the samples in 72 hours and did not show a decrease in sensory properties during 6 months. Panelists assessed the importance of sensory attributes including taste, mouthfeel, fragrance, and color, as well. Although, sensory evaluation did not decrease the sensory of juice, significant decrease of pH (19.83%), increase in acidity (0.31%) and a decrease in antioxidant activity (about 14%) and phenolic compounds (about 21%) during freezing was observed. According to the obtained results, freezing can be suggested as an acceptable method for long storage of mulberry fruit.

Today, production of prebiotic foods is great important. One of the common prebiotic ingredients that used in most food products is date seed powder. Therefore, in this study, for fast-accurate measurement of physicochemical properties of baguettes containing 0, 2.5, 5, 7.5, 10 and 15% of date seed powder was used in the modeling of fuzzy. Finally, it was found that by increasing the amount of date seed powder in formulation baguettes moisture, L* components and firmness of the produced samples increased but water activity, porosity, a* components and specific volume reduced, while no change was found in amount of b* components. In the range of 0 to 15 percent of date seed powder, fuzzy logic models created as an expert system, with very high accuracy and speed to be able to predict and determine various characteristics as a factor of percent of date seed of product. Also, the system will be able to achieve the amount of each of characteristics of the desired output in the range 0 to 15% date seed powder with very high accuracy and less than a second.

For the first time in case of fraud detection in Iran, the amount of water as a fraud in natural lime juice was detected using fuzzy modeling system in the present study. Four parameters consisting pH, EC (electrical conductivity), contrast and homogeneity (obtained from the samples’ images) were considered as input and the amount of water in natural lime juice was introduced as output of the fuzzy model system. Zero, 5, 10, 15, 25, 30 and 40% of water were used for system training and 7.5, 12.5, 20 and 35 % were used for testing the system. Mamdani’s fuzzy inference system, triangle membership functions and 7 rules were used for fuzzy modeling. The performance of the model was determined using R2 and RMSE statistical parameters. As a result, high value of R2(0.99) as well as low value of RMSE (1.67) demonstrated that fuzzy inference system can be used as an efficient method to estimate the amount of water fraud in natural lime juice.