Coefficient Of Determination Values Research Articles

Pengaruh Metode Lattice Terhadap Hasil Belajar Operasi Perkalian Bilangan Cacah dengan Menggunakan Model PBL di Kelas IV Sekolah Dasar

This study aims to determine the effect of using the lattice method on the learning outcomes of multiplication operations of integers in class IV elementary school. This research is experimental research in the form of Quasi Exsperimental Type Nonequivalent Control Group Design. The sampling technique used cluster random sampling. In hypothesis testing, simple linear regression test, correlation analysis, and t test were used. Based on correlation analysis, the experimental class obtained rxy 0.825 which is categorized as very strong. The coefficient determination value obtained is 68.06%, so it can be interpreted that the use of the lattice method has an effect of 68.06% on the learning outcomes of multiplying small numbers in class IV. Through the t-test in the form of an independent t-test based on the posttest scores of the experimental and control classes, a tcount of 2.04613 was obtained with a ttable of 2.014 at a significance level of 0.05. When the tcount and ttable are compared, 2.04613> 2.014. This means that Ho is rejected, and Ha is accepted, so it can be concluded that there is a significant effect of using the lattice method with the PBL model on the learning outcomes of multiplication operations of small numbers in class IV elementary schools

Experimental and theoretical investigation of water sorption on lemon leaves: New perspectives with statistical physical modeling

A deeper analysis of the water sorption isotherms on lemon leaves (40, 50, and 60 °C) was carried out using statistical physical modeling to obtain information about the sorption mechanism and better understand the process. For this, a theoretical model was developed based on the grand canonical potential to describe the experimental data, and the isotherms were analyzed using the finite multilayer sorption model. The experimental sorption isotherms obtained for lemon leaves are Type II. The model used to analyze the process showed excellent predictive capacity, with coefficient of determination values above 0.97 and root mean square error below 0.08. The interpretation of the estimated parameters indicated that the water molecules are parallel to the surface of the lemon leaves, which allows the formation of finite layers of water. The system is directly affected by temperature, where increasing temperature decreases the density of receptor sites. The process occurs through physisorption since the calculated sorption energies are less than 50 kJ mol−1. The thermodynamic analysis of the process shows that water sorption on leaves is exothermic and spontaneous. This work contributes to a better understanding the drying process through the results obtained.

AI-Enabled Portable E-Nose Regression Predicting Harmful Molecules in a Gas Mixture.

The biomimetic electronic nose (e-nose) technology is a novel technology used for the identification and monitoring of complex gas molecules, and it is gaining significance in this field. However, due to the complexity and multiplicity of gas mixtures, the accuracy of electronic noses in predicting gas concentrations using traditional regression algorithms is not ideal. This paper presents a solution to the difficulty by introducing a fusion network model that utilizes a transformer-based multikernel feature fusion (TMKFF) module combined with a 1DCNN_LSTM network to enhance the accuracy of regression prediction for gas mixture concentrations using a portable electronic nose. The experimental findings demonstrate that the regression prediction performance of the fusion network is significantly superior to that of single models such as convolutional neural network (CNN) and long short-term memory (LSTM). The present study demonstrates the efficacy of our fusion network model in accurately predicting the concentrations of multiple target gases, such as SO2, NO2, and CO, in a gas mixture. Specifically, our algorithm exhibits substantial benefits in enhancing the prediction performance of low-concentration SO2 gas, which is a noteworthy achievement. The determination coefficient (R2) values of 93, 98, and 99% correspondingly demonstrate that the model is very capable of explaining the variation in the concentration of the target gases. The root-mean-square errors (RMSE) are 0.0760, 0.0711, and 3.3825, respectively, while the mean absolute errors (MAE) are 0.0507, 0.0549, and 2.5874, respectively. These results indicate that the model has relatively small prediction errors. The method we have developed holds significant potential for practical applications in detecting atmospheric pollution detection and other molecular detection areas in complex environments.

Effects of Gravel Percentage to Compaction Density and Stability of Embankment

Embankments are frequently used in the construction of road, railway, airport, dams and other types infrastructure. Soil compaction is a crucial part of the construction process of embankment. Thus, soil compaction needs to be conducted and investigated in such a way so embankment will not experience large settlement that could lead to collapse. Generally, soil compaction density testing is performed using the sand cone method according to ASTM D-1556. The material used in this test is not carefully considered, which may lead to inaccurate results. In this study, the sand cone correction test according to ASTM D-4718 which consider the percentage of gravel is established to identify the compaction density of embankment and the effect to its stability. 16 secondary data of compaction density have been collected from the construction of embankment. According to the data, the compaction density of the embankment has met the specifications. The relationship between the gravel percentage and the compaction density is obtained. With a determination coefficient (R2) value of 0.805, it can be understood that these two variables have a highly significant correlation. Furthermore, to understand the relationship between the compaction density and the stability of the embankment, a slope stability analysis was conducted on the embankment using the Fellenius method. The result shows that when the compaction density of the embankment meet the specifications, embankment is stable with the safety factor (SF) of 1.511. Furthermore, both variables have a very strong relationship. The safety factor of the embankment is increased as the compaction density increases.

Performance prediction for network-level flexible pavements using an optimised ANN approach

ABSTRACT Considering that the road network shares traffic flow as a system, this study aimed to predict the network-level flexible pavement performance, using an optimised artificial neural network (ANN) approach to predict the pavement performance of each individual road, and the Ford-Fulkerson algorithm to determine the weight coefficients of roads. ANNs were developed to predict distress conditions, functional conditions and structural performance using the inputs of the pavement age and structural, traffic and climatic conditions. ANNs were trained with Long-Term Pavement Performance (LTPP) program data, and almost all the coefficient of determination values between ANN outputs and measured results are larger than 0.9. This ANN approach was optimised by establishing grey models to provide predictive short-term performance data for training ANNs, and employing Kalman Filter to modify the long-term performance prediction. Applications in typical LTPP sections validated the effectiveness of the optimisation method. This study used the reduction in road network capacity due to the assumed unavailability of a certain road to quantify its role (weight coefficient), which was calculated using the Ford-Fulkerson algorithm. This network-level performance prediction approach was applied in a 25-expressways network in Shanghai, which validated the good generalisation capabilities of ANNs and feasibility of the Ford-Fulkerson algorithm.

Quality detection of alfalfa hay based on multisource information fusion: A preliminary study

The quality detection of alfalfa hay is crucial for the development of animal husbandry. In this study, a method for quality detection of alfalfa hay based on the fusion of multisource information including near-infrared spectroscopy, image processing techniques, and electronic nose is proposed. After SG convolution smoothing, feature wavelengths were extracted using Competitive Adaptive Re-weighting Scheme and Successive Projections Algorithm from the spectral data. The image data were denoised using adaptive wavelet thresholding, and color and texture features were extracted using color histograms and random forest algorithms, respectively. Electronic nose data using principal component analysis was used for data dimensionality reduction. Support Vector Machine, Extreme Learning Machine, and Multi-Layer Perceptron were employed to establish quality detection models of alfalfa hay based on spectroscopy, image, gas information, and their combination, respectively. Experimental results demonstrate that the fusion of near-infrared spectroscopy, image data, and gas information effectively enhances the classification accuracy of the model. The accuracy of the test set reaches 100%, with root mean square error and determination coefficient values of 0.1728 and 0.9239, respectively, surpassing prediction models established solely on individual information. This study provides new insights into alfalfa hay quality detection.

Combustion Characteristics, Kinetics and Thermodynamics of Peanut Shell for Its Bioenergy Valorization

To realize the utilization of peanut shell, this study investigates the combustion behavior, chemical kinetics and thermodynamic parameters of peanut shell using TGA under atmospheric air at the heating rates of 10, 20, and 30 K/min. Results indicate that increasing the heating rate leads to higher ignition, burnout, and peak temperatures, as observed in the TG/DTG curves shifting to the right. Analysis of combustion performance parameters suggest that higher heating rates can enhance combustion performances. Kinetic analysis using two model-free methods, KAS and FWO, shows that the activation energy (Eα) ranges from 93.30 to 109.65 kJ/mol for FWO and 89.72 to 103.88 kJ/mol for KAS. The data fit well with coefficient of determination values (R2) close to 1 and the mean squared error values (MSE) less than 0.006. Pre-exponential factors using FWO range from 2.19 × 106 to 8.08 × 107 s–1, and for KAS range from 9.72 × 105 to 2.25 × 107 s–1. Thermodynamic analysis indicates a low-energy barrier (≤±6 kJ/mol) between activation energy and enthalpy changes, suggesting easy reaction initiation. Furthermore, variations in enthalpy (ΔH), Gibbs free energy (ΔG), and entropy (ΔS) upon conversion (α) suggest that peanut shell combustion is endothermic and non-spontaneous, with the generation of more homogeneous or well-ordered products as combustion progresses. These findings offer a theoretical basis and data support for the further utilization of agricultural biomass.

Automatic non-destructive estimation of polyphenol oxidase and peroxidase enzyme activity levels in three bell pepper varieties by Vis/NIR spectroscopy imaging data based on machine learning methods

The browning process of food products if often formed upon cutting and damage during their processing, transport, and storage, amongst other potential sources and reasons. Enzymic browning can be mainly due to polyphenol oxidase (PPO) and peroxidase (POD) enzymes. Visible/near-infrared (Vis/NIR) imaging spectroscopy in the range of 350–1150 nm was used in this study for automatic and non-destructive evaluation of PPO and POD activity levels in three bell pepper varieties (red, yellow, orange; N = 30), with a total of 30 inputs samples in each variety. The spectral data were then modeled by the partial least squares regression (PLSR) throughout the whole spectral range, without using any subset of the most effective wavelength (EW) values. Regression determination coefficient (R2) values for the estimation (prediction) of POD enzyme activity levels were 0.794, 0.772, and 0.726 for red, yellow, and orange bell peppers, respectively, all over the validation set. At the same time, the activity levels of PPO enzyme over bell peppers showed R2 values of 0.901, 0.810, and 0.859, for red, yellow, and orange bell peppers, respectively, all over the validation set. In addition, a combination of support vector machine (SVM) with either genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), or imperialistic competitive algorithms (ICA) hybrid machine learning (ML) techniques were used to select the optimal (discriminant) spectral EW wavelength values, and regression performance was consistently improved, to judge from higher regression fit R2 values. Either 14 or 15 EWs were computed and selected in order of their discriminative power using previously mentioned ML techniques. The hybrid SVM-PSO method resulted the best one in the process of selecting the most effective wavelength values (nm). On the other hand, three regression methods comprising PLSR, multiple least regression (MLR), and neural network (NN), were employed to model the SVM-PSO selected EWs. The ratio of performance to deviation (RPD), the R2 and the root mean square error (RMSE), over the test set, for the non-linear NN regression method exhibited better results as compared to the other two regression methods, being closely followed by PLSR, and therefore NN regression method was selected as the best approach for modeling the most effective spectral wavelength values in this study.

Pengaruh Leverage, Ukuran Perusahaan, Pertumbuhan terhadap Profitabilitas Perusahaan (Sub sektor makanan & minuman)

The aim of this research is to empirically test that leverage, sales growth and company size influence profitability simultaneously or partially. The data analysis technique uses multiple linear regression, with a sample of 12 food and beverage sub-sector companies listed on the IDX. Partial research results: H1: there is no influence of the leverage variable (X1) on the profitability variable (Y), so hypothesis H1 is rejected; H2: there is an influence of the sales growth variable (X2) on the profitability variable (Y), so hypothesis H2 is accepted; H3: there is an influence of the company size variable (X3) on the profitability variable (Y), so hypothesis H3 is accepted. Simultaneously, it can be interpreted that the variables leverage (X1), sales growth (X2) and company size (X3) together influence the profitability variable (Y). Meanwhile, the determination test provides information about the coefficient of determination value, namely the contribution or influence of variables X1,

Spider silk biological material for Q-switched temperature sensor

Abstract A temperature sensor using compact design and highly sensitivity of side polished fiber and fiber Bragg grating as the sensing elements, Q-switched pulse fiber laser source with spider silk as a saturable absorber is proposed and demonstrated. Spider silk sample was gently collected from a live spider, specifically, the jumping spider of Plexippus sp. which then incorporated within the laser cavity by deposited the silk onto the surface of the fiber ferrule to facilitate the generation of Q-switched pulse fiber laser. The temperature variations were detected by monitoring the pulse train and radio frequency shift from the oscilloscope. The performance of the side polished fiber sensor probe shows a sensitivity of 0.1522 kHz/°C, with 0.9479 coefficient of determination value as the temperature increased from -0.5 °C to +3.1 °C. Besides, a linear temperature re-sponse in the range of 25 °C – 55 °C with a sensitivity of 0.0423 kHz/°C, and a linear correlation coefficient of 0.951 was experimentally achieved for a fiber Bragg grating device. The spider silk as a saturable absorber material is compatible with fiber optic interconnection and the temperature sensing characteristics were successfully demonstrated. The sensor's straight-forward design further enhances its desirability as a sensor for temperature monitoring, including in the field of biological treatments, consumer electronics, detection of chemical analytes, and medical diagnosis.

Deep Learning Prediction of Triplet-Triplet Annihilation Parameters in Blue Fluorescent Organic Light-Emitting Diodes.

The triplet-triplet annihilation (TTA) ratio and the rate coefficient (kTT) of TTA are key factors in estimating the contribution of triplet excitons to radiative singlet excitons in fluorescent TTA organic light-emitting diodes. In this study, deep learning models are implemented to predict key factors from transient electroluminescence (trEL) data using new numerical equations. A new TTA model is developed that considers both polaron and exciton dynamics, enabling the distinction between prompt and delayed singlet decays with a fundamental understanding of the mechanism. In addition, deep learning models for predicting the kinetic coefficients and TTA ratio are established. After comprehensive optimization inspired by photophysics, determination coefficient values of 0.992 and 0.999 are achieved in the prediction of kTT and TTA ratio, respectively, indicating a nearly perfect prediction. The contribution of each kinetic parameter of polaron and exciton dynamics to the trEL curve is discussed using various deep-learning models.

Machine learning in epidemiology: Neural networks forecasting of monkeypox cases.

This study integrates advanced machine learning techniques, namely Artificial Neural Networks, Long Short-Term Memory, and Gated Recurrent Unit models, to forecast monkeypox outbreaks in Canada, Spain, the USA, and Portugal. The research focuses on the effectiveness of these models in predicting the spread and severity of cases using data from June 3 to December 31, 2022, and evaluates them against test data from January 1 to February 7, 2023. The study highlights the potential of neural networks in epidemiology, especially concerning recent monkeypox outbreaks. It provides a comparative analysis of the models, emphasizing their capabilities in public health strategies. The research identifies optimal model configurations and underscores the efficiency of the Levenberg-Marquardt algorithm in training. The findings suggest that ANN models, particularly those with optimized Root Mean Squared Error, Mean Absolute Percentage Error, and the Coefficient of Determination values, are effective in infectious disease forecasting and can significantly enhance public health responses.

Optimizing the model-prediction of date palm fronds-derived producer gas and third generation biodiesel powered dual-fuel engine by employing Bayesian-optimized Boosted Regression Trees for enhanced prognostics

The global issue concerning the dual problem of economic energy shortage as well as greenhouse gas emission needs urgent attention. Due to its abundance and carbon neutrality, waste biomass is a good energy conversion feedstock. This research suggests a unique method for using date palm frond waste biomass-derived producer gas and algal biodiesel as sustainable diesel engine fuels. The Bayesian-optimized neural networks and Bayesian-optimized Boosted Regression Trees were employed to optimize engine combustion and emission performance. This was further enhanced with the application of k-cross fold to prevent the model from overfitting during training. The working of dual-fuel engines is a complex phenomenon owing to the employment of two different types of fuel one is liquid (biodiesel + diesel) while the other one is gaseous (Producer gas). The present endeavor was undertaken to handle this with the application of advanced data learning methods. The engine was tested at different compression ratios, engine loads, and fuel injection pressures for brake thermal efficiency, diesel fuel substitution, and emission. Both the Bayesian-optimized neural networks and Bayesian-optimized Boosted Regression Trees could predict the engine output with good prediction efficiency with comparable performance, however, the Bayesian-optimized Boosted Regression Trees-based models were more robust with a coefficient of determination value in the range of 0.9617–0.9999 and mean squared errors as low as 0.001. The mean absolute percentage error was very low in the range of 0.0316–5.231. The Theil’s U2 values were in the range of 0.0029–0.0126.

Soft Computing Modeling Including Artificial Neural Network, Non-linear, and Linear Regression Models to Predict the Compressive Strength of Sustainable Mortar Modified with Palm Oil Fuel Ash

Producing sustainable concrete and mortar is the idea that have been investigated by many researchers in the world through using waste materials in the mortar or concrete compositions to reduce the thread on the environment. In order to predict the compressive strength of mortar, this article proposes statistical models utilising linear regression (LR), nonlinear regression (NLR), and artificial neural network (ANN) based on experimental data collected from prior research in the field. The pozzolanic material used in mortar is agricultural waste, specifically Palm Oil Fuel Ash (POFA). In order to choose the most efficient model, the proposed models were evaluated using several statistical parameters. When compared to alternative models (Linear regression, nonlinear regression, and ANN), the one developed using ANN proved to be the most efficient in terms of approach, giving lower values for root mean square error (RMSE) and mean absolute error (MAE) which were 5.11, and 4.175 respectively. The suggested ANN model performed well according to the scatter index (SI), and the coefficient of determination value (R2) value was 34% more than the R2 in the LR model and 23% greater in the NLR model.

Desalination RO reject brine as a novel-based porous geopolymer for phosphorus removal from contaminated media

Desalination reverse osmosis reject brine-based porous geopolymer (RO/GP) was produced and investigated as an improved adsorbent for phosphorus (P) removal from tainted seawater, brackish water, river water, and municipal wastewater effluent. The RO reject brine/geopolymer was produced by reacting metakaolin and fly ash with a Na-alkali activator and anhydrous RO brine as a sacrificial template. The influence of RO reject brine content on water absorption, porosity, mechanical, and structural properties were examined. The developed RO-based geopolymers exhibited the greatest porosity (58.3–84.2 % vol%), a significant ratio of open porosity to total porosity (67.7–92.1 %), and outstanding compression strength (3.6–10.4 MPa). The produced RO/GP structure has an adsorption capacity of 92.4 mg-P/g. The sequestration reaction of phosphorus by RO/GP is of pseudo-second-order kinetic behavior via Chi-squared (χ2), RMSE, and determination coefficient (R2) values. Regarding their agreement with Langmuir behavior, the phosphorus adsorption uptakes occur in homogeneous and monolayer states. The reaction is exothermic, spontaneous, and favorable. The RO/GP exhibits significant affinity for phosphorus co-existing with Cl−, Na+, SO42−, K+, HCO3−, and Ca2+. The RO/GP shows high safety during the adsorption investigation, with a total cost of 0.32 $/kg-P.

Pengaruh Cash Flow Volatility, Firm Size, Foreign Debt dan Exchange Rate terhadap Hedging (Studi pada Perusahaan Sektor Energi Tahun 2018-2022)

The energy sector has a sharp increase in the value of imports resulting in a trade deficit that causes losses for the country as the rupiah depreciates against the exchange rate. This makes the energy sector vulnerable to exchange rate risk. Any hedging instruments such as swaps, options, forwards & options and natural hedging are generally used by companies to protect foreign exchange exposures. This study aims to examine the effect of Cash Flow Volatility (CFV), Firm Size (SIZE), Foreign Debt (FD) and Exchange Rate (ER) on the use of hedging using various instruments used by companies. By using purposive sampling, 35 energy sector companies in 2018-2022 samples were chosen for the study, and data were gathered from annual repots available on the Indonesia Stock Exchange website. The analysis employed aims to examine the impact of independent variables on the dependent in this study is logistic regression analysis. The results of this study prove that cash flow volatility, firm size and foreign debt have a positive and significant effect on hedging. While the exchange rate has no effect on hedging. The coefficient value of determination in which the variation of the dependent variable can be explained eh the variability of independent variables by 17 percent.

Performance investigation of osmotically assisted reverse osmosis using explainable machine learning models: A comparative study

Osmotically assisted reverse osmosis (OARO) can treat highly concentrated water and achieve higher recovery rates than reverse osmosis (RO). However, existing mathematical models cannot satisfactorily explain OARO systems due to certain limitations. To explore OARO more dynamically, we aimed to analyze OARO performance based on the laboratory-scale data acquired under different operating conditions, by implementing six machine learning (ML) models with the OARO data, evaluating their predictive capabilities, and investigating the effects of input variables on output variables using Shapley Additive Explanation (SHAP) analysis. In predicting the OARO outputs, the ensemble ML models outperformed the conventional ML models. Incorporating FO/RO membrane datasets significantly enhanced the ML model accuracy and mitigated overfitting issues. Regarding the prediction of water flux, the adjusted determination coefficient values increased by 0.478 for Random Forest and 0.569 for Extreme Gradient Boost in the test step. The SHAP analysis consistently ranked the importance of the input variables for OARO outputs. It revealed the presence of potentially influential but previously unrecognized input variables, such as temperature, for the draw concentration. The results of this study are expected to provide a better understanding of OARO.

Pemodelan Regresi Semiparametrik B Spline (Studi Kasus: Pengaruh Harga Emas dan Minyak Mentah Dunia Terhadap Indeks Harga Saham Gabungan)

<p dir="ltr"><span>The increases of the world gold and crude oil prices have a big role as a main factor that effect composite stock price index, the effect can make investors to buy stock from Bursa Efek Indonesia. Regression semiparametric used in this research for a purpose to get combined parametric and nonparametric with B Spline approach. B Spline is a development of spline to overcome weaknesses in making singular matrix at a high order spline with many knot points and close together. Variable parametric component is composite stock price index with crude oil price, and variable nonparametric component is composite stock price index with gold price that got obtained from January 2015 until December 2022. The result from this research is best regression semiparametric B-Spline modelling can be obtained using some combination of order and knot points. The optimal point is obtained on 2nd order using 4 knot point (1.135;1.319,15;1.320,75;1.323,25) with a minimum GCV value is 100.227,8. The best measure of goodness with a coefficient of determination value (R-Square) obtained a value 78,8%, because the value is more than 67% make it as a strong model. MAPE value is 3,37% that has a value less than 10 %, make this model have a perfect forecasting ability.</span></p><p><span id="docs-internal-guid-09fba3ae-7fff-a25a-8197-7dd90ea8b1e1"><span>Keywords: </span><span>Gold; Crude Oil; Composite Stock Price Index; Semiparametric B Spline; GCV</span></span></p>

Validation of Patient Intrinsic Factors on Pre-Delay Coronary CT Scan Angiography (Coronary CTA) Bolus Tracking

Background: There is a variation in the pre-delay scan time on the Caronaria CTA tracking bolus examination, which give a constitution against the acquisition time and the contrast.Purpose: This study aims to determine the influence’s intrinsic factors on the pre-delay scan time.Method: Analytical research with a retrospective approach’s cross-sectional design measures independent variables: aortic diameter, systolic blood pressure, age, heart rate (HR), urea, creatinine, and body mass index (BMI). The dependent variables: the time pre-delay scan is performed simultaneously; its properties are momentary and observed under current conditions. Each object was observed three times, then the data normality and influence relationships between variables were analyzed with the bivariate and multivariate analysis approaches. Data was surveyed in June–August 2023 at a Hospital in Jakarta.Results: Bivariate analysis showed a significant relationship between aortic diameter and pre-delay scan time with a value r = 0,577, having a positive pattern, as well as having a dependency 33.3% with p = 0,000 significance. While a very strong relationship is indicated by systolic blood pressure with r = 0.534 value and positive patterns, has a dependence of 30.4% with p = 0.000 significance value. Other instrumental factors: creatinine and BMI have a moderate relationship with r = 0.308 and r = 0.338 and are positive with a determination coefficient of 0.095 and 0.115. Urea, age, and heart rate have r = 0.174, 0.200, and 0.116. The determination coefficient values are under 0.04.Conclusion: The intrinsic factors: aortic diameter, systolic pressure, creatinine, urea, age, and heart rate have influenced positive CTA scan delays although different determination coefficients.Keywords: CTA Coronarya, bolus tracking, pre-delay scan time, aortic diameter, systolic blood pressure.

An intelligent indicating label using mulberry pigment, polylactic acid and rutile TiO2 nanoparticle for monitoring the freshness of cold fresh pork

The purpose of this work is to develop a new type of intelligent indicating label with high sensitivity to volatile bioamine, anti-ultraviolet light and anti-pigment leakage characteristics by electrospinning for real-time visual monitoring the freshness of cold fresh pork. By investigating pH discoloration sensitivity and light stability of plant pigments with different structures, mulberry pigment was selected as the indicator of indicating label. The content of label components (mulberry pigment, polylactic acid and TiO2 nanoparticle) significantly affected label performance (light stability, responsiveness to trimethylamine, and anti-leakage of mulberry pigment), and the optimal component content was determined to be 6.01% (w/w) of polylactic acid, 0.62% (w/w) of TiO2 nanoparticles and 0.29% (w/w) of mulberry pigment by response surface test based on label performance as response value. A close correlation (the correlation coefficient R values of 0.86–0.97) between the total color difference (ΔE) of the label and the freshness indexes (total plate count, total volatile basic nitrogen content, pH, thiobarbituric acid value and sensory score) of cold fresh pork during cold storage was revealed, and the freshness level of unknown pork samples could be well quantitatively predicted by partial least squares regression models with the determination coefficient (R2) values of 0.89–0.92, facilitating producers, dealers and consumers to know pork quality promptly.