Egg tart quality detection method based on image and colorimetric analysis and application

Abstract

The invention belongs to the technical field of food processing and detection, and discloses an egg tart quality detection method based on image and colorimetric analysis. The method comprises the following steps that egg tart samples which are processed through different unfreezing processes, baked and cooled are cut open, and surface images and flat section images of the egg tart samples are obtained respectively; based on the surface image, calculating a focal spot area proportion of the surface of the egg tart; based on the section image, calculating the pore area proportion of the section of the egg tart core; the chromatic value of the egg tart core is measured, and the total chromatic aberration is calculated; and carrying out standardization processing on the focal spot area proportion, the pore area proportion and the total chromatic aberration, and carrying out weighted summation according to a preset weight to obtain a comprehensive quality score of the egg tart sample. According to the method, a multi-dimensional quantitative index system is provided, and the system can accurately evaluate the quality of the egg tarts from three independent and complementary dimensions, namely surface browning uniformity, internal structure stability and overall color.

Description

Technical Field

[0001] This invention relates to the field of food processing and testing technology, specifically to a method and application for quality testing of egg tarts based on image and colorimetric analysis. Background Technology

[0002] With the rapid development of the frozen food and chain bakery industries, frozen egg tart filling has become a key semi-finished product in the industrial production of egg tarts due to its advantages in standardized production, storage, and transportation. Before use, frozen egg tart filling must be thawed, and the thawing process directly affects the physicochemical state of the filling, thus having a decisive impact on the quality of the final baked product.

[0003] Current research on thawing processes for frozen egg tart fillings or similar egg-containing products largely focuses on thawing efficiency and the analysis of basic physicochemical properties of the raw materials after thawing. However, these studies have significant limitations: First, they remain at the level of assessing the properties of intermediate raw materials, making it difficult to effectively establish a quantitative and predictable correlation model between thawing process parameters and the overall quality of the final baked product. For manufacturing companies, there is a lack of a decision-making tool for scientifically optimizing thawing processes based on the performance of the final product.

[0004] Existing methods for evaluating the quality of finished egg tarts also have shortcomings: First, sensory evaluation methods rely heavily on the experience of evaluators, are highly subjective and have poor repeatability, making it difficult to form standardized judgments; second, single physicochemical index testing methods are difficult to comprehensively and accurately reflect the combined characteristics of the appearance and internal structure of egg tarts.

[0005] In view of this, the present invention is proposed. Summary of the Invention

[0006] The present invention aims to solve at least one of the above technical problems, and provides a method and application for egg tart quality detection based on image and colorimetric analysis.

[0007] To achieve the above objectives, the first technical solution adopted by the present invention is as follows: A method for quality detection of egg tarts based on image and colorimetric analysis includes the following steps: Egg tart samples that had undergone different thawing processes and were then baked and cooled were cut open, and their surface images and flat cross-section images were obtained respectively. Based on the surface image, calculate the area ratio of scorched spots on the surface of the egg tart; Based on the cross-sectional image, calculate the percentage of the pore area in the cross-section of the egg tart filling; Measure the colorimetric value of the egg tart filling and calculate the total color difference; The proportion of scorched area, the proportion of pore area, and the total color difference are standardized and then weighted and summed according to preset weights to obtain the comprehensive quality score of the egg tart sample.

[0008] Preferably, the method for calculating the proportion of the focal spot area is as follows: using image processing software to identify the focal spot region in the surface image, the proportion of the focal spot area = (total area of ​​the focal spot region / total area of ​​the egg tart surface region) × 100%.

[0009] Preferably, the method for calculating the proportion of the pore area is as follows: using image processing software to identify the pore areas in the cross-sectional image, the proportion of the pore area = (total area of ​​pore areas / total area of ​​the cross-sectional area of ​​the egg tart filling) × 100%.

[0010] Preferably, the standardization process is deviation standardization or Z-score standardization; the preset weights are determined based on the contribution of each indicator to the overall quality of the egg tart.

[0011] Preferably, after obtaining the overall quality score of the egg tart sample, the method further includes the following steps: comparing the overall quality score with a preset threshold range and outputting a quality level of "excellent, good, medium, or poor".

[0012] The second technical solution adopted in this invention is: The application of a method for detecting the quality of egg tarts according to any one of the above in evaluating the thawing process of egg tart filling.

[0013] Preferably, the specific method of application is as follows: The same batch of frozen egg tart filling was processed using at least two different thawing processes; For the egg tart filling processed by each thawing process, egg tart samples were prepared using the egg tart quality testing method described in any one of the first technical solutions, and their comprehensive quality scores were obtained. By comparing the overall quality scores or grades of egg tart samples corresponding to each thawing process, the thawing process with the highest score or the best grade is selected as the recommended process.

[0014] Preferably, the different thawing processes are selected from at least two of the following: 0~10℃ air thawing, 18~28℃ air thawing, 30~40℃ water bath thawing, ultrasonic-assisted water bath thawing, and microwave thawing.

[0015] Preferably, the conditions for ultrasonic-assisted water bath thawing are: water bath temperature 30~40℃, ultrasonic frequency 40kHz, and power 120W~360W.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention completely abandons subjective evaluation relying on human senses and creatively proposes a multi-dimensional quantitative index system consisting of "proportion of scorched area," "proportion of pore area," and "total color difference." This system can accurately evaluate the quality of egg tarts from three independent and complementary dimensions: uniformity of surface browning, stability of internal structure, and overall color, making the evaluation results completely objective, measurable, and comparable.

[0017] The core contribution of this invention lies in its standardized experimental design, which for the first time systematically correlates different thawing process parameters of egg tart filling with the aforementioned quantitative indicators and comprehensive scores of the final product. This forms a mapping model of thawing process input and quality data output, enabling producers to select the thawing process that optimizes the overall quality of the finished product based on explicit quantitative data, rather than fuzzy experience, thus achieving data-driven and scientific decision-making for process optimization.

[0018] The indicators defined in this invention have clear physical meanings. The "percentage of charred area" indirectly reflects the impact of differences in moisture migration caused by different thawing methods on the surface browning reaction; the "percentage of pore area" directly reveals the changes in the internal gas and structural stability of the egg tart filling during thawing, and its expansion behavior during baking. Through these indicators, this invention not only achieves evaluation but also provides intuitive quantitative insights into the process mechanism. Attached Figure Description

[0019] Figure 1 Cross-sectional views and air pocket diagrams of egg tarts baked with egg tart filling under different thawing conditions; Figure 2 This image shows the top surface of egg tarts baked under different thawing conditions, along with a schematic diagram illustrating the percentage of burnt spots. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention provides a method for detecting the quality of egg tarts based on image and colorimetric analysis, comprising the following steps: S101, cut open the egg tart samples after they have been processed by different thawing processes and then baked and cooled, and obtain their surface images and flat cross-section images respectively.

[0022] This step aims to obtain standardized images of the egg tart samples for subsequent quantitative analysis. Its core objective is to eliminate interference from all variables except the thawing process, ensuring that any differences detected later stem from different thawing treatments, thereby guaranteeing the fairness and comparability of the evaluation.

[0023] Different thawing processes refer to applying different thawing conditions to frozen egg tart fillings from the same source, such as different thawing temperatures (e.g., 4°C refrigeration versus 25°C room temperature), thawing media (e.g., air, water bath), or energy fields (e.g., ultrasound, microwave).

[0024] Specifically, all egg tart fillings that have undergone different thawing processes were baked under the same conditions: mass (e.g., 30g), same tart crust, and same oven (preheating temperature, top and bottom heat temperatures, and baking time all fixed, e.g., top heat 190℃, bottom heat 200℃, baking for 22 minutes). After baking, they were cooled for the same amount of time (e.g., 60 minutes) in the same environment (e.g., room temperature). This part involves the standard experimental procedures for controlling variables and is the basis for the reproducibility of this method.

[0025] Cut the cooled egg tarts vertically along their central axis to obtain a flat cut that fully exposes the internal structure of the tart filling.

[0026] Place the entire top surface and the flat cut surface of the egg tart on a solid-color (such as white or black) background. Use a digital camera or high-resolution mobile phone to photograph the tart under fixed lighting conditions (avoiding variations in natural light), a fixed shooting distance, and a fixed vertical shooting angle. Shadows and reflections must be avoided to ensure the authenticity of the image's colors and textures.

[0027] S102, Based on the surface image, calculate the percentage of the area of ​​the scorched spots on the surface of the egg tart.

[0028] The percentage of brown spots refers to the total area of ​​brown spots on the surface of the egg tart caused by the Maillard reaction, relative to the total surface area of ​​the entire tart. This indicator objectively quantifies the uniformity of browning. Improper thawing may lead to uneven moisture distribution, resulting in localized or excessive brown spots.

[0029] The calculation method for the area ratio of the focal spot is as follows: use image processing software to identify the focal spot area in the surface image, and the area ratio of the focal spot = (total area of ​​the focal spot area / total area of ​​the egg tart surface area) × 100%.

[0030] S103, Based on the cross-sectional image, calculate the proportion of the pore area in the cross-section of the egg tart filling.

[0031] The porosity percentage refers to the percentage of the total area of ​​the tart filling's cross-section formed by internal gases during baking. This indicator objectively quantifies the fineness and uniformity of the internal texture. The thawing process disrupts the stability of the air bubbles in the egg tart filling, directly leading to excessively large or unevenly distributed pores.

[0032] The method for calculating the pore area ratio is as follows: use image processing software to identify the pore areas in the cross-sectional image, and the pore area ratio = (total area of ​​pore areas / total area of ​​the egg tart filling cross-sectional area) × 100%.

[0033] S104, measure the color value of the egg tart filling and calculate the total color difference.

[0034] Chromaticity values ​​(L*, a*, b*): Measured using the CIE L*a*b* color space. L* represents lightness (0 for black, 100 for white); a* represents redness / greenness (positive for red, negative for green); b* represents yellowness / blueness (positive for yellow, negative for blue).

[0035] Total color difference ΔE is used to quantify the overall difference between the sample color and a reference standard color (such as the ideal egg tart filling color or the control group sample color). The calculation formula is as follows: The values ​​with the subscript 's' are reference standard values.

[0036] The specific measurement method involves using a calibrated colorimeter (such as a Konica Minolta CM-3700) to measure multiple points on the egg tart filling under a standard light source (such as D65) and a standard observer's perspective. The average value is then taken to obtain a set of (L*, a*, b*) values, and ΔE is calculated. This step is a standard technique for color measurement in the food industry.

[0037] S105, the proportion of scorched area, the proportion of pore area, and the total color difference are standardized and weighted according to preset weights to obtain the comprehensive quality score of the egg tart sample.

[0038] This step aims to integrate the three indicators (scorch mark percentage, porosity percentage, and total color difference), which have different dimensions and magnitudes, into a single, comparable comprehensive quality score, which will ultimately be used to guide decision-making.

[0039] Standardization is necessary because the physical meanings and numerical ranges of the various indicators differ, requiring dimensionless processing. The "deviation standardization" method can be used: X norm =(XX min ) / (X max -X min ), where X is the original value, X min and X maxThese are the minimum and maximum values ​​of the indicator across all samples, respectively, mapping all indicator values ​​to the interval [0, 1]. Other methods, such as Z-score standardization, can also be used.

[0040] The method for obtaining the overall quality score of the egg tart sample by weighted summation based on preset weights is as follows: Weights (W1, W2, W3) are assigned to each indicator according to their importance to the overall quality of the egg tart, and the sum of these weights is 1. For example, the proportion of burnt spots (I1) affects appearance, the proportion of air pockets (I2) affects texture, and the total color difference ΔE (I3) affects color. The weights of these three factors can be determined based on expert experience or empirical analysis (e.g., W1=0.4, W2=0.4, W3=0.2). Therefore, the overall score S = W1*I1 + W2*I2 + W3*I3.

[0041] After obtaining the comprehensive score, the quality can be further classified according to the comprehensive score, for example: Excellent (S ≥ 0.75), Good (0.50 ≤ S < 0.75), Average (0.25 ≤ S < 0.50), and Poor (S < 0.25). This classification can be directly applied to the evaluation of the thawing process.

[0042] The key point of this invention is the construction of a comprehensive evaluation system that includes the three specific indicators mentioned above and is based on a weighted summation model. This is one of the core innovations of this invention. This model is not a simple listing of indicators, but rather, through standardization and weight allocation, it integrates multi-dimensional information into a single decision score that can be directly used for process optimization. This achieves a leap from "multi-indicator description" to "single-score decision-making," solving the problem of one-sided evaluation dimensions and inability to directly guide production in existing technologies.

[0043] Through the detailed implementation described above, this invention not only completes an objective test of egg tart quality, but also naturally realizes its application in evaluating the thawing process of egg tart filling: simply pass egg tart fillings treated with different thawing processes through the complete process described above, calculate their respective comprehensive scores or quality grades, and then compare them to intuitively and quantitatively determine the optimal process.

[0044] Common thawing processes are selected from at least two of the following: 0~10℃ air thawing, 18~28℃ air thawing, 30~40℃ water bath thawing, ultrasonic-assisted water bath thawing, and microwave thawing.

[0045] To verify the effectiveness of this method in evaluating thawing processes, the following experiments were conducted: For the different thawing processes mentioned above, with fixed thawing parameters, the final rating results obtained by using the aforementioned egg tart quality testing method are shown in Table 1. Cross-sectional views and air pocket diagrams of the baked egg tarts after each thawing condition are also shown in Table 1. Figure 1 As shown in the diagram, the top surface of the egg tart and the proportion of the burnt spots are illustrated in the following figures. Figure 2 As shown.

[0046] Table 1 .

[0047] As can be seen from Table 1, the most suitable process for thawing egg tart filling is ultrasonic-assisted water bath thawing.

[0048] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for quality detection of egg tarts based on image and colorimetric analysis, characterized in that, Includes the following steps: Egg tart samples that had undergone different thawing processes and were then baked and cooled were cut open, and their surface images and flat cross-section images were obtained respectively. Based on the surface image, calculate the area ratio of scorched spots on the surface of the egg tart; Based on the cross-sectional image, calculate the percentage of the pore area in the cross-section of the egg tart filling; Measure the colorimetric value of the egg tart filling and calculate the total color difference; The proportion of scorched area, the proportion of pore area, and the total color difference are standardized and then weighted and summed according to preset weights to obtain the comprehensive quality score of the egg tart sample.

2. The method for detecting the quality of egg tarts according to claim 1, characterized in that, The calculation method for the area ratio of the focal spot is as follows: use image processing software to identify the focal spot region in the surface image, and the area ratio of the focal spot = (total area of ​​the focal spot region / total area of ​​the egg tart surface region) × 100%.

3. The method for detecting the quality of egg tarts according to claim 1, characterized in that, The method for calculating the proportion of the pore area is as follows: using image processing software to identify the pore areas in the cross-sectional image, the proportion of the pore area = (total area of ​​pore areas / total area of ​​the cross-sectional area of ​​the egg tart filling) × 100%.

4. The method for detecting the quality of egg tarts according to claim 1, characterized in that, The standardization process is either deviation standardization or Z-score standardization; the preset weights are determined based on the contribution of each indicator to the overall quality of the egg tart.

5. The method for detecting the quality of egg tarts according to any one of claims 1-4, characterized in that, After obtaining the overall quality score of the egg tart sample, the following steps are also included: comparing the overall quality score with a preset threshold range and outputting a quality level of "excellent, good, medium, or poor".

6. The application of a method for detecting the quality of egg tarts according to any one of claims 1-5 in evaluating the thawing process of egg tart filling.

7. The application according to claim 6, characterized in that, The specific method of application is as follows: The same batch of frozen egg tart filling was processed using at least two different thawing processes; For the egg tart filling processed by each thawing process, egg tart samples were prepared using the egg tart quality testing method according to any one of claims 1-5 and their comprehensive quality scores were obtained; By comparing the overall quality scores or grades of egg tart samples corresponding to each thawing process, the thawing process with the highest score or the best grade is selected as the recommended process.

8. The application according to claim 7, characterized in that, The different thawing processes are selected from at least two of the following: air thawing at 0~10℃, air thawing at 18~28℃, water bath thawing at 30~40℃, ultrasonic-assisted water bath thawing, and microwave thawing.

9. The application according to claim 8, characterized in that, The conditions for ultrasonic-assisted water bath thawing are: water bath temperature 30~40℃, ultrasonic frequency 40kHz, and power 120W~360W.

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