Modeling Methods Research Articles

Enhancing Manufacturing Processing Stability and Efficiency with Linear-Regression Analysis: Modeling on a Flow-Drill Screw (FDS) Joining Process

The instability (in processing time) in the flow-drill screwing process is undesired but inescapable due to variations in material property, gauge, and process parameters. A substantial number of materials and lab labor need to be used to test and control the variability of the real manufacturing joining process. To enhance the stability and efficiency of the screwing process, this study seeks multi-disciplinary collaboration by applying linear-regression modeling. Six hundred and forty-eight data points were collected and split into an 80% training set for model building and a 20% test set for model validation. A multiple linear-regression model was built. The results indicated that, compared to variable base level (6000 rpm rotational speed and 1100 N downforce), higher rotational speed (8000 rpm, 7000 rpm), greater downforce (1200 N, 1300 N), and their interaction were significantly associated with passage (processing) time, while the switch point did not significantly affect passage time. The interaction plot and effect size were adopted to provide measurements of the effect magnitude on processing time. The coefficient of determination indicated that 86% of the variability in the passage time can be explained by this model. Statistical analysis, such as data visualization, statistical modeling, and other data-driven analysis methods, can be used to detect underlying relationships between variables, investigate variations, and make predictions in the manufacturing process. The outcomes from the data-driven analysis can benefit from improving the economical manufacturing system, refining the processing setting, and reducing test material costs, labor, and lead time.

Integrating Sustainability and Circular Economy into Consumer-Brand Dynamics: A Saudi Arabia Perspective

This study examines the evolving consumer-brand dynamics within Saudi Arabia, particularly focusing on the integration of sustainability into consumer preferences and brand loyalty. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) and non-parametric statistical methods, this research is anchored in Consumer Culture Theory (CCT) and Self-Congruence Theory, exploring how brand personality and consumer self-congruence influence sustainable consumption behaviors and the adoption of circular economy practices among Saudi consumers. The findings reveal a significant correlation between brand loyalty and sustainable purchase decisions, underscoring the pivotal role of brand identity in fostering eco-conscious consumer choices. Additionally, the research highlights a nuanced landscape of brand loyalty, where attributes, such as social responsibility, though currently less influential, present opportunities for brands to align more closely with consumer values and national sustainability goals. The study also identifies demographic factors, such as age and income level, as significant influencers of sustainable purchasing decisions. This study provides insights into the generational shift towards environmental awareness and the implications for businesses and policymaking within the context of Saudi Arabia’s Vision 2030.

Consequences of slope instability and existing practices of mitigation in hydropower projects of Nepal

IntroductionNepal has witnessed several instances of slope instability associated with Hydroelectric Projects in the recent decades. Despite this, slope instability tends to receive less attention compared to other hazards.ObjectivesThe objective of this study is to investigate fourteen hydroelectric projects with the aim of identifying the types and causes of slope failures. Additionally, it seeks to offer a comprehensive understanding of slope stability conditions and challenges encountered during construction at project sites.MethodsTo accomplish this, the study employs Key Informant Interviews with Questionnaires to delve into the slope stability concerns within Nepal’s hydropower projects. The findings are then validated through an extensive review of pertinent literature. To conduct a thorough assessment of slope stability, the study relies on on-site observations, measurements, investigations, and both in-situ and laboratory tests.ResultsIt becomes evident that the careful selection of study sites, the application of geotechnical methods, and the establishment of regular monitoring are pivotal for ensuring favorable slope stability outcomes.ConclusionA majority of respondents concur that cutslope is the primary factor causing slope instability with 44.4% answering affirmatively. An independent t-test reveals there is no significant difference between the variables. Moreover, the correlation which is closed to 1 suggests that perception of respondents are interconnected and tend to vary in a synchronized manner. Participants in the study widely acknowledge numerical modeling methods as a means to overcome the limitations of slope stability studies.

Prospective on applying machine learning in computational fluid dynamics (CFD) simulation of metallurgical reactors

Metallurgical reactors, especially in ironmaking/steelmaking process, characterise with high-temperature turbulence, multiphase flow, mass/heat transfer and reactions. Computational fluid dynamics (CFD) simulation-based design and optimisation are of significance for efficient metallurgical performance. However, the difficulty and cost to numerically solve the nonlinear controlling equations combined with data pre/post-processing make the whole CFD simulation process time-consuming, which makes it challenging to provide in-time feedback for industrial practices. The popularisation and prosperous development of machine learning bring new opportunities for promoting CFD performance. Discussion has been made on the current research progress of applying machine learning in the whole CFD workflow including pre-processing, solving, and post-processing. Among them, the time consumed by manual pre-processing exceeds 50% of CFD tasks in general. The machine learning or parametric modelling methods can reduce pre-processing time by three orders in the estimate. The solving step is expected to be accelerated by 5 to 1000 times using machine learning. A brief review of machine learning coupled CFD is provided, as is a prospective on its development. Discussion is presented on the main functions, challenges, typical techniques and future directions of applying machine learning in CFD simulation of metallurgical reactors, for the purpose of making CFD faster, more accurate, and better visualised based on the metallurgical practices.

Experimental investigation of collapsible soils under oedometric loading: effect of internal erosion

Collapsible soils, geological formations characterized by pronounced structural instability upon saturation, present a significant challenge in geotechnical engineering. This experimental study aims to deepen our understanding of the mechanisms governing the behavior of these soils, which are particularly prevalent in arid and semi-arid regions subject to extreme hydrological cycles. Collapsible soils, sensitive to climatic variability, undergo profound alterations in their hydromechanical properties due to alternating periods of prolonged drought and intense rainfall events. These fluctuations induce significant modifications in the soil's pore structure, promoting the development of internal erosion phenomena such as suffusion. The results obtained from this experimental investigation on reconstituted samples highlight the combined influence of various factors, such as water content, dry density, particle size, and flow rate, on the collapse potential. It appears that fine particles play a predominant role in the formation of fragile structures, susceptible to collapse upon saturation. Moreover, the intensity and duration of infiltration significantly influence the propagation velocity of ultrasonic waves, thus offering a promising tool for monitoring the evolution of damage within the soil mass. Furthermore, this study provides essential insights for assessing the risks associated with collapsible soils and contributes to the development of more reliable characterization and modeling methods. It emphasizes the need to consider the complex interactions between the physical, chemical, and mechanical properties of the soil, as well as the influence of environmental conditions, for better control of the risks associated with these materials.

H2 norm based parameter optimization of multiple tuned mass dampers for resonance suppression

Flexible dynamic behavior of ultra-precision motion stage significantly deteriorates control performance. To tackle this problem, this paper presents an [Formula: see text] norm based parameter optimization of multiple Tuned Mass Dampers (TMDs) for resonance suppression applied on linear flexible structures, assuming all parameters of multiple TMDs are independent, and considering locations of TMDs as variables. Linear flexible structures with multiple TMDs are modeled as a closed-loop control system through modeling in steps. Genetic algorithm is implemented employing [Formula: see text] norm as the optimization criterion. The effectiveness of the optimization is numerically verified on a free-free thin plate. The effects of uncertainties in TMDs parameters, linear flexible structure parameters, and TMDs failure on the vibration suppression effect are studied. The proposed modeling and optimization methods are promising for application to various flexible structures with provided modal frequencies and mode shape matrix.

Measurement of blood-brain barrier water exchange rate using diffusion-prepared and multi-echo arterial spin labelling: Comparison of quantitative values and age dependence.

Water exchange rate (Kw) across the blood-brain barrier (BBB) is an important physiological parameter that may provide new insight into ageing and neurodegenerative disease. Recently, two non-invasive arterial spin labelling (ASL) MRI methods have been developed to measure Kw, but results from the different methods have not been directly compared. Furthermore, the association of Kw with age for each method has not been investigated in a single cohort. Thirty participants (70% female, 63.8 ± 10.4years) were scanned at 3T with Diffusion-Prepared ASL (DP-ASL) and Multi-Echo ASL (ME-ASL) using previously implemented acquisition and analysis protocols. Grey matter Kw, cerebral blood flow (CBF) and arterial transit time (ATT) were extracted. CBF values were consistent; approximately 50 ml/min/100 g for both methods, and a strong positive correlation in CBF from both methods across participants (r = 0.82, p < 0.001). ATT was significantly different between methods (on average 147.7ms lower when measured with DP-ASL compared to ME-ASL) but was positively correlated across participants (r = 0.39, p < 0.05). Significantly different Kw values of 106.6±19.7min-1 and 306.8±71.7min-1 were measured using DP-ASL and ME-ASL, respectively, and DP-ASL Kw and ME-ASL Kw were negatively correlated across participants (r = -0.46, p < 0.01). Kw measured using ME-ASL had a significant linear relationship with age (p < 0.05). In conclusion, DP-ASL and ME-ASL provided estimates of Kw with significantly different quantitative values and inconsistent dependence with age. We propose future standardisation of modelling and fitting methods for DP-ASL and ME-ASL, to evaluate the effect on Kw quantification. Also, sensitivity and bias analyses should be performed for both approaches, to assess the effect of varying acquisition and fitting parameters. Lastly, comparison with independent measures of BBB water transport, and with physiological and clinical biomarkers known to be associated with changes in BBB permeability, are essential to validate the ASL methods, and to demonstrate their clinical utility.

Comparison of compartmental analytical Blood-Oxygen-Level-Dependent functional Magnetic Resonance Imaging models against Monte Carlo simulations performed over cortical micro-angiograms.

Blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) arises from a physiological and physical cascade of events taking place at the level of the cortical microvasculature which constitutes a medium with complex geometry. Several analytical models of the BOLD contrast have been developed, but these have not been compared directly against detailed bottom-up modeling methods. Using a 3D modeling method based on experimentally measured images of mice microvasculature and Monte Carlo simulations, we quantified the accuracy of two analytical models to predict the amplitude of the BOLD response from 1.5 to 7T, for different echo time (TE) and for both gradient echo and spin echo acquisition protocols. We also showed that accounting for the tridimensional structure of the microvasculature results in more accurate prediction of the BOLD amplitude, even if the values for SO2 were averaged across individual vascular compartments. A secondary finding is that modeling the venous compartment as two individual compartments results in more accurate prediction of the BOLD amplitude compared with standard homogenous venous modeling, arising from the bimodal distribution of venous SO2 across the microvasculature in our data.

DEVELOPMENT OF DEFENSE INDUSTRIAL COMPLEX ENTERPRISES IN CONDITIONS OF DIVERSIFICATION

The article analyzes the relevance of diversification processes for the modern military-industrial complex of the Russian Federation and the difficulties that domestic enterprises face on the path to their deployment. The relevance of the study is due to the role that the military-industrial complex (MIC) plays for the future of the country in general and for its economic dynamics in particular. The study uses modeling methods when forming a model for constructing the interaction of a large regional defense industry enterprise and the external environment, analysis to substantiate the effectiveness of the development of diversification processes implemented in the defense industry enterprise, a scenario approach, when predicting the development of diversification processes based on the object of study and determining its inertial, optimistic and pessimistic development scenarios. It is noted that increasing the production of civilian and dual-use products can become a guarantor of long-term operation for defense enterprises, as well as a path to ensuring their own technological sovereignty and contribution to the technological sovereignty of the country. The question is raised about the optimal way to build integration interaction between defense industry enterprises and other economic entities along the path of diversification processes. The concept of entrepreneurial ecosystems is proposed as the basis for such interaction. A model is presented for the formation of an entrepreneurial ecosystem in the military-industrial complex with the participation of local development institutions, industrial partners, universities, and small and medium-sized businesses. Using a particular example, a conclusion is drawn about the economic feasibility of introducing this model. The obtained materials can be useful to defense industry enterprises in the process of developing programs for the diversification of military production and the development of dual-use technologies.

ADVANCED MACHINE LEARNING AND DEEP LEARNING APPROACHES FOR PREDICTING AVIAN INFLUENZA OUTBREAKS

This study examines avian influenza outbreak identification using advanced machine learning models. The dataset includes geographical coordinates, species information, and temporal data. Initial preprocessing involved converting columns to numerical types and removing outliers with the Isolation Forest algorithm, isolating 5% of the data as outliers. Data cleaning ensured dataset integrity. Feature correlations were analyzed, focusing on those linked to H5 highly pathogenic avian influenza (HPAI). Machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Gradient Boosting, were used to predict the target variable. Performance was evaluated using ROC curve and AUC metrics, with the Random Forest model showing the highest AUC score. Deep learning models, specifically a neural network and a convolutional neural network (CNN), were implemented to enhance predictive accuracy. The CNN outperformed traditional machine learning models, demonstrating the potential of deep learning in epidemiological predictions. The study underscores the efficacy of these techniques in predicting avian influenza outbreaks, highlighting the importance of advanced analytical methods in public health predictive modeling

Radiation Impedance of Rectangular CMUTs.

Recently, capacitive micromachined ultrasound transducers (CMUTs) with long rectangular membranes have demonstrated performance advantages over conventional piezoelectric transducers; however, modeling these CMUT geometries has been limited to computationally burdensome numerical methods. Improved fast modeling methods, such as equivalent circuit models, could help achieve designs with even better performance. The primary obstacle in developing such methods is the lack of tractable methods for computing the radiation impedance of clamped rectangular radiators. This paper presents a method that approximates the velocity profile using a polynomial shape model to rapidly and accurately estimate radiation impedance. The validity of the approximate velocity profile and corresponding radiation impedance calculation was assessed using finite element simulations for a variety of membrane aspect ratios and bias voltages. Our method was evaluated for rectangular radiators with width:length ratios from 1:1 up to 1:25. At all aspect ratios, the radiation resistance was closely modeled. However, when calculating the radiation reactance, our initial approach was only accurate for low aspect ratios. This motivated us to consider an alternative shape model for high aspect ratios, which was more accurate when compared with FEM. To facilitate the development of future rectangular CMUTs, we provide a MATLAB script that quickly calculates radiation impedance using both methods.

Application of Computer Simulation Technology in the Development of Tractor Transmission Systems

Tractors are the most important agricultural power machinery. With the development of tractors toward large-scale and electrification, the design of modern tractor transmission systems increasingly relies on computer simulation technology. However, tractor transmission systems, especially power-shift transmissions and CVTs, are highly complex industrial products that involve specialized knowledge from multiple disciplines. Engineers and researchers find it difficult to choose the correct mathematical model and grasp the trend of technological development when applying simulation technology. To address this issue, we conducted a systematic review of the field and carried out the following work: First, the types and basic principles of tractor transmission systems were introduced; Second, the modeling methods and applications of computer simulation technology in the structural optimization, power-shift control, and energy saving of tractor transmission systems were reviewed; Finally, the method of obtaining simulation parameters through system identification was introduced. Although computer simulation technology has been applied in the development of all tractor transmission systems, there are still some issues that need attention, such as the lack of established shift quality evaluation indicators and driving cycles suitable for tractors, which are crucial for the reliability of simulation results but are rarely studied. These issues need to be addressed in future work.

Geographical and genetic clines in Dracocephalum kotschyi X Dracocephalum oligadenium hybrids: landscape genetics and genocline analyses

Conservation and management of medicinally important plants are among the necessary tasks all over the world. The genus Dracocephalum (Lamiaceae) contains about 186 perennials, or annual herb species that have been used for their medicinal values in different parts of the world as an antihyperlipidemic, analgesic, antimicrobial, antioxidant, as well as anticancer medicine. Producing detailed data on the genetic structure of these species and their response against climate change and human landscape manipulation can be very important for conservation purposes. Therefore, the present study was performed on six geographical populations of two species in the Dracocephalum genus, namely, Dracocephalum kotschyi, and Dracocephalum oligadenium, as well as their inter-specific hybrid population. We carried out, population genetic study, landscape genetics, species modeling, and genetic cline analyses on these plants. We present here, new findings on the genetic structure of these populations, and provide data on both geographical and genetic clines, as well as morphological clines. We also identified genetic loci that are potentially adaptive to the geographical spatial features and genocide conditions. Different species distribution modeling (SDM) methods, used in this work revealed that bioclimatic variables related to the temperature and moisture, play an important role in Dracocephalum population’s geographical distribution within IRAN and that due to the presence of some potentially adaptive genetic loci in the studied plants, they can survive well enough by the year 2050 and under climate change. The findings can be used for the protection of these medicinally important plant.

RESEARCH ON THE DESTRUCTION PROCESSES OF WELDED JOINTS UNDER CONDITIONS OF DETACHMENT OF THE VERTICAL STEEL TANK BODY FROM THE BOTTOM DURING A FIRE

The study presents a comprehensive analysis of welded joints by applying advanced mathematical modelling and experimental research methods. The primary objective was to develop and validate a methodology for mathematical modelling of the behaviour of welded joints under mechanical loading, as well as to assess the effectiveness of the proposed approaches in predicting their strength and durability. Based on the research results, we can draw the following conclusions. The authors obtained the results of welded joint samples and analysed the corresponding curves of the dependence of force factors on their deformations. The study showed that the destruction of welded joints occurs mainly along the weld seam, where the strength is lower than in the base material of the rods. It is consistent with the practical data of the experiment, which confirms that the weld is the weakest link in the structure. The article substantiated a set of provisions and assumptions for implementing mathematical modelling of the behaviour of samples of welded joints imitating welded joints in a reservoir for petroleum products under mechanical tensile tests. The test results justified the mathematical models of the steel material of welded joints of petroleum product tanks and obtained numerical parameters of the mechanical properties of steel of welded joints. Yield strength is in the range of 240–300 MPa, the modulus of elasticity is 190–240 GPa, the hardening modulus is 18–20 GPa, and the ultimate plastic deformation is 0.00334. Based on a complex of well-founded mathematical models of welded joints, we carried out calculations regarding the numerical study of the behaviour of welded joints under the conditions of their mechanical tests. We compared the obtained results with the results of experimental tests and, based on this comparative analysis, established that they were adequate since the Fisher criterion did not exceed the tabular value and the relative error did not surpass 12%. The findings of this research offer valuable insights and practical recommendations for improving the design and evaluation of welded structures, enhancing their performance and safety in engineering applications.

Graph semi-supervised soft sensor modeling based on label propagation algorithm

Abstract Data-driven soft sensor modeling methods have become prevalent in the industry. Nonetheless, the complexity of industrial processes often leads to the absence or difficulty in obtaining key labeled data, and existing methods frequently fail to fully utilize the inherent correlations between variables. This paper proposes a novel graph semi-supervised soft sensor modeling method using the label propagation algorithm to address these issues. This method utilizes correlations within the data to assign pseudo-labels to unlabeled data reasonably and employs Graph Convolutional Networks (GCN) to capture spatial relationships between nodes. Additionally, by embedding a Long Short-Term Memory (LSTM) structure, the model can capture temporal dependencies of the data while focusing on spatial structures. Furthermore, the introduction of a residual structure enables the model to directly learn the differences between inputs and outputs, facilitating information transmission, and improving the model's feature extraction ability. Experiments demonstrate the effectiveness of the method.

Energy-Efficient Algorithm for Data Path Selection in High-Density Wireless Sensor Networks

Relevance. Increasing the number of sensor devices per unit area consequently reduces the physical distance between the devices in the sensor network. Such networks are usually deployed over a large area and the sensor device that wants to transmit a data packet is located far away from the base station. In such a case, the source device is challenged to choose a transmission path that consumes the least amount of energy resources and satisfies the delivery time requirements. The objective of this study is to develop and validate the effectiveness of an empirical algorithm for selecting a transmission path that reduces the energy consumption of high-density wireless sensor networks. Methods of system analysis, analytical modeling, geometry and probability theories are used. Solution. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. When building data transmission routes, any sensor devices can be used as repeaters. At the same time, the increase in the number of repeaters leads to an increase in the time of data delivery. Novelty. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. Any sensor devices may be used as repeaters when constructing data transmission routes.Significance (theoretical). Dependences of power consumption level on various system parameters affecting the processes of functioning of high-density wireless sensor networks have been obtained. Significance (practical). The proposed empirical algorithm for selecting a rational data transmission route in a wireless sensor network allows us to determine, among all alternatives, the route to the coordinator that requires the least power. The effectiveness of the proposed empirical power-saving algorithm is confirmed by simulation modeling.

Analysis of models of measurement and correction of volumetric error of three-axis metal-cutting machine tool

The paper considers the improvement of the quality of products of machined production in connection with the quality of machining of various products and, accordingly, with the accuracy of the equipment used. It is noted that geometrical errors, which account for up to 40 % of the total error of a multi-axis, especially three-axis, metal-cutting machine tool, have the greatest influence on the accuracy of product machining. In order to improve the quality of product machining, it is necessary to correct its parameters according to the measurement information received during machine tool operation, for which the methods of mathematical modelling are used. Models of the volumetric error of a three-coordinate metal-cutting machine tool are developed, taking into account the summands of the first (linear) and second (quadratic) order of smallness. A comparative analysis of the developed models is carried out. The results of experiments on measurement of volumetric errors of the STAN S500 complex and their correction on the basis of the nonlinear theoretical model are given. It is found that in the range of angular errors 0–2.5′ the consideration of quadratic terms of the model in addition to linear ones does not lead to a significant reduction of the volumetric error. It is shown that when processing measurement information of multi-axis metal-cutting machines it is sufficient to limit the consideration of components of the volumetric error not higher than the first order of smallness. The research results are useful for the acceptance and periodic control of the volumetric error of metal-cutting machines, as well as for the programme reduction of the volumetric error.

Modeling and validation of purification of pharmaceutical compounds via hybrid processing of vacuum membrane distillation

This study provides an in-depth examination of forecasting the concentration of pharmaceutical compounds utilizing the input features (coordinates) r and z through a range of machine learning models. Purification of pharmaceuticals via vacuum membrane distillation process was carried out and the model was developed for prediction of separation efficiency based on hybrid approach. Dataset was collected from mass transfer analysis of process to obtain concentration distribution in the feed side of membrane distillation and used it for machine learning models. The dataset has undergone preprocessing, which includes outlier detection using the Isolation Forest algorithm. Three regression models were used including polynomial regression (PR), k-nearest neighbors (KNN), and Tweedie regression (TWR). These models were further enhanced using the Bagging ensemble technique to improve prediction accuracy and reduce variance. Hyper-parameter optimization was conducted using the Multi-Verse Optimizer algorithm, which draws inspiration from cosmological concepts. The Bagging-KNN model had the highest predictive accuracy (R2 = 0.99923) on the test set, indicating exceptional precision. The Bagging-PR model displayed satisfactory performance, with a slightly reduced level of accuracy. In contrast, the Bagging-TWR model showcased the least accuracy among the three models. This research illustrates the effectiveness of incorporating bagging and advanced optimization methods for precise and dependable predictive modeling in complex datasets.

Post-Publication Review: Evolution of the Scientific Publishing Workflow

For a considerable period of time, peer review has been regarded as the cornerstone of academic journals and scientific communication, ensuring the high quality and reliability of published materials. However, in the early decades of the 21st century, a growing number of scholars began to challenge the traditional peer review procedure, questioning its efficacy. This study aims to provide a fresh perspective on the peer review mechanism, with the objective of enhancing the implementation of scientific communication's functionalities. The research employs historical analysis techniques and modelling methods based on Business Process Model and Notation (BPMN) to compare diverse review models in terms of their influence on scientific communication. BPMN is a conventional tool employed for modelling and describing business processes. The evolution of the peer review procedure is explored, encompassing an examination of the factors contributing to current and future transformations in the publishing realm. The author refers to the crisis of the conventional peer review system and the growing prevalence of preprints, serving as exemplars of these transformations. Finally, suggestions for the implementation of the post-publication review workflow in Russia are provided.

Hyperspectral Imaging Combined with Deep Transfer Learning to Evaluate Flavonoids Content in Ginkgo biloba Leaves.

Ginkgo biloba is a famous economic tree. Ginkgo leaves have been utilized as raw materials for medicines and health products due to their rich active ingredient composition, especially flavonoids. Since the routine measurement of total flavones is time-consuming and destructive, rapid, non-destructive detection of total flavones in ginkgo leaves is of significant importance to producers and consumers. Hyperspectral imaging technology is a rapid and non-destructive technique for determining the total flavonoid content. In this study, we discuss five modeling methods, and three spectral preprocessing methods are discussed. Bayesian Ridge (BR) and multiplicative scatter correction (MCS) were selected as the best model and the best pretreatment method, respectively. The spectral prediction results based on the BR + MCS treatment were very accurate (RTest2 = 0.87; RMSETest = 1.03 mg/g), showing a high correlation with the analytical measurements. In addition, we also found that the more and deeper the leaf cracks, the higher the flavonoid content, which helps to evaluate leaf quality more quickly and easily. In short, hyperspectral imaging is an effective technique for rapid and accurate determination of total flavonoids in ginkgo leaves and has great potential for developing an online quality detection system for ginkgo leaves.