Quality Evaluation Method Research Articles

Promotion of a quality standard for Scutellariae radix based on the anti-inflammatory efficacy-oriented quality marker of the effect-constituent index.

The quality control of herbal medicines is key to their clinical efficacy. The multi-component and multi-effect characteristics of herbal medicines have prompted scholars to clarify various factors related to quality evaluation through various methods. Nevertheless, the relationship between chemical properties and their associated clinical efficacy is little reflected in the quality control techniques currently in use. To address the issue, a novel herbal quality standard system based on the efficacy-oriented Q-marker of the effect-constituent index (ECI) is promoted in this study, using Scutellariae Radix (SR), a widely used herbal medicine with anti-inflammation, anti-tumor, anti-viral and other therapeutic effects, as a case study. Combined with chromatographic analysis and bioassay, four Q-markers including baicalin, baicalein, wogonin and oroxylin A were selected based on the anti-inflammatory efficacy of SR. The ECI model of SR was constructed by combining the content determination of the Q-markers via ultra-high-performance liquid chromatography-triple quadrupole mass spectroscopy (UHPLC-QqQ-MS/MS) with the corresponding biological potency obtained from the anti-inflammatory effects on tumor necrosis factor (TNF)-α and interleukin (IL)-6 production. Correlation analysis showed that the ECI was significantly correlated with the measured anti-inflammatory activity (p < 0.01). The ECI exhibited a good ability to determine and predict the bioeffect-based quality grade for SR. Overall, the construction and application of the ECI for SR in this study provides a beneficial reference for quality evaluation methods of other herbs with distinct effects and active ingredients.

A rapid evaluation method for pumpkin sensory quality based on near‐infrared spectroscopy

AbstractThe sensory quality (SQ) of pumpkin fruit is one of the most important quality indexes for consumers. In this study, a rapid pumpkin SQ evaluation method based on near‐infrared spectroscopy (NIRS) was introduced. The starch and moisture contents of pumpkins were first determined by NIRS, and then the SQ of pumpkin could be estimated indirectly by these components. Test was carried out on 97 samples of 34 pumpkin fruits. The pumpkin starch and water NIRS models based on variable dimension particle swarm optimization combined moving window (VDPSO‐CMW) and standard normal variate transformation (SNV) showed the best predictive performances, whose predictive determination coefficients (R2p) were 0.775 and 0.932, respectively. A multiple linear regression model (correlation coefficient = 0.788) was established based on the prediction results of the two models and the SQ values obtained from cooking experiments. The proposed method has the potential to provide effective support for pumpkin breeding and production.

A novel method for quality evaluation of Radix Angelica sinensis based on molecularly imprinted electrochemical sensor

Rapid and sensitive detection of n-butylidenephthalide (NBP) is crucial for quality control of Radix Angelica Sinensis (RAS) and its related pharmaceuticals due to their shared pharmacological effects, such as immune enhancement and anti-tumor properties. Current detection methods struggle to quantify NBP quickly and accurately. A molecularly imprinted polymer (MIP)-based electrochemical sensor has been developed, forming a film on PCN-222(Fe) via electropolymerization for the rapid and selective detection of NBP. o-Phenylenediamine (o-PD) was polymerized onto PCN-222(Fe), utilizing its high surface area and porous structure to create a high-performance MIP (MIP/PCN-222(Fe)) sensor. This sensor detects NBP binding at the molecularly imprinted sites through a redox probe, with current changes reflecting the NBP content in the sample. This sensor exhibits a strong affinity for NBP, with a linear detection range from 200 nM to 1 mM, a detection limit of 76 nM, and high specificity towards similar phthalide compounds. Experimental results show that the MIP/PCN-222(Fe) sensor can accurately quantify NBP in real samples, offering a simplified method with promising applications for RAS quality evaluation.

An Evidential Reasoning Method of Comprehensive Evaluation of Water Quality Based on Gaussian Distribution

This study provides an evidential reasoning method for water quality evaluation based on Gaussian distribution to handle the problem of comprehensive water quality evaluation for a region across a period (multiple sections and multiple time points). The method turns the collection of observed water quality indicator values into a probability distribution of water quality grades by using the Gaussian distribution to compute the confidence assessment of water quality grades over one period. It eliminates the subjectivity involved in determining confidence levels and the problem of information loss during data fusion that arises with conventional approaches. The probability distribution of each assessment grade is then determined by repeatedly synthesizing evidence of the same water quality grade using the improved evidential reasoning synthesis rule. To avoid the subjectivity included in experience-based weight settings, principal component analysis (PCA) is utilized to calculate the weights of water quality indicators based on contribution rates and load coefficients. In the end, utility theory is presented to modify the discrete probability distribution of precise numerical expressions, offering thorough results for the evaluation of water quality and facilitating the comparison of various water quality grades. Using the Xiangjiang River Basin as a case study, the proposed evaluation method is contrasted with popular techniques for assessing water quality, including the Single-Factor Evaluation Method, the Fuzzy Comprehensive Evaluation Method, and the Evidential Reasoning Comprehensive Evaluation Method. The findings suggest that the evidence reasoning approach for evaluating water quality that is based on Gaussian distribution is more rational, accurate, and scientific. Additionally, empirical studies on the annual water quality trends in various regions, the upstream, midstream, and downstream trends, and the water quality trends during wet and dry periods are conducted using this method to assess and analyze changes in water quality in the Xiangjiang River Basin during the “11th Five-Year Plan” and “12th Five-Year Plan” periods. The analysis findings demonstrate that, even if the rate of progress has slowed, the Xiangjiang River Basin’s overall water quality has been steadily improving since management and protection measures were put in place. This shows that the preventive and control efforts implemented in the “11th Five-Year Plan” and “12th Five-Year Plan” periods were successful; nevertheless, carrying out the current tactics might only have a limited impact. As a result, more advanced and creative approaches are required to encourage the ongoing enhancement of the water quality in the Xiangjiang River Basin.

Objective quantification of sound sensory attributes in side-by-side vehicles using multiple linear regression models

The evaluation of sound quality is a pivotal area of research within audio and acoustics. The sound quality evaluation methods commonly used include both objective and subjective, the latter being time-consuming and costly as they rely on listening tests. This research work aims to investigate the use of predictive sound quality models as a way to objectively assess the Desire-to-buy of side-by-side vehicles, in a more efficient, faster, and less costly way than conventional methods. Multiple linear regression algorithms were used to validate the objective models derived from objective physical metrics and perceptual psycho-physical metrics. The sensory profile objective models reported in this paper were constructed using parsimonious linear Lasso and Elastic-net algorithms. Our results show that linear objective models effectively account for each of the perceptual attributes of the sensory profiles and the Desire-to-buy, while only requiring a few physical and psychophysical metrics.

From biological potency absorbed components to artificial intelligence mining: A review of analytical strategies for the discovery and validation of quality marker in traditional Chinese medicine in the past decade

Quality control of Traditional Chinese Medicine (TCM) is fundamental to ensuring its clinical efficacy, with TCM formulas being the primary form used in clinical practice. Current quality control methods for TCM formulas often rely on pharmacopoeial standards for individual medicinal materials, typically encompassing only characteristic or partial active ingredients. These methods fail to fully reflect the clinical efficacy of TCM formulas. Consequently, exploring the multiple efficacious components in TCM formulas and establishing the correlation between multi-component content and efficacy has become an urgent issue in the modern quality assessment of TCM formulas. The quality marker (Q-marker) has emerged as a crucial standard in this field, achieving notable success in recent years. This paper reviews recent progress in the development of the Q-marker system in TCM by highlighting strategies based on the correlation between efficacy and constituents, using analytical techniques to investigate the material basis and efficacy of TCM. However, the aforementioned methods inevitably involve human selection factors. With the widespread application of artificial intelligence (AI) learning algorithms, it is now possible to develop a modern quality evaluation method for the multi-component “efficacy-quality” correlation in TCM formulas. This approach leverages AI techniques to explore novel and quantifiable methods for scientific and rational quality control in TCM formulas. In this paper, important future directions and questions in this field are also discussed.

New method of evaluating spatial quality for innovation districts: A case study of Haidian and Chaoyang districts, Beijing

Science and technology innovation has become the core driving force of urban development, and innovation spaces as the carriers of innovation activities have evolved continuously, forming the emerging model of innovation districts in recent years. However, the evaluation standard of innovation spaces has not changed with the evolution of the model, and the evaluation method that focuses on economic benefits still dominates the mainstream. As a result, the construction of innovation spaces around these indicators and guidelines lags behind the advanced level.In this paper, we have systematically sorted out the relevant research progress of innovation districts, established a set of cases with global typicality, combined the dual attributes of “innovation characteristics + urban characteristics,” constructed a research framework for the spatial quality of innovation districts based on the dual perspectives of “innovation space,” and summarized the spatial quality evaluation model through analysis. We do an empirical study on Haidian District to find out how well innovation districts work in terms of space, find typical clusters, and come up with the most important aspects of their space quality. In order to observe the applicability of this method, it is also applied to the urban health check-up in Chaoyang District, Beijing. Through the expert seminar, the evaluation and analysis method of spatial quality of innovative urban areas is further verified and discussed. The results show that the integration of innovative urban areas and urban structures, and the collaborative development of innovative industries and high-quality spaces are the keys to promote the common development of innovative economy and cities. The new evaluation method can effectively measure the quality of innovation spaces, and has certain transferability.

Subjective and objective quality evaluation for industrial images

Recently, the demand for ever-better image processing technique continues to grow in field of industrial scenario monitoring and industrial process inspection. The subjective and objective quality evaluation of industrial images are vital for advancing the development of industrial visual perception and enhancing the quality of industrial image/video processing applications. However, the scarcity of publicly available industrial image databases with reliable subjective scores restricts the development of industrial image quality evaluation (IIQE). In preparation for a vacancy, this article first establishes two industrial image databases (i.e., industrial scenario image dataset (ISID) and industrial process image dataset (IPID)) for assessing IIQE metrics. Furthermore, in order to avoid overwhelming industrial image nuances due to the wavelet subband summation, we then present a novel industrial application subband information fidelity standard (SIFS) evaluation method using the channel capacity of visual signals in wavelet domain. Specifically, we first build a visual signals channel model based on perception process from human eyes to brain. Second, we compute and compare the channel capacity for reference and distorted images to measure the information fidelity in each wavelet subband. Third, we sum over the subbands for information fidelity ratio to obtain the overall quality score. Finally, we fairly compare some up-to-date and our proposed image quality evaluation (IQE) methods in two novelty industrial datasets respectively. Our ISID and IPID datasets are capable of evaluating most IQE metrics comprehensively and paves the way for further research on IIQE. Our SIFS model show a remarkable performance comparing with other up-to-date IQE methods.

Development of a Quality Evaluation Method for Allii Macrostemonis Bulbus Based on Solid-Phase Extraction–High-Performance Liquid Chromatography–Evaporative Light Scattering Detection Chromatographic Fingerprinting, Chemometrics, and Quantitative Analysis of Multi-Components via a Single-Marker Method

As a traditional Chinese medicine (TCM), Allii Macrostemonis Bulbus (AMB) is a key herb for the treatment of thoracic paralytic cardiac pain, but its quality evaluation method has not yet been fully clarified. In this study, chromatographic fingerprints of AMB were developed using solid-phase extraction–high-performance liquid chromatography–evaporative light scattering detection (SPE-HPLC-ELSD) to evaluate the quality of AMB from various origins and processing methods. This was achieved by employing chemical pattern recognition techniques and verifying the feasibility and applicability of the quality evaluation of AMB through the quantitative analysis of multi-components via a single-marker (QAMS) method. Through the analysis of the fingerprints of 18 batches of AMB, 30 common peaks were screened, and 6 components (adenosine, syringin, macrostemonoside T, macrostemonoside A, macrostemonoside U, and macrostemonoside V) were identified. Moreover, three differential markers (macrostemonoside A, macrostemonoside T, and macrostemonoside U) were screened out using chemometrics techniques, including principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal partial least squares discriminant analysis (OPLS-DA). Subsequently, a QAMS method was established for macrostemonoside T and macrostemonoside U using macrostemonoside A as an internal reference. The results demonstrate the method’s accuracy, reproducibility, and stability, rendering it suitable for the quality evaluation of AMB. This study provides a theoretical basis for drug quality control and the discovery of quality markers for AMB.

A novel stone fruit quality non-destructive evaluation method based on PVR by LF-NMRI

For stone fruits, the pulp ratio, indicating the proportion of pulp to the total fruit, serves as a vital metric in assessing stone fruit quality. Traditionally, this ratio is computed by measuring the mass of each component through destructive sampling, and acquired results remain controversial. In order to obtain this ratio non-destructively and more accurately, a novel evaluation method for stone fruit quality based on pulp volume ratio (PVR) by low-field nuclear magnetic resonance imaging (LF-NMRI) is proposed. This approach integrates the SwinUnet segmentation network to differentiating the fruit pulp and core structure, thereby precisely computing the volume proportion of pulp. Experimental results reveal that SwinUnet exhibits better accuracy and robustness in the LF-NMRI data compared to the other networks. Based on the segmentation results, the disparities between the PVR values computed using the ellipse fitting method and the integral method, compared to the ground truth (GT), are less than 1% and 0.5% respectively. This paper provides a new reference for selecting premium stone fruits and enriches the fruit quality evaluation system.

The Detection of Maize Seedling Quality from UAV Images Based on Deep Learning and Voronoi Diagram Algorithms

Assessing the quality of maize seedlings is crucial for field management and germplasm evaluation. Traditional methods for evaluating seedling quality mainly rely on manual field surveys, which are not only inefficient but also highly subjective, while large-scale satellite detection often lacks sufficient accuracy. To address these issues, this study proposes an innovative approach that combines the YOLO v8 object detection algorithm with Voronoi spatial analysis to rapidly evaluate maize seedling quality based on high-resolution drone imagery. The YOLO v8 model provides the maize coordinates, which are then used for Voronoi segmentation of the field after applying the Convex Hull difference method. From the generated Voronoi diagram, three key indicators are extracted: Voronoi Polygon Uniformity Index (VPUI), missing seedling rate, and repeated seedling rate to comprehensively evaluate maize seedling quality. The results show that this method effectively extracts the VPUI, missing seedling rate, and repeated seedling rate of maize in the target area. Compared to the traditional plant spacing variation coefficient, VPUI performs better in representing seedling uniformity. Additionally, the R2 for the estimated missing seedling rate and replanting rate based on the Voronoi method were 0.773 and 0.940, respectively. Compared to using the plant spacing method, the R2 increased by 0.09 and 0.544, respectively. The maize seedling quality evaluation method proposed in this study provides technical support for precision maize planting management and is of great significance for improving agricultural production efficiency and reducing labor costs.

Towards the perfect soft-boiled chicken eggs: defining cooking conditions, quality criteria, and safety assessments

In recent years, ready-to-eat soft-boiled chicken eggs, with coagulated whites and semi-solid yolks have become popular among Chinese consumers due to their convenience, tender texture, and nutritional benefits. However, the standards for these products are currently inconsistent, and quality evaluation parameters and food safety issues remain unclear. Softness ratio, representing the semi-liquid yolk proportion in a cross-section, was defined through a survey of different brands of ready-to-eat soft-boiled chicken eggs. By accounting for egg weight and cooking time, optimal softness under specific conditions was determined. A quality evaluation method was established based on the softness ratio and shelling score. Finally, the safety of soft-boiled chicken eggs was assessed by measuring yolk center temperature. The optimal softness ratio was 0.46 to 0.64. Optimal cooking times in boiling water (100°C) with a 5:1 water-to-egg ratio were 300 s for 43 to 48 g eggs, 330 seconds for 48 to 53g, 350 s for 53 to 58 g, and 370 s for 58 to 63g. After cooking, eggs were cooled for 6 min in an ice water mixture with a 3:1 water-to-egg ratio. Shelling scores (0-5) depended on the egg white surface exposed post-shelling, peaking at an air cell diameter of 21.55 ± 2.26 mm. Egg weight and shelling score had a correlation of -0.41, while egg white springiness and shelling score had a correlation of 0.86. Ensuring core yolk pasteurization standards was difficult, thus stricter management or whole egg irradiation was suggested for safety.

Multi-level fingerprinting and immune activity evaluation for polysaccharides from Dioscorea opposita Thunb.

To establish the quality control method of Dioscorea opposita Thunb., the multi-level fingerprinting of polysaccharides was established and the relationship between fingerprint and immune activity was analyzed. The two molecular weight segments Mw1 (1.38 × 105–1.63 × 106 Da) and Mw2 (3.27 × 103–4.37 × 103 Da), thirteen infrared absorption peaks (3399.26 cm−1, 2929.32 cm−1, 1631.78 cm−1, 1400.39 cm−1, 1351.80 cm−1, 1123.58 cm−1, 1024.76 cm−1, 931.53 cm−1, 854.76 cm−1, 760.43 cm−1, 708.14 cm−1, 616.47 cm−1, and 526.78 cm−1), and four monosaccharides (Man, Rha, GalA, and Glc) were used to evaluate the quality of Dioscorea opposita Thunb. The molecular weight fragments of Mw1, FT-IR absorption peaks of 1631.78 cm−1, and two monosaccharides (Man and Glc) would be used to identify Dioscorea opposita Thunb. polysaccharide (DOP) from different origins. The relationship of spectrum-effect showed that polysaccharides with features such as higher Mw1, a lower peak height of 1631.78 cm−1, higher content of Man, and lower content of Glc exerted stronger immune activity. In conclusion, this study established a polysaccharide-based quality evaluation method for Dioscorea opposita Thunb. and explored the relationship between polysaccharide fingerprints and in vitro immune activity, which provided a basis for further research on Dioscorea opposita Thunb.

Evaluation Methods and Coupled Optimization at Macro- and Micro-Scales for Profiled Ring Rolling of Inconel718 Alloy.

The forming quality of profiled ring rolling not only encompasses macroscopic accuracy but also emphasizes the microstructure. Due to the multiple process parameters and complex metal flow during profiled ring rolling, the various forming defects are difficult to control and difficult to study theoretically. The objective of this study is to establish a comprehensive method for evaluating the forming quality of profiled rings, which considers both the macroscopic forming accuracy and the microstructure. Firstly, the synthetic size factor was defined, and the evolutionary relation between the section forming rate and the diameter growth rate of E-section ring rolling was analyzed in detail. The synthetic size factor can be used to describe the dimensional evolution and evaluate the forming accuracy of the profiled ring rolling process. Taking into full consideration the features of intermittent deformation in local areas, a microstructure evolution model of the Inconel718 alloy during E-section ring rolling, which can accurately predict the recrystallization volume fraction and average grain size of the final ring, was established. Then, combined with finite element simulation, the influence of the rotation speed of the driving roll on the macro-size evolution and microstructure was systematically analyzed. The results indicate that there is often a discrepancy between dimensional accuracy and microstructure uniformity in the optimization trend. For instance, the higher the rotation speed of the driving roll is, the more uniform the microstructure is, but the more difficult it is for the section profile to form. Finally, combined with response surface methodology (RSM), multi-parameter optimization was carried out with section forming accuracy and grain uniformity as the optimization objectives. By using the optimal parameters, an E-section ring with a complete profile and a uniform microstructure was obtained, with a maximum prediction error of the recrystallization volume fraction lower than 5%. The results show that the macroscopic and microscopic quality evaluation methods proposed in this study, as well as the optimization method combining RSM, can be effectively applied to the process optimization of profiled ring rolling.

No-reference stereoscopic image quality assessment based on binocular collaboration

Stereoscopic images typically consist of left and right views along with depth information. Assessing the quality of stereoscopic/3D images (SIQA) is often more complex than that of 2D images due to scene disparities between the left and right views and the intricate process of fusion in binocular vision. To address the problem of quality prediction bias of multi-distortion images, we investigated the visual physiology and the processing of visual information by the primary visual cortex of the human brain and proposed a no-reference stereoscopic image quality evaluation method. The method mainly includes an innovative end-to-end NR-SIQA neural network with a picture patch generation algorithm. The algorithm generates a saliency map by fusing the left and right views and then guides the image cropping in the database based on the saliency map. The proposed models are validated and compared based on publicly available databases. The results show that the model and algorithm together outperform the state-of-the-art NR-SIQA metric in the LIVE 3D database and the WIVC 3D database, and have excellent results in the specific noise metric. The model generalization experiments demonstrate a certain degree of generality of our proposed model.

Application of Machine Learning and Multi-Dimensional Perception in Urban Spatial Quality Evaluation: A Case Study of Shanghai Underground Pedestrian Street

The exploitation of urban subsurface space in urban inventory planning is closely connected to the quality of urban environments. Currently, the construction of underground pedestrian streets is characterised by inefficiency and traffic congestion, making them insufficient for fulfilling the demand for well-designed and human-centred spaces. In the study of spatial quality, traditional evaluation methods, such as satellite remote sensing and street maps, often suffer from low accuracy and slow updating rates, and they frequently overlook human perceptual evaluations. Consequently, there is a pressing need to develop a set of spatial quality evaluation methods incorporating pedestrian perspectives, thereby addressing the neglect of subjective human experiences in spatial quality research. This study first quantifies and clusters the characteristics of underground pedestrian spaces using spatial syntax. It then gathers multidimensional perception data from selected locations and ultimately analyses and predicts the results employing machine learning techniques, specifically Random Forest and XGBoost. The research results indicate variability in pedestrians’ evaluations of spatial quality across different functionally oriented spaces. Key factors influencing these evaluations include Gorgeous, Warm, Good Ventilation, and Flavour indicators. The study proposes a comprehensive and applicable spatial quality evaluation model integrating spatial quantification methods, machine learning algorithms, and multidimensional perception measurements. The development of this model offers valuable scientific guidance for the planning and construction of high-quality urban public spaces.

Quality evaluation for Ficus hirta Vahl granules, using TLC and HPLC fingerprint combined with chemical pattern recognition.

Ficus hirta Vahl is a healthy food with both medicinal and culinary properties and with anti-inflammatory and anti-aging effects. There is currently no standardized or universally accepted research strategy for evaluating the quality of Ficus hirta Vahl granules (FHGs). Therefore, the development of a comprehensive quality evaluation method is crucial for the quality control of FHGs. In this study, we used n-hexane : trichloromethane : ethyl acetate : glacial acetic acid = 20 : 4 : 7 : 1 as the optimal developing agent for TLC to separate and identify 15 batches of FHGs from different origins. Using HPLC, a fingerprint with 7 common peaks was established, and peaks 6 and 7 were attributed to psoralen and bergapten, respectively. The content of the identified components was determined. Further quality evaluation of FHGs was performed using chemical pattern recognition, and the results showed that hierarchical cluster analysis (HCA) could cluster 15 batches of FHGs into 2 categories. Principal component analysis (PCA) showed that 2 principal components can show the similarities and differences between different batches of FHGs. Orthogonal partial least squares discrimination (OPLS-DA) showed that components 5, 6 (psoralen) and 7 (bergapten) are landmark components that cause differences in FHG quality from different regions. By integrating the analytical modes of TLC, HPLC fingerprint and chemical pattern recognition, a scientific basis is provided for the comprehensive control and evaluation of herbal medicine quality.

Comprehensive quality evaluation method of ultrasound tomography algorithms based on entropy method

Comprehensive quality evaluation method of ultrasound tomography algorithms based on entropy method

Leveraging Observations of Untrained Panelists to Screen for Quality of Fresh-Cut Romaine Lettuce

Fresh-cut romaine lettuce’s high perishability challenges ready-to-eat (RTE) salad production. Selecting cultivars less prone to browning and decay is crucial for extending shelf life. Traditional quality evaluation methods using instrumentation and trained panelists are time-consuming and logistically complex. This study investigated the effectiveness of untrained volunteers in assessing fresh-cut romaine lettuce quality. Given that the average consumer in the USA is familiar with the flavor characteristics of romaine lettuce, this study proposed to investigate the value of having untrained volunteers discern the quality of fresh-cut romaine lettuce. Therefore, six romaine lettuce accessions (Green Forest, King Henry, Parris Island Cos, PI 491224, SM13-R2, and Sun Valley) were assessed for sensory quality attributes (browning, green color, decay, and overall quality) and compared with instrumentation analyses (gas composition including O2 and CO2, electrolyte leakage, and color). The results showed significant quality differences (p &lt; 0.05) among the accessions, with some seasonal variability. Very importantly, the consumers’ (n = 159) assessments revealed similar results to those produced by either instrumentation or a trained panel. The consumers provided sensory scores that allowed for the grouping of accessions based on their postharvest quality, which efficiently matched their pedigree relationship. In conclusion, ad hoc consumer panels can be an effective way to characterize the quality of romaine lettuce for RTE salads.

Research on Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities Under Big Data Environment

In innovation and entrepreneurship education, how to effectively use big data technology to improve the quality of education has become an urgent problem. In this article, the authors summarize the development of big data technology and its application background in higher education and point out the limitations of traditional education quality evaluation methods. They discuss the research status of the quality evaluation of innovation and entrepreneurship education in colleges and universities at home and abroad and the application of big data in education quality evaluation. Using the fuzzy comprehensive evaluation method, they constructed a quality evaluation model of innovation and entrepreneurship education in colleges and universities based on big data and proposed a corresponding evaluation process and index system. Finally, through empirical analysis, they verified the effectiveness and operability of the evaluation model. This research has theoretical and practical significance for improving the quality of innovation and entrepreneurship education in colleges and universities.