Evaluation of Oxidation Development of Soybean Oil Enriched with Essential Oil from Aerial Parts of Ferulago angulata Boiss during Accelerated Storage

Introduction  It is believed that edible oils and fats with high levels of unsaturated fatty acids are susceptible to oxidation. Soybean oil as one of the four important edible oils has high content of polyunsaturated fatty acids and so prone to oxidation. Generally, lipid oxidation leads to deterioration of nutritional quality and organoleptic properties of edible oils and fats as well as accelerate the development or progression of cancer, mutagenesis, carcinogenesis, aging and cardiovascular diseases through the formation of free radicals. Therefore, edible oils and fats fortification with antioxidant compounds in order to protect them against oxidation is essential. In recent years, numerous studies were carried out on exploration of natural and safe antioxidant compounds due to the consumers concerns about potential health risk of synthetic antioxidants, such as butylatedhydroxyanisole (BHA), butylatedhydroxytolene (BHT), tert-butylhydroquinone (TBHQ) and propylgallate (PG). In this regard, TBHQ as the most powerful synthetic antioxidant is prohibited as food additive in Japan, Canada and Europe. Ferulago angulata Boiss which called chavir or chavil belongs to the family of Apiacea consisting of 35–40 species that 8 species grow in Iran. It was reported that Ferulago species are used in folk medicine for their tonic, digestive, sedative, aphrodisiac properties from ancient times. Therefore, in the current study, the oxidation development of soybean oil enriched with F. angulata essential oil (EO) during accelerated storage was investigated.   Materials and Methods  EO from freeze dried aerial parts of F. angulata was extracted through hydrodistillation using Clevenger type apparatus. Gas chromatography-mass spectrometry (GC-MS) was used to identify main components of the EO. Total phenolic and flavonoid content of the EO were assessed using Folin–Ciocalteu and aluminium chloride colorimetry methods, respectively. Antioxidant activity of EO was measured through 2, 2-Diphenyl-1-picrylhydrazyl (DPPH) and reducing power (RP) tests. Then, the EO of F. angulata at three concentrations, i.e. 200 ppm (SO-200), 400 ppm (SO-400), and SO-Mixture (100 ppm TBHQ + 100 ppm EO) were added to soybean oil. The synthetic antioxidant of TBHQ at the concentration of 200 ppm was added as control. The effect of EO from freeze dried aerial parts of F. angulata on oxidative stability of soybean oil stored under accelerated conditions at 65 ºC for 24 days was evaluated through acidity, peroxide (PV), p-anisidine (p-An) and TOTOX values.   Results and Discussion  Extraction yield, total phenolic and flavonoid contents of EO of F. angulata were 2.5% v/w, 188 mg GAE/g and 70.90 mg QE/g respectively. Furthermore, DPPH free radical scavenging activity and RP were 55.45-13.21% and 3.61-2.72 in the concentration range of 1.6-4.6 mg/ml of EO, respectively. Based on GC-MS analysis, the EO contains 41 natural compounds, representing 96.97% of the total EO. F. angulata EO could effectively reduce the acidity, PV and p-An values. For control sample, the maximum values of acidity, PV peroxide, p-An and TOTOX were 1.52 mg KOH/g, 10.60 meq O2/kg, 12.48 and 33.68 respectively after 24 days under accelerated conditions. While these values were 0.085 mg KOH/g, 4.5 meq O2/kg, 9.16 and 18.16 respectively for the soybean oil containing the lowest concentration of EO of F. angulata.   Conclusion  The results confirmed the instability of soybean oil during storage as well as the ability of EO from F. angulata for soybean oil protection against oxidation. As a result, EO from aerial parts of F. angulata could be suggested as a natural and effective antioxidant to be used instead of TBHQ as a synthetic antioxidant for soybean oil stabilization

Applying Adaptive Neuro-Fuzzy Inference System and Artificial Neural Network to the Prediction of Quality changes of Hawthorn Fruit (Crataegus pinnatifida) during Various Storage Conditions

IntroductionIn 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.  Materials and MethodsThe 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.Results and DiscussionTo 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.Conclusions 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

Evaluation of Performance of an Industrial Gas Sweetening Plant by Application of Sequential Modular and Simultaneous Modular Methods

In this study the simultaneous-modular and sequential modular methods are used to predict performance of desorption and absorption columns in the loop of an industrial sweetening plant. Mathematical model of absorption and desorption cycle for acid gases in methyldiethanolamine has been developed. This model is based on mass and energy balance and takes into account the chemical interactions between solvent and gases. Application of the simultaneous-modular method to model of the plant provides 31 equations with 31 unknowns. Simultaneous solution of these equations presents details of operating conditions on each section of the process. In the sequential modular method, the calculations have been carried out for each unit as a single module in the loop. This way, the output of each module supplies the input data to the next unit. Data of a commercial gas refinery has been used to validate the models and compare the two methods. After validating, the model effects of some parameters on the performance of the loop have been investigated

Optimization of supercritical carbon dioxide extraction of bioactive flavonoid compounds from spearmint (Mentha spicata L.) leaves by using response surface methodology

The bioactive flavonoid compounds of spearmint (Mentha spicata L.) leaves were obtained by using supercritical carbon dioxide (SC-CO2) extraction. Extraction was carried out according to face-centred central composite design, and independent variables were pressure (100, 200 and 300 bar), temperature (40, 50 and 60 °C) and co-solvent amount (3, 6 and 9 g/min). The extraction process was optimized by using response surface methodology for the highest crude extraction yield of bioactive flavonoid compounds. The optimal conditions were identified as 209.39 bar pressure, 50.00 °C temperature and 7.39 g/min co-solvent amount. The obtained extract under optimum SC-CO2 condition was analysed by high-performance liquid chromatography. Seven bioactive flavonoids including catechin, epicatechin, rutin, luteolin, myricetin, apigenin and naringenin were identified as major compounds. The results of quantification showed that spearmint leaves are potential source of antioxidant compounds