Journal of Agricultural Machinery

Abstract Introduction
Water is a very important component of many food products and determines their physical properties, texture, sensory quality, and rate of chemical and microbiological reactions. Magnetic fields, as an emerging technological tool, have recently received increasing attention in the food industry due to their strong permeability and non-contact nature. Studies have shown that magnetic fields weaken hydrogen bonds. Researchers reported that when the magnetic field strength increases, the refractive index of water increases by approximately 0.1%. Magnetic fields can also weaken the van der Waals bonds between water molecules. A similar type of magnet was used in another study for a magnetic field of 6 Tesla. They did not evaluate the evaporation rate, but rather some other properties using the air flow contact angle, and suggested that the magnetization of pure water requires air and the relative motion of the water against the magnetic flux. Previous experiments were conducted at room temperature. The effects of magnetic fields on water samples have been studied from various aspects and are still of interest to researchers in this field. The direction of air flow relative to the magnetic field gradient also affects the evaporation rate. However, some experiments are not well-defined, and their repetition will not be easily feasible. Therefore, a review of the literature on the effects of magnetic fields on water properties shows that there is still no coherent view on the mechanism of the effects of such fields. In this study, we focused on studying the effect of a static electromagnetic field with predefined intensities on the water evaporation rate, fields from 30 to 130 mT and a temperature range between 30, 50, and 70 °C with forced air movement at a uniform speed, and the continuous presence of samples in the electromagnetic field, which, to our knowledge, has not been reported before. To this end, the objectives of this study include: (1) quantitative determination of the evaporation rate as a function of the applied magnetic field; (2) finding the energy contribution to the evaporation rate in the presence of a magnetic field.
Materials and Methods
To create a magnetic field, two copper coils with a wire gauge of 1.25 mm, a core diameter of 110 mm, and 2500 turns were used. To measure the level of magnetism, the PHYWE Tesla meter with an accuracy of 10 microteslas and measurement range of 20 to 2000 mT, made in Germany, was used. To measure the weight of the samples at the desired intervals, the AND digital scale model GF6000 with a weighing capacity of 6000 grams and an accuracy of 0.01 grams, made in Japan, was used. For each of the tests, 40 milliliters of Type II distilled water were used in accordance with ASTM D1193 and ISO 3696 standards, with a conductivity of 0.1 μS.cm^-1. Initially, to ensure uniform testing conditions, the device was operated for 15 minutes, after which the samples were placed in petri dishes with a diameter of 90 millimeters and a height of 11 millimeters at a constant temperature of 20 degrees Celsius and prepared for testing. After preparing the samples and the device, the prepared samples were placed inside the device and removed at 15-minute intervals for a duration of 120 minutes, then weighed using a scale with an accuracy of 0.01 grams. This process was carried out separately for each treatment, and the data were collected. The evaporation rate of the sample per unit time was calculated using the unit of milligrams per minute and the trend line equation. The slope of the obtained lines indicated the evaporation rate values. All the trend lines obtained had a coefficient of determination (i.e., linear correlation degree) equal to or greater than 0.99. We chose the magnetic field range of 30 to 130 mT because the working range of the magnetic field generator in the device fell within this range. The experiments were conducted using a factorial test based on a completely randomized design with two replications. The first factor was the intensity of the electromagnetic field at four levels: 0, 30, 60, and 130 mT; the second factor was temperature at three levels: 30, 50, and 70 degrees Celsius; and the third factor was time at eight levels: 15 to 120 minutes. The means were compared at the 5% significance level using Duncan's test. For this purpose, SAS software version 9.2 was used, and Excel 2016 was used for plotting the graphs.
Results and Discussions
The samples were placed in the field generated by the Helmholtz coil, and the results confirmed the effect of the magnetic field on the water evaporation rate. It was demonstrated in a study that, although increasing temperature and decreasing humidity are the dominant factors affecting the rate of water evaporation, a stationary magnetic field with decreasing temperature has an increasing effect on the evaporation rate. This finding contradicts the results of the present study, where the experimental data indicate an increased impact of the magnetic field with rising temperature levels. Considering the results of the analysis of variance, all factors along with their two-way and three-way interactions were significant at the one percent level.
Based on Duncan's multiple range test, for duration, magnetic intensity, and temperature, with the increase in each factor level, the weighted evaporation values of the samples significantly decreased compared to the previous factor level. All the trend lines obtained had a coefficient of determination (i.e., linear correlation degree) equal to or greater than 0.99. The slope of the line equation between weight and time is equal to the evaporation rate (R). From the evaporation rates obtained from experimental data, it is clear that the correlation with temperature is not linear, but rather an exponential function as:
The above model can behave like a linear model. The parameter estimates of the model were obtained using the SPSS software as:
The final model can be expressed in the following form:
At a temperature of 30 degrees Celsius, the energy consumption decreased by 11.4 kJ with the increase of magnetic levels. At temperatures of 50 and 70 degrees Celsius, the reduction in energy consumption with the application of a magnetic field was observed to be 48.3 and 45.2 kJ per gram, respectively. These results demonstrate the effect of magnetism on optimizing energy consumption at different temperature levels, with 50 degrees Celsius and a magnetic field intensity of 130 mT being the optimal conditions in terms of energy consumption.
Conclusion
In this study, a statistical approach was used to investigate the rate of water evaporation under different magnetic fields and temperatures over a specified period. The results indicated that the magnetic field, like temperature, affects water evaporation, and as the field increased, the rate of water evaporation also rose. Specifically, the evaporation rates in the treatments at 30, 50, and 70 degrees Celsius after 120 minutes without applying the magnetic field were 43.7%, 53.3%, and 66.5% of the initial weight of the sample, respectively. After applying the magnetic field from 0 to 130 mT, the evaporation rates were reported as 59.6%, 82.8%, and 94.7% of the initial sample weight, respectively, indicating an increase in the evaporation rate with the application of the magnetic field. Finally, a model was proposed that accurately predicts this trend and can be utilized. The analysis of the energy consumption results for each treatment also showed that the magnetic field can influence the total energy consumption for water evaporation and optimize energy use, with reductions of 14.6% at 30 degrees Celsius, 26.55% at 50 degrees Celsius, and 22.5% at 70 degrees Celsius.
Acknowledgments
The present study pertains to research project number 60993 approved by Ferdowsi University of Mashhad, and it acknowledges the efforts of Dr. Mohammad Farkhari (Associate Professor of Plant Breeding at the University of Agricultural Sciences and Natural Resources of Khuzestan) and Dr. Omid Doosti Irani, alumnus of the Biosystems Engineering Department at Ferdowsi University of Mashhad.

Abstract Introduction
Drying is one of the important steps in starch modification, after applying the modification treatments. Starch is obtained from the seeds and fruits of various plants and used in a dried state to achieve a longer shelf life, potentially saving on transportation and storage costs for commercial purposes. Drying is the final necessary step in starch modification, often performed using a conventional oven, a freeze dryer, or an organic solvent (typically ethanol or acetone). In food drying processes, energy consumption is considered a key parameter. The method used to dry pre-gelatinised starch is crucial, as drying is one of the most important steps in the production of modified starch powder. On the other hand, considering the global tendency to use renewable energies, especially in food drying, to reduce thermal damage, energy consumption and drying time, it is of great importance to investigate drying with reflectance window systems, which are environmentally friendly, have high efficiency and cause less damage to the food product components. The effect of drying modified starch with cold plasma by a reflectance window system at a temperature of 50 °C was investigated, and its results were compared with data from the traditional oven drying system.
 Materials and Methods
Potato starch powder was obtained from Zamen Food Products Manufacturing Company, located in Mashhad Industrial City, Iran, in plastic packs. A laboratory-scale cold plasma generator device available at the Sari University of Agricultural Sciences and Natural Resources Growth Centre was used. This device consists of two main parts: the cold plasma generation section and the sample storage section. The device generated cold plasma through direct contact of the sample with the resulting ionised air. Cold plasma was applied to the sample produced by a plasma reactor with copper and steel electrodes at a voltage of 20 kV, a current of 3 mA, and a frequency of 50 Hz, using atmospheric air. A randomised complete factorial design was implemented with the factors of pre-gelatinisation temperature (55 and 60 ℃), cold plasma treatment time (0, 15, and 30 min), and starch drying temperature in the oven (60, 70, and 80 ℃). To prepare pre-gelatinised samples, 10 g of starch was dissolved in 90 g of distilled water to prepare a 10% (w/w) solution. The energy analysis included calculations of drying efficiency, energy efficiency, thermal efficiency, and specific heat consumption. The resulting data were optimized using Design-Expert software.
Results and Discussion
The results showed that the pre-gelatinisation temperature had a significant effect on all the studied parameters (energy, drying, and temperature efficiency), with a confidence level of p < 0.05. Drying temperature did not significantly affect energy efficiency, but it had a significant impact on both drying efficiency and temperature efficiency. Plasma treatment had a substantial effect on energy efficiency and drying efficiency, but no significant effect was observed on temperature efficiency. Based on regression models, the linear model has the best fit to the experimental data and was able to accurately predict the responses, which indicates the importance of the factors under study in process optimisation. Based on optimisation analysis, the optimal conditions indicate a temperature of 60 ℃ for pre-gelatinisation, 70 ℃ for oven-drying, and 30 min for cold plasma treatment time. This combination results in maximum efficiency and reduced energy consumption.
Conclusion
This analysis shows that the studied temperature changes and different treatments have distinct effects on drying processes and energy consumption, which can be considered in optimising these processes. The results of this research can help improve starch production processes and increase their efficiency in related industries. This research simultaneously investigates two new methods for modifying and drying starch, which can result in practical improvements to starch quality.
Funding Sources: This research was funded by the Sari Agricultural Sciences and Natural Resources University in the form of a Master's thesis with registration number 6490/1403/D and registration date 16/9/2024 from the research budget related to the thesis grant.
Conflict of Interest: No conflict of interest has been declared by the authors.
Acknowledgements: We would like to thank Sari Agricultural Sciences and Natural Resources University for their financial and moral support in conducting this research.

Abstract Okra (Abelmoschus esculentus L.) slices were dried using vacuum-infrared drying method. A laboratory-scale dryer was designed and fabricated to control the drying parameters and monitor the weight change of samples. The parameters examined included temperature (50, 55, 65, 75, and 80ºC) and absolute pressure (5, 21, 53, 85, and 101 kPa). The response factors were drying time, drying rate, effective diffusivity, specific energy consumption, colour change (ΔE and a/a₀), and rehydration ratio. The results from the response surface methodology indicated that the optimal conditions for minimising drying time and maximising green colour preservation were a pressure of 5 kPa and a temperature of 51ºC. Under these conditions, the corresponding responses of dried okra slices were 173 min for drying time, 0.24 %wb min^-1 for drying rate, 1.44×10^-9 m² s^-1 for effective diffusivity, 32.6 kWh kg^-1 for specific energy consumption, 12.64 for colour change, 0.75 for relative greenness, and 6.8 for rehydration ratio. Additionally, the findings demonstrated that the combination of pressures lower than 20 kPa with infrared drying effectively enhanced the performance factors of the dryer while preserving the colour of dried okra slices.

Abstract Introduction
Rosa damascena Mill is a valuable cultivated plant, and for many years, essential oil has been produced from its flowers in Iran. The flower buds are generally dried by spreading them out in the shade or in the sun. Shade drying can lead to prolonged drying times, while sun drying may reduce product quality, affecting colour and essential oil. Therefore, in the current study, Rosa flower buds were dried using different drying processes and periods, and their physicochemical properties and essential oil quality were determined.
Materials and Methods
Rosa flower buds were collected from a farm located in Markazi province, Iran, in late May, 2024. Buds were dried using shade, a cabinet dryer (30 and 40 °C), an indirect solar dryer, a freeze-dryer, and a vacuum-dryer. Colour specifications (Lab) of the inner and outer petals of dried buds were measured by a colorimeter. Titratable acidity was determined by the titration method using 0.1 normal sodium hydroxide solution. Ascorbic acid was also measured by the titration method with 2 and 6 dichlorophenol indophenol. Experiments were carried out in three replications using a completely randomised design, and data were analysed using one-way ANOVA. Afterwards, the means of the data were compared using the Duncan test. The extraction of essential oil was applied by the water distillation method. After determining the essential oil yield, the compounds’ percentages were identified using GC and GC-MS devices.
Results and Discussion
The results revealed that drying in shade took too long (more than 12 days), and the shortest drying time happened in the vacuum-dryer (18 hours). The maximum colour index (L*) in the outer and inner petals of dried buds was observed in the freeze-dryer as 49.42 and 44.94, respectively. The maximum value of the a* index of 25.61 was acquired in the outer petal buds, which were dried at 50 °C in the cabinet dryer. After the fresh buds, the a* index of the inner petal buds was highest in the vacuum dryer at 16.62. The b* index of the outer and inner petal buds dried in the cabinet dryer, solar dryer, shade, and vacuum dryer did not have any significant differences. The maximum titratable acidity value was related to buds dried in the solar dryer (2.3%), and the minimum was observed in the vacuum dryer (1.23%). In the cabinet dryer (40 and 50 °C), ascorbic acid of dried buds had the highest values (1.57 and 1.49 mg per 100 g wet matter) in comparison with the other treatments. The quality of the essential oil extracted from dried buds in shade was similar to that of fresh buds. After the shade drying method, the best quality of essential oil was observed in buds dried in the cabinet dryer at 40 °C (30.4%).
Conclusion
Based on the results, applying vacuum-drying considerably shortened the drying period in comparison with shade drying. Lightness (L) is the most important specification that had the maximum value in outer and inner petals of buds dried in the freeze-dryer, and the least in the solar dryer. There were no significant differences between the lightness of inner petals of buds dried in shade and the two treatments of the cabinet dryer. Drying caused an increase in titratable acidity and a decrease in ascorbic acid of the flower buds. The maximum titratable acidity was depicted in buds dried by an indirect solar dryer, and the minimum was in the vacuum dryer. The flower buds dried in the cabinet dryer contained significantly higher levels of ascorbic acid compared to other drying methods. Cabinet drying at 50 °C yielded the highest amount of essential oil. The most aroma compounds and the lowest waxy compounds were observed in fresh buds, buds dried in shade, and buds which dried at 40 °C in the cabinet dryer, respectively.

Abstract A study was carried out to investigate a motorised, integrated cassava tuber slicing and chopping machine. The integrated machine was investigated in terms of key performance metrics at three levels of speeds (950, 1150, and 1350 rpm for chopping; 300, 450, and 600 rpm for slicing) and two levels of feed rates (10 and 15 kg min^-1 for chopping; 5 and 10 kg min^-1 for slicing). According to the investigation findings, the maximum chopping capacity of 229.7 kg h^-1 was recorded at an operating speed of 1350 rpm and a feeding rate of 10 kg min^-1. The maximum chopping efficiency of 82.1% was obtained at an operating speed of 1350 rpm and a feeding rate of 15 kg min^-1, whereas the minimum mechanical loss of 9.06% was found at an operating speed of 1350 rpm and a feed rate of 15 kg min^-1. Based on the investigation results, at 600 rpm rotational speed and 5 kg min^-1 feed rate, the greatest slicer capacity of 114.8 kg h^-1 was noted. At 600 rpm rotational speed and 10 kg min^-1 feed rate, the greatest slicer efficiency of 71.6% was obtained. At this speed and feeding rate, the smallest loss of 11.06% was observed. Due to the low-key performance criteria recorded during the research, optimisation of the drum and hopper of the chopping unit, as well as the hopper and blade geometry of the slicing unit, is recommended.

Abstract Introduction
The electronic nose (e-nose) system analyses the volatile compounds in products by mimicking the human olfactory system and is capable of providing both chemical and sensory information. One of the most important applications of the electronic nose is gas sensors widely used in various industries, including construction, chemical and petrochemical, environmental monitoring, medical and pharmaceutical, agricultural, and food industries, as well as in many other processes where gas monitoring and analysis are essential. Due to the increasing cost of experimental testing, computational fluid dynamics (CFD) can be employed to investigate the effects of key factors in the electronic nose. CFD is a branch of numerical methods used to solve the governing equations describing various flow phenomena. In CFD, different methods and algorithms are utilised to obtain solutions; however, in all cases, the problem domain is discretised into a large number of small elements, and the governing equations are solved for each element. A review of previous studies indicates that simulation processes aimed at achieving reliable results in this field have received considerable attention, and the data obtained from these simulations can be highly accurate and efficient. Moreover, the sensor chamber plays an important role in enhancing the performance, stability, and sensitivity of an electronic nose. Therefore, four different configurations of 3D sensing chambers were simulated using ANSYS FLUENT software, examining both air and CO₂ as fluids. Numerical simulations were carried out to investigate the gas flow behaviour inside these four chambers and specify the optimal chamber design with the best stability time.
Materials and Methods
In the present study, four chamber geometries namely cylindrical, pyramidal, conical, and hemispherical, were designed using CATIA software. Subsequently, three-dimensional simulations were performed using Ansys Fluent software. To predict the fluid flow behaviour, the continuity equation and the Navier–Stokes equations were employed. The boundary conditions were identical for all geometries, and given the equal inlet and outlet cross-sectional areas across all configurations, the only difference among the models lies in their overall geometric structure. The electronic nose chambers were tested with a laminar flow and the SIMPLEC method.
Results and Discussion
The 3-Dimensional simulations were conducted for the four geometries under two fluid injections, air and carbon dioxide, while maintaining identical boundary conditions. To investigate the fluid behaviour inside the geometries, flow field contours were presented at 25 s, 50 s, 75 s, and under approximate stability conditions when the contours became nearly time-invariant. In the air injection case, the fluid inside the hemispherical geometry reached steady-state conditions by 25 s, faster than in the other geometries, and maintained a similar flow pattern at later times. Based on the simulation results, the hemispherical geometry exhibited the best overall performance. This favourable behaviour can be attributed to the geometric dimensions of the hemisphere in the sensor surface region. In contrast, the behaviour of carbon dioxide differed significantly from that of air. Under carbon dioxide injection, similar to the air injection case, the fluid in the hemispherical geometry reached steady-state conditions by 25 s and fully covered the outlet region. In this geometry, the flow attained a nearly uniform distribution at 25 s, and the fluid fully contacted the sensor surface. Also, results showed that small vortices produced near the sensor surface can improve the stability time, and they can help to bring the fluid to the surface faster.
Conclusion
In the present study, four geometries were investigated under two injection conditions: air and carbon dioxide. The evaluations were conducted to identify the optimal geometry in terms of fluid flow behaviour, sensor surface coverage, and flow stability. The results demonstrated that the hemispherical geometry exhibited the best performance under both air and carbon dioxide injection conditions. This indicates that providing a larger fluid–sensor contact area along with a balanced geometric configuration can lead to improved fluid behaviour and overall system performance.

Abstract Introduction
Ensuring the authenticity and safety of almonds is a critical food safety challenge, primarily due to the presence of amygdalin in bitter varieties, a cyanogenic glycoside that can release toxic hydrogen cyanide upon hydrolysis. Traditional detection methods, such as chromatography, are often destructive, time-consuming, and unsuitable for industrial-scale applications. While the current laboratory-scale analysis relies on a sample preparation method (KBr pellet) that is inherently destructive, the ultimate goal of this research is to develop a non-destructive, automated sorting system suitable for industrial implementation. This can be achieved through the use of alternative FTIR techniques, such as Attenuated Total Reflectance (ATR)-FTIR, which allows for direct analysis of the almond shell surface without the need for sample preparation. This study addresses the need for rapid, non-destructive, and automated sorting by developing an integrated framework that combines Fourier-transform infrared (FTIR) spectroscopy of almond shells with machine learning and mechatronic automation. The research specifically focuses on exploiting the rich phenolic content of almond shells as a novel, low-cost substrate for reliable classification.
Materials and Methods
A set of 200 almonds (100 bitter, 100 sweet) was collected. Their shells were separated, vacuum-dried, ground into powder, and pressed into potassium bromide (KBr) pellets for analysis. FTIR spectra were recorded in the range of 400–4000 cm⁻¹. The raw spectral data underwent preprocessing using first and second Savitzky–Golay derivatives, Standard Normal Variate (SNV), and Multiplicative Scatter Correction (MSC). Four supervised learning algorithms, Support Vector Machine (SVM), Random Forest (RF), Multi-Layer Perceptron (MLP), and an Autoencoder-MLP hybrid, were trained and compared, using a 70-30 train-test split with fivefold cross-validation. The best-performing model's output was integrated with a custom-built laboratory-scale mechatronic sorting system. This system featured a conveyor belt, a microcontroller-based control board, and a mechanical deflection mechanism to physically separate the almonds based on the classification decision.
Results and Discussion
Among the tested models, the MLP network achieved the highest classification performance for almond shells, with an accuracy of 95.5% and an Area Under the Curve (AUC) of 0.984. Statistical analysis via the McNemar test confirmed its significant superiority over the best classical model (RF, p < 0.01). This superior performance is attributed to the MLP's ability to model the complex, non-linear relationships within the high-dimensional FTIR spectral data, which traditional linear models like SVM (accuracy 82.5%) failed to capture effectively. Feature importance analysis revealed three key discriminating spectral bands: 1030 cm⁻¹ (associated with phenolic C–O stretching), 1740 cm⁻¹ (related to carbonyl C=O stretching), and 2920 cm⁻¹ (linked to aliphatic C–H stretching). These bands were more pronounced in bitter almond shells, corresponding to their higher phenolic and lipid content, including amygdalin derivatives. Dimensionality reduction visualisations using t-SNE and UMAP on the latent features extracted by the Autoencoder-MLP model further corroborated the superior class separability achieved by deep learning approaches compared to linear methods like PCA. When the MLP model's decisions were deployed on the mechatronic sorter, the system achieved an average physical sorting accuracy of 99.5% across consecutive tests with varying sample compositions. Error analysis indicated that the primary sources of infrequent mis-sorting were related to mechanical synchronisation and sample positioning on the conveyor, rather than errors in the MLP classification algorithm itself. The throughput was measured at 30–35 almonds per minute, demonstrating practical potential for medium-scale processing. This performance aligns with or surpasses the accuracy ranges reported in recent studies utilising HSI or ATR-FTIR for almond discrimination, while uniquely adding the critical step of real-time physical separation.
Conclusion
This study successfully demonstrates the feasibility of a non-destructive, integrated system for almond authentication and sorting. The integration of shell-based FTIR spectroscopy with an MLP classifier yields superior analytical accuracy. Crucially, the direct integration of this classification into an automated mechatronic sorter bridges the gap between laboratory detection and industrial application. The approach offers a scalable, cost-effective, and non-destructive solution for enhancing food safety in almond processing by enabling the real-time removal of toxic bitter almonds from production lines.
Acknowledgement
The authors gratefully acknowledge the staff of the Chemistry Laboratory of Shahid Bahonar University of Kerman for providing access to FTIR equipment and technical support during the spectral acquisition and analysis process.

Abstract This study investigates the effects of high hydrostatic pressure (HHP) processing applying at 600 MPa for 10 min on pH, total soluble solids, total acidity, total phenolic content, vitamin C, pectin methyl esterase activity, and microbial load of lime juice (Citrus Latifolia, Persian variety) immediately after treatment and during 45 days of storage at 20°C in comparison with conventional thermal pasteurisation at 72°C for 20 s. HHP treatment decreased pH and °Brix of lime juice, while thermal treatment had no effect on the mentioned parameters. The influence of HHP on pH and °Brix was not practically considerable. Total acidity was changed insignificantly by both treatments. Storage time caused a significant reduction in the pH and °Brix values of all samples. Total acidity of thermal and HHP of the treated samples remained stable until the 14^th and 29^th days, respectively. But then, there was a significant increase until the end of storage. The best retention of total phenolic (82.51%) and ascorbic acid (91.61%) content was observed by high-pressure processing during storage. Pectin methyl esterase was significantly inactivated by thermal and HHP processing, immediately after treatments. The residual activities were 5.69% and 3.59% at the end of the storage period, respectively. Initial contamination of control lime juice with acidophilic bacteria and total moulds and yeasts was 5.7 ± 0.51 and 4.79 ± 0.28 log CFU mL^-1, respectively. Immediately after treatment, high-pressure processing decreased the microbial load beyond detectable levels. The population of microorganisms remained stable until the 14^th day (for acidophilic) and during the whole time of the storage period (for total moulds and yeasts) under HHP processing.

Abstract Cinnamon is a highly valued aromatic spice widely used in the food and pharmaceutical industries. Due to its extensive applications, cinnamon is susceptible to food fraud, necessitating effective authentication methods. This study uses FTIR spectroscopy and spectral pattern analysis to establish a reliable method for detecting fraud in cinnamon powder by comparing authentic and adulterated samples. The ultimate goal is to provide a practical methodology for identifying adulteration in the spice industry. In this work, Fourier-transform infrared spectroscopy (FTIR) was employed alongside pattern recognition techniques for the authentication of cinnamon powder. To detect and classify fraudulent samples (adulterants include soybean powder, hazelnut shell powder, and dry bread powder, in different concentrations of 5%, 10% and 15% (w/w)) nondestructively, Principal Component Analysis (PCA) was utilised as an unsupervised method, while Partial Least Squares Discriminant Analysis (PLS-DA) and Soft Independent Modelling of Class Analogy (SIMCA) were applied as supervised pattern recognition approaches. The results indicated that while the spectral data of the samples were effectively classified using the PCA technique, some overlap between specific groups was noted. The overall classification accuracy achieved by the SIMCA and PLS-DA classifiers was 83% and 90%, respectively. Based on PCA modelling, SIMCA enabled the classification of samples into two distinct groups (pure vs. adulterated), and the classification accuracy of this two-class model reached 100%. These findings confirm the effectiveness of combining FTIR with pattern recognition techniques for robust and nondestructive authentication of adulterated cinnamon powder, contributing to quality control in the spice industry.

Abstract This research aimed to enhance the design and functionality of an integrated enset processing machine by focusing on key components such as the shaft, cylinder drum, breastplate, and drum blade. Existing enset processing machines suffer from inefficiencies due to component wear, mechanical breakdowns, and suboptimal design, leading to operational challenges. To address these issues, targeted design modifications were planned for the machine’s components. The materials for these components were selected according to ASTM standards. The modified components were rigorously analyzed using the Finite Element Method in the Workbench module of ANSYS 2023 R1 software at Adama Science and Technology University, Adama, Ethiopia. The study reported maximum stresses of 120 MPa, 250 MPa, 400 MPa, and 260 MPa, and minimum stresses of 30 MPa, 70 MPa, 120 MPa, and 80 MPa for the shaft, cylinder drum, blade, and breastplate, respectively. Maximum deformations were found to be 0.15 mm, 0.3 mm, 0.55 mm, and 0.35 mm for these components, with a maximum safety factor of 15 for all. These results indicate that the modifications provide safe working conditions. The design ensures that the drum, drum blade, and breastplate possess sufficient rigidity to withstand operational forces, with minimal deformation (2.39×10⁻⁶ mm for the drum blade), remaining within a safety factor limit of 1.25. Additionally, the machine demonstrated excellent energy dissipation and vibrational response, indicating structural robustness.

Abstract In botanical terms, the classification of plants reveals a multitude of species derived from different sources. The first step for quality control of herbal medicines is to identify their different species and genotypes. The present study investigated the classification of ten different mint genotypes using Gas Chromatography-mass Spectrometry (GC-MS) and an electronic nose (e-nose) system utilizing Metal Oxide Semiconductor (MOS) sensors. Leaf samples were harvested from various mint genotypes, and subsequently, the system sensors' responses to each of these samples were recorded. The classification of plants was performed using biplot diagrams based on GC and GC-MS data, with clustering facilitated by the Ward method. The responses of all e-nose sensors were further analysed through various approaches, including Principal Components Analysis (PCA), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and Artificial Neural Network (ANN). The results from the qualitative analysis of essential oils via GC-MS demonstrate that more than 99% of the identified compounds belong to four chemical groups: hydrocarbon and oxygenated monoterpenes, as well as hydrocarbon and oxygenated sesquiterpenes. Also, based on biplot analysis, different mint populations could be generally divided into 8 groups. The results of principal component analysis showed that the first two main components can cover a total of 97% of the data variance. The classification accuracy achieved through e-nose data for LDA, QDA, and ANN was 98.9%, 99.9%, and 96%, respectively. Proper classification of mint genotypes by e-nose system could be used as a sensitive, reliable, and low-cost alternative to traditional methods.

Abstract Drying is a vital preservation method in the food industry, reducing moisture content while maintaining product quality and extending shelf life. This process involves complex heat and mass transfer mechanisms, necessitating accurate predictive models. This study compares various modeling approaches, including regression models, semi-empirical, and artificial intelligence (AI)-based methods, to simulate the drying process of potato slices. Experimental drying trials were run at 40°C, 50°C, and 60°C, both with and without phase change materials (PCM) and infrared radiation (IR). AI models (ANN, SVM, and RF) were trained and validated using experimental data. Their performance was evaluated against conventional and semi-empirical models using R², RMSE, MAE, and MBE. Results indicate that ANN achieved the highest predictive accuracy (R²= 0.998, RMSE= 0.0656 g water g^-1 dry matter), outperforming other models. SVM also demonstrated strong predictive capability, while RF performed slightly lower. Among semi-empirical models, the Midilli model provided the best fit but was less accurate than AI-based models. These findings highlight the superiority of AI-driven approaches, particularly ANN, in optimizing drying processes for the food industry.

Abstract The present study investigated the use of the cyclic voltammetric electrochemical method and the electronic tongue (e-tongue) method for detecting adulteration in lime juice. Since the measurement of citric acid content in lime juice is an accepted indicator of lime juice adulteration in laboratories, at first, attempts were made to determine its concentration using a potentiostat device and the cyclic voltammetry method, which involved various electrodes including glassy carbon, graphite, gold, and carbon nanotube and gold nanoparticle-modified glassy carbon electrodes. Different conditions were considered by testing citric acid at multiple concentrations in buffers with different pH levels. The results showed that the electrochemical behavior of citric acid was weak, so conventional electrochemical methods could not be used to check its behavior. In the second part, a portable electronic tongue system (e-tongue) was evaluated. Eight samples of adulteration levels (from 5% up to 95%) were created in lemon juice (0, 5, 10, 20, 40, 70, 95, and 100% impurity). Unsupervised models including Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA), and supervised models including Multilayer Perceptron (MLP) neural networks and Support Vector Machine (SVM) were used. Based on the results, the PCA fingerprint showed good discrimination between different levels of adulteration, and HCA further confirmed this. The results of the analysis of supervised methods showed that the MLP model outperformed the SVM model in predicting fraud levels with a success rate of 99.33% and high correlation coefficients (R² = 0.9973, RMSE = 0.09). These results show that the proposed system can separate different levels of adulteration in lemon juice and can be used as a taste quality control system.

Abstract Seaweeds are well known for their technological, nutritional, and health values, and their preservation by drying is essential to stabilize and maintain the quality of the product during storage. The research presents the obtaining of mathematical models in polynomial functions using the response surface methodology. The influence of the independent drying variables was studied: load density (1.70-15 kg m^-2), incandescent lamp wattage (0-500 W), temperature (30-70 °C) and air velocity (0.5-2.5 m s^-1) on the response variables: global acceptance (--), total phenolic content (mg GAC/100 g_db) and drying time (min). The study also showed that the conditions of temperature and incandescent lamp wattage during drying significantly affected the total phenolic content. The optimum conditions were: load density 9.13 kg m^-2, incandescent lamp wattage 374.5 W, temperature and drying air velocity of 63.3 °C and 1.88 m s^-1, respectively. The results show that increasing the power of the incandescent lamps leads to a shorter drying time of approximately 40-45%. For these optimized conditions, mathematical models were applied to simulate the drying curve and kinetics of the material studied. Using the Quasi-Newton Simplex method, the models of Midilli et al. and Page in second place, achieved a better performance in the quality of fit of the curves to the experimental data. Under these conditions, the value of the effective diffusivity of water was of the order of 2.03×10^-11 m² s^-1, a value very similar to those published for agro-industrial products. The information obtained can be of great help in the use of the obtained parameters and applied techniques for the development of equipment and process control in the drying of red seaweed.

Abstract In this study, X-ray computed tomography (CT) as a non-destructive method for internal quality evaluation of apple fruit was investigated. For this purpose, three local apple fruit cultivars including: Red Delicious, Golden Delicious, and Golab were used. The CT number of the images, which indicates the amount of X-ray absorption, was extracted using K-PACS software. Quality parameters such as the amount of soluble solids content, titratable acidity, flavor index, and pH of studied cultivars were measured. The relationship between quality parameters and CT number obtained from tomography images of fruits in the form of linear regression models was investigated. According to the results, the correlation between CT number and quality parameters in all models was more than 0.900. For different cultivars, CT number had a positive correlation with the amount of titratable acidity, flavor index, pH, and soluble solids. The evaluation of quality parameters for the Red Delicious cultivar had the highest accuracy, achieving coefficients of determination (R²) of 0.952 for flavor index, 0.964 for soluble solids, 0.941 for acidity, and 0.969 for pH. For all cultivars, the highest correlation was observed between the pH and the number of CT (with coefficients of explanation 0.969, 0.972, and 0.966 for Red Delicious, Golden Delicious, and Golab cultivars, respectively). This indicates that X-ray CT can reliably assess internal quality attributes without damaging the fruits. The established linear regression models provide a validated and reproducible method for non-destructive quality evaluation of apple fruits.

Abstract In this study, ultrasonic radiation (US), packaging film, controlled atmosphere packaging, and controlled storage temperature were utilized for tomaoto packaging. Prior to packaging, the samples underwent ultrasonic treatment and were subsequently packed using polyethylene film (PE) and polyethylene film equipped with 2% nanoclay particles (Nano film) under both normal atmospheric conditions and modified atmosphere (MA) (5% O₂ + 3% CO₂). These tomatoes were stored at 25°C and 4°C for 28 days. Weekly assessments of storage properties included an examination of physical aspects such as moisture and color indices, chemical factors like pH, total soluble solids (TSS), lycopene, and total phenolic content, as well as mechanical properties encompassing penetration force and elastic modulus. The results indicated that the storage had a detrimental effect on the trends of property changes. Utilizing a modified atmosphere, appropriate storage temperatures, and applying ultrasonic treatment and Nanofilm were found to regulate specific properties effectively. Statistical analysis revealed a significant impact of the applied treatments on most properties at both the 1% and 5% significance levels. On the other hand, an Artificial Neural Network (ANN) was employed for data prediction, and the results showed that the best structure in predicting the physical, mechanical, and chemical properties was 5-10-11. O₂ and CO₂ were predicted with high accuracy with R² = 0.93 and R² = 0.86, respectively, which has shown the accurate performance of the ANN in predicting the data with the selected structure.

Abstract Introduction
Various methods have been used to dry grapes. The main purpose is to increase shelf life, produce high-quality dried grapes, and also produce raisins to reduce post-harvest losses. Different methods can be used to dry grapes. Sun drying is the method traditionally used to dry commercial raisins. However, this process is very slow and depends mainly on weather conditions, which can cause microbial and insect contamination in dried fruits and hence, reduce their quality. Recently, advanced vacuum drying techniques have been used in order to increase the amount of water removal and ensure better quality of raisins. Vacuum drying (VD) is a process in which wet materials are dried under subatmospheric pressure. Vacuum pressure reduction increases the mass of water between the fruit and its surroundings, thereby reducing the heat needed for rapid drying. Therefore, vacuum drying is a promising technology for drying grapes and has been used in current works. Preserving the quality of raisins and maintaining their essential nutritional indicators is a vital aspect of effective management strategies aimed at enhancing product quality. This improvement boosts demand for raisins in both domestic and international markets. Finding new methods of drying while maintaining the desired quality and preventing contamination are other factors that determine the quality of raisins. On the other hand, it is very important to determine the right time to harvest grapes according to the climatic conditions of each region and its effect on the quality of raisins. For this purpose, in this study, some quantitative, qualitative, and nutritional indicators related to raisins were compared between the sun-dried and vacuum drying methods for the white Quchan cultivar, evaluating the potential of each method in this field.
Materials and Methods
This research was conducted in 2021-2023 in one of the vineyards of the Quchan region in Iran. Quchan city is located within the geographical coordinates of 36 to 37 degrees north latitude and 58 degrees 10 minutes to 58 degrees 58 minutes east longitude. The relative humidity of this city is 40% in summer, 65% in spring, and 60% in autumn. Based on 10-year statistics, the average annual rainfall in this area is 274 mm. This research project was done in the form of a split plot, based on a randomized complete block design with four replications. Experimental factors include three harvesting times (August 27^th, September 6^th, and 16^th) and four modes of drying (sun drying, and vacuum drying at 60, 70, and 80 °C). Fruits were harvested at three different stages, with time intervals of 10 days from August 27 to September 16, based on the sugar content in the pods and the ratio of total soluble solids (TSS) to titratable acidity (TA). At each harvest time, the grapes were dried in four different ways. In the first method, the grapes were dried traditionally in the open environment and in front of the sunlight. In the second method, the grapes were dried using a vacuum system at three different temperatures of 60, 70, and 80°C.
Results and Discussion
In general, the interaction of harvesting time and drying method had a significant effect on most of the studied traits. The grape drying methods employed in this research significantly influenced the levels of phenolic compounds, flavonoids, and the antioxidant capacity of the resulting white seedless raisins. The amount of these compounds in sun-dried raisins was lower than the raisins produced using the vacuum drying method. The interaction effect of harvesting time and drying method on the production raisin yield was significant at the 1% probability level. The highest yield was related to the third harvest under the vacuum dryer at 60°C (305.52 g kg^-1), and the lowest yield was related to the first and second harvests with an average of 270.29 g kg^-1 in the sun-dried method. In general, the highest amount of TSS was related to the treatments of the third harvest, which was observed in vacuum drying at 60°C. After that, no significant difference was observed in temperatures of 70 and 80°C. The amount of antioxidant, phenol, flavonoid, and total sugar content in the vacuum drying treatment was higher than the sun drying method. The total soluble sugars in sun-dried raisins were, on average, 22.68% lower compared to those dried using the vacuum method. In terms of total microbial count, the highest microbial load (126.51 Cfu g^-1) was related to sun-dried raisins. The treatments under vacuum drying at all three temperatures of 60, 70, and 80°C showed the lowest amount of microbial load (almost zero). The low level of microbial contamination in raisins produced by the vacuum method in this research can be attributed to the short drying time and also the lack of contact with the surrounding environment.
Conclusion
Vacuum drying is a new technology that has been developed in recent years, employing a lower pressure in the chamber to increase the moisture transfer during the drying process. In this method, due to the lack of oxygen in the environment, some undesirable biochemical reactions such as browning, oxidation, and degradation reactions are reduced. In addition, the periodic pressure change can create fissured and porous structures in the skin of the sample, thereby increasing the mass transfer through the pores. Overall, the results of this research showed that the raisins produced in the third harvest and using vacuum drying at 60°C had better quality than other treatments in terms of biochemical and sensory characteristics, including flavor, texture, and color. It can also be concluded that the vacuum drying method is a good alternative to traditional drying methods.

Abstract In this study, the air drying of cumin seeds was boosted by cold plasma pre-treatment (CPt) followed by high-power ultrasound waves (USp). To examine the impact of included effects, different CP exposure times (0, 15, and 30 s), sonication powers (0, 60, 120, and 180 W), and drying air temperatures (30, 35, and 40 ºC) were selected as input variables. A series of well-designed experiments were conducted to evaluate drying time, effective moisture diffusivity, and energy consumption, as well as color change and rupture force of dried seeds for each drying program. Numerical investigations can effectively bypass the challenges associated with experimental analysis. Therefore, the wavelet-based neural network (WNN), the multilayer perceptron neural network (MLPNN), and the radial-basis function neural network (RBFNN), as three well-known artificial neural networks models, were used to map the inputs and output data and the results were compared with the Multiple Quadratic Regression (MQR) analysis. According to the results, the WNN model with an average correlation coefficient of R² > 0.92 for the train data set, and R² > 0.83 for the test data set provided the most beneficial tool for evaluating the drying process of cumin seeds.

Abstract Introduction
Drying shrimp is one of the storage methods that, while increasing the shelf life, leads to the production of a versatile product with various uses, from consumption as snacks to use as one of the main components of foods. Drying is preferred over other preservation methods because it offers numerous advantages, including extended shelf life, enhanced microbial stability, convenient consumption, reduced transportation costs, increased value, and product diversity.
To accurately model these processes and thus obtain information on factors such as shelf life and energy consumption, it is necessary to determine the product’s initial and final temperatures, its geometry and dimensions, and its thermo-physical characteristics. Simulation of different drying processes requires accurate estimation of the effective moisture diffusion coefficient, which is highly dependent on temperature and humidity. Its dependence can be shown by an equation with an Arrhenius structure as an empirical function of humidity and temperature, or by considering the activation energy.
It is necessary to have sufficient knowledge about heat and mass transfer characteristics, such as diffusion or penetration coefficient and the heat transfer coefficient to estimate the final temperature and drying time. This study investigated the drying process of peeled farmed shrimp (Litopenaeus vannamei) using a convective hot air dryer. Various parameters such as shrinkage and the effective moisture diffusion coefficient were examined.
Materials and Methods
A drying device was built to conduct experimental studies on drying shrimp samples. The experiments were conducted on sliced shrimp meat samples at temperatures of 40, 50, and 60 degrees Celsius, with a constant air velocity of 1.5 m/s. The experimental drying models were based on diffusion theory. In these models, it is assumed that the resistance to moisture diffusion occurs from the outer layer of the food. In most cases, Fick's second law was used to describe the phenomenon of moisture penetration.
The study used the standard method of immersion in toluene to measure volume changes in the samples. During the drying process, the volume of the samples was measured at 45-minute intervals, and their volume changes were calculated. To measure the moisture content of the samples, each test started by recording the initial weight of the samples using a digital scale with an accuracy of ±0.001 g. During the drying process, the samples were weighed each time their volume was measured.
Shrinkage during the drying process is commonly modeled by finding a relationship between shrinkage and moisture, using linear and non-linear models. In most cases, effective permeability is defined as a function of humidity and temperature. For this purpose, curve-fitting methods were employed to analyze the data collected from experimental tests. The appropriate function was extracted by incorporating the Arrhenius equation, which is applicable to most food items.
Results and Discussion
Based on the results of statistical indices, the linear model was the best model for depicting the relationship between shrinkage changes versus moisture ratio changes among the various experimental models evaluated for shrinkage and drying kinetics. Similarly, the Weibull distribution demonstrated superior performance in expressing variations in moisture ratio over time. A moisture dependent experimental model was used to express the variations in the apparent density of shrimp, resulting in a computed range of 1017-1117 kg m^-3. Furthermore, an Arrhenius equation was derived to express the effect of moisture content and temperature on the effective diffusion coefficient of shrimp. According to the results, the effective diffusion coefficient of shrimp exhibited variations ranging from 0.08 ×10^-9 m² s^-1 to 7.39×10^-9 m² s^-1. When deriving the effective diffusion coefficient, the impact of the number of terms in Fick's second law on the variation of the moisture ratio was studied. The findings revealed that increasing the number of terms beyond 100 did not significantly affect the model’s outputs.
Conclusion
The linear model had the highest coefficient of determination (R²) among the evaluated shrinkage models, as well as the lowest root mean square error and sum of square error (SSE). This makes it the most optimal model for interpreting shrinkage at the tested temperature levels. The Weibull distribution experimental model proved to be the most suitable for expressing changes in the moisture ratio of shrimp meat slices over time within the evaluated temperature range. The Arrhenius model accurately predicts changes in the effective diffusion coefficient of shrimp slices with respect to temperature and moisture content within the tested temperature range.

Abstract Introduction
Tannins are a type of phenolic compound usually found in plants, with high molecular weights typically ranging from 500 to more than 3000 Da and even up to 20000 Da. The chemical structure of tannins is very diverse and varied. Tannin exists in plant cells in two forms: hydrolyzable and condensed. The tannin content in plants can vary from 0.2% to 25% of the dry weight of the plant. This can vary depending on the plant species, harvest time, plant habitat, and extraction method. Currently, tannin is used in various fields such as leather making, medicine, food, beverages, ink and glue making, paint and tanning industries, plastic resins, water treatment, and surface coatings. The application of tannins depends on the tannin concentration. Extraction of tannin from agricultural products is done by different methods. Maceration, decoction, pressurized water extraction, Soxhlet extraction, supercritical fluid extraction, ultrasound, and microwave are among these methods. Ultrasound extraction is an effective method for extracting chemical compounds, which is performed in a shorter period of time compared to other methods, and can be used for heat-sensitive compounds such as tannins.
Materials and Methods
Potato peels were randomly selected, dried, and ground. Extraction was performed with an ultrasonic device, and after centrifugation, the total amount of phenolic compounds was measured using the Folin-Ciocalteu method. Afterward, utilizing the method used by Makkar et al. (2001), the amount of total tannins was calculated, and the condensed tannin content was calculated using the method introduced by Porter et al. (1986).
Results and Discussion
The average amount of total tannin extracted by using water as solvent was 142.8 ± 50.9 mg per 100 grams of dry powder in a period of 15 minutes, which was the highest amount of extraction. After water, methanol was the second solvent, yielding an extracted amount of 0.63 ± 55.9 mg per 100 grams of dry powder in 15 minutes. The lowest amount of extraction was related to the ethanol solvent in which was measured over a period of 10 minutes.
Due to its higher polarity, water is the best-performing solvent for extraction. Comparing the results of this experiment with previous research, water is suitable for extracting tannins from potato peels. Additionally, water is a non-toxic and environmentally friendly solvent, and making it an ideal choice for extraction. Increasing the extraction time from 10 to 15 minutes, significantly affects the total amount of extracted tannin, more tannin being extracted during the longer the extraction period.
The effect of extraction time on the amount of condensed tannin is not significant, and no variable in this study had a significant effect on the amount of extracted condensed tannin. It is expected that the increase in the total amount of tannin with the increase in extraction time is related to the increase in the amount of hydrolyzable tannin extracted from the sample.
Conclusion
In this research, the amount of tannin extracted from potato peel was measured. The ultrasound method was used to prepare potato peel, which is a less expensive and faster alternative to other methods. The effect of different solvents were investigated over various extraction times. The results showed that the total amount of extracted tannin increases with the ultrasound extraction time, specifically from 10 to 15 minutes. However, with the increased extraction time, the amount of condensed tannin does not significantly increase. Among the studied solvents, water accounted for the highest amount of extracted tannin. After water, methanol was the second-best solvent, followed by acetone and ethanol. Water is an effective and environmentally friendly solvent for tannin extraction. Potato peels are rich in tannin and contain significantly less condensed tannin than hydrolyzable tannin.

Abstract Introduction
The growing consumer demand for high-quality products has led to the development of new technologies for assessing the quality of agricultural products. Iran is the 9th largest orange producer in the world. Every year, large quantities of agricultural products lose their optimal quality due to mechanical and physical damage during various operations such as harvesting, packaging, transportation, sorting, processing, and storage. This study is performed to identify the natural frequencies and vibration modes of the Thomson orange fruit using finite element modal analysis by ANSYS software. In addition, physical properties including mass, volume, density, and principal dimensions were measured, and mechanical properties were determined using Instron Texture Profile Analysis. The dynamic behavior of the orange fruit was simulated using the pendulum impact test. Afterward, the obtained impact was applied to the orange fruit by force gauge and three-axis accelerometer sensors in both polar and equatorial directions. The three-dimensional geometric model of the orange fruit was drawn in the ANSYS software. After meshing and applying the boundary conditions, the first 20 modes and corresponding natural frequencies were obtained. Since the objective of this study was to identify the natural frequencies of the orange fruit, it was considered to have free movement and rotation in space. The results showed that the natural frequencies of orange fruit are in the range of 0 to 248.41 Hz. Knowledge of the texture characteristics and dynamic behavior of horticultural products is essential for the design and development of agricultural machinery. Furthermore, the design and development of agricultural machinery are directly related to the biological properties of agricultural products.
Materials and Methods
The Thomson orange variety was used in the present study. The oranges used for the experiments were harvested from the Citrus and Subtropical Fruits Research Institute in Ramsar, Iran, located at coordinates 50° 40′ E and 36° 52′ N. The oranges were subsequently divided into two groups: large (average diameter 82 mm) and small (average diameter 66 mm). Conducting the finite element analysis requires knowledge of the physical and mechanical properties of the flesh and skin of the orange fruit. The physical and mechanical properties of the tested samples include geometric dimensions, modulus of elasticity, Poisson’s ratio, and density. In the present study, the dynamic behavior of the orange fruit under dynamic loads was investigated by performing an impact test using a pendulum. The orange fruit was hung from the ceiling using a thin thread to perform experimental tests and extract the modal parameters. The orange samples were subjected to impact at three angles: 7° (below the yield point), 10° (at the dynamic yield point), and 20° (above the dynamic yield point).
Results and Discussion
The comparison of the experimental (laboratory) natural frequencies and simulation validates the simulation results. The experimental natural frequencies of the first, second, and third modes in the large-group oranges are 125.4, 146.9, and 180.4 Hz, respectively. Additionally, the simulation (modal) frequencies are 133.80, 146.16, and 196.66 Hz for the first three modes, respectively. The lowest and the highest differences were observed in the second (0.5%) and third (9.01%) modes, respectively. In the small-group oranges, the first, second, and third modes have experimental natural frequencies of 152.2, 188.8, and 242.2 Hz, respectively, and simulation frequencies are 167.79, 187.50, and 248.30 Hz. The second and first modes exhibited the smallest and largest disparities between experimental and simulated natural frequencies, respectively, at 0.68% and 10.24%.
Conclusion
While there are certain limitations, it is undeniable that Computer Aided Engineering (CAE) applications are advantageous for predicting the natural frequencies and vibration modes of spherical fruits such as oranges. Utilizing the obtained frequencies, especially the resonance frequency and the vibrational mode shape, enables us to avoid the resonance frequency in the actual transportation of oranges. This is possible through the implementation of suitable packaging and transportation methods, thereby mitigating the deterioration of fruit quality and ensuring an accurate prediction of its shelf life.

Abstract Introduction
Drying is one of the oldest methods of food preservation. To increase the efficiency of heat and mass transfer while maintaining product quality, the study of the drying process is crucial scientifically and meticulously. It is possible to conduct experimental tests, trial and error, in the drying process. However, this approach consumes time and cost, with a significant amount of energy resources. By harnessing available software and leveraging technological advancement to develop a general model for drying food under varying initial conditions, the drying process can be significantly optimized.
Materials and Methods
This study was conducted with the aim of simulating heat and mass transfer during Refractance window drying for aloe vera gel. Comsol Multiphysics version 5.6 is a three-dimensional model used to solve heat and mass transfer equations. For this purpose, the differential equations of heat and mass transfer were solved simultaneously and interdependently. The above model considered various initial conditions: water temperature of 60, 70, 80, and 90℃, and aloe vera gel thickness of 5 and 10 mm. The initial humidity and temperature of the aloe vera is uniform. The initial temperature is 4℃ and the initial humidity of the fresh aloe vera sample is 110 g_water/g_{dry matter}. Heat is supplied only by hot water from the bottom surface of the product.
Results and Discussion
The drying time was needed to reduce the moisture content of aloe vera gel from 110 to 0.1 g_water/g_{dry matter} during Refractance window drying. Aloe vera gel with a thickness of 5 mm dried in 120, 100, 70, and 50 minutes at water temperatures of 60, 70, 80, and 90℃, respectively. For a 10 mm thick layer of aloe vera gel, the drying time was 240, 190, 150, and 120 minutes, for water temperatures of 60 to 90℃, respectively. These results demonstrate the importance of both the water temperature and thickness on the drying time. Furthermore, the drying rate of aloe vera gel increased as the water temperature increased from 60 to 90℃, the drying rates were 0.915, 1.099, 1.57, and 2.198 g_water/min for 5 mm thickness and 0.457, 0.578, 0.732, and 0.915 g_water/min for 10 mm thick layer of aloe vera gel, respectively.
Conclusion
Based on the simulation results, the optimal model is with a water temperature of 90℃ and an aloe vera gel thickness of 5 mm. Overall, the modeling results are consistent with the results of experimental data.

Abstract Introduction
Due to the disadvantages of using chemical materials as pretreatment before grape drying, the application of non-chemical methods that not only take the environmental issues into account but also increase the drying rate and improve the quality of the produced raisins is vitally important. The high-humidity hot air impingement blanching (HHAIB) is one of the non-chemical methods that can be used as a suitable alternative for chemical pretreatment in grape drying. In this research, the design, construction, and evaluation of a high-humidity hot air impingement blanching system are discussed in terms of the drying kinetics of white seedless grapes. The results are compared against the control and chemical pretreatment.
Materials and Methods
High-humidity hot air impingement blanching (HHAIB) system
The HHAIB system is composed of the steam generator, steam transfer pipes, side channel pump, closing and opening valves, air recycling channel, electric air heater, hot-humid air transfer channel, pretreatment chamber, hot-humid air distribution chamber, nozzles, temperature and humidity sensors and controllers. The performance of the system depends on the humid air temperature, the output fluid velocity from the nozzle, the distance of the nozzles from the product surface, as well as the diameter and arrangement of the nozzles. In order to achieve optimal design of the nozzle array, the relationships existed for the heat transfer coefficient, air mass flow, and blowing power were considered.
Application of the HHAIB pretreatment and evaluation of its effect on the grape drying process
Experiments were conducted to investigate the effect of temperature and duration of HHAIB pretreatment on the kinetics of grape drying. A two-factor completely randomized factorial design with three replications was used to analyze the data.
According to the studies, the air at temperatures of 90, 100, and 110°C, a velocity of 10 m s^-1, and relative humidity in the range of 40-45% was applied to the product. Pretreatment durations of 30, 60, 90, 120, and 150 s were also considered. Experiments were conducted with three replicates and control treatment and acid pretreatment were used to compare the drying process. Due to the high quality of shade-dried raisins, this method was used to study the process.
The effect of the pretreatment duration on the drying kinetics of white seedless grapes was assessed by observing variations in moisture ratio and drying rate over time, as well as determining the effective diffusivity of water.
For the color evaluation of the produced raisins, chroma (C), hue angle H°, and total color difference (ΔE) parameters were calculated after measuring L^*, a^*, and b^* values.
Results and Discussion
The comparison of the drying process among the control, chemical, and HHAIB showed the positive efficacy of HHAIB on the drying rate of grapes. Compared to fresh grapes, the increase in drying rate under the influence of HHAIB varied from 8% for a duration of 30 s at 90°C to 68% for a duration of 150 s at 110°C. The values of the diffusion coefficient of grapes for the HHAIB pretreatment at temperatures of 90, 100, and 110°C and durations of 30, 60, 90, 120, and 150 s, as well as for the control and chemical pretreatments were determined. The values of the coefficient changed from 2.28×10^-10 m² s^-1 for 30 s of applying pretreatment at 90°C to 3.53×10^-10 m² s^-1 for 150 s of applying the pretreatment at 110°C. The highest value of this coefficient (7.46×10^-10 m² s^-1) was associated with the chemical pretreatment. The value of the diffusion coefficient increased with increasing temperature and duration of the HHAIB pretreatment. In general, this increase in the drying rate and the diffusion coefficient can be attributed to the effect of the HHAIB pretreatment on the texture and destruction of the cell wall, as well as the microcracks created on the skin of the grapes. Moreover, the findings reveal that, in comparison with the hot air temperature, the duration of the HHAIB pretreatment was more effective in enhancing the drying rate. Additionally, based on the color analysis, a temperature of 110°C and a duration range of 90-150 s were achieved as suitable conditions for applying pretreatment.
Conclusion
The HHAIB pretreatment, which combines the benefits of hot air blanching with jet technology, affects the texture and skin of grapes, accelerates the drying process, and increases the quality of the produced raisins. However, the correct application of this pretreatment depends on the proper design of the system and appropriate conditions, including duration, temperature, and relative humidity. The results of drying kinetics showed that the drying rate increased with an increase in the temperature and duration of the pretreatment. The findings indicate that the HHAIB pretreatment could improve the color indices of the raisins, resulting in an increase in the drying rate and acceptable quality of the final product. This provides a basis for the use of HHAIB on larger and industrial scales.

Abstract Introduction
The use of corn oil in diets is due to its positive effects on cardiovascular and immune systems. Corn oil is composed of 99% triacylglycerol, with 59% unsaturated fatty acids and 13% saturated fatty acids. Of the unsaturated fatty acids, 24% contain a double bond. Because of this composition, corn oil can be a good alternative to other oils high in saturated fatty acids, as it reduces blood cholesterol levels.
This study employed an electrical nasal system to detect the amount of palm oil present in corn oil. The properties extracted from the signals obtained by the device were processed using principal component analysis, artificial neural networks, infusion, and response surface methods. The results were then compared to find the best method for detecting palm oil levels in corn oil.
Materials and Methods
The required palm oil was obtained from the Nazgol Oil Agro-industrial Plant, while the corn oil was obtained from natural lubrication centers. To prepare samples with different percentages of palm oil, 75 grams of palm oil and corn oil with the specified percentages were mixed and stored in special containers.
In the electrical nose system, ten metal oxide semiconductor sensors (MOS) were used to collect output data. Pre-processing operations were performed on this data using RSM, ANFIS, PCA, and ANN methods to estimate the percentage of palm oil in corn oil. The Unscrambler V.9 software, Design Expert 8.07.1, and MATLAB R2013a were used to analyze the results.
Results and Discussion
Based on the Score plot, PC-1 and PC-2 explain 53% and 25%, respectively, describing the variance between samples for a total of 78 data points. The analysis indicates that sensors 7 and 8 have minimal impact on the detection process and can be removed from the sensor array. When reducing the cost of the olfactory system's sensor array, sensor 6 plays a more significant role than other sensors in detecting corn oil with palm composition.
According to the loading diagram of palm percentage in corn oil, the MQ6 sensor had the least effect in classifying different percentages of palm in corn oil and pattern identification. Out of all functional parameters (accuracy, sensitivity, and specificity), the RSM method is deemed more appropriate for determining the percentage of palm in corn oil.
Regarding the separation of corn oil and palm oil by ANFIS, RSM, and ANN, the results in Table 3-1 indicate that the RSM method is better suited for classifying corn and palm oil.
Conclusion
In this study, we used an electronic multi-sensor system based on metal oxide sensors to analyze various aromatic compounds in different oil and palm samples and to detect the presence of palm. The system provided comparable information for classifying different samples of palm oils. Using PCA, ANN, ANFIS, and RSM methods, we evaluated the system's performance in differentiating and classifying various oil and palm samples.
The results obtained from the loading diagrams for the detection of palm in corn oil indicated that the MQ6 sensor had the least impact on the detection process. Therefore, this sensor can be removed from the sensor array.
Additionally, our analysis showed that using the RSM method is more effective in detecting different percentages of palm in corn oil. Overall, our study demonstrates the efficacy of the electronic multi-sensor system in analyzing different oil and palm samples and detecting the presence of palm.

Abstract Accurate investigation of kinetics and development of high-precision seed drying models will help better studying the drying process by identifying effective parameters. Present study investigates the application of cold plasma (CP), as a pretreatment process, in air drying of canola seeds. This may bring about some complication into the drying kinetics investigation. Canola seeds with an initial moisture content of 27.5±1% (dry basis) were exposed to CP for 0, 15, 30, and 60 s prior to fluidization by air at temperatures of 40, 50 and 60 °C in a pilot scale fluidized bed heated by a solar panel.  The results showed a decreasing trend in drying time from 40 to 60 ^oC. The shortest drying time corresponds to samples dried at 60 ^oC with no CP pretreatment. The longest period however occurred for samples dried at 40 ^oC with 60 s of CP pretreatment. The greatest effect of CP on reducing the drying time was observed at temperatures of 40 and 50 °C at the CP exposure time of 15 and 60 s, respectively.  A reasonably accurate study of drying kinetics was accomplished using the superposition method. Accordingly, using experimental data, curves correspond to different drying conditions were plotted and in two steps these were shifted to a reference curve to acquire a final drying curve. The curve then was fitted to a second-order equation, and was validated using the experimental data. The correlation coefficients, mean square error and mean absolute error were 0.99, 0.03, and 0.023, respectively.