Model Of Biological Tissue Research Articles

МОДЕЛЮВАННЯ РОЗПОДІЛУ ТЕМПЕРАТУРНОГО ПОЛЯ ПРИ ТЕРМІЧНИХ УРАЖЕННЯХ БІОЛОГІЧНИХ ТКАНИН

Early determination of the thermal lesion degree in case of scald accelerates the treatment process and increases its effectiveness. The thermal lesion degree can be evaluated by determining the temperature difference between healthy and injured areas of biological tissue. For this purpose, a model of biological tissue in the form of a multilayer structure can be used. Heat exchange processes in such a structure are described by a generalized thermal model. Such structure contains conditionally flat heat sources located in each layer, which have the form of a developed network of blood vessels. The considered model of biological tissue quite accurately describes the heat exchange processes in body tissues. The article considers heat exchange processes that take place in biological tissue and a number of assumptions that should be used to mathematically describe these processes were identified. During the analysis of heat transfer process, the equations of temperature distribution in the tissue layers and the boundary conditions that describe the thermal interaction of the model with the environment are determined. As a result, the model of the stationary thermal regime of a biological tissue fragment in the form of a generalized thermal model and a mathematical model of the temperature field distribution in this fragment is obtained. This model is determined by many parameters, which are divided into 3 groups: thermophysical parameters; structural and topological parameters; parameters of the blood vascular system. Models of the particular fragment thermal regime are unequivocally determined by a combination of these parameters. For the analysis of temperature in any point of biological tissue modelled part mathematical model of temperature field distribution in stationary mode was developed. This model allows reasonable approach to the thermal lesion degree evaluation on the basis of the surface temperature difference between healthy and injured areas of tissue.

Finite-element kalman filter with state constraint for dynamic soft tissue modelling

This research work proposes a novel method for realistic and real-time modelling of deformable biological tissues by the combination of the traditional finite element method (FEM) with constrained Kalman filtering. This methodology transforms the problem of deformation modelling into a problem of constrained filtering to estimate physical tissue deformation online. It discretises the deformation of biological tissues in 3D space according to linear elasticity using FEM. On the basis of this, a constrained Kalman filter is derived to dynamically compute mechanical deformation of biological tissues by minimizing the error between estimated reaction forces and applied mechanical load. The proposed method solves the disadvantage of costly computation in FEM while inheriting the superiority of physical fidelity.

Theory and simulation for equilibrium glassy dynamics in cellular Potts model of confluent biological tissue

Glassy dynamics in a confluent monolayer is indispensable in morphogenesis, wound healing, bronchial asthma, and many others; a detailed theoretical framework for such a system is, therefore, important. Vertex-model (VM) simulations have provided crucial insights into the dynamics of such systems, but their nonequilibrium nature makes theoretical development difficult. The cellular Potts model (CPM) of confluent monolayers provides an alternative model for such systems with a well-defined equilibrium limit. We combine numerical simulations of the CPM and an analytical study based on one of the most successful theories of equilibrium glass, the random first-order transition theory, and develop a comprehensive theoretical framework for a confluent glassy system. We find that the glassy dynamics within the CPM is qualitatively similar to that in the VM. Our study elucidates the crucial role of geometric constraints in bringing about two distinct regimes in the dynamics, as the target perimeter P_{0} is varied. The unusual sub-Arrhenius relaxation results from the distinctive interaction potential arising from the perimeter constraint in such systems. The fragility of the system decreases with increasing P_{0} in the low-P_{0} regime, whereas the dynamics is independent of P_{0} in the other regime. The rigidity transition, found in the VM, is absent within the CPM; this difference seems to come from the nonequilibrium nature of the former. We show that the CPM captures the basic phenomenology of glassy dynamics in a confluent biological system via comparison of our numerical results with existing experiments on different systems.

A microstructurally motivated constitutive description of collagenous soft biological tissue towards the description of their non-linear and time-dependent properties

A versatile constitutive model for load-carrying soft biological tissue should incorporate salient microstructural deformation mechanisms and be able to reliably predict complex non-linear viscoelastic behavior. The advancement of treatment and rehabilitation strategies for soft tissue injuries is inextricably linked to our understanding of the underlying tissue microstructure and how this defines its macroscopic material properties. Towards this long-term objective, we present a generalized multiscale constitutive framework based on a novel description of collagen, the most mechanically significant extracellular matrix protein. The description accounts for the gradual recruitment of undulated collagen fibrils and introduces proteoglycan mediated time-dependent fibrillar sliding. Crucially, the proteoglycan deformation allows for the reduction of overstressed fibrils towards a preferential homeostatic stress. An implicit Finite Element implementation of the model uses an interpolation strategy towards collagen fiber stress determination and results in a memory-efficient representation of the model. A number of test cases, including patient-specific geometries, establish the efficiency of the description and demonstrate its ability to explain qualitative properties reported from macroscopic experimental studies of tendon and vascular tissue.

Simple implementations of fractional-order driving-point impedances: Application to biological tissue models

A novel procedure for the circuit implementation of the driving-point impedance of frequency-domain material models, constructed from fractional-order elements of arbitrary type and order, is introduced in this work. Following this newly introduced concept, instead of emulating separately each fractional-order element in the model under consideration, the direct emulation of the complete model can be achieved through the approximation of the total impedance function. The magnitude and phase frequency responses of the impedance function are first extracted and approximated through curve-fitting-based techniques. A rational integer-order driving-point impedance function is then obtained and realized using appropriately configured passive and/or active topologies. Comparison between the conventional method and the proposed method reveals that the achieved benefit is the significant reduction of the passive and/or active components count. Verification of the introduced concept is performed through circuit simulation results using OrCAD PSpice in the case of a root/stem/electrode interface model, as well as through experimental results where the driving-point input impedance of the human respiratory system under different health conditions is synthesized on a Field-Programmable Analog Array device.

Poisson-bracket formulation of the dynamics of fluids of deformable particles.

Using the Poisson-bracket method, we derive continuum equations for a fluid of deformable particles in two dimensions. Particle shape is quantified in terms of two continuum fields: an anisotropy density field that captures the deformations of individual particles from regular shapes and a shape tensor density field that quantifies both particle elongation and nematic alignment of elongated shapes. We explicitly consider the example of a dense biological tissue as described by the Vertex model energy, where cell shape has been proposed as a structural order parameter for a liquid-solid transition. The hydrodynamic model of biological tissue proposed here captures the coupling of cell shape to flow and provides a starting point for modeling the rheology of dense tissue.

The mechanical properties of tibiofemoral and patellofemoral articular cartilage in compression depend on anatomical regions

Articular cartilage in knee joint can be anatomically divided into different regions: medial and lateral condyles of femur; patellar groove of femur; medial and lateral plateaus of tibia covered or uncovered by meniscus. The stress–strain curves of cartilage in uniaxially unconfined compression demonstrate strain rate dependency and exhibit distinct topographical variation among these seven regions. The femoral cartilage is stiffer than the tibial cartilage, and the cartilage in femoral groove is stiffest in the knee joint. Compared with the uncovered area, the area covered with meniscus shows the stiffer properties. To investigate the origin of differences in macroscopic mechanical properties, histological analysis of cartilage in seven regions are conducted. The differences are discussed in terms of the cartilage structure, composition content and distribution. Furthermore, the commonly used constitutive models for biological tissues, namely Fung, Ogden and Gent models, are employed to fit the experimental data, and Fung and Ogden models are found to be qualified in representing the stiffening effect of strain rate.

A novel hyperelastic model for biological tissues with planar distributed fibers and a second kind of Poisson effect

Constitutive models are of fundamental importance to many biomedical problems such as the rupture prediction of aortic aneurysms. Existing structure-based constitutive models such as the widely used Gasser–Ogden–Holzapfel (GOH) model usually need to specify the number of fiber family which may be difficult to identify for many kinds of tissues. In this study, we developed a novel hyperelastic model for biological tissues which does not need the information of the number of fiber family. We illustrated that a single generalized structure tensor (GST) with one parameter can characterize the structure of arbitrary planar fiber distribution as long as an in-plane symmetric axis exists. Based on the GST, we developed three novel fiber strain energy functions. Moreover, we proposed a novel second kind of Poisson effect which can capture deformation coupling properties of bio-tissues that the traditional well-known Poisson effect can hardly capture. According to the second kind of Poisson effect, the models constructed from the three novel strain energy functions exhibit different deformation couplings, referred to as strong, medium and weak coupling. The model of medium coupling has the best fitting for experimental data of porcine aortic tissues, and the model of weak coupling has the worst fitting. The newly proposed second kind of Poisson effect is a useful and essential supplement to the traditional Poisson effect and can be applied to investigate the accuracy of nonlinear constitutive models.

Model of microwave radiation absorption by biological tissues

А model of absorption of electromagnetic energy of radiofrequency range by biological tissues is described in the article. The problems of modeling the interaction of microwave radiation and biological tissues represented as multilayer structures are considered. Patch-antenna models for six sub-bands overlapping the 500–3500 MHz range are developed. The model of biological tissue was developed on the basis of MRI imaging, which allows for modeling under near real-life conditions. Based on the developed models of transceivers and biotissue, models have been created that allow to analyze the absorption of electromagnetic energy in the near- and far fields of the transmitter. From the results of modelling in the near field we can see that there are certain absorption maxima at frequencies of 750, 938, 1250 and 1357 MHz. Based on the results of the far field modeling it can be noted that in the range of 750 to 1000 MHz there is no absorption peak at 938 MHz. Also, as a result of the simulation, a decrease in the magnitude of absorption starting from 750 MHz was registered. Absorption peak absence is also observed in the area of 1357 MHz frequency. In the range of 2.5–3 GHz both in the near and far fields practically linear decrease of absorption value is observed. When analyzing the influence of structures' sizes on electromagnetic energy absorption in biological tissues, it was found that the nature of change in absorption value is a nonlinear value. In the range of 0.5–2 GHz both increase and decrease of absorption at thickening or thinning of layers is observed. It should also be noted that when the size of each layer increases by 10 %, the peak of absorption in the area of 1156 MHz frequency is observed. For the 2–3.5 GHz range there are no significant changes in the chart shape when the layer thickness changes.

Quantifying the link between local structure and cellular rearrangements using information in models of biological tissues.

Machine learning techniques have been used to quantify the relationship between local structural features and variations in local dynamical activity in disordered glass-forming materials. To date these methods have been applied to an array of standard (Arrhenius and super-Arrhenius) glass formers, where work on "soft spots" indicates a connection between the linear vibrational response of a configuration and the energy barriers to non-linear deformations. Here we study the Voronoi model, which takes its inspiration from dense epithelial monolayers and which displays anomalous, sub-Arrhenius scaling of its dynamical relaxation time with decreasing temperature. Despite these differences, we find that the likelihood of rearrangements can nevertheless vary by several orders of magnitude within the model tissue and extract a local structural quantity, "softness," that accurately predicts the temperature dependence of the relaxation time. We use an information-theoretic measure to quantify the extent to which softness determines impending topological rearrangements; we find that softness captures nearly all of the information about rearrangements that is obtainable from structure, and that this information is large in the solid phase of the model and decreases rapidly as state variables are varied into the fluid phase.

Exploring the potential of transfer learning for metamodels of heterogeneous material deformation

From the nano-scale to the macro-scale, biological tissue is spatially heterogeneous. Even when tissue behavior is well understood, the exact subject specific spatial distribution of material properties is often unknown. And, when developing computational models of biological tissue, it is usually prohibitively computationally expensive to simulate every plausible spatial distribution of material properties for each problem of interest. Therefore, one of the major challenges in developing accurate computational models of biological tissue is capturing the potential effects of this spatial heterogeneity. Recently, machine learning based metamodels have gained popularity as a computationally tractable way to overcome this problem because they can make predictions based on a limited number of direct simulation runs. These metamodels are promising, but they often still require a high number of direct simulations to achieve an acceptable performance. Here we show that transfer learning, a strategy where knowledge gained while solving one problem is transferred to solving a different but related problem, can help overcome this limitation. Critically, transfer learning can be used to leverage both low-fidelity simulation data and simulation data that is the outcome of solving a different but related mechanical problem. In this paper, we extend Mechanical MNIST, our open source benchmark dataset of heterogeneous material undergoing large deformation, to include a selection of low-fidelity simulation results that require ≈ 2 − 4 orders of magnitude less CPU time to run. Then, we show that transferring the knowledge stored in metamodels trained on these low-fidelity simulation results can vastly improve the performance of metamodels used to predict the results of high-fidelity simulations. In the most dramatic examples, metamodels trained on 100 high fidelity simulations but pre-trained on 60,000 low-fidelity simulations achieves nearly the same test error as metamodels trained on 60,000 high-fidelity simulations (1 − 1.5% mean absolute percent error). In addition, we show that transfer learning is an effective method for leveraging data from different load cases, and for leveraging low-fidelity two-dimensional simulations to predict the outcomes of high-fidelity three-dimensional simulations. Looking forward, we anticipate that transfer learning will enable us to better capture the influence of tissue spatial heterogeneity on the mechanical behavior of biological materials across multiple different domains.

Chemometrics-Enabled Raman Spectrometric Qualitative Determination and Assessment of Biochemical Alterations during Early Prostate Cancer Proliferation in Model Tissue

The use of Raman spectroscopy combined with multivariate chemometrics for disease diagnosis has attracted great attention from researchers in recent years. This is because it is a noninvasive and nondestructive detection approach with enhanced sensitivity. However, a major challenge when analyzing spectra from biological samples has been the detection of subtle biochemical alterations buried in background and fluorescence noise. This work reports a qualitative chemometrics-assisted investigation of subtle biochemical alterations associated with prostate malignancy in model biological tissue (metastatic androgen insensitive (PC3) and immortalized normal (PNT1a) prostate cell lines). Raman spectra were acquired from PC3 and PNT1a cells at various stages of growth, and their biochemical alterations were determined from difference spectra between the two cell lines (for prominent alterations) and principal component analysis (PCA) (for subtle alterations). The Raman difference spectra were computed by subtracting the normalized mean spectral intensities of PNT1a cells from the normalized mean spectral intensities of PC3 cells. These difference spectra revealed prominent biochemical alterations associated with the malignant PC3 cells at 566 ± 0.70 cm−1, 630 cm−1, 1370 ± 0.86 cm−1, and 1618 ± 1.73 cm−1 bands. The band intensity ratios at 566 ± 0.70 cm−1 and 630 cm−1 suggested that prostate malignancy can be associated with an increase in relative amounts of nucleic acids and lipids, respectively, whereas those at 1370 ± 0.86 cm−1 and 1618 ± 1.73 cm−1 suggested that prostate malignancy can be associated with a decrease in relative amounts of saccharides and tryptophan, respectively. In the analysis using PCA, intermediate-order and high-order principal components (PCs) were used to extract the subtle biochemical fingerprints associated with the cell lines. This revealed subtle biochemical differences at 1076 cm−1, (1232, 1234 cm−1), (1276, 1278 cm−1), (1330, 1333 cm−1), (1434, 1442 cm−1), and (1471, 1479 cm−1). The band intensity ratios at 1076 cm−1 and 1232 cm−1 suggested that prostate malignancy can be associated with an increase in subtle amounts of nucleic acids and amide III components, respectively. The method reported here has demonstrated that subtle biochemical alterations can be extracted from Raman spectra of normal and malignant cell lines. The identified subtle bands could play an important role in quantitative monitoring of early biomarker alterations associated with prostate cancer proliferation.

Manifold learning based data-driven modeling for soft biological tissues

Data-driven modeling directly utilizes experimental data with machine learning techniques to predict a material’s response without the necessity of using phenomenological constitutive models. Although data-driven modeling presents a promising new approach, it has yet to be extended to the modeling of large-deformation biological tissues. Herein, we extend our recent local convexity data-driven (LCDD) framework (He and Chen, 2020) to model the mechanical response of a porcine heart mitral valve posterior leaflet. The predictability of the LCDD framework by using various combinations of biaxial and pure shear training protocols are investigated, and its effectiveness is compared with a full structural, phenomenological model modified from Zhang et al. (2016) and a continuum phenomenological Fung-type model (Tong and Fung, 1976). We show that the predictivity of the proposed LCDD nonlinear solver is generally less sensitive to the type of loading protocols (biaxial and pure shear) used in the data set, while more sensitive to the insufficient coverage of the experimental data when compared to the predictivity of the two selected phenomenological models. While no pre-defined functional form in the material model is necessary in LCDD, this study reinstates the importance of having sufficiently rich data coverage in the date-driven and machine learning type of approaches. It is also shown that the proposed LCDD method is an enhancement over the earlier distance-minimization data-driven (DMDD) against noisy data. This study demonstrates that when sufficient data is available, data-driven computing can be an alternative method for modeling complex biological materials.

A generic physics-informed neural network-based constitutive model for soft biological tissues

Constitutive modeling is a cornerstone for stress analysis of mechanical behaviors of biological soft tissues. Recently, it has been shown that machine learning (ML) techniques, trained by supervised learning, are powerful in building a direct linkage between input and output, which can be the strain and stress relation in constitutive modeling. In this study, we developed a novel generic physics-informed neural network material (NNMat) model which employs a hierarchical learning strategy by following the steps: (1) establishing constitutive laws to describe general characteristic behaviors of a class of materials; (2) determining constitutive parameters for an individual subject. A novel neural network structure was proposed which has two sets of parameters: (1) a class parameter set for characterizing the general elastic properties; and (2) a subject parameter set (three parameters) for describing individual material response. The trained NNMat model may be directly adopted for a different subject without re-training the class parameters, and only the subject parameters are considered as constitutive parameters. Skip connections are utilized in the neural network to facilitate hierarchical learning. A convexity constraint was imposed to the NNMat model to ensure that the constitutive model is physically relevant. The NNMat model was trained, cross-validated and tested using biaxial testing data of 63 ascending thoracic aortic aneurysm tissue samples, which was compared to expert-constructed models (Holzapfel-Gasser-Ogden, Gasser–Ogden–Holzapfel, and four-fiber families) using the same fitting and testing procedure. Our results demonstrated that the NNMat model has a significantly better performance in both fitting (R2 value of 0.9632 vs 0.9019, p=0.0053) and testing (R2 value of 0.9471 vs 0.8556, p=0.0203) than the Holzapfel–Gasser–Ogden model. The proposed NNMat model provides a convenient and general methodology for constitutive modeling.

Biochemical basis of Quantum-like neuronal dynamics

The nervous system is a complex dynamical system that incorporates higher order biology (e.g., multicellular architecture) and lower-order biology (e.g., intra cellular pathway) that can be modeled via classical laws such as reaction-diffusion models. Simple reaction-diffusion models of neuronal tissue are shown to support bio-chemical wave effects that are analogous to quantum phenomena. These phenomena include quantum-like superpositions and classical entanglement which will not be affected by decoherence n the wet and warm brain environment. These classical phenomena could enable quantum-like complexity of brain functions. Conventional reaction-diffusion models of biological tissues challenge the current quantum brain hypothesis and suggest that the brain should perhaps be thought of as a classical quantum-like system. Statement of SignificanceThis manuscript introduces the notion of nonseparability (classical entanglement) in the case of biochemical waves in arrays of coupled axons. We use a linear reaction-diffusion model with cross diffusion to address nonseparability between degrees of freedom (along and across the axon array). Perturbation theory applied to a nonlinear model with quadratic nonlinearity is used to illustrate nonseparability between modes along the axons. This paper suggests that the brain should perhaps be thought of as a classical quantum-like system.

Characterization of the Nonlinear Biaxial Mechanical Behavior of Human Ureter Using Constitutive Modeling and Artificial Neural Networks

Characterization of the mechanical properties of soft biological tissues is a fundamental issue in a variety of medical applications. As such, constitutive modeling of biological tissues that serves to establish a relationship between the kinematic variables has been used to formulate the tissue’s mechanical response under various loading conditions. However, the validation of the developed analytical and numerical models is accompanied by a length of computation time. Hence, the need for new advantageous methods like artificial intelligence (AI), aiming at minimizing the computation time for real-time applications such as in robotic-assisted surgery, sounds crucial. In this study, at first, the mechanical nonlinear characteristics of human ureter were obtained from planar biaxial test data, in which the examined specimens were simultaneously loaded along their circumferential and longitudinal directions. To do so, the biaxial stress-strain data was used to fit the well-known Fung and Holzapfel-Delfino hyperelastic functions using the genetic optimization algorithm. Next, the potential of Artificial Neural Networks (ANN), as an alternative method for prediction of the mechanical response of the tissue was evaluated such that, multilayer perceptron feedforward neural network with different architectures was designed and implemented and then, trained with the same experimental data. The results showed both approaches were practically able to predict the ureter nonlinearity and in particular, the ANN model can follow up the tissue nonlinearity during the entire loading phase in both low and high strain amplitudes (RMSE<0.02). Such results confirmed that neural networks can be a reliable alternative for modeling the nonlinear mechanical behavior of soft biological tissues.

Is there any objective and independent characterization and modeling of soft biological tissues?

The characterization of soft tissue raises several difficulties. Indeed, soft biological tissues usually shrink when dissected from their in vivo location. This shrinkage is characteristic of the release of residual stresses, since soft tissues are indeed often pre-stressed in their physiological configuration.During experimental loading, large extension at very low level of force are expected and assumed to be related to the progressive recruitment and stretching of fibers. However, the first phase of the mechanical test is also aiming at recovering the pre-stressed in vivo behavior. As a consequence, the initial phase, corresponding to the recovering of prestress and/or recruitment of fiberes, is questionable and frequently removed. One of the preferred methods to erase it consists in applying a preforce or prestress to the sample: this allows to easily get rid of the sample retensioning range. However this operation can impact the interpretation of the identified mechanical parameters.This study presents an evaluation of the impact of the data processing on the mechanical properties of a numerically defined material. For this purpose, a finite element simulation was performed to replicate a uniaxial tensile test on a biological soft tissue sample. The influence of different pre-stretches on the mechanical parameters of a second order Yeoh model was investigated.The Yeoh mechanical parameters, or any other strain energy density, depend strongly on any pre- and post-processing choices: they adapt to compensate the error made when choosing an arbitrary level of prestretch or prestress. This observation spreads to any modeling approach used in soft tissues. Mechanical parameters are indeed naturally bound to the choice of the pre-stretch (or pre-stress) through the elongation and the constitutive law. Regardless of the model, it would therefore be pointless to compare mechanical parameters if the conditions for the processing of experimental raw data are not fully documented.

Application of Upconversion Luminescence of NaYF4:Yb,Er Nanoparticles to Study Protein Coagulation Dynamics

We present a method for measuring the dynamics of the internal temperature of biological tissues and the thickness of a denatured layer during laser thermolysis. This method is based on measuring the intensities of three luminescence bands of NaYF4:Yb,Er upconversion nanoparticles in the green and red parts of the visible spectrum. Ovalbumin was used as a model biological tissue, the general denaturation patterns of ovalbumin were also studied using plasmonic photothermal therapy.

How to implement user-defined fiber-reinforced hyperelastic materials in finite element software

Finite element modeling is often used in biomechanical engineering to evaluate medical devices, treatments and diagnostic tools. Using an adequate material model that describes the mechanical behavior of biological tissues is essential for a reliable outcome of the simulation. Pre-programmed material models for biological tissues are available in many finite element software packages. However, since these pre-programmed models are presented to the user as a black box, without the possibility to modify the material description, many researchers turn to implementing their own material formulations. This is a complex undertaking, requiring extensive knowledge while documentation is limited.This paper provides a detailed description, at the level of the biomedical engineer, of the implementation of a nonlinear hyperelastic material model using user subroutines in Abaqus®, in casuUANISOHYPER_INV and UMAT. The Gasser-Ogden-Holzapfel material model is used as an example, resulting in four implementation variations: the built-in implementation, a UANISOHYPER_INV formulation, a UMAT with analytical tangent stiffness formulation and a UMAT with numerical tangent stiffness formulation. In addition, three different element formulations are used: a continuum compressible, a continuum incompressible and a plane stress incompressible. All cases are thoroughly verified by applying a series of deformations on a single cube element and by simulating an extension-inflation experiment with non-homogeneous deformations and multiple elements. In these test cases, stresses, displacements, reaction forces, the required number of iterations and the total CPU time were compared.The results show that the four implementation variations are very similar, with total relative errors between 10−3 and 10−15, number of iterations that varied by maximum one iteration, and a comparable CPU time. In addition to this detailed overview, the user subroutines are added as supplementary material to this tutorial, which can be used as the ideal starting point for biomechanical engineers to implement their own material models at different levels of complexity.

Optical properties of a simple model of soft biological tissue

Optical phantoms that simulate light propagation processes through soft biological tissue are required to evaluate the performance and calibrate different medical imaging modalities. Liquid lipid based low-cost phantoms were prepared, with cow’s milk as scattering material, red India ink as the absorbing material and demineralized water as the matrix material since it provides a soft medium biologically compatible with the addition of organic molecules. Two experiments were carried out for characterization of this soft tissue model. First, collimated and diffuse transmittance and reflectance spectra were measured using phantoms with milk of different fat content. From the data, dependence between: total extinction coefficient, Kubelka Munk’s ratio, absorption and scattering coefficients on the wavelength were estimated. Second, using collimated transmittance measurements the effect of the phantom components was observed. The absorption peak, around 550 ± 0.3 nm, increases as ink was added to milk phantoms; and when lipid concentration was varied, by fixed ink, the scattering growths. The extinction coefficient’s dependence on the wavelength was determined, and fluorescence was observed with a 31 ± 0.3 nm Stokes shift. Results confirm the possibility of spectroscopic identification of milk kinds, as well as the feasibility of low cost controllable phantom for preliminary biophotonic studies.