Quantitative Analysis Model Research Articles

Real Time-Cutting Algorithmic Trading

a. Brief overview of algorithmic trading. Algorithmic trading, also known as algo trading or automated trading, refers to the use of computer algorithms and mathematical models to execute trading orders in financial markets. The primary goal of algorithmic trading is to achieve efficient and optimized execution of trading strategies, leveraging the speed and precision of computer systems. Here is a brief overview of algorithmic trading: 1. **Automation:** Algorithmic trading involves automating the process of buying or selling financial instruments, such as stocks, bonds, currencies, or commodities. The trading decisions are based on predefined rules and algorithms, eliminating the need for manual intervention. 2. **Speed:** One of the key advantages of algorithmic trading is its ability to execute trades at extremely high speeds. Algorithms can analyze market conditions, make decisions, and execute trades in a fraction of a second, responding to market changes much faster than human traders. 3. **Quantitative Analysis:** Algorithmic trading relies heavily on quantitative analysis and statistical models. Traders develop algorithms that are based on mathematical formulas, historical price data, technical indicators, and other relevant factors to identify trading opportunities. 4. **Market Making:** Some algorithmic trading strategies are designed for market making, where traders provide liquidity to the market by quoting bid and ask prices. Market-making algorithms aim to profit from the spread between the buying and selling prices. 5. **Arbitrage:** Algorithmic trading is often employed for arbitrage strategies, which involve exploiting price discrepancies between different markets or instruments. These discrepancies may arise due to inefficiencies, delays in information dissemination, or other market imperfections. 6. **Risk Management:** Algo trading systems typically incorporate risk management measures to control exposure and mitigate potential losses. These measures may include setting stop-loss orders, position sizing algorithms, and other risk controls. 7. **Backtesting:** Before deploying algorithms in live markets, traders often perform extensive backtesting. This involves running historical market data through the algorithm to simulate how it would have performed in the past. This helps refine and optimize the algorithm for real-world conditions. 8. **Machine Learning:** Some advanced algorithmic trading strategies incorporate machine learning techniques to adapt and learn from changing market conditions. Machine learning algorithms can analyze vast amounts of data to identify patterns and make predictions. 9. **Regulatory Considerations:** Algorithmic trading is subject to various regulations to ensure fair and orderly markets. Regulatory bodies may impose rules regarding algorithmic trading practices, transparency, and risk management to prevent market manipulation and protect investors. Algorithmic trading has become increasingly prevalent in financial markets, with institutional investors, hedge funds, and proprietary trading firms utilizing sophisticated algorithms to execute trades efficiently and capitalize on market opportunities.

The Role of Generative AI in Shaping Millennials and Gen Z’s Orientation Toward Luxury Products

This study investigates the impact of Artificial Intelligence (AI)-generated promotional content and status motivations on the perception of luxury brands among Millennials and Generation Z. The author adopts a mixed-method research approach that integrates a quantitative survey and structural equation modeling analysis with qualitative Natural Language Processing (NLP). The findings reveal that generative AI promotional content impacts millennials and Gen Z’s purchase intention due to its perceived entertainment, transparency, and usefulness. Status motivations had a stronger impact on purchase intention, and demographic characteristics were significantly different in their results. The study is the first to examine the human characteristics of perceived expertise, transparency, and entertainment in an AI context in light of status consumption motivation. As AI is expected to have more human-like characteristics and behavior, testing theories centered on human influence in an AI context is crucial for marketing theory and practice.

Research Advances of Groundwater Nitrate Pollution and Source Apportionment in China

Groundwater nitrate (NO3-) contamination in China has become a serious environmental problem, especially in agricultural production areas, which greatly affects the safety of drinking groundwater and requires urgent attention. In this study, the main sources of groundwater nitrate in China were reviewed, including atmospheric deposition, soil nitrogen, agricultural fertilization, and fecal sewage, among which fecal sewage and agricultural fertilization were the main reasons for the excessive groundwater nitrate. The application of hydrochemical analysis, multivariate statistical analysis, stable isotope tracer method, and microbial source tracking in the source apportionment of groundwater nitrate was summarized. All types of source apportionment methods had certain limitations. It is suggested that a variety of methods should be used to identify the source of groundwater nitrate, and the contribution rate of different pollution sources should be calculated using multivariate statistical analysis and isotope quantitative analysis models. The source apportionment of nitrate pollution has undergone a process from qualitative to quantitative research. At present, the SIAR and MixSIAR models based on δ15N-NO3-and δ18O-NO3- have been used widely to analyze the source of nitrate. However, due to the overlap of isotope characteristic values of different input end-members, the difference in δ15N-NO3-and δ18O-NO3- values under different spatial and temporal changes, and the influence of isotope fractionation in the process of nitrogen migration and transformation, the results calculated by the model remain uncertain. It is necessary to further optimize the analytical method of the model to obtain the source of nitrate pollution and its contribution rate more accurately to further aid in the scientific management of groundwater resources.

Transforming architecture: The synergy of digital fabrication and parametric design

This paper delves into the transformative synergy between digital fabrication and parametric design in the field of architecture, tracing their evolution, principles, and integration that herald a new era of architectural innovation. Digital fabrication, with its roots in the mid-20th century, has evolved from basic CNC machinery to advanced 3D printing and robotic assembly, enabling a shift from mass production to mass customization. Parametric design, through its algorithmic nature, allows for the manipulation of complex geometries based on set parameters, facilitating designs that adapt to environmental data for enhanced sustainability. The integration of these two methodologies has fostered a powerful design-to-production workflow, enabling architects to materialize complex designs with unprecedented precision and efficiency. Highlighted through cutting-edge techniques and notable case studies, this paper provides a comprehensive overview of the current landscape and potential future directions of architectural practices shaped by these technologies. It underscores the significant efficiency gains, structural optimization, and innovation potential that this integration offers, backed by quantitative analysis and mathematical modeling. This exploration not only emphasizes the shift towards more sustainable, efficient, and innovative architectural solutions but also reflects on the broader implications for the future of architectural design and construction.

Exploring the structural, functional, and biocompatibility aspects of marine bacterial extracellular polysaccharides for biopharmaceutical applications

Marine bacterial extracellular polysaccharides (EPS) represent a unique class of biopolymers with vast potential in biopharmaceutical research due to their diverse structural characteristics, functional properties, and biocompatibility. This study delves into the comprehensive analysis of the monosaccharide composition, molecular weight distribution, and conformational dynamics of marine bacterial EPS, employing advanced analytical techniques such as Nuclear Magnetic Resonance (NMR) spectroscopy, Mass Spectrometry (MS), and Size-Exclusion Chromatography coupled with Multi-Angle Laser Light Scattering (SEC-MALLS). Furthermore, we explore the functional applications of these EPS in biofilm inhibition, immunomodulation, and as innovative drug delivery systems, highlighting their role in enhancing antibiotic efficacy and mitigating resistance. Through quantitative analysis and mathematical modeling, including rheological studies and kinetic modeling of biodegradation, this work assesses the EPSs biocompatibility and biodegradability, essential for their safe use in biomedicine. Our findings underscore the potential of marine bacterial EPS in developing novel biopharmaceutical formulations that are not only effective but also environmentally sustainable and biocompatible, paving the way for future advancements in medical and therapeutic applications.

A systematic 3-D magnetic particle imaging simulation model for quantitative analysis of reconstruction image quality

Background and objectiveMagnetic particle imaging (MPI) is an emerging imaging technology in medical tomography that utilizes the nonlinear magnetization response of superparamagnetic iron oxide (SPIO) particles to determine the in vivo spatial distribution of nanoparticle contrast agents. The reconstruction image quality of MPI is determined by the characteristics of magnetic particles, the setting of the MPI scanner parameters, and the hardware interference of MPI systems. We explore a feasible method to systematically and quickly analyze the impact of these factors on MPI reconstruction image quality. MethodsWe propose a systematic 3-D MPI simulation model. The MPI simulation model has the capability of quickly producing the simulated reconstruction images of a scanned phantom, and quantitative analysis of MPI reconstruction image quality can be achieved by comparing the differences between the input image and output image. These factors are mainly classified as imaging parameters and interference parameters in our model. In order to reduce the computational time of the simulation model, we introduce GPU parallel programming to accelerate the processing of large complex matrix data. For ease of use, we also construct a reliable, high-performance, and open-source 3-D MPI simulation software tool based on our model. The efficiency of our model is evaluated by using OpenMPIData. To demonstrate the capabilities of our model, we conduct simulation experiments using parameters consistent with a real MPI scanner for improving MPI image quality. ResultsThe experimental results show that our simulation model can systematically and quickly evaluate the impact of imaging parameters and interference parameters on MPI reconstruction image quality. ConclusionsWe developed an easy-to-use and open-source 3-D MPI simulation software tool based on our simulation model incorporating all the stages of MPI formation, from signal acquisition to image reconstruction. In the future, our simulation model has potential guiding significance to practical MPI images.

Carbon Emission Measurement Method of Regional Power System Based on LSTM-Attention Model

With the acceleration of the green and low-carbon transformation of the power system, it is very important to calculate and analyze the carbon emissions of the urban power system. In order to effectively grasp the carbon emission distribution of power system and reduce the carbon emission of power system, this paper proposes a systematic carbon emission measurement method for regional power system. Firstly, the quantitative analysis model of driving factors for regional power system carbon emissions is constructed, and the direction and measures of low-carbon transformation and green collaborative development of regional power system are proposed. Secondly, energy consumption scenarios under different constraints are established to support the collaborative control path of CO2. It provides key data and a theoretical basis for the low-carbon development of the power industry. Finally, a systematic carbon emission measurement method based on LSTM-Attention is proposed. Finally, the effectiveness of the proposed method is verified by an example analysis, and the effective path of carbon emission reduction in the future is analyzed.

The intervention of local public authorities and the impact of the COVID-19 pandemic in Romania: a subnational analysis.

The COVID-19 pandemic had a strong territorial dimension, with a highly asymmetric impact among Romanian counties, depending on pre-existing vulnerabilities, regions' economic structure, exposure to global value chains, specialization, and overall ability to shift a large share of employees to remote working. The aim of this paper is to assess the role of Romanian local authorities during this unprecedented global medical emergency by capturing the changes of public spending at the local level between 2010 and 2021 and amid the COVID-19 pandemic, and to identify clusters of Romanian counties that shared similar characteristics in this period, using a panel data quantitative model and hierarchical cluster analysis. Our empirical analysis shows that between 2010-2021, the impact of social assistance expenditures was higher than public investment (capital spending and EU funds) on the GDP per capita at county level. Additionally, based on various macroeconomic and structural indicators (health, labour market performance, economic development, entrepreneurship, and both local public revenues and several types of expenditures), we determined seven clusters of counties. The research contributes to the discussion regarding the increase of economic resilience but also to the evidence-based public policies implementation at local level.

A Study of China's Electricity Supply Based on Correlation and Time Series Modeling

In this study, the relationship between electricity supply and various indicators is deeply explored through multi-factor correlation analysis, quantitative analysis and prediction modeling. First, the Pearson correlation coefficient analysis was used, and it was revealed that electricity supply is significantly and positively correlated with gross domestic product (GDP), per capita GDP, urban population ratio, and consumption level of residents. Subsequently, linear regression methods were used for quantitative analysis, and forecasting models for electricity supply were established, including ARIMA model and Holt-Winters seasonal exponential smoothing model. Finally, the forecast value of China's electricity supply for the next 37 years was derived by synthesizing the forecasting models. The results of this study provide an important reference for optimizing the structure of electric power supply and improving the supply efficiency, and are of guiding significance for the formulation of relevant policies and planning.

Quantitative analysis model based on surface‐enhanced Raman spectroscopy of malachite green adsorbed on gold nanoparticles film substrates

AbstractIn order to investigate the adsorption process of malachite green (MG) on gold nanoparticles, a simple gold nanoparticles‐assembled film was prepared as a substrate of surface‐enhanced Raman spectroscopy (SERS), and it was soaked in MG solutions of different concentrations. The kinetic adsorption process was investigated by SERS method and density functional theoretical calculations. When saturated adsorption was achieved, the relationship between the characteristic SERS band signal intensity and the logarithm of solution concentration of MG was consistent with Temkin adsorption isotherm model, where the R2 value was greater than 0.995, and the linear range was 1 × 10−3–1 × 10−7 M. Finally, a SERS quantitative analysis model of the relationship between the adsorption properties of surface species and the bulk concentration was established. According to the electrostatic interaction and co‐adsorption, we proposed the surface adsorption configurations and adsorption process of MG on the nanostructured gold films.

Analysis of Spatial Spillover Effects of Digital Inclusive Finance on Urban-Rural Income Gap

This article mainly analyzes the changes in urban-rural disparities under digital inclusive finance, which can be divided into three levels of research objectives, gradually and layer by layer. Firstly, based on the theoretical foundation of accelerating the development of rural financial markets and reducing economic disparities between rural and urban areas through digital inclusive finance, a panel threshold model is constructed to quantitatively analyze the threshold effect of digital inclusive finance on the urban-rural income gap, and to study how to reduce the threshold effect and narrow the digital divide between rural and urban areas; Secondly, from the perspective of spatial spillover, this study examines the positive or negative spatial spillover effects of digital inclusive finance on regional urban-rural income disparities, in order to analyze the direction of the impact of digital inclusive finance on urban-rural income disparities. Using the Spatial Panel Lag Model (SAR) and Spatial Durbin Model (SDM) to empirically test their inherent correlation and impact; Thirdly, there are differences in policies and resource allocation among provinces and cities, as well as differences in urban-rural income disparities in different regions. Based on this, a quantitative analysis model is constructed to analyze the path and methods of digital inclusive finance promoting urban-rural income disparities.

Predicting fatigue crack growth rate of cement-based and asphalt paving materials based on indirect tensile cyclic loading tests

Fatigue crack growth rate (FCGR) is a critical indicator that reflects the rate at which material deterioration occurs under cyclic loads. However, there is currently a lack of a mechanistic quantitative analysis and prediction of the FCGR of paving materials under external indirect tensile (IDT) cyclic loads. To address this issue, this study aims to develop a mechanistic model for quantitative analysis and prediction of the FCGR in paving materials subjected to external IDT cyclic loads based on the J-integral based Paris’ law. Two types of paving materials, namely cement-treated aggregates and asphalt mixtures, were selected to demonstrate the results and model of the FCGR. The results indicate that the derived J-integral serves as the driving force for crack growth and is influenced by horizontal tensile stress, initial resilient modulus, damage density, specimen size, and surface energy of the paving materials. The fatigue life of cement-treated aggregates is primarily influenced by the crack initiation stage, while for asphalt mixtures, the crack growth stage plays a more significant role. The application of Paris’ law is found to be effective in predicting the FCGR at stable crack growth stage. The coefficients of Paris’ law exhibit minimal variation across different stress levels and loading frequencies, and can be utilized to evaluate the fatigue crack resistance of different paving materials.

Quantifying the Impact of Single-Event Upsets on Neural Networks with Uncertainty Analysis

The Convolutional neural networks have gradually penetrated into various fields of social life, in addition to aerospace devices, neural network chips are also indispensable for target recognition and detection, etc. Due to the complex environment in space, neural network chips are extremely easy to be damaged by cosmic rays, so it is urgent to evaluate and enhance the robustness of neural network chips. However, most of the existing researches are monolithic error injection of neural networks and do not build relevant models for quantitative analysis. The goal of this paper is to analyse the effects of single-event upsets (SEUs) induced by the penetration of SRAM storage areas in a quantized neural network chip by energetic particles from space. Firstly, a SEU probability model as well as an uncertainty assessment model are established, and the effect of SEU on the reliability of CNNs is evaluated by fault injection with the upset rate of four chip types. The results of the error injection experiments show that the Virtex-5 type of rollover rate decreases the average accuracy of VGG-16 by 20%, while the other three types of error injection have an impact of about 11% on the accuracy of the neural network. Quantitative experimental results confirm the hypothesis that SEU increases the uncertainty of neural networks. The quantification of these effects contributes to the study of reinforcement of neural network chips.

International application of global forest product model and the reference to China

PurposeThis article summarizes the international scientific research output of global forest product models, infers future research trends and provides reference for quantitative analysis and mathematical modeling of Chinese forest product problems, with the aim of contributing to promoting domestic production of Chinese forest products and strengthening international trade competitiveness of forest products.Design/methodology/approachIn 1999, Joseph Buongiorno, a scholar at the University of Wisconsin in the United States of America, proposed the global forest products model (GFPM), which was first applied to research in the global forestry sector. GFPM is a recursive dynamic model based on five assumptions: macroeconomics, local equilibrium, dynamic equilibrium, forest product conversion flow and trade inertia. Using a certain year from 1992 to present as the base period, it simulates and predicts changes in prices, production and import and export trade indicators of 14 forest products in 180 countries (regions) through computer programs. Its advantages lie in covering a wide range of countries and a wide variety of forest products. The data mainly include forest resource data, forest product trade data, and other economic data required by the model, sourced from the Food and Agriculture Organization (FAO) of the United Nations and the World Bank, respectively.FindingsCompared to international quantitative and modeling research in the field of forest product production and trade, China's related research is not comprehensive and in-depth, and there is not much quantitative and mathematical modeling research, resulting in a significant gap. This article summarizes the international scientific research output of global forest product models, infers future research trends, and provides reference for quantitative analysis and mathematical modeling of Chinese forest product problems, with the aim of contributing to promoting domestic production of Chinese forest products and strengthening international trade competitiveness of forest products.Originality/valueOn the basis of summarizing and analyzing the international scientific research output of GFPM, sorting out the current research status and progress at home and abroad, this article discusses potential research expansion directions in 10 aspects, including the types, yield and quality of domestic forest product production, international trade of forest products, and external impacts on the forestry system, in order to provide new ideas for global forest product model research in China.

Qualitative and quantitative detection of microplastics in soil based on LIF technology combined with OOA-ELM/SPA-PLS

Traditional methods for detecting soil microplastics include chemical digestion, density separation, staining, etc. These methods often only provide qualitative analysis of microplastics and are difficult to accurately quantify. To address this issue, laser induced fluorescence (LIF) technology is introduced, which can achieve high sensitivity, non-destructive, and rapid detection, and improve the accuracy and efficiency of qualitative and quantitative analysis of soil microplastics. Therefore, based on LIF technology, a qualitative prediction model for optimizing Extreme Learning Machine (ELM) using Osprey Optimization Algorithm (OOA) and a quantitative prediction model combining Continuous Projection Algorithm (SPA) with Partial Least Squares Algorithm (PLS) were established. Qualitative analysis used eight types of microplastics, with AS, PA66, ABS, PS, POM, PBT, PET, and PVC as experimental samples. Initially, spectral data is obtained using LIF technology and the raw spectral data is pre-processed using polynomial smoothing algorithm (SG) and moving average (MA) methods. Subsequently, factor analysis (FA) and local linear embedding (LLE) techniques are applied to dimensionality reduction and then input into the ELM model. At the same time, the ELM parameter is optimized by using OOA algorithm. In the quantitative analysis, PA66 powder and soil are mixed in different proportions (10:0, 8:2, 5:5, 2:8, 0:10), SG and MA pre-processing are performed on the spectral data obtained by LIF technology, and SPA is used to select the spectral characteristic wavelength for the original data and the pre-processed spectral data, and then a linear regression model is established combined with PLS. In order to facilitate the comparison of data, the experimental results are reserved to 5 decimal places, the experimental results indicate that LIF technology is faster than traditional methods in extracting raw data. The convergence of the OOA-optimized ELM model is better than that of the PSO-optimized ELM model. Among all the models of qualitative analysis, SG-LLE-OOA-ELM had the best prediction effect, with mean square error (MSE) of 0.00834, coefficient of determination (R2) of 0.99841, root mean square error (RMSE) of 0.09134, and mean absolute error (MAE) of 0.07038. Among the models of quantitative analysis, the best effect is SG-SPA-PLS model with R2 of 0.99951.

Quantitative analysis and stochastic modeling of osteophyte formation and growth process on human vertebrae based on radiographs: a follow-up study

Osteophytes are frequently observed in elderly people and most commonly appear at the anterior edge of the cervical and lumbar vertebrae body. The anterior osteophytes keep developing and will lead to neck/back pain over time. In clinical practice, the accurate measurement of the anterior osteophyte length and the understanding of the temporal progression of anterior osteophyte growth are of vital importance to clinicians for effective treatment planning. This study proposes a new measuring method using the osteophyte ratio index to quantify anterior osteophyte length based on lateral radiographs. Moreover, we develop a continuous stochastic degradation model with time-related functions to characterize the anterior osteophyte formation and growth process on cervical and lumbar vertebrae over time. Follow-up data of anterior osteophytes up to 9 years are obtained for measurement and model validation. The agreement test indicates excellent reproducibility for our measuring method. The proposed model accurately fits the osteophyte growth paths. The model predicts the mean time to onset of pain and obtained survival function of the degenerative vertebrae. This research opens the door to future quantification and mathematical modeling of the anterior osteophyte growth on human cervical and lumbar vertebrae. The measured follow-up data is shared for future studies.

A Long-term Interpretation of the Effect of COVID-19 on Food Security: A Lesson for the Future Pandemic Diseases

Background: The global spread of coronavirus disease (COVID-19) could trigger a food security crisis affecting not only impoverished individuals but also urban residents in low- and middle-income countries. Objectives: Given that various strains of COVID-19 impact food security differently, we assessed the food security status of households in Semnan province, Iran, during and after the containment of the omicron variant. Methods: Data from 231 households were gathered using a standard questionnaire that evaluated socioeconomic characteristics, food security based on the U.S. Household Food Security Survey Module, and vulnerability to food insecurity. A quantitative model was utilized to identify factors associated with food insecurity during and post-pandemic. Results: The findings indicated an improvement in household food security following the management of COVID-19, with significant differences observed in food security levels, especially concerning severe food insecurity, during versus after the COVID-19 period. The quantitative model analysis revealed that, with the exception of the head of household's occupation status, personal savings, and the number of educated and employed members within a household, all variables significantly correlated with food security (P < 0.05). Conclusions: These outcomes highlight how the relationship between socioeconomic factors and food security levels may vary depending on the pandemic's timing, environmental conditions, and household resilience, underlining the importance of adaptive policymaking that responds to current circumstances and the evolving links between socioeconomic determinants and food security.

Classification and quantification of adulterants in Alaska pollock based on fourier transform near-infrared spectroscopy with chemometrics

Alaska pollock is one of the most economically valuable and consumed cod species, but the adulterants in the market affect its quality and value. This study used a combination of FT-NIR spectroscopy and chemometrics to construct a qualitative and quantitative analysis model. Before the establishment of detection models, different pre-processing techniques were used to eliminate background interference. Principal component analysis (PCA) and analysis of variance (ANOVA) showed the feasibility of spectral methods for the detection of Alaska pollock adulteration. The modeling results show that the RF classification model had the most suitable prediction results (accuracy: 92.18%, precision: 98.04%, recall: 92.59%, and F1-score: 95.29%), and SVR was the optimal prediction model for quantitative analysis, with an RC2 of 0.941, an RMSEC of 0.060, an RP2 of 0.946, and an RMSEP of 0.062. These results indicate that, based on the developed detection model, adulterants in Alaska pollock could be rapidly detected and accurately quantified. In addition, it provides a reliable basis for rapid and non-destructive detection of and has great potential for determining other seafood species.

Spatiotemporal development of the neuronal accumulation of amyloid precursor protein and the amyloid plaque formation in the brain of 3xTg-AD mice

The amyloid plaque is a hallmark of Alzheimer's disease. The accumulation of the amyloid precursor protein (APP) in the neuronal structure is assumed to lead to amyloid plaque formation through the excessive production of β-amyloid protein. To study the relationship between the neuronal accumulation of APP and amyloid plaque formation, we histologically analyzed their development in the different brain regions in 3xTg-AD mice, which express Swedish mutated APP (APPSWE) in the neurons. Observation throughout the brain revealed APPSWE-positive somata in the broad regions. Quantitative model analysis showed that the somatic accumulation of APPSWE developed firstly in the hippocampus from a very early age (<1 month) and proceeded slower in the isocortex. In line with this, the hippocampus was the first region to form amyloid plaques at the age of 9–12 months, while amyloid plaques were rarely observed in the isocortex. Females had more APPSWE-positive somata and plaques than males. Furthermore, amyloid plaques were observed in the lateral septum and pontine grey, which did not contain APPSWE-positive somata but only the APPSWE-positive fibers. These results suggested that neuronal accumulation of APPSWE, both in somatodendritic and axonal domains, is closely related to the formation of amyloid plaques.

Accurate identification of methanol and ethanol gasoline types and rapid detection of the alcohol content using effective chemical information

Methanol and ethanol gasoline are two emerging clean energy sources with different characteristics. To achieve the qualitative identification and quantitative analysis of the alcohols present in methanol and ethanol gasoline, effective chemical information (ECI) models based on the characteristic spectral bands of the near-infrared (NIR) spectra of the methanol and ethanol molecules were developed using the partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) algorithms. The ECI model was further compared with models built from the full wavenumber (Full) spectra, variable importance in projection (VIP) spectra, and Monte Carlo uninformative variable elimination (MC-UVE) spectra to determine the predictive performance of ECI model. Among the various qualitative identification models, it was found that the ECI-PLS-DA model, which is built using the differences in molecular chemical information between methanol and ethanol, exhibited sensitivity, specificity and accuracy values of 100%. The ECI-PLS-DA model accurately identified methanol gasoline and ethanol gasoline with different contents. In the quantitative analysis model for methanol gasoline, the methanol gasoline and ethanol gasoline ECI-PLS models exhibited the smallest root mean squared error of predictions (RMSEP) of 0.18 and 0.21% (v/v), respectively, compared to the other models. Meanwhile, the F-test and T-test results revealed that the NIR method employing the ECI-PLS model showed no significant difference compared to the standard method. Compared with other spectral models examined herein, the ECI model demonstrated the highest recognition success and determination accuracy. This study therefore established a highly accurate and rapid determination model for the qualitative identification and quantitative analysis based on chemical structures. It is expected that this model could be extended to the NIR analysis of other physicochemical properties of fuel.