Polynomial Regression Equation Research Articles

Mapping algal bloom dynamics in small reservoirs using Sentinel-2 imagery in Google Earth Engine

Freshwater algal blooms have caused ecological damage and public health concerns throughout the world. Monitoring such blooms via in situ sampling is both costly and time-consuming, and satellite imagery provides a rapid and relatively inexpensive way to supplement these techniques. Sentinel-2 MultiSpectral Imager data have effectively detected chlorophyll-a, a proxy for algal biomass, in large bodies of water, but few studies have shown the applicability in small (<10 km2) reservoirs, which are critically important for aquatic species, drinking water, irrigation, cultural activities, and recreation. This study provides a test of the use of Sentinel-2 imagery in Google Earth Engine for algal bloom detection in two small freshwater reservoirs in northern California, USA, from October 2015 to December 2020. Google Earth Engine’s cloud computing allows for the analysis of extensive datasets and time series, expanding the capacity to analyze the spatial and temporal heterogeneity of floating algal blooms. Here we analyzed four spectral indices - Normalized Difference Vegetation Index (NDVI), Normalized Difference Chlorophyll Index (NDCI), B8AB4, and B3B2 - to retrieve chlorophyll-a data for algal bloom identification in two highly dynamic freshwater systems. We assessed the relationship between spectral indices and monthly in situ water samples that were collected at three sites within the reservoirs using cubic polynomial regression equations. NDCI, which leverages the red-edge wavelength, most accurately identified chlorophyll-a across all study sites (highest adjusted R2 = 0.84, lowest RMSE = 0.02 µg/l), followed by NDVI. We demonstrate that Sentinel-2 imagery can capture greater spatial and temporal heterogeneity of algal blooms than typical in situ sampling. This suggests that remote sensing may be an increasingly important tool in monitoring algal bloom dynamics in small reservoirs and other aquatic environments.

DETERMINATION OF THE EFFECTIVE DOSE OF PHOSPHORUS IN LEMON EUCALYPTUS PLANTS (Corymbia citriodora)

Phosphorus (P) is one of the most limiting nutrients for plant species and its intensive use can exhaust world reserves. In general, forest production is unviable without the availability of P. The objective of the study was to determine the effect of phosphate fertilization on plant growth of four families of lemon eucalyptus (Corymbia citriodora). Plants produced from seeds of four seed trees were used, which were subjected to seven doses of phosphorus: 0, 25, 50, 75, 100, 125 and 150 mg P dm-3. The experiment was carried out in a 7 × 4 factorial arrangement, with four replicates and using containers with 5 dm3 of a Red Eutropheric Latosol type soil. The response variables measured were monthly plant height, plant collar diameter at 90 d, dry biomass weight of leaves, roots, stems and branches, and phosphorus use efficiency of leaves. The variations of the characteristics evaluated as a function of phosphorus concentration were analysed by means of polynomial regression equations. Phosphate fertilization is necessary for the growth and development of C. citriodora plants. Plants of the four C. citriodora seed trees from different provenances responded differentially to phosphorus doses. It was found that the optimum amount of phosphorus ranges between 30 and 100 mg dm-3 according to the characteristics evaluated and the seed tree.

Seamless topo-bathymetric surveys of Maiyegun Estate Waterfront Lagos State, Nigeria

Topo-bathymetric surveys are mostly desired in coastal area where water and land perpetually interact. This work explore the ground-based methods of topographic and bathymetric surveys in collecting data for the appropriate description of the Mayegun Estate water front and the adjoining land topography. To acquire depth data at pre-defined transects lines, hydrographic vessel and a trained diver were engaged. The topo-bathy DEM was produced at 1m and contour at 0.5m interval. The wave pattern was determined to be semi-diurnal. The fitted second order polynomial regression equation for wave amplitude against time yielded (y = 2E-07x2 - 0.0003x - 0.0276 at R² = 0.0064) while that of depth versus sounded points gave (y = 5E-09x2 - 5E-06x - 7.1512) at R² = 0.0019. The vertical and horizontal uncertainties were computed as 0.015m and 5m + (-0.47) at 95% confidence level based on (IHO) specification for order 1a. This principle can be adopted with caution as a viable alternative for topo-bathy surveys to minimize cost.

Design and Preparation of White High-Strength Concrete with Ground Limestone Powder by Means of Response Surface Methodology.

This paper investigates the properties of white high-strength concrete (WHSC) prepared with ground limestone powder (GLP). Response surface methodology (RSM) was used to design the proportions of mixes and evaluate the influence of the water–binder ratio (w/b), slurry volume fraction (Vs), and the content of GLP in a binder (Cg) on the slump, whiteness and compressive strength of WHSC via Box–Behnken equations. Results indicate that quadratic polynomial regression equations can be used to predict the performance of WHSC as influenced by combined factors. Both slump and compressive strength of WHSC are found highly influenced by w/b while GLP significantly improves the whiteness of WHSC. An optimal mix proportion of WHSC is provided by the multi-objective optimization with high-accuracy predictions. This paper demonstrates the feasibility of preparing WHSC with GLP and presents the potential of using RSM in the mix proportioning of concrete.

Calibration and comparison of various sensors for soil moisture measurement

The research paper introduces the development of a soil moisture sensor in accordance with the field condition and has a capacity to measure soil moisture up to 50 cm depth. In this study, the Arduino-based soil moisture sensors such as Stainless Steel 316L grade (SS316L) probe and Copper Based Probe (CBP) are introduced, which fit best with the conventional method for measuring soil moisture. The paper also presents the calibration and performance comparison of three sensors, including SS316L and CBP sensors (developed) and Manual Soil Moisture (MSM) sensors. The globally accepted and proven Oven Dry Method (ODM) is adopted for calibration purpose during soil moisture measurement. Linear, polynomial, and logarithmic regression equations, along with Adaptive Neuro-Fuzzy Inference System (ANFIS), are utilized for the best fitting. Thus, it proves that ANFIS non-linear system gives the best result. It is observed that the SS316L sensor performs well with the desired accuracy in the given field condition compared to other soil moisture sensors. The outcome of this study will assist the government authorities, policymakers, and researchers working on water-saving objectives.

SPATIAL PATTERN AND ALLOMETRY OF A KEY TREE FOR THE AGROINDUSTRY SECTOR: Hancornia speciosa Gomes

The fruits of the mangabeira tree (Hancornia speciosa) are used mainly in the Brazilian Northeast region to supply the agroindustry sector to produce juices, sweets, ice cream and other derivatives. To support conservation and population management, we analyzed the spatial pattern and the allometric relations of H. speciosa in natural populations. For the spatial distribution pattern, we used a second-order neighborhood density function (NDF). Polynomial regression equations were fitted from the allometric data based on the coefficient of determination (R²). We also used descriptive statistics. The H. speciosa trees studied did not occur in a random pattern in the studied areas, and the aggregation pattern (up to 15 and 20 m) was associated with the limited dispersion of seeds (e.g., barochory). The positive relation between the DBH (diameter at breast height) and total height showed good fit of the equation (R² = 0.72). Also, there were high correlations between the crown area and the overall height and DBH (R² = 0.83; R² = 0.81, respectively). The results obtained reflect adaptive strategies of H. speciosa in its natural habitat and provide information to support management and conservation of natural populations submitted to intensive fruit harvesting.

Leaching of Ibute-Nze kaolin iron-oxide impurity with oxalic acid process optimization of dissolution conditions using response surface methodology

The goal of this research was to remove iron oxide from Ibute-Nze kaolin by dissolving the clay mineral in an aqueous oxalic acid solution and optimizing the process. The chemical composition of the raw and modified clay was determined using x-ray fluorescence, and the morphology of the solid sample was determined using a scanning electron microscope. The best conditions for the oxalic acid leaching of iron-oxides impurity from Ibute-Nze kaolin were determined using response surface methodology based on Box-Behnken design. The studies were carried out within the following process parameter ranges: 40–90 leaching temperature; 0.075-0.355mm particle size; 1–6 mol/dm3 acid concentration; 0.02–0.12 g/cm3 kaolin sample to acid ratio and 30-240 min contact time. The characterization revealed that Ibute-Nze clay is kaolinitic in nature and calcination at 750 opens more pores for its leaching. According to the analysis of variance, a second-order polynomial regression equation provided the best fitting for the experimental data. The predicted and experimental response values were shown to be correlated (R2 = 0.9276) in the experimental runs. The following were found to be the best conditions for the leaching process variables: 83.2051 leaching temperature, 0.0827mm particle size, 3.6179mol/dm3 acid concentration; 0.0287g/cm3 kaolin to acid ratio and 217.932min reaction time. The chemical leaching process was 92.6035 per cent under these conditions, which made the clay good for industrial applications.

Seam Strength Prediction for Different Stitch Types Considering Stitch Density of Cotton Woven Fabrics

Seam strength is critical in achieving the proper and adequate quality seam, which eventually determines the overall quality of any garment. The target of this research was to investigate exactly how different stitch density affected seam strength for different stitch types. Commercially available, 100% cotton plain weave structured woven fabric with mass per unit area of 270 g/m2 was used in this analysis. Two different stitch types, such as lock stitch (ISO#301) and chain stitch (ISO#401), along with four stitch densities like 8, 10, 12 and 14 per inch were utilized. The seam strength of the prepared samples was determined using a tensile strength tester followed by the ASTM D1683 standard procedure. The impact of independent variables on superimposed seam strength was statistically analyzed using a regression correlation with the help of SPSS software to construct a regression equation to predict seam strength before the manufacturing process. The study found a non-linear relationship between seam strength and stitch density. When the stitch density is too low, the seam strength is shown low as well, because the cloth cannot be held under tensile load. In a very compact woven fabric, structural jamming or intrinsic puckering occurs if it is too high. As a result, the best stitch density should be chosen to achieve the best seam strength. This study will help the manufacturers choose a suitable SPI for lock and chain stitches from polynomial regression equations before the sewing production of cotton woven fabric, which was not predicted by any researcher before. Therefore, the optimum stitch density can be chosen using regression equations to get the best result for seam strength.

Parametric optimization of blowdown operated double-effect vapour absorption refrigeration system

The present work investigates the thermodynamic optimality of a double-effect vapor absorption refrigeration system (VARS) driven by blowdown heat of a 210 MW thermal power plant at Badarpur using response surface methodology (RSM). RSM helps to develop a relation amongst the decision variables and the second law efficiency vi a se cond-order nonlinear polynomial regression equation. In addition, the analysis of variance technique (ANOVA) is applied to identify the decision variables having a significant effect on th e system’s thermal performance. Moreover, coefficient of structural bond (CSB) analysis of the evaporator is carried out. The decision variables are the temperatures of the high-pressure generator, low-pressure generator, condenser, absorber, and evaporator. Results conclude that the second law efficiency is affected significantly by absorber temperature (F value = 2049.4), followed by condenser temperature (F value = 1596.4), and is least affected by high-pressure generator temperature (F value = 495). CSB value of the Evaporator is 0.5851.

Оптимизация условий мицеллярно-ферментативной экстракции пектина из плодов боярышника ( Crataegus monogyna Jacq.)

Introduction. Commercial pectin is usually obtained from apples or citrus fruits. However, some wild fruits, such as hawthorn, are also rich in pectin with valuable nutritional and medical properties. The research objective was to study and improve the process of combined surfactant and enzyme-assisted extraction of pectin from hawthorn fruits.&#x0D; Study objects and methods. The study involved a 1% solution of Polysorbate-20 surfactant and a mix of two enzymes, namely cellulase and xylanase, in a ratio of 4:1. The response surface methodology with the Box-Behnken experimental design improved the extraction parameters. The experiment featured three independent variables – temperature, time, and solvent-to-material ratio. They varied at three levels: 20, 40, and 60°C; 120, 180, and 240 min; 15, 30, and 45 mL per g. Their effect on the parameters on the pectin yield was assessed using a quadratic mathematical model based on a second order polynomial equation.&#x0D; Results and discussion. The response surface methodology made it possible to derive a second order polynomial regression equation that illustrated the effect of extraction parameters on the yield of polyphenols. The regression coefficient (R2 = 98.14%) and the lack-of-fit test (P &gt; 0.05) showed a good accuracy of the model. The optimal extraction conditions were found as follows: temperature = 41°C, time = 160 min, solvent-to-material ratio = 32 mL per 1 g. Under the optimal conditions, the predicted pectin yield was 14.9%, while the experimental yield was 15.2 ± 0.4%. The content of galacturonic acid in the obtained pectin was 58.5%, while the degree of esterification was 51.5%. The hawthorn pectin demonstrated a good complex-building ability in relation to ions of copper (564 mg Cu2+/g), lead (254 mg Pb2+/g), and cobalt (120 mg Co2+/g).&#x0D; Conclusion. Combined surfactant and enzyme-assisted extraction made improved the extraction of pectin from hawthorn fruits. The hawthorn pectin can be used to develop new functional products.

Establishing empirical relationships and process parameter optimization of tungsten inert gas welded butt joints of nickel-based alloy using statistical approach of regression analysis and response surface methodology

ABSTRACT This investigation presents the process parameter optimisation of high-frequency concentrated arc TIG (HFCA-TIG) welded butt joints of nickel-based alloy 718 using response surface methodology to enhance the performance characteristics of joints. The polynomial regression equations of second order were formulated for predicting the performance characteristics of alloy 718 butt joints from the process parameters. Analysis of variance (ANOVA) was performed to determine the significant process parameters and to estimate the proficiency of empirical relationships. The experimental and predicted results were compared for validation of empirical relationships. It disclosed less than 4% error at 95% confidence level. The numerical and graphical optimisation complying the optimisation criteria was executed to find the optimum conditions of process parameters depending on the desirability function. The interaction effect of process parameters on performance characteristics of joints was analysed from three-dimensional response surface plots.

Годовая динамика плотности и таксономического состава рыбного населения русловой ямы (влияние факторов)

The article reports the results of the study on the annual dynamics of fish density, taxonomic composition of the fish population in a section of a riverbed depression in the lower reaches of the Irtysh River. A complex system of interaction between environmental factors and fish relationships in the predator-prey system in the studied section of the river under changing environmental factors is considered. The study was conducted using echometric sounding of the water column of the riverbed depression with an acoustic complex; during the open water period it was performed from a boat and during the ice cover period — from a snowmobile. The study also analysed the dynamics of environmental factors (turbidity, temperature, water level) and their effect on fish density. The maximum fish density in the riverbed depression was observed in summer, when juveniles migrate from the floodplain to the main channel. Fish density differed significantly during periods of open water and ice cover. Groups of cyprinids and percids had the largest share in the fish population. Their joint share varied from 68.53 to 87.42% of the total fish density; these values were noted in January and August, respectively. The maximum fish density was noted in June, the minimum density — in October and December: 4.49, 1.19 and 1.45 thousand ind./ha, respectively. The dynamics of the total fish density in the water area of the riverbed depression is described by a polynomial regression equation, and the only influencing factor is water temperature. The dynamics of the density of taxonomic groups in the water area of the riverbed depression demonstrated the following characteristic features: for the density of cyprinids the influencing factors were temperature and the water level; for percids, coregonids and pikes — the density of cyprinids, cyprinids and percids, respectively; for burbots and sturgeons — the water level. The study revealed that turbidity has high reliable correlation with the water level and temperature.

DIRECT SOLUTION OF POLYNOMIAL REGRESSION OF ORDER UP TO 3

This article presents results related to the direct solution of the polynomial regression parameters based on the analytical solving of regression equations. The analytical solution is based on the normalization of the values of independent quantity with equidistance steps. The proposed solution does not need to directly solve a system of polynomial regression equations. The direct expressions to calculate estimators of regression coefficients, their standard deviations, and also standard and expanded deviation of polynomial functions are given. For a given number of measurement points, the parameters of these expressions have the same values independently of the range of input quantity. The proposed solution is illustrated by a numerical example used from a literature source.

Design and Testing of Water Jet-Based Fresh Lotus Seed Peeling Machine

Highlights Lotus seed coats are often peeled using water jets, which has not been studied in-depth. A water jet-based lotus seed peeling system is proposed and investigated in detail. Response surface method experiment was used to enhance peeling-machine performance. Proposed system performs well in terms of cost, stability, and percentage of seed peeled and damaged. Abstract. Lotus seed coats are often peeled using water jets. However, currently, existing fresh lotus seed peeling machines could process lotus seeds only if their size differences are small. Due to a lack of research on water jet operation parameters, the peeling process is often associated with a low percentage of seed peeled (PSP) and a high percentage of seed damage (PSD). A new type of water jet-based fresh lotus seed peeling machine was designed and tested to solve the aforementioned problems. Three-factor, three-level response surface method (RSM) was used in experiments to enhance peeling-machine performance, and quadratic polynomial regression equations were obtained via analysis of the experimental data. Results show that these independent variables had significant effects on PSP and PSD: water jet pressure (X1), water jet angle (X2), and processing speed (X3). Specifically, X1 and X2 had an interactive effect on PSD, whereas X1 and X3 both had a strong interactive effect on PSP and PSD. The optimal parameters were a water jet pressure of 0.60 MPa, water jet angle of 20°, and processing speed of 62 rpm, leading to predicted PSP and PSD of 93.13% and 1.65%, respectively (similar to the validation experimental PSP and PSD of 93.89% and 1.76%, respectively); thus, proving the accuracy of the RSM model. In general, the proposed peeling machine provided a high machine capacity (19.65 kg/h), a high percentage of seed peeled, and a low percentage of seed damaged, and all performance indices fulfilled the requirements for use. Keywords: Lotus seed, Parameter optimization, Peeling, Water jet.

The Impact of Seasonality of Agricultural Production on Product Prices in Romania

"This paper aims to determine the possible impact of the seasonality of agricultural production on the prices of these products. Three main products were analysed, the majority of which are wheat, maize and sunflower production. With the help of national databases, product prices were analysed quantitatively and qualitatively, as well as statistically, over the last 5 years by calendar month. A series of researches have been carried out on the data, such as: analysing the dynamics of both annual and monthly prices, as well as determining the correlations between the price level for these products and the calendar month to which these prices refer. Following this analysis, the third-degree polynomial regression equations were determined, showing that product prices follow a sinuous trend, with high prices in the first part of the year, when supply is running out, then, during the harvesting period, there is a drop in price, given the sudden increase in supply on the market, and in the last months of the calendar year, the highest prices are recorded, given the additional cost of storing production following the harvesting campaign. "

Analysis of the unsaturated behaviour of compacted lateritic fine-grained tropical soils for use in transport infrastructure

This article evaluates the unsaturated behaviour of fine-grained tropical soils for application in transport infrastructure. An extensive experimental campaign was conducted covering physical, chemical, mineralogical and mechanical tests. Simulations of a pavement structure were carried out applying the new Brazilian pavement design method (MeDiNa). The results showed highly weathered soils, with classification ranging from A-4 to A-7-6 (TRB), SC, SM, ML and CL (USCS), but of the same class MCT (LA’ – lateritic sandy soils), bimodal SWCC and excellent resilient modulus (394–680 MPa). The compaction directly influenced its unsaturated behaviour. The soils were proved to be suitable for use in the pavement with high traffic, thus enabiling the exploitation of the local materials and reducing costs with transport and acquiring scarce granular materials. Polynomial and linear regression equations were proposed to predict the MR as a function of confining and deviation stresses acting in the subgrade and sub-base layers.

Application of Box-Behnken Design for Ultrasonic-assisted Extraction of Glycyrrhiza Polysaccharide and Antioxidant Activity Analysis

In the present paper, Glycyrrhiza was selected as raw materials, and ultrasonic-assisted water bath extraction method was used to investigate crushed particle size, liquid-material ratio, ultrasonic temperature and ultrasonic time. The ultrasonic-assisted extraction technology of polysaccharide from Glycyrrhiza was optimized by Box-Behnken design methodology and the quadratic polynomial regression equation of the prediction model was established, then the antioxidant activity of the polysaccharide in vitro was determined. According to the model regression analysis, the optimum conditions were as follows: particle size of 127 μm, liquid-material ratio of 19.32 mL/g, ultrasonic temperature of 65 ℃ and ultrasonic time of 30.2 min. Under these conditions, the yield of polysaccharide reached 10.867%. Glycyrrhiza polysaccharide showed ability to scavenge both DPPH· and ABTS+· with IC50 values of 2.80 mg/mL and 1.96 mg/mL, respectively, and there was a certain dose-effect relationship between the scavenging rate of two different free radicals and the concentration of crude polysaccharide extract. The model established in this study can analyze and predict the yield of Glycyrrhiza polysaccharide to determine the natural antioxidant activity, which can provide an experimental basis for the development and utilization of Glycyrrhiza resources.

Petroleum Physical Properties Prediction Application in Enhanced Oil Recovery Process

The Enhanced Oil Recovery (EOR) process is one of the ways in the petroleum exploitation process so that thick oil can be lifted to the surface and produced. The EOR process referred to in this study is the EOR process carried out in previous studies at the EOR laboratory of UPN Veteran Yogyakarta Indonesia by adding biosurfactants and adjusting the temperature. In laboratory experiments, each time an amount of biosurfactant concentration is added and the temperature is adjusted, the calculation must be done repeatedly to determine the amount of viscosity, interfacial tension (IFT), and density. This experiments takes a long time, requires high cost and variety limitation of the condition. The previous research has succeeded in building a model with multivariate polynomial regression equations to predict the value of the physical properties of crude oil from existing data then classify it into three categories using Naive Bayes, i.e., light oil, medium oil, and heavy oil. The physical properties of petroleum measured in the research are viscosity, interfacial tension, and density. The model uses laboratory data which are taken from the test results of Pertamina's KW-55 well as validation. The validation result shows that Multivariate Polynomial Regression has succeeded in predicting the value of viscosity, interfacial tension, and density with error values ranging from 0% to 1% from the sample data. With a low error value, the application can make forecasting with more variable conditions. The model still cannot be used independently without the Python environment, so to be used easily by more users, the model must be built into an independent application that can be installed on the user's device. In this research, the prediction application of petroleum physical properties has been built. The application is made using the Multivariate Polynomial Regression method according to the model in the previous study to predict the physical properties of petroleum, then uses Naïve Bayes to classify the oil. The application completed the several adjustment to shift from model to application, including user interface, system, and database adjustments.

PR-FCM: A polynomial regression-based fuzzy C-means algorithm for attribute-associated data

Partitioning data into internally homogeneous parts is an important problem when mining in situ engineering data. In this paper, a polynomial regression-based fuzzy c-means (PR-FCM) clustering algorithm that utilizes the functional relationships among the attributes of the input dataset is proposed. In this algorithm, a polynomial regression equation is taken as the center of each cluster instead of the cluster prototype used in conventional FCM, and the difference between a sample and a cluster prototype is defined as the distance between the actual value of one attribute and the corresponding predicted value provided by its own polynomial regression equation. An alternating optimization method is designed to optimize the new clustering objective function of the proposed algorithm. A series of experiments on synthetic and real-world datasets are conducted to evaluate the performance of the PR-FCM algorithm, which exhibits higher effectiveness and possesses more advantages than the original FCM algorithm. The PR-FCM algorithm is applied to tunnel boring machine (TBM) operation data from a TBM project in China. The experimental results show that the proposed algorithm can effectively cluster TBM operation data.

The Use of the Dynamics of Changes in Table Eggs during Storage to Predict the Age of Eggs Based on Selected Quality Traits.

Simple SummaryThe freshness is the most important characteristic of table eggs. EU legislation does not provide clear guidelines how to store table eggs or how to elongate their shelf life. Changes occurring in eggs after laying are a natural consequence of the passage of time, and there is no method for precise determination of “age” in a randomly chosen egg. The dynamics of changes of individual quality features of the raw material during its extended storage period of up to 35 days were determined. For this purpose, the evaluation of quality traits was performed daily, and the data thus obtained made it possible to create a multivariate mathematical model which, after further statistical processing, makes it possible to determine with high certainty (above 95%) the age of an egg on the basis of its measurable traits, both non-destructive and destructive. The study allowed us to select easily measurable egg quality traits, whose values clearly change in time. The detailed data of daily variability and methods of data statistical analysis are not only of scientific importance, but are also a useful diagnostic tool in assessing the freshness of table eggs on the basis of their quality characteristics.The aim of the study was to determine daily changes in some egg quality parameters, indirectly reflecting egg freshness, and to assess the possibility of predicting time from laying using mathematical methods. The study material consisted of 365 table eggs of medium (M, ≥53 g and <63 g) and large (L, ≥63 g and <73 g) weight classes (commercial stock, cage system, brown-shelled eggs) collected on the same day. Eggs were numbered individually and placed on transport trays and stored (14 °C, 70% RH). Every day, for 35 days, egg quality characteristics were analyzed (10 eggs per group). The change of traits in time was analyzed on the basis of linear and polynomial regression equations, depending on the trait. Based on model fitting, eight traits were selected as those most affected by storage time: egg weight and specific weight, Haugh units, albumen weight, air cell depth, yolk index, albumen and yolk pH. These traits, excluding those related to the weight, were then used in a multiple linear regression model to predict egg age. All regression models presented in this study were characterized by high predictive efficiency, which was confirmed by comparison of the observed and estimated values.