Advanced Analysis Methods Research Articles

Mapping membrane biophysical nano-environments

AbstractThe mammalian plasma membrane is known to contain domains with varying lipid composition and biophysical properties. However, studying these membrane lipid domains presents challenges due to their predicted morphological similarity to the bulk membrane and their scale being below the classical resolution limit of optical microscopy. To address this, we combine the solvatochromic probe di-4-ANEPPDHQ, which reports on its biophysical environment through changes in its fluorescence emission, with spectrally resolved single-molecule localisation microscopy. The resulting data comprises nanometre-precision localisation coordinates and a generalised polarisation value related to the probe’s environment – a marked point pattern. We introduce quantification algorithms based on topological data analysis (PLASMA) to detect and map nano-domains in this marked data, demonstrating their effectiveness in both artificial membranes and live cells. By leveraging environmentally sensitive fluorophores, multi-modal single molecule localisation microscopy, and advanced analysis methods, we achieve nanometre scale mapping of membrane properties and assess changes in response to external perturbation with methyl-β-cyclodextrin. This integrated methodology represents an integrated toolset for investigating marked point pattern data at nanometre spatial scales.

Analysing Food Safety Compliance in Toronto: Identifying Current Hazards and Challenge

Abstract. This research conducts an in-depth investigation into the current state of food safety compliance in Toronto, thoroughly examining the present condition of food safety in the city. By utilizing a comprehensive dataset that includes a wide variety of dining establishments and their respective inspection results, the study employs advanced statistical methods for both analysis and visualization. These methods include the development of logistic regression models to identify significant patterns and trends. The goal is to highlight areas that require immediate attention or policy adjustments through a detailed examination of inspection reports, types of violations, violation frequencies, inspection dates, and the nature of the violations. Finally, the findings of the study strongly suggest that the government should increase the frequency of inspections, particularly during certain seasons, across various types of food businesses, and for specific types of violations, to enhance local food safety and protect the health of every Toronto resident.

Research on identification and zoning control of territorial spatial risk pattern based on deep learning: A case study of Shenzhen, China

In the era of globalization, digitalization, and diversification, currently societal cities face risks intertwined by uncertainty and complexity. Thus, it is crucial to address risks concerns to achieve modernization of spatial management. Moreover, how to scientifically identify the risk pattern of territorial space and how to map and control it in space becomes a critical and urgent issue to be addressed for achieving modernization of spatial management. Understanding the intricate relationship between land use, environmental factors, and population density is essential for identifying risk patterns that can inform effective disaster management strategies. By employing advanced analytical methods, this study aims to provide a comprehensive framework for recognizing and mitigating territorial spatial risks in an increasingly complex urban landscape. This study used a case study to conduct the unsupervised algorithm of stack autoencoder self-organizing map to analyze the distribution and spatial clustering of Shenzhen's land spatial risk pattern and reveal the driving mechanism of its risk pattern formation in detail. It was found that: (1) The overall territorial spatial risk pattern of Shenzhen presents a distribution trend of "high in the west and low in the east, high in the south and low in the north"; that is, I and II risk areas are clustered in the hilly areas in the northeast and IV and V risk areas in the coastal areas in the southwest; (2) Population density, land use intensity, seawater intrusion, geological disasters, relief amplitude, soil type, soil erosion intensity are the main driving forces for the formation of Shenzhen's territorial spatial risk pattern. In particular, seawater intrusion, population density and land use intensity, soil type were found having significantly synergistic impact on the risk pattern.

Multisource Data-Driven Carbon Price Composite Forecasting Model: Based on Feature Selection and Multiscale Prediction Strategy

Accurate prediction of carbon trading prices is crucial for guiding investors to make informed decisions and assisting governments in formulating scientific carbon trading policies. This paper introduces a multisource data-driven carbon price composite forecasting model, aimed at enhancing prediction accuracy through advanced data processing and analysis methods. The model initially employs a secondary decomposition strategy, including Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD) methods, to decompose the original data series into three sub-sequences of different frequencies. Subsequently, it utilizes the Partial Autocorrelation Function (PACF) and Random Forest algorithm for feature selection to determine the input variables for different frequency sequences and conducts in-depth analysis and selection of influencing factors, including unstructured data. Furthermore, the model employs a multiscale forecasting strategy, combining Particle Swarm Optimization (PSO) enhanced Bidirectional Long Short-Term Memory (BiLSTM) and Extreme Gradient Boosting (XGBoost) models, along with the Autoregressive Integrated Moving Average (ARIMA) method, to predict based on the characteristics of different frequency components. Finally, the forecasts are integrated using PSO-BiLSTM to form a comprehensive forecast. Notably, given the high correlation between the trend series and influencing factor variables, the model jointly predicts them. A case study based on the Guangdong carbon market in China demonstrates that the proposed composite forecasting model outperforms other benchmark models, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) values of 0.4009, 0.2699, and 0.5183%, respectively. This forecasting model provides an effective tool for predicting and analyzing carbon price fluctuations, offering new insights for precise carbon market price predictions.

Facial and dental arch shape in individuals with different bite force levels : A pilot study using geometric morphometrics.

This pilot study aimed to assess the relationship between bite force variation and dental arch and facial shape using geometric morphometrics, an advanced method of statistical analysis that provides adetailed shape analysis of astructure considering the spatial relationship of its parts. The sample consisted of 16German adult men and women. For each individual, maximum bite force was recorded in four positions: maximum intercuspation, protrusion, laterotrusion to the right and to the left. Facial and three-dimensional (3D) dental reconstructions were obtained from 3D facial photographs and 3D scans of dental stone models. Atotal of 51landmarks were placed. General shape variation was assessed by principal component analysis. Partial least squares analyses were performed to evaluate the covariation between bite force, facial shape, and dental shape. There was no clear pattern or statistically significant covariation between our variables. Our results suggest aweak relationship between bite force, dental arch, and facial shape. Considering previous work in this field, we propose that low masticatory loads, characteristic in Western urban populations, may explain this. Further studies should, therefore, address this issue, taking into account effect size, the mechanical properties of the diet, and other relevant variables.

A Non-Invasive Gait-Based Screening Approach for Parkinson’s Disease

Parkinson's disease (PD) presents a significant global health challenge, characterized by the progressive degeneration of dopamine-producing neurons in the brain, resulting in both motor and non-motor symptoms that severely impact quality of life. This study addresses the complexities of PD, highlighting the critical need for early diagnosis to slow disease progression. This research addresses the challenges of early diagnosis, such as the use of unreliable diagnostic techniques and limited healthcare resources. It uses the MMU Parkinson Disease Dataset and applies camera-based data collection to analyze gait patterns that can identify a risk of Parkinson's Disease. The study utilizes computer vision and the AlphaPose framework to analyze video data and detect body key points. By employing machine learning algorithms, including Support Vector Machines (SVM) and CatBoost, showing highly effective in identifying temporal dependencies in gait patterns. The algorithms achieved a high accuracy of 83.33% on the MMU dataset. This method enhances the accuracy of PD detection and enables immediate detection and control of the disease. The combination of advanced data analysis methods and medical knowledge offers new possibilities to develop targeted treatments that improve patient outcomes, demonstrating the potential of machine learning in effectively managing and treating Parkinson's disease. To enhance the generalizability of models, future research should collect extensive and diverse datasets covering various backgrounds and different stages of Parkinson's disease and utilize advanced techniques for extracting features to improve the accuracy of gait analysis.

A review on depth perception techniques in organoid images

Organoids are an in vitro model that can simulate the complex structure and function of tissues in vivo. Functions such as classification, screening and trajectory recognition have been realized through organoid image analysis, but there are still problems such as low accuracy in recognition classification and cell tracking. Deep learning algorithm and organoid image fusion analysis are the most advanced organoid image analysis methods. In this paper, the organoid image depth perception technology is investigated and sorted out, the organoid culture mechanism and its application concept in depth perception are introduced, and the key progress of four depth perception algorithms such as organoid image and classification recognition, pattern detection, image segmentation and dynamic tracking are reviewed respectively, and the performance advantages of different depth models are compared and analyzed. In addition, this paper also summarizes the depth perception technology of various organ images from the aspects of depth perception feature learning, model generalization and multiple evaluation parameters, and prospects the development trend of organoids based on deep learning methods in the future, so as to promote the application of depth perception technology in organoid images. It provides an important reference for the academic research and practical application in this field.

Application of Advanced Algorithms in Port State Control for Offshore Vessels Using a Classification Tree and Multi-Criteria Decision-Making

This article examines the methods and application of classification trees and multi-criteria decision-making in the process of holding offshore vessels in port (Port State Control—PSC). This work aims to improve the efficiency and precision of the control processes in the detention of offshore vessels by using advanced analytical methods. Methodologically, a classification decision tree was used to identify the most important risk factors, enabling a faster and more accurate assessment of the possibility of detaining offshore vessels in port. Multi-criteria decision-making (MCDM) also enabled the simultaneous assessment of multiple factors, ensuring a balanced, robust, accurate, and objective approach. The research results show that the integration of these methods into the PSC process can significantly increase the safety of shipping and reduce the operating costs of offshore vessels. The application of these analytical tools can lead to a more systematic and transparent inspection process. This paper suggests further research and training of inspectors in the use of these techniques to maximize their applicability and effectiveness. Finally, this paper emphasizes the potential of classification trees and MCDM for safer and more efficient maritime transport by improving PSC procedures.

Methods for separating the noise produced by the wheels and track during a train pass-by

AbstractRolling noise is produced by vibration of the wheels and track, induced by their combined surface roughness. It is important to know the relative contributions of the different sources, as this affects noise control strategies as well as acceptance testing of new rolling stock. Three different techniques are described that aim to use pass-by measurements to separate the wheel and track components of rolling noise. One is based on the TWINS model, which is tuned to measured track vibration. The second is based on the advanced transfer path analysis method, which provides an entirely experimental assessment. The third is based on the pass-by analysis method in combination with static vibroacoustic transfer functions which are obtained using a reciprocity method. The development of these methods is described and comparisons between them are presented using the results from three experimental measurement campaigns. These covered a metro train, a regional train and a high-speed train at a range of speeds. The various methods agree reasonably well in terms of overall trends, with moderate agreement in the mid-frequency region, and less consistent results at low and high frequency.

Exploring the Impact of In Vitro-Transcribed mRNA Impurities on Cellular Responses.

Advances in mRNA technology have enabled mRNA-based therapies to enter a new era of medicine. Such therapies benefit from a single, standardized in vitro transcription (IVT) manufacturing process applicable to a wide range of targets. This process includes several downstream purification steps, which aim to eliminate impurities that potentially affect safety and efficacy. However, it is not fully understood which impurities are the most critical; hence, some efforts are still needed to establish the correlation between RNA impurities and their role in limiting therapeutic efficacy. To study this relationship, we produced in vitro-transcribed mRNAs using several bacteriophage T7 RNA polymerases, including one wild-type and four engineered variants. Important attributes of the mRNA such as integrity, purity, and functional activity were then measured using advanced physicochemical and cellular assays. For impurities including abortive transcripts, mRNAs containing partial poly(A) tails, and double-stranded (ds)RNA byproducts, structure-function relationships have been established by tracking cellular responses (i.e., protein expression, reactogenicity) in multiple cell models. By varying the T7 RNA polymerase, different levels of sense-antisense dsRNA byproducts were measured by mass photometry, contributing directly to immunological reactogenicity in bone marrow-derived dendritic cells. T7 RNA polymerase differences with regard to short (<20 nucleotides) 3'-loopback dsRNA byproducts were also further investigated using native mass spectrometry by precisely resolving these impurities at the nucleotide level. Overall, this study highlights the importance of developing sensitive and advanced analytical methods to characterize IVT mRNA impurities and understand their interaction with cellular machinery in order to ensure quality control of RNA-based therapies.

Construction of engineered heterojunction based on CdS and MgO material co-integrated into a flat plane-like graphene for tetracycline decontamination: Ecological hazard assessment and toxicity alleviation

This study encompasses the photocatalytic decomposition activity of an appreciable ternary CdS/MgO/graphene heterostructured composite (denoted as CMG) for the decontamination of recalcitrant tetracycline (TTC) from aqueous environment under LED light illumination. The physicochemical characteristics of the as-prepared catalysts were elucidated utilizing a series of advanced analytical methods including XRD, FTIR, DRS, BET, EIS, TEM, and FESEM. The CMG architectures reveal vigorous photocatalytic performance towards the decontamination of TTC upon exposure to the LED light, the decontamination rate is approximately 5.5, 4 and 3 times higher than neat graphene, MgO and CdS, respectively. Under the optimized conditions (i.e., pH: 7, CMG dosage: 0.5 g. L−1, light intensity: 75 W and TTC content: 30 mg. L−1), the remarkable degradation rate of TTC (98 % in 120 min) was achieved by the CMG/LED system. An inhibitory impact of anions during the photocatalyst process was recorded as follows: Cl− > NO3− > SO42− > PO43−. To provide a comprehensive understanding of the photocatalyst's behavior and shed light on its mechanism, various analytical techniques were utilized including band structure evaluation, EIS, and capture experiments. The active agents trapping experiments evidenced that OH radicals are the predominant decomposing agents participated in the TTC decontamination. Furthermore, the CMG composite exhibited a noticeable performance during six cycling treatment experiments, inducing its remarkable capability for practical applications. The photocatalytic mechanism of the TTC degradation route over the CMG/LED system was unraveled on the basis of the LC-MS analysis. The ECOSAR software calculations forecasted that CMG/LED system could be regarded as an ecologically benign technology to eliminate antibiotic-related hazards to the living individuals and the environment. In extension, the ecological risk assessment of TTC and its intermediates was scrutinized over CMG/LED system for the first time ever, revealing the excellent performance of the target system in diminishing the actual potential ecological risk.

Multiple damage detection in sandwich composite structures with lattice core using regression-based machine learning techniques

The abstract discusses the importance of Structural Health Monitoring (SHM) in ensuring the reliability and health of structures. It introduces a novel approach for recognizing cracks and delamination in sandwich composite structures using advanced methods for data analysis and machine learning algorithms. The research leverages artificial intelligence and machine learning to accurately identify and locate damage such as delamination and cracks in composite sandwich layers, which can significantly enhance troubleshooting performance, improve detection accuracy, and reduce the time and cost associated with repairs. The article presents a damage detection technique utilizing regression analysis for sandwich composite structures with a lattice core, capable of identifying and locating multiple cracks and delamination, as well as simultaneous defects in the structure. The analysis is conducted based on the sandwich composite structure’s healthy and damaged conditions in Abaqus software, and acceleration responses under random forces obtained through the finite element method were used to train various machine learning models, including regression algorithms like k-Nearest Neighborhood Regression (KNN), Light Gradient Boost Machine (LGBM), and Decision Tree Regression (DTR) to detect the damage location. The results indicate that the regression (LGBM), k-Nearest neighborhood, and decision tree regression (DTR) were the most successful functions, respectively, and the damage classification models accurately identified the damage and its location in the composite structure, achieving an accuracy rate of approximately 98.8%.

Advanced Analytical Methods for Risk Mitigation in Multimodal Freight Transport

Stakeholders in multimodal freight transport encounter significant challenges due to the multitude of unknowns and inherent risks that can adversely affect operations. The subjective nature of the information complicates the identification and assessment of these risks, making them particularly challenging in the context of multimodal transport, where the potential consequences can be substantial. This research intends to provide a comprehensive understanding of the asymmetries in risks associated with multimodal freight transport by identifying and evaluating quantitative hazards. By integrating fuzzy set theory and failure mode and effects analysis (FMEA), the study offers a structured approach to statistically forecast risks, addressing imprecision in traditional risk assessments. Qualitative interviews conducted with multimodal freight transport operators in Thailand reveal critical insights, including the identification of high-priority risks such as delays from regulatory compliance, inadequate infrastructure, and inefficiencies in stakeholder communication. The findings of this study not only highlight these pressing issues but also provide actionable strategies to mitigate risks, thereby enhancing the operational resilience of multimodal freight transport systems.

Analysis of the Spatiotemporal Distribution Characteristics and Influencing Factors of High-Grade Cultural Heritage in the Region Surrounding Mount Song

Situated in central China, the region surrounding Mount Song is a pivotal area within the Central Plains, renowned for its extensive and high-grade cultural heritage. This study aims to analyze the spatiotemporal distribution of this heritage using advanced spatial pattern analysis methods, including the Average Nearest Neighbor Ratio and Kernel Density Estimation within ArcGIS. Our findings reveal a distinct continuity in temporal distribution and a pronounced clustering in spatial terms. By investigating the underlying factors such as the geographical setting, historical political capitals, cultural and religious influences, water transportation systems, and traditional Fengshan ceremonies, this research elucidates the dynamics shaping this distribution. This study’s outcomes provide essential insights for policymakers and stakeholders involved in the protection and revitalization of cultural heritage, ensuring informed decision making for sustainable cultural conservation.

Developing Methods for Assessing the Impact of Digital Innovations on the Economic Efficiency of Enterprises: Methodology for Measuring ROI of Digital Projects

This study aims to develop methodologies for assessing the return on investment (ROI) of digital projects, crucial for understanding the economic impact of digital innovations on enterprises. Traditional ROI metrics often fail to capture the comprehensive value generated by digital projects. The proposed integrative framework employs advanced analytical methods, including cost-benefit analysis, real options analysis, and predictive analytics, to evaluate both tangible and intangible benefits. It incorporates dynamic risk management and scenario planning to address uncertainties and future challenges. Continuous feedback and iterative improvements ensure the framework's accuracy and relevance, enabling enterprises to optimize economic efficiency and strategic decision-making. This holistic approach offers an understanding of the multifaceted value of digital innovations.

Thermodynamic and advanced exergy analysis of Rankine Carnot battery with cascaded latent heat storage

Cascaded latent heat storage offers significant advantages in Rankine Carnot battery systems by minimizing exergy destruction during heat transfer processes. However, the system performance and the sources of exergy destruction remain unclear, and the configuration of multiple phase change materials remains ambiguous. This paper investigates a Rankine Carnot battery system integrated with cascaded latent heat storage using energy, conventional exergy, and advanced exergy analysis methods. First, a novel phase change material partitioning configuration scheme is proposed, considering the phase change of the working fluid, significantly improving temperature matching within the storage. The number of stages in heat storage devices significantly affects system performance, with roundtrip and exergy efficiencies increasing by 32.2 % and 15.2 % as the number of stages rises from 1 to 4, though further increases have minimal impact. Advanced exergy analysis on a 4-stage system indicates that the endogenous avoidable exergy destruction of the expander, compressor, evaporator, and condenser constitutes 12.4 %, 12.0 %, 10.5 %, and 7.0 % of the total system exergy destruction, respectively, highlighting the priority components for system improvement. Furthermore, raising the heat storage temperature by 45 °C reduces roundtrip efficiency by 26.5 % while increasing power output by 63.2 %. Increasing the heat pump’s evaporation temperature by 30 °C improves roundtrip efficiency by 151.1 % but reduces power output by 33.7 %. Reducing the amount of waste heat and surplus electricity results in a significant decrease in the roundtrip efficiency and exergy efficiency, which is mainly caused by the reduction in the isentropic efficiencies of compressor and expander under partial load conditions. The findings on the priority components for system improvement and the configuration of cascaded latent heat storage provide an effective approach for enhancing Rankine Carnot batteries.

Smart Supervision of Public Expenditure: A Review on Data Capture, Storage, Processing, and Interoperability with a Case Study from Colombia

Effective fiscal control and monitoring of public management are critical for preventing and mitigating corruption, which in turn, enhances government performance and benefits citizens. Given the vast amounts of data involved in government operations, applying advanced data analysis methods is essential for strengthening fiscal oversight. This paper explores data management strategies aimed at enhancing fiscal control, beginning with a bibliometric study to underscore the relevance of this research. The study reviews existing data capture techniques that facilitate fiscal oversight, addresses the challenges of data storage in terms of its nature and the potential for contributing to this goal, and discusses data processing methods that yield actionable insights for analysis and decision-making. Additionally, the paper deals with data interoperability, emphasizing the importance of these practices in ensuring accurate and reliable analysis, especially given the diversity and volume of data within government operations. Data visualization is highlighted as a crucial component, enabling the detection of anomalies and promoting informed decision-making through clear and effective visual representations. The research concludes with a case study on the modernization of fiscal control in Colombia, focusing on the identification of user requirements for various data-related processes. This study provides valuable insights for modern audit and fiscal control entities, emphasizing that data capture, storage, processing, interoperability, and visualization are integral to the effective supervision of public expenditure. By ensuring that public funds are managed with transparency, accountability, and efficiency, the research advances the literature by addressing both the technological aspects of data management and the essential process improvements and human factors required for successful implementation.

Assessment of ChatGPT-4 in Family Medicine Board Examinations Using Advanced AI Learning and Analytical Methods: Observational Study.

This research explores the capabilities of ChatGPT-4 in passing the American Board of Family Medicine (ABFM) Certification Examination. Addressing a gap in existing literature, where earlier artificial intelligence (AI) models showed limitations in medical board examinations, this study evaluates the enhanced features and potential of ChatGPT-4, especially in document analysis and information synthesis. The primary goal is to assess whether ChatGPT-4, when provided with extensive preparation resources and when using sophisticated data analysis, can achieve a score equal to or above the passing threshold for the Family Medicine Board Examinations. In this study, ChatGPT-4 was embedded in a specialized subenvironment, "AI Family Medicine Board Exam Taker," designed to closely mimic the conditions of the ABFM Certification Examination. This subenvironment enabled the AI to access and analyze a range of relevant study materials, including a primary medical textbook and supplementary web-based resources. The AI was presented with a series of ABFM-type examination questions, reflecting the breadth and complexity typical of the examination. Emphasis was placed on assessing the AI's ability to interpret and respond to these questions accurately, leveraging its advanced data processing and analysis capabilities within this controlled subenvironment. In our study, ChatGPT-4's performance was quantitatively assessed on 300 practice ABFM examination questions. The AI achieved a correct response rate of 88.67% (95% CI 85.08%-92.25%) for the Custom Robot version and 87.33% (95% CI 83.57%-91.10%) for the Regular version. Statistical analysis, including the McNemar test (P=.45), indicated no significant difference in accuracy between the 2 versions. In addition, the chi-square test for error-type distribution (P=.32) revealed no significant variation in the pattern of errors across versions. These results highlight ChatGPT-4's capacity for high-level performance and consistency in responding to complex medical examination questions under controlled conditions. The study demonstrates that ChatGPT-4, particularly when equipped with specialized preparation and when operating in a tailored subenvironment, shows promising potential in handling the intricacies of medical board examinations. While its performance is comparable with the expected standards for passing the ABFM Certification Examination, further enhancements in AI technology and tailored training methods could push these capabilities to new heights. This exploration opens avenues for integrating AI tools such as ChatGPT-4 in medical education and assessment, emphasizing the importance of continuous advancement and specialized training in medical applications of AI.

Microfluidic paper-based colorimetric chemo-sensing of formaldehyde using tetramethylbenzidine-conjugated gold nanoflowers: A point-on-demand approach for efficient chemosensing of volatile organic compounds

The presence of formaldehyde, a common environmental aldehyde and a recognized carcinogen, poses a significant health risk to humans. Monitoring its levels in environmental samples and human biofluids is crucial for both pollution control and advancements in health science. This study introduces an effective colorimetric method utilizing UV–Vis spectroscopy to identify formaldehyde in real samples. Various types of gold nanoparticles (AuNPs) with diverse sizes and structures were employed to develop a photochemical chemosensing device. The method exhibited high sensitivity in detecting formaldehyde, particularly through the use of gold nanoflowers (AuNFs) at a pH of 6.15, with a low quantification limit of 0.1 µM and a linear range of 0.6 µM to 1 M. Furthermore, the method was successfully utilized to assess formaldehyde in human biofluids. Additionally, a paper-based microfluidic colorimetric opto-device was incorporated for on-site and on-demand formaldehyde screening. This study showcases the application of AuNPs for the precise and sensitive detection of formaldehyde in human biofluids, offering potential for advanced analytical methods targeted at formaldehyde and other volatile organic compounds (VOCs). The practicality, affordability, and portability of the microfluidic paper-based colorimetric device (µPCDs) make it a promising choice for point-of-care VOC testing. This represents the first demonstration of µPCDs for the selective photochemical sensing of toxic substances, and its one-step process that does not rely on novel sorbents, membranes, or external stimuli, making it easily scalable. The potential for µPCDs to be broadly applicable to the selective monitoring of formaldehyde from complex bio-matrices is evident. This method demonstrated robust performance in terms of accuracy, sensitivity, reproducibility, and selectivity.

Migration of Chemical Compounds from Packaging Materials into Packaged Foods: Interaction, Mechanism, Assessment, and Regulations.

The migration of chemical compounds from packaging polymers to food presents a multifaceted challenge with implications for food safety and public health. This review explores the interaction between packaging materials and food products, focusing on permeation, migration, and sorption processes. The different migration mechanisms of contact migration, gas phase migration, penetration migration, set-off migration, and condensation/distillation migration have been discussed comprehensively. The major migrating compounds are plasticizers, nanoparticles, antioxidants, light stabilizers, thermal stabilizers, monomers, oligomers, printing inks, and adhesives, posing potential health risks due to their association with endocrine disruption and carcinogenic effects. Advanced analytical methods help in the monitoring of migrated compounds, facilitating compliance with regulatory standards. Regulatory agencies enforce guidelines to limit migration, prompting the development of barrier coatings and safer packaging alternatives. Furthermore, there is a need to decipher the migration mechanism for mitigating it along with advancements in analytical techniques for monitoring the migration of compounds.