Analysis of the combinative effect of ultrasound and microwave power on Saccharomyces cerevisiae in orange juice processing

High temperature in conventional method for juice pasteurization causes adverse effects on nutrients and nutritional value of food. The objective of this study was to examine the effect of microwave output power, temperature, ultrasound power, and ultrasonic exposure time on Saccharomyces cerevisiae in orange juice. Based on our findings, microwave output power, ultrasound power, ultrasonic exposure time orange juice temperature were the most effective factors to reduce S. cerevisiae. The results showed that the quadratic model included was the best model for account. The model showed that regarding decrease of S. cerevisiae account microwave-induced temperature was more effective than microwave output power. Also, compared to microwave power, the ultrasound power was more effective on S. cerevisiae reduction. The optimum processing condition was 350 W microwave power, 35 °C temperature, 778.2 W ultrasonic power, and 11 min of exposure. Based on our result, the consumption energy was 142.77 J/mL with no remaining of S. cerevisiae. The results showed that the given scores by panelists to the combinative and conventional methods for color and flavor indices were significant (P b 0.05). Industrial Relevance: In order to reduce the adverse effects (loss of vitamins, flavor, and non-enzymatic browning) of the thermal pasteurization method, other methods capable of inactivation of microorganisms can be applied. In doing so, non-thermal methods are of interest, including pasteurization using high hydrostatic pressure processing (HPP), electric fields, and ultrasound waves. The ultrasound technology has been the main focus of studies in recent years. However, the main challenge facing the non-thermal technologies in food processing is the inactivation of pathogenic microorganisms and food spoilage agents, which can be achieved by various methods. The aim of present research was examined simultaneous effect of ultrasonic and microwave to remove microorganism. This research introduces new, innovative, and combined method for fruit juice pasteurization, and this method can benefit the food industry

The simultaneous effect of electromagnetic and ultrasound treatments on Escherichia coli count in red grape juice

Introduction: The thermal pasteurization is a common method for maintaining fruit juice and increasing shelf life, but the thermal processing changes the flavor and color of the products. The aim of this study was to investigate the effect of a new method of combining heat and ultrasound on the number of the Escherichia coli present in grape juice. Methods: In this study, the effects of the microwave power, temperature, ultrasound power and ultrasonic exposure time were evaluated on E. coli count of red grape juice. In order to determine the microbial inactivation by microwave and ultrasound, E. coli at a concentration of 6×106 per mL was inoculated to red grape juice. Results: The effects of microwave power, grape juice temperature, ultrasound power and ultrasonic exposure time on the reduction of E. coli were significant (P<0.05). The model showed that in reducing E. coli the importance of the final temperature of the juice was higher than the microwave power. In addition, the ultrasonic power was more effective in E. coli reduction as compared to the microwave power. Conclusion: Both sample temperature and ultrasonic duration were important independent variables and effective factors on E. coli reduction

Artificial neural networks, genetic algorithm and response surface methods: The energy consumption of food and beverage industries in Iran

In this study, the energy consumption in the food and beverage industries of Iran was investigated. The energy consumption in this sector was modeled using artificial neural network (ANN), response surface methodology (RSM) and genetic algorithm (GA). First, the input data to the model were calculated according to the statistical source, balance-sheets and the method proposed in this paper. It can be seen that diesel and liquefied petroleum gas have respectively the highest and lowest shares of energy consumption compared with the other types of carriers. For each of the evaluated energy carriers (diesel, kerosene, fuel oil, natural gas, electricity, liquefied petroleum gas and gasoline), the best fitting model was selected after taking the average of runs of the developed models. At last, the developed models, representing the energy consumption of food and beverage industries by each energy carrier, were put into a finalized model using Simulink toolbox of Matlab software. Results of data analysis indicated that consumption of natural gas is being increased in Iran food and beverage industries, while in the case of fuel oil and liquefied petroleum gas a decreasing trend was estimated

An overview to current status of waste generation, management and potentials for waste-to-energy (Case study: Rasht City, Iran)

This paper presents an overview of the current municipal solid waste (MSW) management in Rasht City, Guilan Province, Iran, followed by evaluating the potential for waste-to-energy. The data of different MSW functional elements were collected from previous works, available reports, interviews and meetings with specialists in the field. About 800 tons MSWs are generated in Rasht per day, of those, over 75% are organic wastes followed by paper and cardboard comprising 5.9%. The daily theoretical energy contained in the city MSWs was estimated to be over 591.62 megawatt hour (MWh, over 215942.54 MWh per year). Almost 500 tons of daily MSWs are directly transferred to Saravan as the biggest landfill in north of Iran with an area of about 30 ha, while the remaining portion is treated in the Guilan composting plant. Landfill mining calculations showed that we could recycle about 3008947, 36793, 61443 and 18366  tons of plastics, textile, wood and  rubbers collected from Saravan landfill respectively. A simple assessment of waste-to-energy potentials from organic wastes using operational conversion coefficients revealed that by employing the combination of waste-to-energy and gas turbine technology, an estimated energy of 227.668 MWh can be produced from the Rasht daily food wastes. Although MSW management in Rasht  has been improved over the last decade owing to the establishment of waste recycling and composting organization, however it is still far from the standard situation due to lack of comprehensive waste management planning, financial resources and infrastructure

Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared-assisted continuous hybrid solar dryer

Drying characteristics of stevia leaves were investigated in an infrared (IR)-assisted continuous-flow hybrid solar dryer. Drying experiments were conducted at the inlet air temperatures of 30, 40, and 50°C, air inlet velocities of 7, 8, and 9 m/s, and IR lamp input powers of 0, 150, and 300 W. The results indicated that inlet air temperature and IR lamp input power had significant effect on drying time (p &lt; .05). A comparative study was performed among mathematical, Artificial Neural Networks (ANNs), and Adaptive Neuro-Fuzzy System (ANFIS) models for predicting the experimental moisture ratio (MR) of stevia leaves during the drying process. The ANN model was the most accurate MR predictor with coefficient of determination (R2), root mean squared error (RMSE), and chi-squared error (χ2) values of 0.9995, 0.0005, and 0.0056, respectively, on test dataset. These values of the ANFIS model on test dataset were 0.9936, 0.0243, and 0.0202, respectively. Among the mathematical models, the Midilli model was the best-fitted model to experimental MR values in most of the drying conditions. It was concluded that artificial intelligence modeling is an effective approach for accurate prediction of the drying kinetics of stevia leaves in the continuous-flow IR-assisted hybrid solar dryer

Optimization of the Efficiency of Electromagnetic Waves Dryer Power on Chemical Composition and Yield of Satureja bachtiarica Essential Oil Using Response Surface Methodology

The aims of this research were to study and optimize the effects of different electromagnetism wave (microwave) powers on drying procedure of Bakhtiari savory. The essential oils from shade and microwaves dried samples were isolated by hydrodistillation in a Clevenger apparatus and analysed by gas chromatography-mass spectrometry (GC-MS). The results showed that with increasing the microwave power, drying time and essential oil yield decreased. The highest values of thymol and carvacrol and sum of them that show the quality of essential oil was obtained with 800 W microwave power. The results of optimization using Response Surface Methodology revealed that the optimum point was 561.3 W microwave power. At this point, the analytical software obtained the p-cymene, γ-terpinene, thymol, carvacrol, yield and drying time values were 5.61%, 12.61%, 12.87%, 43.28%, 1.79% and 445.37 s, respectively. Hence, if we consider the quality of essential oil together with shorter time of drying, the microwave power of 800 W is recommended and if essential oil yield is more important than its quality, shade-dried is recommended

Challenges in Well Testing Data From Multi-layered Reservoirs; a Field Case

The analysis of well test data in multilayer reservoirs is usually a challenging problem due to the complexity of interlayer flow within reservoir; these problems are as a result of insufficient data from unique layer flow into the wellbore. Also, these kinds of tests often could not be interpreted during shut-in period. The analyzer which is used for this kind of analysis should be verified first, to achieve this purpose some attempts are made and finally the best method is provided. In this study; a numerical simulation model is used to design synthetic models of multilayer reservoirs, and a methodology is developed to get individual layer rate versus time. The output of the model is then exported to an analytical well test analyzer, Ecrin, and the tests are analyzed as actual data. Different scenarios for testing layered reservoirs are examined to get the least errors. It was found that in order to get the right reservoir parameters it is necessary to define some efficacy coefficients, then, the well test data of a well in South Pars field were analyzed and the individual layer parameters and reservoir heterogeneity were obtained. Finally some suggestions and conclusions are made to make a complete analysis and simulating multi-layer reservoirs and a method is specified to affect the unique layer rates on analysis to have better results. Using this methodology one can evaluate productivity of wells in less time and there will be less necessity for reservoir numerical simulation

Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics

Abstract Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral wavelengths of 450 nm to 900 nm were pretreated by applying moving window smoothing (MWS), standard normal variates (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1st derivative, and Savitzky–Golay 2nd derivative algorithms. In addition, three different wavelength selection algorithms, namely; competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), and successive projection algorithm (SPA), were executed on the spectra to invoke the most informative wavelengths. The linear discriminant analysis (LDA), developed with SNV-filtered spectral data, was the most accurate classifier to differentiate the contaminated and non-contaminated kiwifruits with accuracies of 96.67% and 96.00% in the cross-validation and evaluation stages, respectively. The system was able to detect infected samples before the appearance of disease symptoms. Results also showed that the gray-mold infection significantly influenced the kiwifruits’ firmness, soluble solid content (SSC), and titratable acidity (TA) attributes. Moreover, the Savitzky–Golay 1st derivative-CARS-PLSR model obtained the highest prediction rate for kiwifruit firmness, SSC, and TA with the determination coefficient (R2) values of 0.9879, 0.9644, 0.9797, respectively, in calibration stage. The corresponding cross-validation R2 values were equal to 0.9722, 0.9317, 0.9500 for firmness, SSC, and TA, respectively. HSI and chemometric analysis demonstrated a high potential for rapid and non-destructive assessments of fungal-infected kiwifruits during storage

Representing the Human Experts Judgment on Quality Indices of White Rice by Image Processing and Artificial Intelligence Techniques

In the present study, a grading system based on fuzzy logic was developed to simulate the behavior of an expert in the evaluation and classification of physical properties of rice grains (paddy) for pricing the product. Based on two desired quality indices in this study and the input linguistic variables of fuzzy grading system, 250 samples were prepared with different quality conditions which include all the possible states for the rice grains (paddy). Lighting and imaging were carried out from each 250 samples of rice products in the same condition. Image processing algorithm was conducted to extract geometric features and light intensity of grains and also fuzzy product pricing model was developed in MATLAB software. Fuzzy inference system was designed with the help of fuzzy toolkit. The input variables of the fuzzy system designed in this study were degree of milling (DOM) and percent of broken kernels (PBK) that were obtained as a real numbers of an image processing algorithm. In total, 25 rules of If-Then were formulated with considering the number of inputs’ fuzzy sets. Fuzzy inputs for degree of milling and the percentage of broken kernels were five membership functions of very low, low, medium, high and very high that were selected based on the evaluations conducted from quality of rice production within the rice field factories in north of the country. The results of pricing through fuzzy logic indicated good overall matching with results of product pricing by an expert (overall accuracy of 92 percent)