Model Of Mixture Research Articles

Preliminary Study on Multi-Scale Modeling of Asphalt Materials: Evaluation of Material Behavior through an RVE-Based Approach

This study employs a microstructure-based finite element modeling approach to understand the mechanical behavior of asphalt mixtures across different length scales. Specifically, this work aims to develop a multi-scale modeling approach employing representative volume elements (RVEs) of optimal size; this is a key issue in asphalt modeling for high-fidelity fracture modeling of heterogeneous asphalt mixtures. To determine the optimal RVE size, a convergence analysis of homogenized elastic properties is conducted using two types of RVEs, one made with polydisperse spherical inclusions, and another made with polydisperse truncated cylindrical inclusions, each aligned with the American Association of State Highway and Transportation Official’s maximum density gradation curve for a 12.5 mm Nominal Maximum Aggregate Size (NMAS). The minimum RVE lengths for this NMAS were found to be in the range of 32–34 mm. After the optimal RVE size for each inclusion shape is obtained, computational models of heterogeneous Indirect Tensile Asphalt Cracking Test samples are then generated. These models include the components of viscoelastic mastic, linear elastic aggregates, and cohesive zone modeling to simulate the rate-dependent failure evolution from micro- to macro-cracking. Examination of load-displacement responses at multiple loading rates shows that both heterogeneous models replicate experimentally measured data satisfactorily. Through micro- and macro-level analyses, this study enhances our understanding of the composition-performance relationships in asphalt pavement materials. The procedure proposed in this study allows us to identify the optimal RVE sizes that preserve computational efficiency without significantly compromising their ability to capture the asphalt material behavior under specific operational conditions.

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast-charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of DiffMix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

Modelling of wall-bounded cavitating flow and spray mixing in multi-component environments using the PC-SAFT equation of state

This work introduces a numerical multiphase model for multi-component mixtures, utilizing tabulated data for physical and transport properties across a spectrum of conditions from near-vacuum pressures to supercritical states. The property data are derived using Perturbed Chain Statistical Associating Fluid Theory (PC-SAFT), vapor-liquid equilibrium (VLE) calculations, entropy scaling methodologies, and Group Contribution (GC) methods. These techniques accurately reflect the thermodynamic behaviors of real fluids, avoiding the empirical estimation of Equation of State (EoS) input parameters. Implemented in OpenFOAM, the fluid dynamics solver is designed to address the three-dimensional Navier-Stokes equations for multi-component mixtures. The methodology integrates operator splitting to manage hyperbolic and parabolic steps distinctively. Hyperbolic terms are solved using the HLLC (Harten-Lax-van Leer-Contact) solver with temporal integration performed via a third-order Strong-Stability-Preserving Runge–Kutta (SSP-RK3) method. Viscous stress tensor contributions in the momentum equation are handled through an implicit velocity correction equation, while parabolic terms in the energy equation are explicitly solved. The simulation efficiency is further enhanced by adaptive Local Time Stepping and the Immersed Boundary (IB) method, which addresses interactions between the fluid and solid boundaries. Turbulence is resolved using the Wall Adaptive Large Eddy (WALE) model. Applied to high-pressure diesel fuel spray injections into non-reacting (nitrogen) gas environments, the model has been validated against Engine Combustion Network (ECN) data for the Spray-C configuration, featuring a fully cavitating multi-hole orifice. Results demonstrate that the model achieves accurate predictions across a broad range of tested conditions without the need for tuning or calibration parameters.

Kinetic model of multi-component polyatomic gas mixture considering internal energy excitation

In this paper, a kinetic Boltzmann model equation with internal degrees of freedom is established for thermodynamic non-equilibrium multi-component polyatomic gas mixture by using continuous energy levels to deal with rotational energy and vibrational energy. The normalization and conservativeness of the kinetic model are analyzed and proved. Then, numerical algorithm and wall boundary conditions for solving the model equation are given. Then, normal shock-wave structure flow problem and pressure/temperature gradient driven micro-channel flow problem for the two-component gas mixture are used to verify the reliability of the model. The simulation results are in good agreement with those obtained by other methods, which verifies the reliability of the model and algorithm in this paper. Finally, taking 25 N attitude control engine two-dimensional profile as the object, the study for two-dimensional profile nozzle internal and external mixed flow problem is carried out, and the influence of different inlet rarefaction levels on the molecular transports of mixed flow field and the molecular transport phenomena of mixed flow field with different number of components are discussed.

Statistical Thermodynamic Description of Self-Assembly of Large Inclusions in Biological Membranes.

Recent studies revealed anomalous underscreening in concentrated electrolytes, and we suggest that the underscreened electrostatic forces between membrane proteins play a significant role in the process of self-assembly. In this work, we assumed that the underscreened electrostatic forces compete with the thermodynamic Casimir forces induced by concentration fluctuations in the lipid bilayer, and developed a simplified model for a binary mixture of oppositely charged membrane proteins with different preference to liquid-ordered and liquid-disordered domains in the membrane. In the model, like macromolecules interact with short-range Casimir attraction and long-range electrostatic repulsion, and the cross-interaction is of the opposite sign. We determine energetically favored patterns in a system in equilibrium with a bulk reservoir of the macromolecules. Different patterns consisting of clusters and stripes of the two components and of vacancies are energetically favorable for different values of the chemical potentials. Effects of thermal flutuations at low temperature are studied using Monte Carlo simulations in grand canonical and canonical ensembles. For fixed numbers of the macromolecules, a single two-component cluster with a regular pattern coexists with dispersed small one-component clusters, and the number of small clusters depends on the ratio of the numbers of the molecules of the two components. Our results show that the pattern formation is controlled by the shape of the interactions, the density of the proteins, and the proportion of the components.

Development and verification of rutting prediction model based on variable frequency load test

ABSTRACT An innovative rutting test method was developed based on the existing rutting prediction model to improve the rutting prediction model used in the asphalt pavements. The loading mould was improved based on test requirements and equipment characteristics, and small-scale and full-scale variable frequency load rutting tests were conducted. Nonlinear multiple regression analysis was conducted on the test results using numerical analysis software, converting driving speed into loading frequency and incorporating it into the rutting prediction model. This resulted in a permanent deformation prediction model for asphalt mixtures, incorporating factors such as temperature, pressure, loading cycles, loading frequency, and pavement thickness. Fourteen highways were selected as validation sections. The validation results showed that the average error of the prediction model established in this study was 15.48%, compared to 24.42% and 27.94% for the other two models. For different grades of highways, the load frequency parameter can distinguish the rutting conditions under different driving speeds, making the rutting prediction model established in this study more accurate compared to measured values.

Coal-gangue recognition for top coal caving face based on electromagnetic detection

Identifying the coal-gangue mixing ratio during top coal caving is essential for automating the coal caving process efficiently. In this article, a block impression reconstruction method is proposed to create 3D models of coal-gangue mixtures with varying gangue ratios and morphological distributions, based on real coal-gangue blocks that reflect actual coal-falling conditions. These 3D models are then input into CST software for electromagnetic forward simulation. The relationship between electromagnetic signal propagation characteristics and gangue ratio is analyzed, resulting in the creation of a coal-gangue mixture electromagnetic signal dataset. An Optuna-XGBoost-based model is then designed to identify the coal-gangue mixing ratio and the recognition performance is firstly verified by using the electromagnetic forward simulation data. Finally, to verify the method’s practicality, a microwave detection test bench for top coal caving is set up and some comparative experiments are conducted. The experimental results indicate that the electromagnetic signals of coal and rock with different gangue contents exhibit significant differences, and the proposed coal-gangue identification model has significant advantages in accuracy and overall performance compared to other competing models.

P19-24 Risk modeling of mixtures of endocrine disrupting chemicals relevant to human exposure, using zebrafish (Danio rerio) embryo as model organism – the RiskMix project

P19-24 Risk modeling of mixtures of endocrine disrupting chemicals relevant to human exposure, using zebrafish (Danio rerio) embryo as model organism – the RiskMix project

Establishment and evaluation of dynamic viscoelasticity constitutive model for asphalt mixtures based on PCA model: Utilizing coal-based synthetic natural gas slag and phosphogypsum whisker as substitute fillers

To promote the secondary utilization of solid waste in asphalt pavement and reduce resource waste and environmental damage, the fine solid waste powder from the secondary treatment of solid waste was used as a substitute filler to prepare asphalt mixtures. To more precisely characterize the effects of filler type and substitution ratio on the dynamic viscoelastic mechanical characteristics of asphalt mixtures, the dynamic modulus test was conducted on asphalt mixtures, while the dynamic modulus master curve of asphalt mixtures was established based on the time-temperature equivalent principle and the Sigmoidal function. The improved generalized Sigmoidal (IGS) model, five-parameter fractional Zener (FFZ) model, six-parameter fractional Zener (SFZ) model and ten-parameter fractional Zener (TFZ) model were used to construct the shrinkage frequency curves of different asphalt mixtures under different constitutive relationships, whereas the applicability of the constitutive models was further evaluated by the principal component analysis (PCA) model. The research results showed that the dynamic modulus and phase angle of the mixture exhibited a similar change rule with the variation of temperature and frequency, and the use of solid waste-based fillers could effectively reduce the effect of frequency variation on the viscoelasticity of the mixture. The dynamic modulus master curve constructed by Sigmoidal function could accurately describe the functional relationship between the dynamic modulus with loading frequency and temperature, and the variation trend of dynamic modulus in a wider frequency range was flattened with the increase in frequency, and this change rule was not affected by the substitution ratio of the filler, while the use of solid waste-based filler could reduce the dependence of the mixture on the temperature. Compared to several fractional constitutive models, the IGS model exhibited a higher fitting accuracy for the dynamic viscoelasticity of several mixtures, and with the use of solid waste-based fillers, the deviation of fitting curves gradually decreased. A comparative study showed that the fractional constitutive model was difficult to accurately describe the dynamic viscoelasticity of the mixture, and the IGS model was preferred to be recommended as a calculation model to investigate the constitutive relationship on the dynamic viscoelasticity of the mixture.

Nonlinear thermal-mechanical coupled isogeometric analysis for GPLs reinforced functionally graded porous plates

This work presents an isogeometric model for thermal-mechanical coupled analysis of geometrically nonlinear responses of graphene platelets reinforced functionally graded porous (GPLs-FGP) plates. The closed-cell Gaussian random field (GRF) scheme, Halpin-Tsai micromechanics model and rule of mixture are utilized to determine the material properties of GPLs-FGP plates with three types of porosity distribution and GPLs dispersion pattern. Unlike previous studies, the influences of GPLs and porosity on the thermal conductivity of GPLs-FGP plates are considered simultaneously. A geometrically nonlinear isogeometric analysis (IGA) model is developed base on a four-variable refined plate theory (RPT), using the von Kármán nonlinear theory, Hamilton’s principle and Total Lagrangian (TL) incremental scheme. After ensuring the accuracy and reliability of the present model through comparison with several examples, the thermal conductivity, temperature distribution, geometrically nonlinear static bending and transient responses of the plate, taking into account the temperature effects of porosity and graphene under thermal-mechanical loads are investigated. Numerical results show that porosity exerts influences on the thermal conductivity and temperature distribution of GPLs-FGP plates, which subsequently impact their geometrically nonlinear thermal-mechanical coupled responses. The results of this study are helpful for designing and manufacturing GPLs-FGP structures with improved performance.

High-Resolution Identification of Sound Sources Based on Sparse Bayesian Learning with Grid Adaptive Split Refinement

Sound source identification technology based on a microphone array has many application scenarios. The compressive beamforming method has attracted much attention due to its high accuracy and high-resolution performance. However, for the far-field measurement problem of large microphone arrays, existing methods based on fixed grids have the defect of basis mismatch. Due to the large number of grid points representing potential sound source locations, the identification accuracy of traditional grid adjustment methods also needs to be improved. To solve this problem, this paper proposes a sound source identification method based on adaptive grid splitting and refinement. First, the initial source locations are obtained through a sparse Bayesian learning framework. Then, higher-weight candidate grids are retained, and local regions near them are split and updated. During the iteration process, Green’s function and the source strength obtained in the previous iteration are multiplied to get the sound pressure matrix. The robust principal component analysis model of the Gaussian mixture separates and replaces the sound pressure matrix with a low-rank matrix. The actual sound source locations are gradually approximated through the dynamically adjusted sound pressure low-rank matrix and optimized grid transfer matrix. The performance of the method is verified through numerical simulations. In addition, experiments on a standard aircraft model are conducted in a wind tunnel and speakers are installed on the model, proving that the proposed method can achieve fast, high-precision imaging of low-frequency sound sources in an extensive dynamic range at long distances.

The recycling potential of fibre reinforced concrete with 4D Dramix® fibres: Experimental analysis and model verification

This experimental study aims to advance sustainable construction practices by exploring the recycling potential of steel fibre reinforced concrete with 4D Dramix® fibres from the company Bekaert. In this research steel fibres from reinforced concrete specimens with varying fibre quantities of two 4D Dramix® fibre types, more precisely, the 55/50 BG and 65/50 BG fibres are recycled. A jaw crusher at the recycling company AC Materials (Flanders, Belgium) and an impact crusher at the research and development company CTP (Wallonia, Belgium) are employed to optimize the quantity and quality of the recycled fibres. Subsequently, the recycled steel fibres are reintegrated into fresh concrete mixes. The recycling process of the fibres results in a general decrease of the residual tensile strength capacity for the concrete mixtures with recycled fibres (RSFRC) compared to those with new fibres (NSFRC), and the deviation increases with increasing crack mouth opening displacement (CMOD). It was found that fibres recovered by the impact crushing process are (generally) shorter than those obtained from the jaw crusher, which results in a fibre reinforced concrete (FRC) with a somewhat lower post-cracking tensile strength. Nevertheless, most recycled fibres retain 75 % of their original capacity, considering the mean residual tensile strength of the FRC mixtures. Consequently, current findings show promising results for the recycling potential of steel fibres and the residual strength behaviour of RSFRC compared to NSFRC. Additionally, this study highlights the potential for an improved constitutive tensile model for FRC mixtures, where the design parameters ka and kc can be estimated based on the fR3k/fR1k-ratio.

An interaction-based mixing model for predicting porosity and tensile strength of directly compressed ternary blends of pharmaceutical powders

Predicting the mechanical properties of powder mixtures without extensive experimentation is important for model driven design in solid dosage form manufacture. Here, a new binary interaction-based model is proposed for predicting the compressibility and compactability of directly compressed pharmaceutical powder mixtures based on the mixture composition. The model is validated using blends of MCC, lactose and paracetamol or ibuprofen. Both compressibility and compactability profiles are predicted well for a variety of blend compositions of ternary mixtures for the two formulations. The model performs well over a wide range of compositions for both blends and better than either an ideal mixing model or a ternary interaction model. A design of experiments which reduces the amount of API required for fitting the model parameters for a new formulation is proposed to reduce amount of API required. The design requires only three blends containing API. The model gives similar performance to the well-known Reynolds et al. model (2017) when trained using the same data sets. The binary interaction model approach is generalizable to other powder mixture properties. The model presented in this work is limited to curve-fitting of empirical compaction models for mixtures of common pharmaceutical powders and is not intended to provide guidance on the practical operating space (or design space) limits.

Policy-guided Monte Carlo on general state spaces: Application to glass-forming mixtures.

Policy-guided Monte Carlo is an adaptive method to simulate classical interacting systems. It adjusts the proposal distribution of the Metropolis-Hastings algorithm to maximize the sampling efficiency, using a formalism inspired by reinforcement learning. In this work, we first extend the policy-guided method to deal with a general state space, comprising, for instance, both discrete and continuous degrees of freedom, and then apply it to a few paradigmatic models of glass-forming mixtures. We assess the efficiency of a set of physically inspired moves whose proposal distributions are optimized through on-policy learning. Compared to conventional Monte Carlo methods, the optimized proposals are two orders of magnitude faster for an additive soft sphere mixture but yield a much more limited speed-up for the well-studied Kob-Andersen model. We discuss the current limitations of the method and suggest possible ways to improve it.

Mechanical behaviour of bridge expansion joint interfaces filled with novel polyurethane-modified asphalt

The use of Asphalt Plug Joints (APJs) in bridge construction has rapidly expanded due to their distinct advantages in driving comfort, shock absorption, noise reduction, and maintenance ease. However, prolonged exposure to vehicular loads and environmental factors renders this expansion joint susceptible to interface effects and stress concentration, leading to damage, particularly at the interface layers between the expansion joint pavement and the expansion joint-cross joint plate contact areas. To address issues such as stress concentration, this study proposes a polyurethane-modified asphalt concrete mixture as the joint filling material. Pull-out and inclined shear tests are used to identify a bonding agent with superior adhesive properties. Additionally, a finite element model of pull-out specimens containing bilinear cohesive elements is established to simulate the cohesive damage process of the interface layers. Through parameter design analysis, the study examines the influence of individual parameter variations on interface damage behavior, obtaining comprehensive simulated analysis data on interface crack resistance performance. Based on interface test data, a bilinear cohesive force model and a viscoelastic material model of the asphalt concrete mixture are employed to establish a scaled-down model of the improved geometry design of the new APJ. This model simulates the interface mechanical characteristics of APJs under temperature effects and vehicle loads. The results demonstrate that the bilinear cohesive force model effectively simulates the nonlinear behavior of interface bonding slip. Furthermore, the use of SZ interface bonding agents and improvements in cross joint plate form can effectively mitigate issues such as uneven settlement and low-temperature cracking.

Development of laminar burning velocity prediction model and correlation of iso-octane air mixtures using artificial neural network

Surrogate fuels provide an economical alternative for forecasting the combustion properties of transportation fuels like diesel, gasoline, and kerosene. Iso-octane (2,2,4-trimethylpentane), a primary reference fuel for gasoline, is extensively used as a surrogate component. This study utilizes a Feed-Forward Artificial Neural Network (FFANN) with back-propagation (BP) to predict the laminar burning velocity (LBV) of iso-octane/air mixtures. The ANN model was developed using a dataset of 6339 data points, including 3788 experimental data points from literature since 2004 and 2551 simulation data points from reduced kinetic reaction mechanism (RKM) CHEMKIN simulations. The grid search cross-validation (CV) method was employed to optimize the ANN hyperparameters. Implemented in Python using Keras, the ANN model addresses research gaps by forecasting LBV for hydrocarbons beyond n-heptane and modelling LBV under conditions reflective of actual engine operations. Unlike prior studies, we optimized various ML models, their complexity, size, and hyperparameters to achieve high prediction accuracy. We also integrated a hybrid model combining Particle Swarm Optimization (PSO) with Genetic Algorithms (GA) for hyperparameter optimization, ensuring efficient configuration of the ANN. The constructed ANN model was compared to other ML models, including generalized linear regression (GLR), support vector machine (SVM), random forest (RF), and XGBoost regression. For the testing set (20 % of the total dataset), the ANN model outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R2) of 0.9903, root mean square error (RMSE) of 1.911, and mean absolute error (MAE) of 1.206. Optimizing the ANN model with PSO further enhanced prediction accuracy, achieving a correlation coefficient of 0.9973 and a reduced mean absolute error of 0.753. To evaluate the ANN model's predictive ability, a reduced mechanism from the Lawrence Livermore National Laboratory (LLNL) was used. Compared to the LLNL RKM, the ANN predicted values showed lower percentage deviation from experimental data. Additionally, computation cost comparisons revealed that while RKM simulations in CHEMKIN took 244.2 s per case, the PSO-optimized ANN model required only 900 s for 150 cases. A 16-term correlation for laminar burning velocity (LBV) was derived from ANN predictions, serving as a practical tool to predict LBV based on pressure, temperature, and equivalence ratio. Unlike existing literature correlations, this newly developed correlation is valid across a wide range of operating conditions, offering a fundamental database for real-time combustion applications.

Analysis of combustion models of hydrogen-air mixtures using ANSYS FLUENT

Abstract This paper details an experimental investigation into flame propagation of a stoichiometric hydrogen mixture within a fixed 40 cm3 volume channel, utilizing ANSYS Fluent for simulation. The geometry was delineated as a 200x20 mm plane, with ignition positioned at the center. Certain input parameters were determined using MATLAB’s Cantera. The study explored different configurations of ANSYS Fluent’s channel combustion models, specifically examining both laminar and turbulent scenarios. Overpressure within the chamber was gauged at points 50 mm from the channel’s center to assess the impact of the relaxation factor on these measurements. These models underwent comparative analysis with actual experimental results, focusing on the highest achieved pressures and the flame’s shape at 1.9 ms, 2.9 ms, and 4.8 ms post-ignition. The findings underscore the complexity of modelling hydrogen mixture combustion in enclosed channels, highlighting the necessity for a specialized approach. Standard modules struggled to accurately replicate pressure variations over time, leading to significant discrepancies between the resulting models.

Implementation of a Drift Flux Model into SAM with Development of a Verification and Validation Test Suite for Modeling of Noncondensable Gas Mixtures

The advanced thermal-hydraulic system code, System Analysis Module (SAM), was originally developed for the modeling of single-phase flow in advanced reactors. It has since been expanded to include a four-equation drift flux model for the modeling of two-phase flows containing a noncondensable gas. The model was expanded to support the modeling of molten salt reactor (MSR) designs in which the fuel is directly dissolved in the circulating coolant. These designs have shown that circulating gas bubbles can play an important role in the management of fission products and the operational behavior of the reactor. A drift flux model was implemented to more accurately capture the localized behavior of the void in the core and its impact on the mass transfer of fission products. A thorough assessment of the new model was performed by developing a verification and validation test suite. Verification problems were designed to test all major terms in the new governing equations. The new model converged to the correct solution at the expected order of accuracy for all verification cases. The validation cases included a wide range of flow and void conditions in different pipe geometries. Although higher void experiments show a slight underprediction of void by the drift flux model, experiments that aim to reproduce Molten Salt Reactor Experiment (MSRE) experimental conditions show good agreement with the model. The gas transport model was activated for a SAM model of the MSRE to demonstrate that it can be used in a more complex model. This gas transport model will be used along with an interfacial area transport equation being implemented in SAM for the prediction of mass transport behavior in MSR conditions.

M-estimate based diffusion active noise control algorithm over distributed networks and its performance analysis

Multi-channel active noise control (ANC) systems can achieve effective attenuation of low-frequency noise in spatial areas and have been extensively studied. Recent works have focused on the application of multitask diffusion strategies in ANC systems based on wireless acoustic sensor and actuator networks (WASAN), which show the advantages of saving computational burden and enhancing flexibility. However, these works all adopt the mean-square error minimization as the optimization criterion, which leads to severe performance degradation in the presence of impulsive noise interference. Therefore, in this paper, we exploit the M-estimate function and the augmented weight vector of the control filter to formulate a robust minimization problem and propose a distributed augmented diffusion filtered-x least mean M-estimate (DADFxLMM) algorithm for multi-channel ANC systems. The proposed algorithm leverages the M-estimate function to overcome the influence of outliers in the error signal and achieves robustness against impulsive noise, and also eliminates the dependence on the symmetry of the acoustic paths by integrating the augmented weight vectors. Moreover, we replace the score function with the update probability of the weight vector to avoid the need for integration and Price’s theorem, and analyze the mean and mean-square performance of the proposed DADFxLMM algorithm under the contaminated Gaussian (CG) noise model. Simulation results under different system configurations demonstrate that the proposed algorithm outperforms the existing multi-channel ANC algorithms in the presence of impulsive noise interference.

Rapid determination of chicken meat ratios in Beef Mixtures and Beef Sausages by Near Infrared Reflectance (NIR) spectroscopy

This study aims to determine the percentage of chicken meat in beef and chicken mixtures, which is the most common form of beef adulteration. Ground beef and beef sausages were prepared with mixtures containing chicken meat, ranging from 0.0% to 100.0% with 5.0% increments, and analyzed using a near-infrared spectroscopy device. Optimal analysis conditions were determined through the examination of a wide range of regression models. The best regression model for ground beef mixtures yielded the following results: RMSEC (Root Mean Square Error of Calibration): 2.35, RMSEV (Root Mean Square Error of Validation): 3.36, R2C (R-Value Calibration): 0.99, R2V (R-Value Validation): 0.98. The results for beef sausages were as follows: RMSEC: 2.56, RMSEV: 3.66, R2C: 0.99, R2V: 0.98. As a result, the chicken meat content in beef mixtures was detected with a margin of error of 2.05%, while the chicken meat content in beef sausages was detected with a margin of error of 2.12%.