Process Parameters For Production Research Articles

Leveraging machine learning to streamline the development of liposomal drug delivery systems

Drug delivery systems efficiently and safely administer therapeutic agents to specific body sites. Liposomes, spherical vesicles made of phospholipid bilayers, have become a powerful tool in this field, especially with the rise of microfluidic manufacturing during the COVID-19 pandemic. Despite its efficiency, microfluidic liposomal production poses challenges, often requiring laborious, optimization on a case-by-case basis. This is due to a lack of comprehensive understanding and robust methodologies, compounded by limited data on microfluidic production with varying lipids. Artificial intelligence offers promise in predicting lipid behaviour during microfluidic production, with the still unexploited potential of streamlining development. Herein we employ machine learning to predict critical quality attributes and process parameters for microfluidic-based liposome production. Validated models predict liposome formation, size, and production parameters, significantly advancing our understanding of lipid behaviour. Extensive model analysis enhanced interpretability and investigated underlying mechanisms, supporting the transition to microfluidic production. Unlocking the potential of machine learning in drug development can accelerate pharmaceutical innovation, making drug delivery systems more adaptable and accessible.

Theoretical analysis of the forming process of closed die forging with flash and optimization design method of flash gutter

The subject of this article is the forming process of closed die forging with flashes and the optimal design of the flash gutter dimensions. The goal is to conduct an in-depth theoretical analysis of the forming process of closed die forging with flash and to research the optimal design of the flash gutter dimension, aiming to improve the scientificity and efficiency of the forging process, guide the optimization of process parameters in actual production, extend the service life of dies, reduce material waste, and enhance product quality and production efficiency. The tasks were to establish an accurate mathematical model for closed die forging with flash to analyze metal plastic deformation and stress distribution, to conduct in-depth research on key factors such as stress distribution, position of the neutral plane, and flash gutter design during the forming process, and to validate the mathematical model through finite element simulation using DEFORM-2D software. The methods used include theoretical analysis, mathematical modeling, and finite element simulation validation. The theoretical analysis provides a foundation for understanding the forming process and stress distribution, whereas the mathematical model allows for quantitative analysis. Validation of the finite element simulation provides a means to test and refine the theoretical analysis and mathematical model. The following results were obtained: the existing mathematical model underestimates the height of the main deformation zone, which results in an unreasonable flash gutter design. After verification and correction, the error in the optimized mathematical model did not exceed 10 %, and the flash amount was significantly reduced. Additionally, a stress analysis of difficult-to-fill cavity positions revealed that the entrance radius of the cavity significantly affects the final filling. Conclusions. The scientific novelty of the results obtained is as follows: A mathematical model of closed die forging with flash was established to analyze the metal plastic deformation and stress distribution, providing theoretical support for die design optimization, forging quality improvement, cost reduction, and productivity improvement. Using the Deform-2D finite element simulation, the optimal design criterion for the flash was refined, resulting in a substantial reduction in the flash amount and material savings.

Rupture energy coupling of alkali washing tower cylinder

The energy generated by the container during the explosion induces destructive effects on the surrounding objects and itself, such as deformation, compression, rupture, etc. The explosive energy is one of the richest pieces of evidence in the analysis of the accident cause, and therefore a comprehensive analysis of the accident explosive energy is essential for determining accident properties. Considering the rupture accident of an alkali washing tower in an aromatic hydrocarbon plant as an example, the destructive energy and the rupture internal pressure are estimated to ascertain the explosive properties of the accident by analyzing the field damage characteristics. This study aims to conduct a comprehensive analysis and calculation of the energy associated with an alkali washing tower rupture accident. The objective is to determine the cause of the accident, which can be attributed to the combined effect of wall thickness reduction and fluctuations of production process parameters. The findings of this analysis will serve as a scientific basis for the examination of similar accidents and the formulation of safety protection measures.

Optimizing process parameters and materials for the conversion of plastic waste into hydrogen

Abstract This study has investigated hydrogen production from waste plastics using pyrolysis, steam methane reforming, and water-gas-shift reactions modelled via Aspen Plus. After evaluating multiple alternatives, polypropylene (PP) was selected as the feedstock. The research has been focused on how reformer temperature, steam-to-fuel ratio (S/F), reformer pressure, and pyrolysis temperature impact syngas composition, heating values, syngas (H2/CO) ratios, and yields of hydrogen (H2), methane (CH4), and carbon dioxide (CO2). Key findings have indicated that raising reformer temperatures to around 1000°C maximizes hydrogen production in syngas, reaching peak levels of 2360 Nm3/Ton and 2525 Nm3/Ton for reformer temperature and steam-to-fuel ratio (S/F) ratios, respectively, via processes like steam methane reforming and the water-gas-shift reaction. Moreover, other parameters like steam-to-fuel (S/F) ratio and reformer pressure have produced the highest amount of hydrogen at 0.25 and 1 atm, respectively. Optimizing reformer temperature and steam-to-fuel ratio (S/F) have been selected as key in hydrogen production, with peak lower heating values (LHV) of 1.15 MJ/kg for temperature and 1.035 MJ/kg for S/F ratios, highlighting the importance of balancing these parameters for efficiency. Additionally, syngas' hydrogen (H2) composition increased with pyrolysis temperature, peaking at 8.5% at 700°C. Finally, this research has provided valuable insights into optimizing process parameters for sustainable hydrogen production. Moreover, the simulation process has provided cost-effective adjustments and informed decision-making for sustainable and scalable technologies, benefiting researchers, investors, engineers, and policymakers involved in innovative hydrogen generation.

Effects of Process Parameters on Microstructure Evolution of Pure Aluminum Target under Clock 135° Collaborative Hot Rolling: A Simulation Study

Based on the results of current research and experiments, clock 135° hot rolling has been widely considered to be the preferred method in target rolling. However, it has been found that the process parameters are of vital importance during the rolling. Therefore, this work focuses mainly on the investigation of the effects of the roll speed ratio and offset distance on the microstructure evolution of a pure aluminum (Al) target under clock 135° collaborative hot rolling. Taking the ultra-thick pure Al metal circular billet as a research object, firstly, the evolutionary behavior of the effective strain (ES) and grain refinement for a rolled piece under the clock counterclockwise 135° synchronous and asynchronous (the speed ratios between rollers are set to be 1:1.05, 1:1.1, 1:1.2, respectively) rolling modes has been comparatively studied based on numerical simulation by DEFORM-3D software. It has been shown that a large ES value of 5.835 mm/mm is obtained in the lower surface layer by counterclockwise 135° asynchronous rolling with a roll–speed ratio of 1:1.2. Meanwhile, the average grain size below 80 µm accounts for ~61.8% of the total grains. These results demonstrate that the clock counterclockwise 135° asynchronous rolling method with a roll speed ratio of 1:1.2 should be an ideal strategy in obtaining finer grains. Unfortunately, nevertheless, the maximum degree of the bad plate shape is generated after forging by clock asynchronous rolling with a speed ratio of 1:1.2. As a consequence, clock counterclockwise 135° snake rolling with a 30 mm offset distance was proposed on the basis of a rolling speed ratio of 1:1.2, which perfectly corrected the warping plate shape caused by clock asynchronous rolling. What is more important, the minimum damage value of 1.60 was achieved accordingly. Meanwhile, the ES value increased in the core of the plate for the clock counterclockwise 135° snake rolling with all of the four offset distances compared to clock synchronous rolling. This study should be significantly conducive to guidance on setting process parameters in the industrial production of hot rolling metal or alloy targets.

Enhancing tensile properties of polymer‐based triply periodic minimal surface metamaterial structures: Investigating the impact of post‐curing time and layer thickness via response surface methodology

AbstractThis research aims to explore the influence of post‐curing time and layer thickness on the tensile characteristics of various triply periodic minimal surface (TPMS) structures produced by mask stereolithography (MSLA). The study determined the best post‐curing duration, layer thickness, and TPMS lattice type to improve ultimate tensile strength (UTS) and absorbed energy. To experimentally evaluate the tensile characteristics, a dog bone‐shaped specimen was utilized. Three distinct TPMS structures, Gyroid (G), Neovius (N), and Diamond (D), were present in the test region. After investigating many process factors with response surface methodology (RSM), optimization methods are applied to find their best printing procedure. The work shows the novel use of RSM to optimize post‐curing and printing parameters on TPMS structure mechanical properties during manufacturing. According to the optimization results, the biggest factor affecting UTS is layer thickness, while the most significant factor increasing energy is curing time. The optimal operating parameters for MSLA printing based on the optimization results are a layer thickness of 0.05 mm, a post‐curing period of 40 min, and a lattice type of N. The optimum responses corresponding to the optimum parameters were determined as 7.16 MPa for UTS and 18.16 J for energy.Highlights Optimized the production process parameters of TPMS geometries. Compared TPMS structures for mechanical performance. Identified optimal input parameters to improve UTS and energy absorption. Conducted comprehensive experimental evaluations to validate the optimization.

Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II

Optimization of production process parameters for polishing machine tools in crankshaft abrasive belt based on BP neural network and NSGA-II

HEAR set: A ligHtwEight acoustic paRameters set to assess mental health from voice analysis

BackgroundVoice analysis has significant potential in aiding healthcare professionals with detecting, diagnosing, and personalising treatment. It represents an objective and non-intrusive tool for supporting the detection and monitoring of specific pathologies. By calculating various acoustic features, voice analysis extracts valuable information to assess voice quality. The choice of these parameters is crucial for an accurate assessment. MethodIn this paper, we propose a lightweight acoustic parameter set, named HEAR, able to evaluate voice quality to assess mental health. In detail, this consists of jitter, spectral centroid, Mel-frequency cepstral coefficients, and their derivates. The choice of parameters for the proposed set was influenced by the explainable significance of each acoustic parameter in the voice production process. ResultsThe reliability of the proposed acoustic set to detect the early symptoms of mental disorders was evaluated in an experimental phase. Voices of subjects suffering from different mental pathologies, selected from available databases, were analysed. The performance obtained from the HEAR features was compared with that obtained by analysing features selected from toolkits widely used in the literature, as with those obtained using learned procedures. The best performance in terms of MAE and RMSE was achieved for the detection of depression (5.32 and 6.24 respectively). For the detection of psychogenic dysphonia and anxiety, the highest accuracy rates were about 75 % and 97 %, respectively. ConclusionsThe comparative evaluation was carried out to assess the performance of the proposed approach, demonstrating a reliable capability to highlight affective physiological alterations of voice quality due to the considered mental disorders.

Formation of build-up in a cement kiln preheater: qualitative and quantitative mineral characterisation

Extracting more information about the reactions and parameters governing the formation of build-up in the preheater systems of clinker burning lines could be an efficient way of better attenuating build-up formation. In order to control the appearance of build-up, diagnosis steps of mineralogical analysis application for process optimisation (MAA-PO) are suggested. Mineralogical characterisation was carried out on three samples taken from different layers of the same build-up. Qualitative and quantitative X-ray diffraction, chemical analysis and Fourier transform infrared spectroscopy revealed that ternesite is the key mineralogical phase responsible for the growth and consolidation of build-up. Its appearance is governed by solid–solid reactions promoted by the available sulfate liquid phases, mainly calciolangbeinite, as well as C12A7 and C4AF. The hot-powder calcination degree, amount of sulfur trioxide, lime saturation factor, silica ratio and alumina ratio are the operational data that were correlated to the mineralogical analysis of build-up. Based on this correlation, it is possible to predict the reaction kinetics involved in each layer formation and to estimate the development time of each. The results showed that MAA-OP is a promising novel approach to optimise the process parameters in clinker production.

Production of Amylase by Solid State Fermentation Using Agricultural Waste

This study presents a comprehensive investigation into the production of amylase, a crucial enzyme with wide-ranging industrial applications, using locally sourced substrates from Kachchh, Gujarat. The research employed the Bacillus licheniformis strain and substrates such as coconut, rice husk, wheat bran, paddy straw, and maize straw. The study found paddy straw to be the most promising substrate for amylase production. The research also systematically optimized various process parameters for amylase production in Solid-State Fermentation (SSF) using the One Variable at a Time (OVAT) method. These parameters included incubation period, temperature, inoculum level, additional carbon sources, starch concentrations, additional nitrogen sources, initial pH, different mineral salt ions, initial moisture level, and surfactants. The results showed that the optimal conditions for maximum amylase yield were an incubation period of 48 hours, an incubation temperature of 35°C, an inoculum level of 10%, starch as the additional carbon source, a starch concentration of 2.5%, yeast extract as the additional nitrogen source, an initial pH of 7, NaCl as the mineral salt, an initial moisture level of 75%, and Tween 80 as the surfactant. This research provides a reliable and sustainable approach to enzyme production, offering valuable insights for the optimization of the solid-state fermentation process for maximum amylase production.

Experimental investigation of the multi-layer wetting behavior of electrode-separator-composites in lithium-ion batteries

The success of lithium-ion batteries and their application in electromobility is highly dependent on their scalable and cost-efficient manufacturing. Herein, electrolyte filling and wetting are time-consuming and quality-critical process steps, which have been, on the material level, primarily researched on an intra-material level for single-cell components while lacking overarching investigations for inter-material effects during wetting. This study demonstrates for the first time an experimental methodology designed to systematically quantify the interaction effects between different electrode-separator-composites during wetting by varying product components and process parameters. The investigation reveals a quantifiable interaction within a two- or three-layer material combination of anode, separator, and cathode, which can be discerned into distinct anode and cathode interaction effects, maintaining consistency across the product and parameter variations. Through the analysis of activation energy using the Arrhenius equation, the study observe a combinatorial relationship for modeling multi-layer wetting. Multi-layer wetting rates increase by 215 % compared to single material rates. Higher temperatures boost penetration rates due to lower viscosity, but reduce the interaction effect. These insights contribute to an application-orientated understanding of electrode-separator-composite wetting behavior and open up more possibilities for an optimized manufacturing process through expanding methodical exploration of strategies for counterbalancing poor wettability.

A Systematic Investigation of Process Parameters for Small-Volume Aqueous Suspension Production by the Use of Focused Ultrasonication

Aqueous suspensions containing crystalline drug in the sub-micron range is a favorable platform for long-acting injectables where particle size can be used to obtain a desired plasma-concentration profile. Stabilizers are added to the suspensions and screened extensively to define the optimal formulation composition. In the initial formulation screening the amount of drug compound can be limited, necessitating milling methods for small-volume screening predictable for scale-up. Hence, adaptive focused ultrasound was investigated as a potential milling method for rapid small-volume suspensions by identifying the critical process parameters during preparation. Suspensions containing drug compounds with different mechanical properties and thereby grindability, i.e., cinnarizine, haloperidol, and indomethacin with brittle, elastic, and plastic properties, respectively, were investigated to gain an understanding of the manufacturing with adaptive focused acoustics as well as comparison to already established milling techniques. Using a DoE-design, peak incident power was identified as the most crucial process parameter impacting the milling process for all three compounds. It was possible to decrease the sizes of drug particles to micron range after one minute of focused ultrasound exposure which was superior compared to other milling techniques (e.g., non-focused ultrasound exposure). The addition of milling beads decreased the drug particle sizes even further, thus to a lower degree than other already established milling techniques such as milling by dual centrifugation. This study thereby demonstrated that adaptive focused ultrasonication was a promising method for rapid homogenization and particle size reduction to micron range for different compounds varying in grindability without altering the crystalline structure.Graphical

Applications of simulation modeling in mining project risk management: criteria, algorithm, evaluation

Project risk management in the mining industry is necessary to identify, analyze and reduce uncertainty. The engineering features of mining enterprises, by their nature, require improved risk management tools. This article proves the relevance of creating a simulation model of the production process to reduce uncertainty when making investment decisions. The purpose of the study is to develop an algorithm for deciding on the economic feasibility of creating a simulation experiment. At the same time, the features and patterns of the cases for which the simulation experiment was carried out were studied. Criteria for feasibility assessment of the model introduction based on a qualitative parameters became the central idea for algorithm. The relevance of the formulated algorithm was verified by creating a simulation model of a potassium salt deposit with subsequent optimization of the production process parameters. According to the results of the experiment, the damage from the occurrence of a risk situations was estimated as a decrease in conveyor productivity by 32.6%. The proposed methods made it possible to minimize this risk of stops in the conveyor network and assess the lack of income due to the risk occurrences.

A local space transfer learning-based parallel Bayesian optimization with its application

The optimization of process parameters in polyolefin production can bring significant economic benefits to the factory. However, due to small data sets, high costs associated with parameter verification cycles, and difficulty in establishing an optimization model, the optimization process is often restricted. To address this issue, we propose using a transfer learning Bayesian optimization strategy to improve the efficiency of parameter optimization while minimizing resource consumption. Specifically, we leverage Gaussian process (GP) regression models to establish an integrated model that incorporates both source and target grade production task data. We then measure the similarity weights of each model by comparing their predicted trends, and utilize these weights to accelerate the solution of optimal process parameters for producing target polyolefin grades. In order to enhance the accuracy of our approach, we acknowledge that measuring similarity in a global search space may not effectively capture local similarity characteristics. Therefore, we propose a novel method for transfer learning optimization that operates within a local space (LSTL-PBO). This method employs partial data acquired through random sampling from the target task data and utilizes Bayesian optimization techniques for model establishment. By focusing on a local search space, we aim to better discern and leverage the inherent similarities between source tasks and the target task. Additionally, we incorporate a parallel concept into our method to address multiple local search spaces simultaneously. By doing so, we can explore different regions of the parameter space in parallel, thereby increasing the chances of finding optimal process parameters. This localized approach allows us to improve the precision and effectiveness of our optimization process. The performance of our method is validated through experiments on benchmark problems, and we discuss the sensitivity of its hyperparameters. The results show that our proposed method can significantly improve the efficiency of process parameter optimization, reduce the dependence on source tasks, and enhance the method's robustness. This has great potential for optimizing processes in industrial environments.

Evolution of transition metal dichalcogenide film properties during chemical vapor deposition: from monolayer islands to nanowalls

Unique properties possessed by transition metal dichalcogenides (TMDs) attract much attention in terms of investigation of their formation and dependence of their characteristics on the production process parameters. Here, we investigate the formation of TMD films during chemical vapor deposition (CVD) in a mixture of thermally activated gaseous H2S and vaporized transition metals. Our observations of changes in morphology, Raman spectra, and photoluminescence (PL) properties in combination with in situ measurements of the electrical conductivity of the deposits formed at various precursor concentrations and CVD durations are evidence of existence of particular stages in the TMD material formation. Gradual transformation of PL spectra from trion to exciton type is detected for different stages of the material formation. The obtained results and proposed methods provide tailoring of TMD film characteristics necessary for particular applications like photodetectors, photocatalysts, and gas sensors.

Multi-physics coupled numerical simulation study to optimize process parameters for electromagnetic stirring of semi-solid A356 aluminum alloy under the influence of skin effect

The electromagnetic stirring (EMS) method stands as a widely used technique in creating semi-solid aluminum alloy slurry due to its convenience and precision in parameter control compared to other methods. In this study, a comprehensive model that combines electromagnetics, flow, heat transfer and solidification was formed. By analyzing the influence of input current and frequency on the electromagnetic force, the behavior of various process parameters in velocity field and temperature field under influence of the skin effect is investigated. The aim is to find the optimal process parameters for production of slurry using the low-frequency EMS. The results show that within the investigated range, by adjusting the input current and frequency to 150 A and 15 Hz, higher fluid velocities and appropriate distribution of vortex zones can be observed, while achieving a uniform temperature field, the highest cooling rate and the minimum radial temperature difference. In addition, the sample prepared under the optimal process parameters shows that the melt edge is least affected by the skin effect, which leads to a higher stirring strength and a smaller overall grain size, which is consistent with the theoretical skin effect research found within the simulation section.

Experimental and Theoretical Investigation of Thermal Parameters Influencing the Freezing Process of Ice Cream

Freezing time stands out as the most critical parameter in ice cream production, significantly influencing the final product’s quality and production efficiency. Therefore, it is essential to accurately estimate the freezing time based on ice cream recipes. This research paper focuses on determining the thermal properties of ice cream samples by leveraging insights from previous studies. Moreover, a new specific heat correlation is proposed to account for the latent heat effect of water during the freezing process. The results demonstrated that the new specific heat correlation aligns well with established formulas from previous research as well as experimental results with maximum 2.5% deviation. To validate the accuracy of the proposed numerical model, experimental studies were compared with numerical results for the first time in the context of ice cream freezing inside a mold. Additionally, a parametric analysis was conducted using numerical modelling to discern the relative importance of various production process parameters. Notably, the glycol–water mixture temperature emerged as the most influential parameter affecting freezing time, while the amount of air inside the ice cream was identified as the least significant factor.

Nano-CaO as a heterogeneous catalyst for biodiesel synthesis by transesterification of Jatropha oil

BackgroundThis research employs response surface methodology, specifically Central Composite Design (CCD), to optimize the process parameters for the effective production of biodiesel. Jatropha oil was utilized as the raw material to minimize expenses. A nanocatalyst was utilized as a solid catalyst, developed from CaCO3 via waste snail shells, offering advantages such as recyclability and improved catalytic activity during a transesterification process. The developed nanocatalyst was analyzed using various techniques, including dynamic light scattering (DLS), scanning electron microscopy (SEM), Brunauer–Emmett–Teller (BET) analysis, and Fourier-transform infrared (FTIR). The BET analysis revealed a surface area of 5.1m2/g and the Barrett-Joyner-Halenda (BJH) analysis provided insights into the pore volume and diameter of the synthesized nano-CaO, showing values of 0.002556 cc/g and 1.1 nm, respectively, indicating the presence of both microspores and active sites on the external surface of the nano-CaO catalyst. Biodiesel conversion was controlled by adjusting factors like the methanol to oil ratio, catalyst weight, reaction time, reaction temperature, and agitation speed. A quadratic model was established to explore the correlation between the independent variables and the biodiesel conversion rate. The results showed a maximum biodiesel conversion rate of 96.73% under the optimal conditions: methanol to oil ratio (6:1), catalyst weight (1.4 wt%), reaction time (60 min), reaction temperature (55 °C), and agitation speed (250 rpm). These parameters were determined through 32 experimental trials. The RSM technique yielded impressive results with a determined coefficient of determination (R2) of 0.9834, adjusted R2 of 0.8503, predicted R2 of 0.8309, and a coefficient of variance (CV) 0.75%. Based on the analysis of variance (ANOVA) findings, the model exhibits a high level of significance (p<0.0001), which is less than 0.05 and F- Value 29.71.The study aims to enhance the yield and efficiency of the transesterification process, thereby increasing the overall production of fatty acid methyl ester from Jatropha oil. This innovative approach efficiently generates biodiesel from renewable resources, in a manner that is both environmentally friendly and maximizes the effectiveness of the process parameters. The evaluation conform that the quality of the biodiesel met the standards set by ASTM D 6751 and EN 14214.

Recycling of Waste PET Plastic on Asphalt, by Using Application of Response Surface Methodology: Effect of Production Process Parameters

Plastic, polymers of variable compositions have become object of common use and difficult to digest by micro-organisms, especially single-use plastic waste, such as polyethylene terephthalate (PET) water canisters. These polymers are source of environmental pollution. Therefore, it is important to manage theme in the good way to protect environment. In this study, different conditions of waste plastic PET (Polyethylene Terephthalate) recycling on asphalt were optimized. Response Surface Methodology (RSM) using the Doehlert experimental design has been employed in the optimization. The independent variables considered were bitumen (5-8%), PET (0-12%), Mixing temperature (150-160°C) and Mixing time (20-30min). Four-second order polynomial models were generated. The responses obtained by the models were well described as: specific density (Y.&amp;lt;SUB&amp;gt;SD&amp;lt;/SUB&amp;gt;), penetrability (Y.&amp;lt;SUB&amp;gt;P&amp;lt;/SUB&amp;gt;), softening point (Y.&amp;lt;SUB&amp;gt;SP&amp;lt;/SUB&amp;gt;), and flash point (Y.&amp;lt;SUB&amp;gt;FP&amp;lt;/SUB&amp;gt;) of the process with satisfactory fits in terms of absolute average deviation, bias factor and accuracy factor. The optimum responses were 1,04 as specificity gravity (Y.&amp;lt;SUB&amp;gt;SG&amp;lt;/SUB&amp;gt;), 60*(1/10mm) as penetrability at 25°C, 100g and 5sec (Y.&amp;lt;SUB&amp;gt;P&amp;lt;/SUB&amp;gt;), 50°C as softening point (Y.&amp;lt;SUB&amp;gt;SP&amp;lt;/SUB&amp;gt;), and 242°C as flash point (Y.&amp;lt;SUB&amp;gt;FP&amp;lt;/SUB&amp;gt;). The statistical relation between the four independent variables and the process responses were well described.

Influence of baking time and formulation of part-baked wheat sourdough bread on the physical characteristics, sensory quality, glycaemic index and appetite sensations.

Raw materials and process parameters in bread production can modulate the glycemic index, which on itself has been linked with provision of better hunger satisfaction and maintaining better satiation. The objective of this research was to investigate if using unrefined wheat flour or the addition of intact cereals in formulation or alternating the baking time would have an effect on physical characteristics, sensory quality, glycaemic index and appetite sensations in wheat sourdough bread. In the study, three types of commercial part-baked frozen sourdough bread, baked to the final baking for two different times (long and short baking time) were used. A randomized controlled crossover trial was performed with 10 healthy adults who consumed sufficient quantity of bread to ingest 50 g available carbohydrates. Participants self-reported appetite sensations (desire to eat, hunger, fullness, satisfaction, appetite) on a 10 cm visual analog scale (VAS) scale in a course of 180 min. In addition, bread products were subjected to overall acceptability and different sensory attributes were examined on JAR "just about right" scale. Different bread formulations (refined flour, unrefined wheat flour, cereal flour or intact cereals) and different length of baking time significantly influenced (p < 0.005) physical, textural and sensory features of products. The alternation of aforementioned parameters decreased the glycemic index, but not significantly (p > 0.005). No correlation was found between lower GI, satiety and satiation. Liking score and incremental area under the curve (iAUC) of satiety and satiation were calculated as highest in sourdough bread with added cereals.