Processing Methods Research Articles

Starting in the Middle: A Method Trial

In the MIM121 Architectural Design I "Starting in the Middle" studio group, which we conducted in the 2023-2024 fall semester at Gebze Technical University, we aimed to question how architecture establishes and transforms the relations between human and non-human worlds under the theme "Umwelt: Building a World". For this, we have adopted Deleuze and Guattari's rhizomatic idea of "being in the middle/starting in the middle" as the studio's processing method and Baruch Spinoza's idea of "being the source of virtuous effects" as our way of acting. We have determined the German biologist Jakob Johann von Uexküll's concept of Umwelt as the theme, considering that the effort to understand the worlds of other living beings we share our environment with has not only a biological or architectural but also an ethical/political side. Uexküll's concept of environment-world (Umwelt), which examines the behavior of animals in the environments they live in, positions itself against the objective, homogeneous and human-centered view of space by arguing that each living thing has its own spatio-temporal understanding and meaning-making process. This concept lays the groundwork for evaluating space as a subjective, multi-agent, heterogeneous sum of pluralities. Within the scope of this article, we document and analyze the syllabus of the MIM121 Architectural Design I studio group from the specific perspective and method of the studio. In doing so, we rely on the results of the 2015 "Architecture Schools First Year Studio Meeting II" workshop. Ten years after this workshop, which revealed the problems and goals of first-year architecture studios on a national scale, we are analyzing our own studio in the context of the workshop's outputs, and we are considering making a contribution to these outputs. Thus, we hope that by discussing the limits, opportunities and threats of the proposed methodology, it will provide a basis for similar studios.

Influence of Processing Techniques on the Proximate Composition, Anti-Nutritional Factors, and Amino Acid Profile of Red Kidney Beans (Phaseolus vulgarisL).

The effects of traditional processing techniques-cooking, autoclaving, and roasting-on the physicochemical properties, anti-nutritional factors (ANF), and amino acid composition of red kidneybeans (RKB) were investigated. Significant alterations in proximate composition were observed: cooking generally increased protein levels, while roasting decreased fiber content. Autoclaving produced diverse effects. Evaluation of functional properties revealed changes in water activity, density, and color. Cooking increased water activity and lightness, whereas roasting and autoclaving led to darker colors and reduced water activity. Cooking effectively enhanced protein content and reduced ANF, while texture profile analysis (TPA) showed variations in hardness, adhesiveness, and resilience. Autoclaving resulted in softer beans, and cooking influenced adhesiveness and springiness. ANF analysis showed reductions post-processing, especially with cooking and autoclaving. Amino acid composition varied across processing methods, with notable effects from cooking and roasting. Microstructural changes observed via Scanning Electron Microscopy (SEM) suggested improved nutrient bioavailability. Fourier Transform Infrared (FTIR) spectroscopy indicated alterations in the chemical composition of proteins, lipids, and carbohydrates, attributed to the different processing methods. This comprehensive study highlights the complex interactions between cooking techniques and the physicochemical properties of legumes, providing valuable insights for optimizing their nutritional value. Traditional method of cooking RKB enhances the nutritional value.

Visual identification of material attributes in wakame: exploring thickness, strength, and chlorophyll content

This study investigates the potential of using visual features to predict key material attributes in wakame, focusing on thickness, strength, and chlorophyll content (SPAD values). We compared frozen and salted wakame samples to understand how different processing methods affect these predictions. Using a combination of RGB, L*a*b*, and HSV color features, we developed and evaluated various regression models, including simple linear regression, quadratic regression, and random forests. Our results indicate that color features can effectively predict SPAD values, particularly in frozen samples, with the best models achieving an R2 of 0.900. However, predicting thickness and strength proved more challenging, with models showing limited predictive power. Interestingly, strength predictions were more accurate for salted samples, suggesting that salt curing may enhance the relationship between visual features and physical strength. We found that processing methods significantly impact the effectiveness of prediction models. Freezing appears to better preserve the original optical properties of wakame, while salt curing introduces greater complexity, necessitating more sophisticated modeling approaches. This study contributes to the development of rapid, non-destructive methods for assessing wakame quality, which is crucial for the growing wakame industry. Our findings highlight the potential of visual analysis in wakame quality assessment while also emphasizing the need for tailored approaches based on processing methods. Future work should focus on refining these models and exploring additional factors that influence wakame properties.

Force Feedback Method for Statically Indeterminate Steel Structure Construction Based on Staged Temperature Measurement

Structural design usually adopts uniform temperature action. However, during the actual construction of the structure, the temperature field acting on the structure is inhomogeneous. Therefore, the simulation of the construction of statically indeterminate steel structures considering only the uniform temperature field cannot truly reflect the temperature action after structural molding and the evolution of the stress performance of the temporary stress system of structural construction. This paper proposes a force feedback method for the construction of statically indeterminate steel structures based on staged temperature measurements. In this method, the construction sequence and the measured temperature are taken into account, and the structural response under the action of temperature is obtained by using the first processing method in the matrix displacement method and the deformation coordination formula to obtain the deformation and stress values of the structure. The application results show that the structural forces calculated by the method proposed in this paper are closer to the measured data in the field, and the error is reduced by 10~40%. This paper provides a reference for the calculation of construction forces in statically indeterminate steel structures considering the effect of temperature inhomogeneity and provides a basis for construction safety assessment and schedule management.

Effects of salt concentration, temperature, and blanching time on insect protein extraction yield: optimisation by response surface methodology

Abstract This study aimed to optimise the protein extraction yield from black soldier fly larvae by employing a blanching process to enhance protein solubility and mitigate oxidation. Through response surface methodology, we determined the optimal blanching conditions, with the temperature set at 85.152 °C, a duration of 12.411 s, and a sodium chloride concentration of 0.46 M. These parameters were selected to maximise protein solubility while minimising oxidation. The results of this study reveal significant improvements in protein functionality after blanching, particularly with respect to increased emulsification stability. Notably, no significant effect on foam formation was observed. These findings underscore the efficacy of blanching as a pre-processing step to enhance the quality of proteins extracted from black soldier fly larvae. Moreover, these optimised conditions have promising applications in various food and feed formulations, contributing to sustainable protein sourcing and processing methods. By addressing key challenges related to food security and environmental sustainability, this study provides valuable insights into the utilisation of insect proteins as an alternative protein source in the face of global challenges, such as climate change and dwindling natural resources.

A Systematic Review: Assessment of the Metabolomic Profile and Anti-Nutritional Factors of Cannabis sativa as a Feed Additive for Ruminants

Background: Cannabis sativa is a high-value crop that can be cultivated for ruminant’s feed and medicinal purposes. The demand for Cannabis and Cannabis products has increased since the beginning of 21st century. Objectives: The increase in the production cost of high-protein feeds such as lucerne has led to an urgent need to investigate alternative high-protein sources. Methods: Cannabis has been identified as an alternative to lucerne due to its high protein content. Results: However, the cultivation and uses of Cannabis and its by-products in South Africa is limited due to the strict legislation. The metabolites and nutritional value of Cannabis are influenced by growing conditions and soil type. Furthermore, the available literature has shown that Cannabis contains anti-nutritional factors that may affect feed intake or bioavailability and digestibility. Conclusion: Therefore, it is crucial to employ a processing method that can reduce anti-nutritional factors to promote the feed intake and growth rate of sheep. Fermentation, as a processing method, can reduce anti-nutritional factors found in Cannabis, which will make it a palatable alternative feed supplement for ruminants such as Dorper sheep. Overall, this review paper aimed to examine the available literature on the use of Cannabis as an alternative high-protein feed supplement for Dorper sheep in South Africa.

Isolation of bioactive beta-glucans from mycelium of <i>Pleurotus ostreatus</i> mushroom

Background: Beta-glucan, a compound found in higher fungi, possesses significant properties important for the human body, including immunomodulatory effects. Developing efficient extraction technology is crucial to ensure a high yield of beta-glucan component while preserving its bioactive properties. Context and purpose of this study: This study aimed to evaluate the extraction efficiency of beta-glucans from Pleurotus ostreatus mycelium powder using microwave-assisted extraction and to compare its performance with traditional extraction methods—specifically water and ethyl alcohol-based extractions. Objective: The aim of this study is to increase the yield of the aqueous extract of beta-glucan from the mycelium of the fungus Pleurotus ostreatus, while ensuring its biological activity is preserved for subsequent use in the fortifying food products. To achieve this, various extraction methods were employed. Results: Traditional methods of beta-glucan extraction, including water and ethyl alcohol-based processes, yielded the largest amount of beta-glucan of 1.45 ± 0.02 g of beta-glucan. The stimulation index, a measure of beta-glucans on the human phagocytosis system, reached 1.28 under optimal conditions — double treatment with ethanol followed by a double extraction with water, each extraction lasting three hours. When extracting beta-glucan using microwave radiation, 2.97 g of beta-glucan was obtained—2.05 times greater than traditional methods. The simulation index also improved by 13%, reaching 1.47% while reducing the extraction time. Conclusions: Based on the conducted research, it has been found that the extraction of beta-glucan from the mycelium of the Pleurotus ostreatus fungus is advisable to carry out using microwave processing, which significantly reduces the extraction time while maintaining the biological activity of beta-glucan. Keywords: Basidiomycetes, Pleurotus ostreatus, beta-glucan, water extraction, ethyl alcohol extraction, microwave radiation, immunomodulatory properties.

Deep learning for polymer scaffold bioimage analysis: Opportunities and challenges

Significant efforts have been made to advance bioprinted scaffold research in cell biology, tissue engineering, and drug screening studies. Ideal scaffolds should demonstrate suitable mechanical properties, excellent biocompatibility, and bioactivities. However, the design and preparation of such scaffolds are challenging. Imaging modalities including magnetic resonance imaging (MRI), micro-computed tomography (Micro-CT), ultrasound imaging (UI), optical coherence tomography (OCT), and confocal laser scanning microscopy (CLSM) are commonly used to visualize the interior architecture of bioprinted scaffolds as well as the surrounding cells and tissues. The obtained bioimages provide the first-hand information of scaffold biological functionalities, while the interpretation of such images can readily give rise to divergent viewpoints and even arguments. This study is to review and explore deep learning (DL) methods in those image analysis through restoration, segmentation, and classification. Firstly, the current DL methods for biological image processing are summarized, such as convolutional neural network, U-Net and generative adversarial network. The corresponding outcomes of these methods can reveal the cell-scaffold and tissue-scaffold interactions and guide scaffold design in each specific task. Last but not least, the challenges and limitations of DL applications in this area are highlighted, such as building DL models using smaller bioimage datasets, interpreting DL models, vision-language model guided bioimage analysis, and developing intelligent analysis platforms. These studies would mark a paradigm shift in polymer scaffold designs and the associated performance.

Oxidative Stress Monitoring Platform: A Longitudinal In vitro Multinuclear (1H/19F) MR Spectroscopic Study.

Glutathione (GSH) is a master antioxidant that counters oxidative stress. Clinical studies have confirmed significant depletion of GSH in the hippocampus and the substantia nigra as an early diagnostic biomarker for Alzheimer's disease (AD) and Parkinson disease (PD), respectively. External agents like anesthetics (inhaled and intravenous) have a different impact on GSH. There is significant depletion of the serum GSH peroxidase level after surgery with isoflurane anesthesia that is not found in patients administered intravenous propofol. The objective of this study is to evaluate the GSH level associated with isoflurane in vitro phantom model using non-invasive magnetic resonance (MR) spectroscopy and to detect residual isoflurane in a solution. MRS data was generated utilizing a 3T MR scanner (Prisma, Siemens) equipped with a 64-channel 1H head coil and dual tune (19F/1H) head coil. The GSH data acquisition was performed using the MEGA-PRESS pulse sequence using experimental parameters: ON = 4.40 ppm, OFF = 5.00 ppm, TE = 120 ms, TR = 2500 ms, voxel size = 25 × 25 × 25 mm and average = 32. Isoflurane was detected using 19F MRS studies using 19F/1H head coil. GSH data was processed using KALPANA package and 19F data was processed using Siemens package. The GSH peak area (without isoflurane) in a phosphate-buffered solution (PBS) solution (control) showed a slow decline over time due to natural oxidation of GSH to dimeric glutathione (GSSG). On the contrary, the GSH peak area in similar model is reduced significantly (p = 0.016) due to isoflurane induced oxidation of GSH to GSSG compared to control. We also report a concise general method for data generation and processing of 1H MRS data for GSH as well as 19F monitoring platform using 19F MR spectroscopy. This is the first report wherein both 1H and 19F spectroscopy are applied to generate MRS data along with a unique data processing method. This method is highly sensitive and specifically detects GSH without ambiguity as well as isoflurane due to the unique chemical shift patterns of CF3 and CHF2 moieties. This non-invasive MRS approach is developed to monitor GSH-isoflurane interaction leading to oxidative stress and this approach can be extended for other inhaled anesthetics. This methodology using non-invasive 19F MR spectroscopy needs further development for future clinical studies.

Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning

BackgroundAiming at the problem that traditional transfer methods are prone to lose data information in the overall domain-level transfer, and it is difficult to achieve the perfect match between source and target domains, thus reducing the accuracy of the soft sensor model.MethodsThis paper proposes a soft sensor modeling method based on the transfer modeling framework of substructure domain. Firstly, the Gaussian mixture model clustering algorithm is used to extract local information, cluster the source and target domains into multiple substructure domains, and adaptively weight the substructure domains according to the distances between the sub-source domains and sub-target domains. Secondly, the optimal subspace domain adaptation method integrating multiple metrics is used to obtain the optimal projection matrices Ws and Wt that are coupled with each other, and the data of source and target domains are projected to the corresponding subspace to perform spatial alignment, so as to reduce the discrepancy between the sample data of different working conditions. Finally, based on the source and target domain data after substructure domain adaptation, the least squares support vector machine algorithm is used to establish the prediction model.ResultsTaking Pichia pastoris fermentation to produce inulinase as an example, the simulation results verify that the root mean square error of the proposed soft sensor model in predicting Pichia pastoris concentration and inulinase concentration is reduced by 48.7% and 54.9%, respectively.ConclusionThe proposed soft sensor modeling method can accurately predict Pichia pastoris concentration and inulinase concentration online under different working conditions, and has higher prediction accuracy than the traditional soft sensor modeling method.

Hands-on training about data clustering with orange data mining toolbox.

Data clustering is a core data science approach widely used and referenced in the scientific literature. Its algorithms are often intuitive and can lead to exciting, insightful results that are easy to interpret. For these reasons, data clustering techniques could be the first method encountered in data science training. This paper proposes a hands-on approach to data clustering training suitable for introductory courses. The education approach features problem-based training that starts with the data and gradually introduces various data processing and analysis methods, illustrating them through visual representations of data and models. The proposed training is suitable for a general audience, does not require a background in statistics, mathematics, or computer science, and aims to engage the audience through practical examples, an exploratory approach to data analysis with visual analysis, experimentation, and a gentle learning curve. The manuscript details the pedagogical units of the training, motivates them through the sequence of methods introduced, and proposes data sets and data analysis workflows to be explored in the class.

Promoting sustainable performance of smallholder oil palm farmers: an analysis of key determinants and strategic priorities

PurposeThis study investigates the factors affecting the performance of smallholder oil palm farmers in Indonesia, with an emphasis on sustainable productivity.Design/methodology/approachThe study involved interviews with regulators, practitioners and experienced farmers in Riau, West Kalimantan, Central Kalimantan and the Bangka Belitung Islands, Indonesia. A confirmatory and explanatory approach was used to explore the relationships among farmer competency, social capital, institutional support, sustainable productivity and overall performance. Data from 757 farmers were analyzed using partial least squares structural equation modeling (PLS-SEM), while the analytical network process (ANP) method identified strategic priorities.FindingsThe results indicate that the sustainability of oil palm farming was low. Social capital, institutional support and sustainable productivity are the key performance factors. Sustainable productivity mediates these relationships. Farmers’ competence indirectly affects performance through sustainable productivity, social capital and institutions. Institutional support needs to be improved.Research limitations/implicationsThis study suggests expanding sustainability indicators by following the latest standards of RSPO principles and criteria, simplifying language for better farmer understanding and assessing sustainability before and after policy implementation.Practical implicationsThe proposed policy framework emphasizes social capital, institutional support and sustainable productivity to improve sustainability and effectiveness.Social implicationsThis study highlights the critical role of social capital, institutional support and sustainable productivity in enhancing Indonesian palm oil farmers’ sustainability and performance.Originality/valueThis unique integrated approach combining PLS-SEM and ANP methodologies provides a comprehensive understanding of the factors affecting smallholder performance and data-driven strategic priorities for policy interventions.

Reviving Brasília Through Analyzing Historical Aerial Photographs

This study presents an optimized method for processing and retrieving digital data from 1950s aerial photographs of the Plano Piloto area in Brasília. In analogous contexts, internal orientation parameters are often unavailable, and external orientation parameters must be obtained through non-standard approaches, as the identification and geolocation of control points in the field becomes impractical in substantially altered landscapes. Nevertheless, advancements in computer vision techniques, integrated into modern photogrammetry software such as Agisoft Metashape®, provide a viable solution for mapping historical analog aerial photographs. The software successfully processed 156 aerial photographs, using 17 control points, yielding a historical orthoimage and a digital surface model with medium-scale accuracy. The generated model enabled precise identification of key features, including vegetation cover, land use, and terrain characteristics, providing valuable insights for the analysis of land occupation dynamics in the study area. The proposed methodology exhibits substantial potential for replication in similar cases, establishing a robust framework for the processing of historical aerial imagery.

Proteinoid-polyaniline neuromorphic composites for audio recognition

Abstract We present an innovative neuromorphic system using a proteinoid-polyaniline (PANI) composite for recognition of audio inputs of the English alphabet. Neuromorphic devices, which draw inspiration from the neural networks of the brain, have emerged as very promising potential solutions for efficient signal processing. The proteinoid-PANI composite was synthesized through a template-free method, resulting in a unique nanostructure consisting of both nanorods and nanospheres. Principal component analysis (PCA), spectrogram analysis, and temporal spiking response analysis were among the signal processing methods used to examine the composite's audio response to English alphabet stimuli. The system showed a moderate positive correlation between input and output signals, unique time-frequency response patterns, and convoluted spiking behaviour. In addition, the output amplitude showed less variation compared to the input, while maintaining the same temporal characteristics.
Microscopic analysis provided detailed information about the morphology of the composite. The nanorods displayed an optimal aspect ratio and had diameters of around 
100~nm, while the nanospheres varied in size, ranging from 200 to 500~nm in diameter. The nanostructure, morphological characteristics, and signal processing properties of the proteinoid-PANI composite demonstrate its potential for advanced applications in neuromorphic computing and signal processing, particularly in speech recognition and human-machine interaction.

A Review of the Application of Artificial Intelligence in Climate Change-Induced Flooding—Susceptibility and Management Techniques

A fresh paradigm for classifying current studies on flood management systems is proposed in this review. The literature has examined methods for managing different flood management activities from a variety of fields, such as machine learning, image processing, data analysis, and remote sensing. Prediction, detection, mapping, evacuation, and relief efforts are all part of flood management. This can be improved by adopting state-of-the-art tools and technology. Preventing floods and ensuring a prompt response after floods is crucial to ensuring the lowest number of fatalities as well as minimizing environmental and financial damages. The following noteworthy research questions are addressed by the framework: (1) What are the main methods used in flood control? (2) Which stages of flood management are the majority of research currently in existence focused on? (3) Which systems are being suggested to address issues with flood control? (4) In the literature, what are the research gaps regarding the use of technology for flood management? To classify the many technologies that have been studied, a framework for classification has been provided for flood management. It was found that there were few hybrid models for flood control that combined machine learning and image processing. Furthermore, it was discovered that there was little use of machine learning-based techniques in the aftermath of a disaster. To provide efficient and comprehensive disaster management, future efforts must concentrate on integrating image processing methods, machine learning technologies, and the understanding of disaster management across all phases. The study has proposed the use of Generative Artificial Intelligence.

Evaluation of Thermal Liquid Biopsy Analysis of Saliva and Blood Plasma Specimens as a Novel Diagnostic Modality in Head and Neck Cancer

Background: Over the past decade, saliva-based liquid biopsies have emerged as promising tools for the early diagnosis, prognosis, and monitoring of cancer, particularly in high-risk populations. However, challenges persist because of low concentrations and variable modifications of biomarkers linked to tumor development when compared to normal salivary components. Methods: This study explores the application of differential scanning calorimetry (DSC)-based thermal liquid biopsy (TLB) for analyzing saliva and blood plasma samples from head and neck cancer (HNC) patients. Results: Our research identified an effective saliva processing method via high-speed centrifugation and ultrafiltration, resulting in reliable TLB data. Notably, we recorded unique TLB profiles for saliva from 48 HNC patients and 21 controls, revealing distinct differences in thermal transition features that corresponded to salivary protein denaturation. These results indicated the potential of saliva TLB profiles in differentiating healthy individuals from HNC patients and identifying tumor characteristics. In contrast, TLB profiles for blood plasma samples exhibited smaller differences between HNC patients and had less utility for differentiation within HNC. Conclusions: Our findings support the feasibility of saliva-based TLB for HNC diagnostics, with further refinement in sample collection and the incorporation of additional patient variables anticipated to enhance accuracy, ultimately advancing non-invasive diagnostic strategies for HNC detection and monitoring.

Deriving the Importance of Defects in Multi-Unit Residential Buildings Using the Analytic Hierarchy Process Method

Defects in apartment buildings significantly impact the convenience and safety of residents, and their prevalence has been steadily increasing. This study prioritizes the key defects in apartment buildings to enhance quality management. The primary causes of defects were identified by focusing on frequently occurring issues such as tile installation, flooring, wallpaper, PL windows, and kitchen furniture. A literature review was conducted to analyze the main types of defects, and expert focus group interviews were held to investigate the causes of defects and potential countermeasures. Following this, the analytic hierarchy process was employed to assess the relative importance of these defects and establish their prioritization. The findings indicated that structural issues and material-related factors, particularly flooring and window installation, were the primary causes. Based on these insights, this study proposes a strategic approach to improving quality management in apartment buildings. This research serves as a valuable reference for developers, contractors, and policymakers in defect prevention and management.

Effect of Recycling Chicken Eggshell Waste as a Pore-Forming Mineral Source in Low-Water-Absorption Bi-Layered Red Ceramic Tiles

This work evaluated the effects of incorporating chicken eggshell waste in a low-water-absorption bi-layered red ceramic tile composition, focusing on its porosity. Red ceramic tile formulations were prepared with incorporations of 0, 5, 10, 15, 20 and 25 wt.% of chicken eggshell waste. The bi-layered red floor tile processing method consisted of dry powder granulation, double uniaxial pressing and firing at 1220 °C using a fast-firing cycle. The physical properties and microstructural development of the tile specimens were investigated. It was found that chicken eggshell waste exhibited good chemical compatibility for use in red ceramic tile formulations, enabling its recycling. The novel bi-layered red ceramic tiles presented water absorption between 0.34 and 0.97% and apparent density between 2.09 and 2.14 g/cm3. The results demonstrated that chicken eggshell waste, when incorporated up to 15 wt.%, can be used as an efficient pore-forming carbonate source to manufacture low-water-absorption bi-layered red ceramic tiles (BIa and BIb groups—ISO 13006), which allows use in ventilated façades. It was concluded that the proposed approach is suitable for recycling chicken eggshell waste into red ceramic tile formulations, with relevant repercussions for the circular economy.

Weighted Median Filtering for Terrain and Contour Generalization

Abstract. When working with terrain elevation data, as in image processing, it is often desirable to smooth the data, for purposes such as map generalization or removal of noise. Median filtering is one common technique that can be used for this purpose. It differs from linear filtering techniques like local averaging or Gaussian blurring by its ability to smooth while retaining sharp edges in an image. When applied to elevation data, this means that median filtering can better preserve steep slopes and cliffs while otherwise reducing noise or excessive detail in the terrain. However, median filtering as typically applied can also introduce new artifacts, such as lopping off the tops of peaks and ridges to create flat plateaus that don’t exist in the original landscape. A lesser known technique, a weighted median filter, can reduce or eliminate these artifacts. This method shows promise as a way to generalize digital elevation models, as well as their associated contour lines. It can also be used to smooth hillshaded images, preserving the sharp transition in shading that occurs across ridges. And due to its ability to retain discontinuities in the data, it can be used to locate latent terracing effects hidden in elevation data, which may represent real terrain features or may indicate artifacts of the processing methods used to generate the data.

Impact of Thermal, Ultraviolet and Ultrasonic Treatment on Mechanical, Physicochemical and Sensory Characteristics of Pear Jelly

The purpose of this study is to evaluate the influence of thermal, ultraviolet, and ultrasonic processing by examining the mechanical, physicochemical, and sensory characteristics of pear fruit jelly. Samples were processed using thermal pasteurization (TP), ultraviolet light (UV-C,) and ultrasound (US) at 65°C/50 min for all methods. US increases the most mechanical performance values compared to TP and UV-C treatment which reduces them. TP does not statistically significantly alter most physicochemical characteristics except for sugars, where it exhibits a slight reducing effect. UV-C irradiation increases water activity and moisture content the most compared to other treatments and has the least reducing effect on sugars. The US treatment significantly reduces the glucose, fructose, and sucrose in the highest amount and increases total solids and soluble solids contents. In terms of color characteristics, TP treatment makes the color of the samples darker, UV-C irradiation more saturated, and US preserves the original colour best. The strongest correlations between physicochemical and colour parameters have positive and statistically significant Pearson coefficients. Different processing methods had different effects on the sensory characteristics, with UV-C improving aroma, US taste, and texture, and TP having no significant effect on these.