Journal of Researches in Mechanics of Agricultural Machinery

Abstract Introduction
Legumes are the second most consumed food source for humans, and beans rank first among water legumes in Iran based on the area under cultivation. The two-spotted spider mite (Tetranychus urticae) is a major pest of common bean, causing reduced growth, yield losses, and, in severe cases, plant mortality. Without timely intervention, infestations can cause significant economic damage, prompting reliance on chemical control methods. According to reports, bean fields are sprayed with various pesticides and acaricides using sprayers, on average 3 to 5 times (in some cases 8 times) each year, to control this pest. This level of pesticide spraying, along with environmental pollution, increases production costs, eliminates natural enemies, and increases unauthorized pesticide residues in the product. Today, the use of unmanned aerial vehicle (UAV) to control pests and diseases of various crops has been proposed as a new method of spraying. According to the results of the research conducted, spraying with UAV has been more effective and efficient than other spraying methods. Therefore, considering the problems of spraying beans with conventional sprayers and the capabilities of UAV in pest management, the aim of this study is to assess the technical efficacy of unmanned aerial vehicle (UAV) sprayers in managing of two-spotted spider mite in bean fields compered to tractor-mounted lancer sprayers as a conventional method.
Material and Methods
In order to investigate the effect of spraying rate and height on the consumption of the solution pesticide, the drift, the spraying uniformity, and control of two-spotted spider mite, a randomized complete block design with three replications was employed in two crop years at the station of the Agricultural and Natural Resources Research and Education Center of Markazi Province. The experimental treatments included: T1- UAV sprayer (spraying rate of 1.2 L/min- spraying height of 1.5 m), T2- UAV sprayer (spraying rate of 1.2 L/min- spraying height of 2.5 m), T3- UAV sprayer (spraying rate of 2.4 L/min- spraying height of 1.5 m), T4- UAV sprayer (spraying rate of 2.4 L/min- spraying height of 2.5 m), and T5- tractor-mounted lancer sprayers (conventional method). In all tests, the flying speed of the UAV sprayer was 6 m/s. Conventional tractor-mounted lancer sprayers, the standard regional method, served as the control. Data were analyzed using SPSS, and ANOVA revealed significant treatment differences at the 1% level.
Results and Discussion
The results demonstrated that the UAV sprayer not only reduced pesticide consumption but also significantly decreased the spray drift rate compared to tractor-mounted lancer sprayers. This dual effect substantially minimized pesticide waste and mitigated environmental contamination. The highest drift (34.17%) occurred with the lancer sprayer, whereas the lowest drift (7.5%) was observed for the UAV treatment 3 (2.4 L/min, 1.5 m height). The highest consumption rate of the UAV sprayer was 26.4 L/hr, achieved at a spraying rate of 2.4 L/min and a speed of 6 m/s. This was substantially lower than the amount of solution used by the lancer sprayer by 502.2 L/hr. The average effective field capacity was 7.8 ha/hr and 6.7 ha/hr for the UAV sprayer at 2.5 m and 1.5 m heights, respectively, compared to 0.83 ha/hr for the lancer sprayer. The field efficiency of the UAV sprayer was 72.33% and 69.49% at heights of 1.5 m and 2.5 m, respectively.
In contrast, the lowest field efficiency (42.33%) was attained with the lancer sprayer. Accordingly, the effective field capacity of the UAV sprayer at heights of 1.5 m and 2.5 m, and the lancer sprayer was obtained 6.72, 7.8, and 0.83 hr/hr, respectively. Increasing the spray flow rate at constant speed enhanced the spraying uniformity coefficient. Among the UAV treatments, treatment 3 (2.4 L/min, 1.5 m) showed the lowest spraying coefficient. All UAV treatments outperformed conventional spraying in mite control while using less pesticide. The lancer sprayer showed the lowest effectiveness in controlling the two-spotted spider mite on beans at 7 and 14 days after spraying. Although there was no significant difference between the experimental treatments 14 days after spraying, both spraying methods were effective in controlling the two-spotted spider mite. Notably, Treatment 2 (1.2 L/min, 2.5 m) achieved the highest efficacy at 7 and 14 days after spraying. In conclusion, from a technical standpoint, the UAV sprayer is superior to the lance sprayer for controlling bean pests, offering significant advantages in efficiency, drift reduction, and chemical usage.
Conclusions
This study was conducted to evaluate the performance of a UAV sprayer with two spray rates and two spray heights for controlling the two-spotted spider mite pest of common beans, and to compare it with the conventional spraying method (a tractor-mounted lancer sprayer).
- In spraying with the UAV sprayer, in addition to reducing the consumption of pesticide solution, the amount of drift was lower than with a lancer sprayer.
- The effective field capacity and field efficiency of the UAV sprayer are higher than that of the lancer sprayer.
- The UAV sprayer has acceptable spraying quality factors.
- The lance sprayer showed the lowest percentage of effectiveness in controlling the two-spotted spider mite of beans at 7 and 14 days after spraying.
Due to its greater field efficiency, the UAV sprayer is advantageous for large areas and in conditions of pest epidemics. Also, in fields where tractors cannot enter due to flat bean cultivation (or rainfall and post-irrigation conditions), the best spraying method is to use a UAV. In general, when spraying with the UAV sprayer to control bean spider mites, a flight height of 1.5 m, a maximum flight speed of 6 m/s, and a flow rate of 2.4 L/min are recommended.

Abstract Introduction
Drying of a variety of food product is carried out to reduce their moisture levels in order to increase the shelf life, reduce the probability of developing fungi and to facilitate further processing to obtain the final product. Drying of almonds exhibit many characteristic features such as non-spherical shape, swelling/ shrinkage as a function of moisture content, uneven drying because of their peculiar shape and proximity to other almond kernels and so on. In recent years, different drying methods have been developed for different types of food materials, including hot air (HA) drying, infrared drying, vacuum drying, etc. For tree nuts, microwave drying, radiofrequency drying, and ultrasound assisted HA drying have also been studied in bench scale. However, HA drying in the deep-bed bin dryers is the most commonly applied technology in the industry, considering its large drying throughputs and relatively low cost.
Material and Methods
The main objectiveof this research is to dry almond nuts optimally using 3 combined microwave-vacuum, continuous and infrared dryers and finding the best method by artificial neural networks. 3 microwave power levels of 250, 450 , 650 watts, 3 levels of vacuum pressure of 25, 45, 65 (kPa), 3 levels of linear speed of 15, 39, 63 〖cm m〗^(-1) of belt, and 3 levels of infrared radiation power of 500, 1000, 1500 watts were performed. In during the process of drying almond kernels, its initial moisture content was reduced from about 46% to about 6% based on the weight of dry matter. In order to predict the moisture content of almond kernels, Mathematical Model was selected based on the lowest value of root mean square error (RMSE) and χ2 and the highest value of coefficient of determination (R^2).
Results and Discussion
Based on the Midili model, in the combined microwave-vacuum dryer, the percentage of shrinkage is from 8.5 to 15%. General color changes from 2.6 to 14, activation energy from 21.2to 39.7 (kJ mol-1), effective moisture diffusion coefficient from1.2×10-8 to 6.16×10-8 (m2 s-1), and specific energy consumption from 0.06 to 0.17 (GJ kg-1) was variable. Combined microwave-continuous dryer, shrinkage percentage from 7.1 to 12.1%, color changes from 8.5 to 19, activation energy from 11.75to 24.7 (kJ mol-1), effective moisture diffusion coefficient from 1.1×10-8 to 9.9×10-8 (m2 s-1) and specific energy consumption values were calculated from 0.9 to 10.5 (GJ kg-1). Combined microwave-infrared dryer, fading percentage from 8.7 to 15%, color changes from 7.1 to 22.17, activation energy from 10.95 to 21.05 (kJ mol-1), effective diffusion coefficient humidity from1.4×10-8 to4.5×10-8 (m2 s-1) and the specific energy consumption was calculated from 0.2 t 0.6 (GJ kg-1).
The optimal point of drying almond kernels of combined microwave-infrared dryer at air temperature of 45 (0C) and power of 446 (W) of microwave and power of 1500(W) of infrared radiation was obtained by forward backpropagation network and Bayesian adjustment training function. Combined microwave-vacuum dryer, The most optimal point at air temperature of 45(0C), microwave power of 650(W) and vacuum pressure of 25 (kPa) was obtained by the pre-propagation network and Lunberg-Marquat training function. Combined microwave-continuous dryer, the most optimal point at air temperature of 45(0C) and microwave power of 450(W) and belt linear speed of 59(cm/min) was obtained by feed back propagation network with Lunberg-Marquat training function. Optimum brightening of almond kernels in the response surface method of combined microwave-infrared dryer, the most optimal point in shrinkage is 7% and color change is 10.5, and the amount of specific energy consumption is 0.2 (GJ kg-1) and the amount of the effective diffusion coefficient of moisture is 2.5×10-8 (m2/s) at a temperature of 45(0C) and 417(W) of microwave power and 1490(W) of infrared power were obtained. Combined microwave-vacuum dryer has the most optimal point in shrinkage of9%, color changes of 8.5the amount of specific energy consumption of 0.13(GJ kg-1) and the amount of effective diffusion coefficient of 2.3×10-8(m2/s) at the air temperature of 45 (0C) and the microwave power of 650 watts, the vacuum pressure of 26.5 kilopascals was achieved.
Conclusions
This study demonstrated that the application of combined drying
technologies significantly improves the drying performance and quality attributes of almond kernels compared to conventional approaches. Among the evaluated methods, combined microwave–infrared, microwave–vacuum, and microwave–continuous drying systems effectively reduced the moisture content from 46% to approximately 6% (dry basis) while influencing shrinkage, color change, energy consumption, and moisture diffusion behavior. The Midilli model provided the most accurate prediction of drying kinetics for all systems, confirming its suitability for modeling almond drying processes. Artificial neural network analysis successfully identified optimal operating conditions for each dryer configuration, highlighting the critical role of air temperature and microwave power in process efficiency. Overall, the combined microwave–infrared dryer showed superior performance in terms of lower specific energy consumption and favorable quality characteristics, while the microwave–vacuum dryer exhibited competitive results under optimized conditions. These findings indicate that hybrid drying technologies, coupled with intelligent modeling and optimization tools, offer a promising and energy-efficient approach for industrial-scale drying of almond kernels and potentially other tree nuts.

Abstract Introduction
Cavitation is one of the main causes of performance degradation, flow instability, and reduced service life of flow control valves in hydraulic power transmission systems. High-velocity flow through throttling orifices generates local low-pressure regions, leading to the formation and collapse of vapor bubbles. This phenomenon results in pressure fluctuations, vibration, noise, seat erosion, and reduced system efficiency. These effects are particularly significant in needle-type flow control valves due to their specific seat geometry and complex internal flow patterns, especially under transient conditions and rapid variations in valve opening. Recently, numerous numerical and experimental studies have investigated cavitation in hydraulic valves. By employing multiphase flow models, advanced CFD simulations, high-speed visualization, and fluid–structure interaction analyses, the influence of operating pressure, fluid properties, transient behavior, internal geometry, and movable element dynamics on cavitation inception and development has been examined. These studies consistently demonstrate that internal valve geometry plays a decisive role in cavitation intensity. However, most existing research has focused on pressure and directional control valves, while comprehensive analyses of cavitation in needle-type flow control valves, particularly considering the combined effects of seat geometry and operating conditions, remain limited. In this study, the effects of different seat curvature radii and pressure levels at various valve openings on flow characteristics and cavitation intensity in needle-type flow control valves are systematically investigated. Numerical predictions are validated using experimental data. The results provide practical guidelines for optimizing design, reducing cavitation, and improving flow stability in hydraulic valves used in agricultural and industrial machinery.
Material and Methods
In this study, the flow behavior and cavitation characteristics of a needle-type hydraulic flow control valve were investigated using combined numerical and experimental approaches. The numerical simulations were performed based on a two-phase mixture model coupled with the Schnerr–Sauer cavitation model, accounting for slight oil compressibility. Turbulent flow was modeled using the k–ω SST turbulence model. Transient simulations were conducted at different inlet pressures, valve openings, and seat curvature radii to evaluate their effects on cavitation development. Cavitation intensity was quantified using the cavitation number and vapor volume fraction. Numerical results were validated through experiments conducted on a dedicated hydraulic test rig under controlled operating conditions.
Results and Discussion
The results obtained from numerical and experimental analyses of the flow through needle-type control valves demonstrated that the employed numerical model accurately predicts both the hydrodynamic behavior and cavitation phenomena. Comparison of simulation results with experimental data for both simple and rounded-seat geometries (with a 4 mm radius) revealed that the predicted flow rate differences were generally below 6%, decreasing further at higher pressures. This convergence is attributed to cavitation suppression at elevated pressures and to the flow approaching a single-phase regime. Investigation of the inlet pressure effect indicated that increasing oil pressure consistently enhances flow rate; however, the rate of increase is strongly dependent on the needle displacement. At small displacements, the severe constriction and high local pressure drop make the flow highly sensitive to pressure fluctuations, leading to intense and persistent cavitation. With moderate displacements, the constraining effect diminishes, and phenomena such as flow separation, turbulence, and partial cavitation become more prominent. At larger displacements, the flow transitions to a more stable regime, reducing the flow rate's sensitivity to pressure variations. Analysis of minimum pressure, vapor volume fraction, cavitation number, and cavitation index revealed that higher inlet pressures-particularly at moderate and large displacements-lead to increased minimum pressures, reduced vapor volume, and improved flow stability. Conversely, at small displacements, the severe local pressure drops limit the effectiveness of even high absolute pressures in suppressing cavitation. This behavior confirms the nonlinear and multifactorial nature of cavitation in needle-type control valves. The effect of seat geometry was found to be crucial in enhancing valve performance. Increasing the seat radius improves the flow profile, reduces boundary-layer separation, and decreases local pressure losses, resulting in higher flow rates, higher minimum pressures, and substantially lower cavitation intensity. Seats with radii between 4 and 6 mm not only increase flow capacity but also significantly improve cavitation indices and numbers. Overall, these findings highlight the importance of optimizing seat geometry while accounting for cavitation effects in numerical modeling of control valves, especially at low pressure and small openings.
Conclusions
This study investigated the transient cavitation behavior in needle control flow valves using a combined numerical-experimental approach, analyzing the simultaneous effects of inlet pressure and seat geometry on the valves' hydrodynamic performance. The results demonstrated that the proposed numerical model exhibits high accuracy, showing less than 6% deviation from experimental data. Findings indicate that although increasing inlet pressure and moving part displacement generally reduce cavitation intensity, the influence of seat geometry on cavitation control is significantly more pronounced. Optimizing the seat curvature, particularly with a 6 mm radius, led to a 91% reduction in the cavitation index and a 23% increase in flow rate, accompanied by streamlined flow patterns and reduced local pressure drops. The study confirms that modifying seat geometry alone, without altering operating pressure, can substantially enhance hydraulic performance, stability, and service life of the valve. These results provide a practical and cost-effective design guideline for hydraulic power transmission systems.

Abstract Introduction
Potato (Solanum tuberosum L.) is a major tuber crop with significant global nutritional importance, providing higher energy and protein yield per unit area compared to wheat, rice, and maize (Akhavan et al., 2005). Iran ranks 14th worldwide in potato production, with an annual output of approximately 5 million tons, of which about 200,000 tons are exported to neighboring countries. Approximately 180,000 hectares of Iranian farmland are dedicated to potato cultivation, predominantly under irrigated conditions. Hamadan Province leads national production, contributing 24%, followed by Ardabil, Isfahan, and other provinces.
Accurate identification of potato cultivars is critical in the food and processing industries for determining dry matter content and potential pesticide residues. Traditional methods are often destructive and time-consuming. Machine vision technology offers a non-destructive, rapid, and precise alternative by exploiting differences in shape, size, color, and texture among cultivars. Artificial neural networks (ANNs) are powerful computational tools capable of learning complex relationships between input features and desired outputs through training, making them suitable for pattern recognition and classification tasks (Hristev, 1998).
Previous studies have successfully applied image processing and ANNs for cultivar classification in crops such as rice (Golpour et al., 2014; Shantaiya & Ansari, 2012). This study aimed to develop and evaluate algorithms for extracting morphological, color, and texture features from potato tuber images and to design optimal ANN architectures for classifying ten common Iranian potato cultivars using these features.

Material and Methods
Ten widely cultivated potato cultivars (Agria, Marfona, Granola, Arinda, Santa, Milva, Jelly, Ramos, Spirit, and one additional cultivar referred to as Bamba) were collected from Hesamabad village, Bahar County, Hamadan Province, Iran. At least 30 healthy tubers per cultivar were selected after washing to remove soil.
Images were acquired using a Canon PC1742 digital camera (12.1 megapixels) at a fixed distance of 48 cm against a blue background, yielding 30 images per cultivar (4000×3000 pixels). Pre-processing involved thresholding, binary conversion, noise removal based on small area elimination, and background subtraction.
Morphological features (e.g., area, major/minor axis length, perimeter, solidity), color features (mean, range, variance, and standard deviation in RGB and HSI spaces, totaling 24 features), and texture features (120 features derived from Gray-Level Co-occurrence Matrices in four directions) were extracted using custom algorithms in MATLAB R2012a.
Feed-forward ANNs with one or two hidden layers were trained using the Levenberg-Marquardt algorithm. Data were normalized to [0,1] and randomized; 70% were used for training and 30% for testing. Performance was evaluated using mean squared error (MSE) and coefficient of determination (R²).
Results and Discussion
Image processing successfully segmented tubers, removing background and noise, enabling reliable feature extraction.
When using morphological features (16 inputs) with a single-hidden-layer ANN, the best performance was achieved with a 16-17-10 architecture (log-sigmoid hidden layer, linear output), yielding 88.09% classification accuracy, MSE of 0.1359, and R² of 0.9965 after 17 epochs. Two-hidden-layer networks performed slightly worse (86.19 %).
Color features (24 inputs) produced lower accuracy with single-hidden-layer networks (maximum 27.61%). However, a two-hidden-layer network (24-10-11-10 architecture, tan-sigmoid functions) improved accuracy to 39.04%, indicating that color alone is insufficient for robust discrimination due to high intra-cultivar variability and overlap among cultivars.
Texture features (120 total) were analyzed separately for red, green, and blue channels (40 features each) and grayscale levels. Single-hidden-layer networks yielded low accuracies (20–24.28%). Two-hidden-layer networks performed better: green channel achieved the highest accuracy of 28.57% (40-16-17-10 architecture, linear-tan-sigmoid), followed closely by blue channel (28.09%). The green channel provided the most discriminative texture information.
Overall, morphological features provided the highest classification accuracy (88.09%), followed by color and texture. This aligns with prior research showing morphological traits (shape and size) are more stable and distinctive among potato cultivars than color or texture, which can be influenced by growing conditions and post-harvest handling.
Lower accuracies with color and texture may result from similarities among certain cultivars and environmental effects on surface properties. Combining feature sets was not explored but could potentially improve performance. The non-destructive nature of the approach and high accuracy with morphological features demonstrate its practical potential for industrial sorting and quality control.
Conclusions
This study demonstrated that image processing combined with artificial neural networks can effectively classify potato cultivars non-destructively. Among the tested feature types, morphological features yielded the highest classification accuracy (88.09%) using a single-hidden-layer ANN, outperforming color and texture features. For color and texture, two-hidden-layer networks generally performed better than single-layer ones. The green channel provided the most useful texture information.
Morphological characteristics proved most reliable for discrimination, likely due to greater genetic stability. The developed system offers a rapid, accurate tool for cultivar identification in food processing and trade, with potential for further improvement through feature fusion or larger datasets.

Abstract Introduction
Milk, one of the most nutritious food sources, is rich in protein, fat, vitamins, and minerals, making it an essential component of human diets. However, raw milk provides an ideal environment for microbial growth due to its high moisture and nutrient content, leading to rapid spoilage and potential health risks if not properly processed. Traditional thermal pasteurization, the standard method for ensuring microbial safety and extending shelf life, often results in uneven heat distribution, causing degradation of heat-sensitive compounds and compromising sensory and nutritional quality. These limitations have driven the search for non-thermal alternatives. Hydrodynamic cavitation emerges as a promising, innovative approach, generating localized bubble formation and collapse that produce pressure shocks capable of disrupting microbial structures without causing an overall temperature increase, thereby preserving desirable food properties. Oscillatory flow reactors, particularly single-orifice designs, excel at creating controlled cavitation and enhanced mixing, even at low Reynolds numbers, through reciprocating fluid motion and specialized baffle geometry. Turbulent kinetic energy (TKE) is a key parameter that determines cavitation intensity, mixing efficiency, and microbial inactivation efficacy. Computational Fluid Dynamics (CFD) combined with Response Surface Methodology (RSM) provides a powerful tool for modeling flow dynamics and optimizing reactor geometry to maximize TKE. This study aims to optimize a single-orifice oscillatory flow reactor using CFD-RSM for non-thermal pasteurization of milk. The primary objectives are to achieve maximum TKE through geometric and operational parameter optimization, enhance hydrodynamic cavitation for microbial inactivation, and maintain milk's nutritional and sensory qualities, offering a sustainable alternative to thermal methods.

Material and Methods
This study employed a computational approach integrating CFD and RSM for reactor optimization. A single-orifice oscillatory-flow reactor with annular baffles was modeled in COMSOL. Turbulence was simulated with the standard k-ε model under time-dependent conditions. Milk was treated as the working fluid (density: 1030 kg/m³; viscosity: 0.002 Pa·s). Key independent variables included baffle-to-reactor diameter ratio (d₀/D: 0.4–0.8), oscillation frequency (5–15 Hz), baffle spacing (10–20 mm), and baffle orifice diameter (3–7 mm). Box-Behnken design in Design-Expert software generated experimental runs to optimize maximum and average TKE. For microbial validation, fresh milk samples were sterilized, inoculated with E. coli, and processed through the reactor under optimized conditions derived from simulations. Bacterial reduction was assessed via plate counting on MacConkey agar. Milk samples were sourced locally and processed in a controlled laboratory setting. Inoculation and plating were performed in triplicate for accuracy. Optimization relied on 29 RSM runs.
Results and Discussion
CFD-RSM optimization revealed that oscillation frequency exerted the strongest influence on TKE, with significant positive linear and quadratic effects. A higher frequency (15 Hz) markedly increased both the maximum and average TKE by enhancing vortex formation, increasing shear rates, and intensifying oscillatory motion. In contrast, increasing baffle-to-reactor diameter ratio (d₀/D from 0.4 to 0.8) and baffle orifice diameter (3–7 mm) generally reduced TKE by diminishing flow constriction and weakening cavitation intensity at orifice regions. Baffle spacing exerted a mixed influence: closer spacing (10 mm) substantially increased maximum TKE via localized turbulence intensification, whereas wider spacing (up to 20 mm) slightly improved average TKE through enhanced axial dispersion and a more uniform energy distribution. The optimal configuration was determined as d₀/D = 0.4, oscillation frequency = 15 Hz, baffle spacing = 10 mm, and baffle orifice diameter = 3 mm. Under these conditions, CFD simulations predicted a maximum TKE of 0.36683 m²/s², concentrated primarily at the baffle orifices where rapid acceleration triggers bubble collapse, and an average TKE of 0.071449 m²/s², representing a substantial improvement over non-optimized geometries. Contour visualizations confirmed peak TKE localization in orifice zones, validating effective cavitation generation. Microbial validation experiments demonstrated strong inactivation efficacy. Milk samples processed under optimized conditions showed a significant logarithmic reduction in Escherichia coli counts, which was directly correlated with elevated TKE levels. Cavitation-induced mechanisms, including high-pressure shocks (megapascal range), high-velocity microjets (>100 m/s), extreme shear stresses, and localized production of reactive oxygen species (•OH radicals) disrupted bacterial cell membranes, caused cell lysis, and induced DNA damage without bulk temperature rise. Quality analyses confirmed preservation of key milk attributes. Contents of fat, protein, lactose, and solids-non-fat remained statistically comparable to untreated controls, with negligible changes in pH and overall acidity. No significant degradation of heat-sensitive components was observed, addressing critical limitations of conventional thermal pasteurization such as whey protein denaturation, Maillard reactions, and partial vitamin loss. Regression models exhibited excellent predictive performance (R² > 0.95), underscoring methodological robustness. The optimized single-orifice oscillatory flow reactor thus generates intense hydrodynamic cavitation sufficient for effective non-thermal pasteurization, with TKE established as a reliable indicator of microbial inactivation potential and process efficiency.
Conclusions
This study successfully optimized a single-orifice oscillatory flow reactor using computational fluid dynamics and response surface methods (CFD-RSM), maximizing turbulent kinetic energy to drive hydrodynamic cavitation for non-thermal milk pasteurization. The identified optimal parameters (d₀/D = 0.4, 15 Hz frequency, 10 mm baffle spacing, 3 mm orifice diameter) yield high TKE, enabling effective inactivation of Escherichia coli through turbulent flow, mechanical shear, pressure shocks, and reactive species generation, without compromising milk's compositional quality. The approach offers a sustainable, energy-efficient alternative to thermal methods, preserving heat-sensitive nutrients and sensory properties while ensuring microbial safety. By linking TKE directly to cavitation intensity and inactivation efficacy, this work provides a scalable framework for industrial adoption in dairy processing. Future research should focus on pilot-scale validation, energy consumption analysis, and extension to other liquid foods with varying viscosities and microbial loads. Ultimately, this technology advances non-thermal processing paradigms, contributing to higher-quality, safer dairy products.

Abstract Introduction
Converted internal combustion engines refer to engines whose systems are modified from their original configurations to meet the requirements of another engine type, such as converting a diesel engine to an engine that operates on natural gas as fuel, which can reduce the formation of nitrogen oxides (NOx), carbon monoxide (CO), and soot emissions. Now, many worldwide companies involved in the design and manufacturing of internal combustion engines have converted diesel engines to natural gas-fueled engines; Fait Powertrain is an example. This company has successfully presented some commercial types of converted diesel-to-natural gas-fueled engines. Given Iran's natural gas resources and the lower cost of producing and handling natural gas compared to gasoline/diesel, as well as the lower pollution from natural gas engines, the conversion of gasoline-fuelled engines to natural gas-fuelled engines has been successfully implemented in Iran. However, converting diesel-fuelled engines to natural gas-fuelled engines received less attention. The literature survey also indicated that the simultaneous investigation of compression ratio, ignition timing, and turbocharger map variables on the performance and emission parameters of a diesel engine converted to a natural gas engine has received limited attention. The present study aimed to investigate the effects of compression ratio, ignition timing, and turbocharger map variables on the performance and emission parameters of a diesel engine converted to natural gas using one-dimensional computer simulation. The findings of this study, achieved through advanced modeling of a converted diesel-to-natural gas-fuelled engine using the efficient GT-Power software, can lead to the proper selection of the aforementioned variable values to obtain nearly equal performance parameters, along with reduced emissions, compared to the base diesel engine.
Material and Methods
A four-stroke, four-cylinder, water-cooled, direct-injection, turbocharged diesel engine from Fiat Powertrain was studied. The engine was installed in IPCo's engine test room. The dynamometer and other instruments in the test room measured the engine's performance parameters. The diesel engine was simulated using GT-Power software. The simulated model consisted of a fuel tank, transfer fuel pump, injector pump, fuel common rail, fuel filtration, pressure regulator, injectors, turbocharger, exhaust gas recirculation system, input and output manifolds, input and output valves, cylinder, crankcase, and so on. The developed diesel engine model was validated using experimental data. The necessary modifications were applied to the simulated diesel engine model to enable operation on natural gas. The effects of independent variables, including engine speed at 11 levels, compression ratio at 3 levels, ignition timing at 3 levels, and turbocharger operating map at 5 levels, were investigated on the performance parameters and emissions of the converted-to-natural-gas engine model. Based on the considered levels of the independent variables, modeling of the dependent variables was performed at 495 operating points. The dependent variables were included brake torque, brake mean effective pressure (BMEP), carbon monoxide (CO), carbon dioxide (CO₂), nitrogen oxides (NOₓ), and unburned hydrocarbons (HC).
Results and Discussion
The validation of diesel engine simulation results against experimentally measured data from IPCo and manufacturer catalogs demonstrated that the developed model has acceptable accuracy and can be used as a reliable tool for analyzing engine performance. The results showed that the converted engine BMEP and brake torque values are highly dependent on the ignition timing. For all independent variable levels, the maximum and minimum values of the converted engine BMEP and torque were observed at 13° before top dead center (BTDC) and 21° BTDC, respectively. These results indicated that at 13° BTDC, combustion occurred at an optimal point in the engine cycle, facilitating timely combustion and generating maximum effective mechanical work. An examination of the engine BMEP and output torque values revealed that the maximum BMEP (13.77 bars) and brake torque (328 N.m) can be achieved with proper selection of the turbocharger parameters. However, at turbocharger operating maps 4 and 5, with a compression ratio of 14 and an ignition time of 13° BTDC, the engine model would produce a BMEP of about 13.40 bars and a brake torque of about 320 N.m at 2500-3250 rpm, which could be preferred for heavy-duty vehicles. The investigation of engine combustion Nox and CO emissions indicated that the greatest and lowest values occurred at ignition timings of 21° BTDC and 13° BTDC, respectively. A comparison of NOx and CO emissions between the base diesel engine and the converted natural gas engine demonstrated that the NOx and CO levels for the converted engine were at least 60% and 50% lower than those of the base diesel engine, respectively. An investigation of CO2 emissions in the converted engine across all studied cases indicated that the highest values occurred at 21° BTDC and the lowest at 13° BTDC. The maximum CO2 pollutant level of the converted engine decreased by at least 40% compared to the base diesel engine.
Conclusions
This study demonstrated that an appropriate combination of compression ratio, ignition timing, and turbocharger map parameters for the converted engine can lead to an optimal design that simultaneously achieves comparable performance metrics to the base diesel engine while significantly reducing exhaust emissions. Considering that the converted engine would be used in place of a diesel engine in commercial vehicles, it is recommended that further research be conducted to design turbocharger operating maps that enable the converted engine to produce maximum power and torque within almost the same engine speed ranges as the base diesel engine.

Abstract Introduction
Tragacanth gum is one of the most valuable natural gums, widely used for its renewability, availability, cost-effectiveness, hypoallergenicity, non-carcinogenicity, and non-toxicity. Its applications span wastewater treatment, extending the shelf-life of agricultural and food products, and various hygienic and pharmaceutical industries (Boamah et al., 2023; Anon, 2006). Statistics indicate that Iran accounts for approximately 70% of the world’s tragacanth gum exports (Qomshi Bozorg et al., 2012). Generally, tragacanth gum is classified into two major types (ribbon and flake), which exhibit distinct physical and rheological properties. Key determinants of tragacanth gum quality and commercial value include aqueous solution viscosity, color indices, and microbial load (Abasi & Rahimi, 2006). A critical technological challenge in the export, grading, and processing of tragacanth gum is the development of a rapid, accurate, cost-effective, and non-destructive method for measuring the viscosity of its gels. In this study, a novel approach is proposed for estimating tragacanth gum viscosity, using the optical properties of the samples as input and machine learning algorithms for modeling. To address the problem, five regression-based modeling techniques, including multiple linear regression (MLR), partial least square regression (PLSR), support vector regression (SVR), standard Lasso regression, and cross-validated Lasso regression, were applied to predict viscosity from optical data. By comparing their performance and prediction accuracy, the best model for estimating tragacanth gum viscosity based on optical features was identified.
Material and Methods
This study employed an experimental design to evaluate the feasibility of predicting the viscosity of tragacanth gum gel using optical properties and machine learning. A total of 36 gum samples from two commercially available grades (strip and flake) were analyzed. Optical color indices (L*, a*, b*) were measured using a calibrated colorimeter (Lutron RGB-1002). Gum gels were prepared according to standard protocols, and their viscosities were measured with Brookfield DV_II viscometer. All samples were prepared under identical conditions to ensure consistency. A machine learning approach was used to develop predictive models for viscosity determination. MLR, PLSR, SVR, Lasso, and LassoCV models were developed, trained, and tested in Python. Model performance was evaluated using R², RMSE, and MAE with k-fold cross-validation to minimize overfitting and ensure replicability.
Results and Discussion
- Initial Data Pattern Analysis: To examine the initial patterns and possible relationships between the input variables (optical and physical features) and the output variable (viscosity), the variations in viscosity with respect to each of the measured features were analyzed. As observed, there is a clear distinction in viscosity between the two gum types (ribbon and flake); ribbon-type samples generally exhibit higher viscosity, whereas flake-type samples tend to have lower viscosity. Among the optical features, the L*, a*, and b* parameters exhibit distinct patterns of scatter with respect to viscosity.
- Evaluation of Machine Learning Models: The results of evaluating the performance of different models in predicting the viscosity of tragacanth gum showed that the Lasso regression model provided the highest accuracy compared to the other models. This model demonstrated superior predictive capability for viscosity.
-Residual Plot Analysis: Ideally, residuals should be randomly scattered around the zero axis (y= 0), with no systematic patterns or clustering. In this dataset, due to the nature of the samples, the outputs belong to two distinct types of tragacanth gum with separate numerical ranges. One type has low viscosity values (200 to 500 cP), while the other shows high viscosity values (2000 to 3000 cP), and there are no data points in the intermediate range.
A closer look at the Lasso residual plot shows that the residuals mostly fluctuate within a narrow range around zero and exhibit a more random pattern than those of the other models. This indicates that the Lasso model has achieved a better balance between variance and bias.
Analysis of Predicted vs. Actual Value Plots: These plots are essential tools for assessing model fit, as the closer the points lie to the diagonal reference line (y = x), the higher the model accuracy and the smaller the difference between actual and predicted values. In the plots of all models, an upward trend near the reference line can be observed, indicating that all five models can predict the output values. However, differences in point density and the degree of dispersion around the reference line distinguish the accuracy and stability of each model.
The Lasso and LassoCV models exhibit the best agreement between actual and predicted values. In these two models, most points align very closely with the reference line, indicating high correlation and low prediction error.
Conclusions
This research showed that using machine learning and exploiting the optical properties of various types of tragacanth gum enables the determination of its viscosity quickly, at low cost, and with high accuracy. In this study, five linear regression models MLR, Lasso, LassoCV, and PLSR and one nonlinear regression model, SVR, were compared.
The Lasso model, due to its superior statistical indices, high consistency of points around the reference line (lower error), and minimal dispersion in the residual plots, was identified as the most stable and accurate model among all the methods examined. This confirms the Lasso model's high efficiency and reliability in predicting complex, multidimensional data. Moreover, in this model, applying the L1 penalty to the coefficients effectively eliminated one of the features, reducing dimensionality and yielding a lighter, more interpretable model. Overall, the results indicated that the Lasso model outperformed the other models in terms of statistical metrics (R², RMSE, MAE), residual distribution, and prediction accuracy and speed, making it an optimal choice for non-destructive viscosity estimation. Given the practical nature of this research and its strong potential for application in the production and trade industry of tragacanth gum, it is recommended that future research develop a model capable of automatically determining viscosity values through direct image processing of tragacanth samples.

Abstract Introduction
Pistachio (Pistacia vera L.) is one of the world's most valuable nuts, rich in unsaturated fatty acids, high-quality proteins, phenolics, vitamins, and minerals. In 2022, Iran was the second-largest producer after the USA, and Turkey was in third place after Iran. Due to its high economic value and growing demand, pistachio powder is highly susceptible to adulteration, including mixing with cheaper powders and adding artificial colorants. Such fraud harms consumer rights and can pose health risks, particularly for individuals with allergies or favism.
Among various adulterants, faba bean (Vicia faba) powder is commonly used due to its low cost, similar color and texture, lack of strong taste or odor, and easy mixability. Traditional methods, such as sensory evaluation and simple chemical tests, cannot reliably detect this adulteration. Destructive techniques like chromatography and mass spectrometry are accurate but time-consuming, expensive, and unsuitable for rapid screening.
Recently, non-destructive methods have gained attention. Research on pistachio adulteration has focused on near-infrared, mid-infrared, and Raman spectroscopy, as well as image processing and deep learning. Hyperspectral imaging, which combines spectral and spatial information, has been successfully used to assess pistachio kernel quality, moisture content, aflatoxin, and fungal contamination. However, its application for detecting faba bean adulteration in pistachio powder has not been systematically reported. Given the complexity of hyperspectral data, artificial neural networks are ideal for pattern recognition and accurate adulteration detection.
This study aims to develop a rapid, non-destructive, and accurate method based on hyperspectral imaging combined with an artificial neural network to detect the levels of adulteration in pistachio powder with faba bean powder.
Material and Methods
Pistachio kernels (cv. Ahmad Aghaei) and faba beans were purchased from a local market. One day before the experiments, raw samples were separately milled, sieved through a 30-mesh (600 μm) sieve to achieve uniform particle size, and stored in nylon bags at room temperature. Adulterated samples were prepared by mixing faba bean powder with pure pistachio powder at 0% (pure), 25%, and 50% weight ratios. For each adulteration level, 40 samples (total of 120) were prepared. Approximately 6.5 g of each sample was placed in a wooden cup, and the surface was flattened to reduce shadow effects.
A VA1000 hyperspectral imaging system (NIT Optics) with a spectral range of 400–1000 nm was used. Reflectance calibration was performed using white and dark references. Min Max normalization was applied for standardization. Principal Component Analysis was used for dimensionality reduction. A multilayer perceptron neural network with one hidden layer was trained using 70% of the data and tested on 30%, using IBM Modeler v.18.
Results and Discussion
After observing the spectra, the initial and final wavelengths were removed due to high noise, and the spectral region from 450 to 1000 nm was analyzed. To examine differences among sample spectra, their mean spectra were plotted.
In principal component analysis, new variables are created, each being a linear combination of the original variables. These principal components retain the maximum information from the original variable matrix. The first principal component contains the largest variance, the second captures information not explained by the first, and so on. PCA performed in IBM Modeler ranked all principal components by importance. In this study, only PC1, PC2, PC4, and PC5 were identified as important. The distribution of samples in the coordinate system of the first three principal components showed good separation between classes, which is promising for achieving a high-accuracy classification model.
The software selected a three-layer perceptron neural network with one hidden layer containing three neurons. The input layer had four neurons (corresponding to the four principal components), and the output layer had three neurons for classification into classes A, B, and C. The confusion matrix showed accuracies of 98.80% for training and 94.59% for testing. Errors occurred only for class C, while classes A and B achieved 100% accuracy. Thus, the combination of hyperspectral imaging and an artificial neural network successfully distinguished the three classes, with perfect detection of pure pistachio powder.
Unlike previous studies that used adulterants such as green peas or spinach, this study uniquely employed faba bean powder—an inexpensive and common product in Iran. Hyperspectral imaging enables detailed characterization of pistachio biochemical components, including proteins, carbohydrates, minerals, fatty acids, and phenolic compounds. However, overlapping spectral features of pistachio and faba bean make interpretation challenging.
Conclusions
In this study, a novel, rapid, and non-destructive method based on hyperspectral imaging (400–1000 nm) combined with a multilayer perceptron artificial neural network was developed to detect adulteration of pistachio powder with faba bean powder. The results showed that the proposed method can distinguish pure samples from adulterated ones (25% and 50% faba bean powder) with excellent accuracy. Due to its non-destructive nature and high speed, this approach has significant practical potential for integration into food production lines, quality control laboratories, and inspection systems. Overall, the combination of hyperspectral imaging and neural networks can serve as an effective and reliable tool to preserve pistachio authenticity, prevent economic fraud, and protect consumer rights.
Future research should focus on evaluating the method's performance for detecting other adulterants, expanding the model with larger datasets that include lower adulteration levels (e.g., 5%, 10%, and 15%) and, as a next step, validating the approach on real production lines.