Micro-CT (micro-computed tomography) method-based method for measuring peel retention degree of colored rice

Abstract

The invention discloses a method for measuring the peel retention degree of colored rice based on a Micro-CT (Micro-Computed Tomography) method. Relates to the technical field of crop treatment. According to the method for measuring the skin retention degree of the colored rice based on the Micro-CT method, high-resolution three-dimensional structure information in rice grains is obtained by utilizing the Micro-CT technology, and accurate and quantitative recognition and segmentation of the skin layer and endosperm of the colored rice are realized through the steps of three-dimensional reconstruction and dual image processing. The method guarantees the calculation accuracy of the peel retention degree, maintains the complete structure of rice grains, is high in measurement precision, is simple and rapid in operation, and provides a reliable technical means for the quality control of the processing precision of colored rice.

Description

Technical Field

[0001] This invention relates to the field of crop treatment technology, and in particular to a method for determining the husk retention of colored rice based on Micro-CT. Background Technology

[0002] Currently, the rice processing industry mainly uses sensory inspection, dyeing, and transmission image recognition methods to determine the degree of bran retention. However, these methods have some shortcomings. Sensory inspection relies on experience and has low accuracy; dyeing is cumbersome, and the natural pigments of colored rice (such as black and red rice) can severely interfere with the dyeing effect; transmission image recognition results in a low signal-to-noise ratio and large measurement error due to the dark color of the bran; and destructive detection methods based on component analysis cannot meet the requirements for non-destructive and rapid detection. Therefore, existing technologies cannot effectively solve the problem of difficulty in identifying the bran of colored rice due to its high content of anthocyanins and anthocyanins. There is an urgent need to develop a method for detecting the degree of bran retention that can overcome color interference and achieve non-destructive and accurate measurement. Summary of the Invention

[0003] In view of this, the present invention provides a method for determining the husk retention of colored rice based on Micro-CT, aiming to solve the problems of large measurement error, cumbersome operation and inability to achieve non-destructive testing in traditional detection methods due to the rich anthocyanin, anthocyanin and other pigments in the husk of colored rice.

[0004] To achieve the above objectives, in a first aspect, the present invention provides a method for determining the husk retention of colored rice based on Micro-CT, comprising the following steps: S1. Obtain rice grain samples of colored rice with different milling degrees; S2. Perform Micro-CT tomography on the rice grain sample to obtain tomographic image data of the rice grain sample; S3. Based on tomographic image data, obtain an image sequence containing all cross-sections of the rice grain sample through three-dimensional reconstruction. S4. Based on preset segmentation thresholds and grayscale thresholds, perform batch processing on all cross-sectional images in the image sequence, and calculate the following areas for each cross-section in sequence: (a) For each cross-sectional image, the segmentation threshold is applied to segment the image, the rice grain region is identified from the background, and the cross-sectional area of ​​the rice grain is calculated. (b) For each cross-sectional image, image contrast enhancement processing is first performed to enhance the grayscale difference between the cortex and endosperm, and then the grayscale threshold is applied to perform image segmentation, identify the cortical region, and calculate the cortical area of ​​the rice grain. S5. From the image sequence, select all cross-sections with a cross-sectional area greater than zero as valid cross-sections; sum up the rice grain cortex area of ​​all valid cross-sections as the total rice grain cortex area, and sum up the rice grain cross-sectional area of ​​all valid cross-sections as the total rice grain cross-sectional area. S6. Calculate the ratio of the total area of ​​the rice grain peel to the total cross-sectional area of ​​the rice grain to obtain the peel retention degree.

[0005] This invention, employing the aforementioned technical solution, utilizes Micro-CT tomography to acquire high-resolution three-dimensional structural information of the rice grain's interior. Through three-dimensional reconstruction and dual image processing steps, it achieves precise and quantitative identification and segmentation of the bran and endosperm of colored rice. Specifically, a preset segmentation threshold effectively distinguishes the rice grain from the background, ensuring the integrity of the detected object. Furthermore, image contrast enhancement processing is performed first to strengthen the visual difference between the bran and endosperm, and then combined with grayscale threshold segmentation technology, effectively stripping and statistically analyzing the bran region, thus completely overcoming the color interference problem caused by dark pigments such as anthocyanins and anthocyanins. This method not only achieves non-destructive and rapid determination of the bran retention of a single rice grain, significantly improving the accuracy (consistent with the trend of the national standard dyeing method for ordinary rice) and repeatability, but also provides a reliable and efficient technical means for quality control of the processing precision of colored rice, filling the gap in existing technologies for the specific detection of colored rice.

[0006] In some embodiments of the present invention, step S2, prior to Micro-CT tomography, further includes decoating and removing impurities from the sample. By employing the above technical solution, the present invention can ensure sample purity, thereby further improving the quality and analytical accuracy of subsequent scanned images.

[0007] In some embodiments of the present invention, in step S1, the colored rice is one of black rice, red rice, or purple rice. By employing the above technical solution, the present invention provides a solution specifically for colored rice rich in anthocyanins, anthocyanins, and other pigments, thus solving the problem of interference from the color of the bran layer on traditional detection methods.

[0008] In some embodiments of the present invention, in step S2, the parameters of the Micro-CT tomography scan include: X-ray source voltage 80kV, current 100μA, scanning mode SQ scan, and acquisition frame rate of 1080 FPS. By adopting the above technical solution, the present invention, based on the above-mentioned suitable combination of Micro-CT tomography parameters, can ensure image quality while improving detection efficiency.

[0009] In some embodiments of the present invention, in step S3, the single frame size of the three-dimensional reconstructed cross-sectional image sequence is 350×350 pixels, and the total number of frames is not less than 1000 frames. By adopting the above technical solution and based on the above-mentioned suitable parameters, the present invention can ensure that an image sequence with sufficient resolution is obtained, thereby providing a data foundation for subsequent accurate calculations.

[0010] In some embodiments of the present invention, in step S4: The segmentation threshold mentioned in step (a) has a lower limit of 81 to 116 and an upper limit of 255; And / or, In step S4(b), image contrast enhancement processing is first performed, wherein the grayscale display range of the image is adjusted to a lower limit of 182 to 253 and an upper limit of 184 to 255; then, image segmentation is performed using the grayscale threshold, wherein the lower limit of the grayscale threshold is 128 and the upper limit is 255. By adopting the above technical solution, and based on the aforementioned suitable threshold range setting, this invention can accurately distinguish between rice grain regions and background, and between the cortex and endosperm, thereby improving calculation accuracy.

[0011] In some embodiments of the present invention, in step S6, the formula for calculating the degree of husk retention is: husk retention (%) = (total area of ​​rice grain husk / total cross-sectional area of ​​rice grain) × 100%. By adopting the above technical solution and the above calculation method, the present invention can ensure the accuracy and reliability of the husk retention measurement results.

[0012] In some embodiments of the present invention, in step S4, the statistical analysis of the rice grain cortex area and cross-sectional area is automatically completed using image analysis software. By employing the above technical solution, the present invention can automate the determination of cortex retention, thereby improving detection efficiency and consistency, and reducing human error.

[0013] In some embodiments of the present invention, the method for determining the husk retention of colored rice based on Micro-CT is a non-destructive testing method, and the rice grain sample retains its intact structure after testing. By employing the above-mentioned technical solution, the present invention can maintain sample integrity, thereby achieving repeatable testing and reducing testing costs.

[0014] In some embodiments of the present invention, after step S6, the method further includes: S7. Compare the calculated husk retention rate with the preset processing precision level to determine the milling grade of colored rice. This invention, by adopting the above technical solution, directly converts the test results into processing quality evaluation indicators, facilitating production quality control. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0016] Figure 1 These are comparative images of the appearance morphology of rice grains in Examples 1-9 of the present invention; Figure 2 Comparison chart of skin retention between Examples 1-3 and Comparative Examples 1-3 provided by the present invention; Figure 3 The graphs showing the changes in the husk retention of the two types of black rice in Examples 4-9 of this invention; Figure 4 This is a schematic flowchart of the method for determining the husk retention of colored rice based on Micro-CT in Embodiment 1 of the present invention.

[0017] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Where specific conditions are not specified in the embodiments, conventional conditions or conditions recommended by the manufacturer shall apply. Where the manufacturers of reagents or instruments are not specified, they are all conventional products that can be purchased commercially. Furthermore, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, or solution B, or a solution where both A and B are satisfied simultaneously. In addition, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention. 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.

[0019] Currently, the rice processing industry mainly uses sensory inspection, dyeing, and transmission image recognition methods to determine the degree of bran retention. However, these methods have some shortcomings. Sensory inspection relies on experience and has low accuracy; dyeing is cumbersome, and the natural pigments of colored rice (such as black and red rice) can severely interfere with the dyeing effect; transmission image recognition results in a low signal-to-noise ratio and large measurement error due to the dark color of the bran; and destructive detection methods based on component analysis cannot meet the requirements for non-destructive and rapid detection. Therefore, existing technologies cannot effectively solve the problem of difficulty in identifying the bran of colored rice due to its high content of anthocyanins and anthocyanins. There is an urgent need to develop a method for detecting the degree of bran retention that can overcome color interference and achieve non-destructive and accurate measurement.

[0020] In view of this, the present invention provides a method for determining the husk retention of colored rice based on Micro-CT, aiming to solve the problems of large measurement error, cumbersome operation and inability to achieve non-destructive testing in traditional detection methods due to the rich anthocyanin, anthocyanin and other pigments in the husk of colored rice.

[0021] To achieve the above objectives, this invention proposes a method for determining the degree of husk retention based on Micro-CT tomographic scanning technology. This method acquires tomographic image data of rice grains through 360° omnidirectional scanning. After three-dimensional reconstruction, a dual grayscale threshold segmentation technique is used to separately identify the rice grain region from the background, and the cortex region from the endosperm. Finally, the degree of husk retention is accurately determined by cumulatively calculating the proportion of cortex area. This method not only overcomes the interference of colored rice cortex color but also has the advantages of being non-destructive, efficient, and quantitative, providing a reliable technical means for quality control of colored rice processing precision.

[0022] In a first aspect, the present invention provides a method for determining the husk retention of colored rice based on Micro-CT (computational radiography) method, comprising the following steps: S1. Obtain rice grain samples of colored rice with different milling degrees; S2. Perform Micro-CT tomography on the rice grain sample to obtain tomographic image data of the rice grain sample; S3. Based on tomographic image data, obtain an image sequence containing all cross-sections of the rice grain sample through three-dimensional reconstruction. S4. Based on preset segmentation thresholds and grayscale thresholds, perform batch processing on all cross-sectional images in the image sequence, and calculate the following areas for each cross-section in sequence: (a) For each cross-sectional image, the segmentation threshold is applied to segment the image, the rice grain region is identified from the background, and the cross-sectional area of ​​the rice grain is calculated. (b) For each cross-sectional image, image contrast enhancement processing is first performed to enhance the grayscale difference between the cortex and endosperm, and then the grayscale threshold is applied to perform image segmentation, identify the cortical region, and calculate the cortical area of ​​the rice grain. S5. From the image sequence, select all cross-sections with a cross-sectional area greater than zero as valid cross-sections; sum up the rice grain cortex area of ​​all valid cross-sections as the total rice grain cortex area, and sum up the rice grain cross-sectional area of ​​all valid cross-sections as the total rice grain cross-sectional area. S6. Calculate the ratio of the total area of ​​the rice grain peel to the total cross-sectional area of ​​the rice grain to obtain the peel retention degree.

[0023] This invention, employing the aforementioned technical solution, utilizes Micro-CT tomography to acquire high-resolution three-dimensional structural information of the rice grain's interior. Through three-dimensional reconstruction and dual image processing steps, it achieves precise and quantitative identification and segmentation of the bran and endosperm of colored rice. Specifically, a preset segmentation threshold effectively distinguishes the rice grain from the background, ensuring the integrity of the detected object. Furthermore, image contrast enhancement processing is performed first to strengthen the visual difference between the bran and endosperm, and then combined with grayscale threshold segmentation technology, effectively stripping and statistically analyzing the bran region, thus completely overcoming the color interference problem caused by dark pigments such as anthocyanins and anthocyanins. This method not only achieves non-destructive and rapid determination of the bran retention of a single rice grain, significantly improving the accuracy (consistent with the trend of the national standard dyeing method for ordinary rice) and repeatability, but also provides a reliable and efficient technical means for quality control of the processing precision of colored rice, filling the gap in existing technologies for the specific detection of colored rice.

[0024] The "segmentation threshold" setting involves entering values ​​in the upper and lower limit boxes on the Threshold parameter settings page, or directly dragging the sliders to segment the image into target and background. Then, the target region is selected, and finally, the area is calculated. The goal is to identify rice-grain regions from the background.

[0025] The contrast enhancement parameter is operated by sliding the Brightness / Contrast slider to adjust the dynamic range. By adjusting the grayscale difference between bright and dark areas, it enhances the visual effect of the cortical region, thereby separating the cortex from the endosperm, so that only the brighter cortical region is presented in the image.

[0026] In some embodiments of the present invention, the degree of milling in step S1 is positively correlated with the milling time. The milling time gradient is set to 0 min, 3 min, and 9 min, corresponding to three processing states: unmilled brown rice, unmilled rice, and finely milled rice, respectively. By controlling the milling time, a series of samples with a bran retention rate ranging from 0.1% to 99.9% can be obtained, meeting the testing requirements for different processing precisions.

[0027] In some embodiments of the present invention, in step S1, the colored rice includes one of black rice, red rice, or purple rice. By employing the above technical solution, the present invention provides a solution specifically for colored rice rich in anthocyanins, anthocyanins, and other pigments, thus solving the problem of interference from the color of the bran layer on traditional detection methods.

[0028] In some embodiments of the present invention, step S2, prior to Micro-CT tomography, further includes decoating and removing impurities from the sample. By employing the above technical solution, the present invention can ensure sample purity, thereby further improving the quality and analytical accuracy of subsequent scanned images.

[0029] Furthermore, in some embodiments of the present invention, step S2 before Micro-CT tomography includes a sample pretreatment step: the rice grain sample is vertically adhered to the sample stage with food-grade adhesive to ensure that the long axis of the rice grain is parallel to the rotation center axis during scanning, and the rice grain sample is located in the center of the field of view so that the offset during rotation is as small as possible to obtain the best imaging quality.

[0030] In some embodiments of the present invention, in step S2, the parameters of the Micro-CT tomography scan include: X-ray source voltage 80kV, current 100μA, scanning mode SQ scan, and acquisition frame rate of 1080 FPS. By adopting the above technical solution, the present invention, based on the above-mentioned suitable combination of Micro-CT tomography parameters, can ensure image quality while improving detection efficiency.

[0031] In some embodiments of the present invention, in step S3, the single frame size of the three-dimensional reconstructed cross-sectional image sequence is 350×350 pixels, and the total number of frames is not less than 1000 frames. By adopting the above technical solution and based on the above-mentioned suitable parameters, the present invention can ensure that an image sequence with sufficient resolution is obtained, thereby providing a data foundation for subsequent accurate calculations.

[0032] In some embodiments of the present invention, in step S4: The segmentation threshold mentioned in step (a) has a lower limit of 81 to 116 and an upper limit of 255; And / or, In step S4(b), image contrast enhancement processing is first performed, wherein the grayscale display range of the image is adjusted to a lower limit of 182 to 253 and an upper limit of 184 to 255; then, image segmentation is performed using the grayscale threshold, wherein the lower limit of the grayscale threshold is 128 and the upper limit is 255. By adopting the above technical solution, and based on the aforementioned suitable threshold range setting, this invention can accurately distinguish between rice grain regions and background, and between the cortex and endosperm, thereby improving calculation accuracy.

[0033] In some embodiments of the present invention, in step S6, the formula for calculating the degree of husk retention is: husk retention (%) = (total area of ​​rice grain husk / total cross-sectional area of ​​rice grain) × 100%. By adopting the above technical solution and the above calculation method, the present invention can ensure the accuracy and reliability of the husk retention measurement results.

[0034] In some embodiments of the present invention, in step S4, the statistical analysis of the rice grain cortex area and cross-sectional area is automatically completed using image analysis software. By employing the above technical solution, the present invention can automate the determination of cortex retention, thereby improving detection efficiency and consistency, and reducing human error.

[0035] In some embodiments of the present invention, the method for determining the husk retention of colored rice based on Micro-CT is a non-destructive testing method, and the rice grain sample retains its intact structure after testing. By employing the above-mentioned technical solution, the present invention can maintain sample integrity, thereby achieving repeatable testing and reducing testing costs.

[0036] In some embodiments of the present invention, after step S6, the method further includes: S7. Compare the calculated husk retention rate with the preset processing precision level to determine the milling grade of colored rice. This invention, by adopting the above technical solution, directly converts the test results into processing quality evaluation indicators, facilitating production quality control.

[0037] Furthermore, in some embodiments of the present invention, the preset processing accuracy levels include those shown in Table 1 below: Table 1-

[0038] The technical solution of the present invention will be further described in detail below with reference to specific embodiments and accompanying drawings. It should be understood that the following embodiments are only used to explain the present invention and are not intended to limit the present invention.

[0039] Examples 1 to 9: Methods for determining the husk retention of colored rice based on Micro-CT; Examples 1 to 9 and Comparative Examples 1 to 3 were used Figure 1 The rice grain sample shown in the figure was subjected to the following experiment. Detailed information about the rice grain sample can be found in the attached figure. Figure 1 .

[0040] Figure 1 These are comparative images of the appearance morphology of rice grains in Examples 1-9 of the present invention, arranged in three groups: First row: Ordinary brown rice milling 0 min (Example 1), 3 min (Example 2), 9 min (Example 3); Second row: Black rice 1 milling 0 min (Example 4), 3 min (Example 5), 9 min (Example 6); Third row: Black rice 2 milling 0 min (Example 7), 3 min (Example 8), 9 min (Example 9); the scale in the figure shows the actual size (1 mm / division).

[0041] Examples 1 to 3 are experiments on the husk retention of ordinary rice (non-colored rice) measured by micro-CT method. The husk retention test results of Examples 1 to 3 are shown in [reference needed]. Figure 2 In this diagram, gray bars represent skin retention as determined by the Micro-CT method (Examples 1-3). Black bars represent skin retention as determined by the national standard staining method (Comparative Examples 1-3).

[0042] Example 1: Determination of bran retention in ordinary brown rice (milled for 0 min) using Micro-CT method; S1. Sample Preparation: Prepare ordinary brown rice (with a light-colored bran layer; see [reference needed for ordinary brown rice in this example]). Figure 1 The whole meter is glued vertically to the center of the top of the short column sample stage; S2. Micro-CT Tomography: Turn on the micro-CT host and set the main scanning parameters: dynamic scanning mode, X-ray source voltage of 80 kV, current of 100 μA, sample exposure time of 0.6 s, and acquisition frame rate of 1080 FPS.

[0043] S3. 3D Reconstruction: After scanning, the DCM file is output and imported into VoxelStudio Recon software for analysis. The pixel size of the slice layer image is modified to 350 × 350 × 1120 pixels. The "reconstruction" process obtains the center cross-sectional images of the rice grains in the XY (cross-section), XZ (longitudinal section - front), and YZ (longitudinal section - side) directions, as well as the RAW file of the cross-sectional images of the stacked rice grains. The single frame size of the reconstructed stacked slices is 350 × 350 pixels, with a total of 1120 frames.

[0044] S4. Image Batch Processing and Area Calculation: Import the above RAW file sequence into ImageJ software, and perform the following batch operations on each cross-sectional image in the sequence in sequence: S4(a) Calculation of cross-sectional area of ​​rice grains: Modify the grayscale value of the image to 8 bits, apply the segmentation threshold (lower limit 81, upper limit 255) to segment the image, identify the rice grain region from the background, and the software automatically calculates the cross-sectional area of ​​the rice grains in the section.

[0045] S4(b) Calculation of cortical area of ​​rice grains: On the same cross-sectional image, perform the following operations to accurately segment the cortex and calculate its area: (b.1) Image contrast enhancement processing: First, adjust the grayscale display range of the image to the lower limit of 182 and the upper limit of 201.

[0046] (b.2) Gray-scale threshold segmentation of the cortex: Then, the image is segmented directly by applying gray-scale thresholds (lower limit 128, upper limit 255) to separate the cortex from the endosperm in order to identify the cortical region.

[0047] (b.3) Batch execution and area statistics: Set the above two operations ((b.1) and (b.2)) as a batch processing task, automatically perform the "segmentation of cortex and endosperm" operation on each cross-sectional image in the image sequence, and automatically calculate the cortical area of ​​each slice by the software.

[0048] S5. Area Accumulation: From the entire image sequence, select all cross-sectional areas of rice grains with a cross-sectional area greater than zero as valid cross-sectional areas. Accumulate the cortical area of ​​all valid cross-sectional areas to obtain the total cortical area of ​​the rice grains, and accumulate the cross-sectional area of ​​all valid cross-sectional areas to obtain the total cross-sectional area of ​​the rice grains.

[0049] S6. Calculation of Peel Retention Rate: The peel retention rate is calculated according to the following formula: Peel retention rate (%) = (Total area of ​​rice grain peel / Total cross-sectional area of ​​rice grain) × 100% The calculated result is the three-dimensional bran retention value of the ordinary brown rice sample.

[0050] The flowchart illustrating the method for determining the husk retention of colored rice based on Micro-CT in this embodiment is shown below. Figure 4 .

[0051] Example 2: Determination of bran retention in ordinary brown rice (milled for 3 min) using Micro-CT method; S1. Sample preparation: Mill ordinary brown rice for 3 minutes (see [reference needed] for ordinary brown rice in this example). Figure 1 The whole meter is glued vertically to the center of the top of the short column sample stage; S2. Micro-CT Tomography: Turn on the micro-CT host and set the main scanning parameters: dynamic scanning mode, X-ray source voltage of 80 kV, current of 100 μA, sample exposure time of 0.6 s, and acquisition frame rate of 1080 FPS.

[0052] S3. 3D Reconstruction: After scanning, the DCM file is output and imported into VoxelStudio Recon software for analysis. The pixel size of the slice layer image is modified to 350 × 350 × 1120 pixels. The "reconstruction" process obtains the center cross-sectional images of the rice grains in the XY (cross-section), XZ (longitudinal section - front), and YZ (longitudinal section - side) directions, as well as the RAW file of the cross-sectional images of the stacked rice grains. The single frame size of the reconstructed stacked slices is 350 × 350 pixels, with a total of 1120 frames.

[0053] S4. Image Batch Processing and Area Calculation: Import the above RAW file sequence into ImageJ software, and perform the following batch operations on each cross-sectional image in the sequence in sequence: S4(a) Calculation of cross-sectional area of ​​rice grains: Modify the image grayscale value to 8 bits, apply the segmentation threshold (lower limit 95, upper limit 255) to segment the image, identify the rice grain region from the background, and the software automatically calculates the cross-sectional area of ​​the rice grains in the section.

[0054] S4(b) Calculation of cortical area of ​​rice grains: On the same cross-sectional image, perform the following operations to accurately segment the cortex and calculate its area: (b.1) Image contrast enhancement processing: First, adjust the grayscale display range of the image to the lower limit of 228 and the upper limit of 233.

[0055] (b.2) Gray-scale threshold segmentation of the cortex: Then, the image is segmented directly by applying gray-scale thresholds (lower limit 128, upper limit 255) to separate the cortex from the endosperm in order to identify the cortical region.

[0056] (b.3) Batch execution and area statistics: Set the above two operations ((b.1) and (b.2)) as a batch processing task, automatically perform the "segmentation of cortex and endosperm" operation on each cross-sectional image in the image sequence, and automatically calculate the cortical area of ​​each slice by the software.

[0057] S5. Area Accumulation: From the entire image sequence, select all cross-sectional areas of rice grains with a cross-sectional area greater than zero as valid cross-sectional areas. Accumulate the cortical area of ​​all valid cross-sectional areas to obtain the total cortical area of ​​the rice grains, and accumulate the cross-sectional area of ​​all valid cross-sectional areas to obtain the total cross-sectional area of ​​the rice grains.

[0058] S6. Calculation of Peel Retention Rate: The peel retention rate is calculated according to the following formula: Peel retention rate (%) = (Total area of ​​rice grain peel / Total cross-sectional area of ​​rice grain) × 100% The calculated result is the three-dimensional bran retention value of the ordinary brown rice sample.

[0059] Example 3: Determination of bran retention in ordinary brown rice (milled for 9 min) using Micro-CT method; S1. Sample preparation: Milled ordinary brown rice for 9 minutes (see [reference needed for ordinary brown rice in this example]). Figure 1 The whole meter is glued vertically to the center of the top of the short column sample stage; S2. Micro-CT Tomography: Turn on the micro-CT host and set the main scanning parameters: dynamic scanning mode, X-ray source voltage of 80 kV, current of 100 μA, sample exposure time of 0.6 s, and acquisition frame rate of 1080 FPS.

[0060] S3. 3D Reconstruction: After scanning, the DCM file is output and imported into VoxelStudio Recon software for analysis. The pixel size of the slice layer image is modified to 350 × 350 × 1120 pixels. The "reconstruction" process obtains the center cross-sectional images of the rice grains in the XY (cross-section), XZ (longitudinal section - front), and YZ (longitudinal section - side) directions, as well as the RAW file of the cross-sectional images of the stacked rice grains. The single frame size of the reconstructed stacked slices is 350 × 350 pixels, with a total of 1120 frames.

[0061] S4. Image Batch Processing and Area Calculation: Import the above RAW file sequence into ImageJ software, and perform the following batch operations on each cross-sectional image in the sequence in sequence: S4(a) Calculation of cross-sectional area of ​​rice grains: Modify the grayscale value of the image to 8 bits, apply the segmentation threshold (lower limit 105, upper limit 255) to segment the image, identify the rice grain region from the background, and the software automatically calculates the cross-sectional area of ​​the rice grains in the section.

[0062] S4(b) Calculation of cortical area of ​​rice grains: On the same cross-sectional image, perform the following operations to accurately segment the cortex and calculate its area: (b.1) Image contrast enhancement processing: First, adjust the grayscale display range of the image to the lower limit of 244 and the upper limit of 249.

[0063] (b.2) Gray-scale threshold segmentation of the cortex: Then, the image is segmented directly by applying gray-scale thresholds (lower limit 128, upper limit 255) to separate the cortex from the endosperm in order to identify the cortical region.

[0064] (b.3) Batch execution and area statistics: Set the above two operations ((b.1) and (b.2)) as a batch processing task, automatically perform the "segmentation of cortex and endosperm" operation on each cross-sectional image in the image sequence, and automatically calculate the cortical area of ​​each slice by the software.

[0065] S5. Area Accumulation: From the entire image sequence, select all cross-sectional areas of rice grains with a cross-sectional area greater than zero as valid cross-sectional areas. Accumulate the cortical area of ​​all valid cross-sectional areas to obtain the total cortical area of ​​the rice grains, and accumulate the cross-sectional area of ​​all valid cross-sectional areas to obtain the total cross-sectional area of ​​the rice grains.

[0066] S6. Calculation of Peel Retention Rate: The peel retention rate is calculated according to the following formula: Peel retention (%) = (total area of ​​rice grain husk / total cross-sectional area of ​​rice grain) × 100% The result calculated is the three-dimensional husk retention value of the ordinary brown rice sample.

[0067] The graphs showing the changes in bran retention of the two types of black rice under different milling times in Examples 4 to 9 of this invention are shown below. Figure 3 . Figure 3 Specifically, this includes: the gradient change in bran retention of black rice 1 (Examples 4 to 6) with milling time (0 / 3 / 9 min); and the gradient change in bran retention of black rice 2 (Examples 7 to 9) with milling time (0 / 3 / 9 min).

[0068] The differences between the detection methods for the skin retention value in Examples 4 to 9 and those in Example 1 are shown in Table 2 below.

[0069] Table 2-

[0070] Comparative Examples 1 to 3, national standard staining method; Comparative Example 1: Weigh 5 g of ordinary brown rice (without a dark bran color) and place it in a petri dish. Add ultrapure water to submerge the sample and let it stand for 1 min. After washing away the bran powder, immediately add eosin Y-methylene blue staining agent to submerge the sample and let it stand for 2 min. After discarding the staining solution, immediately add 80% ethanol solution to submerge and wash the sample surface. Repeat the rinsing process 3 times, rinsing for 1 min each time. After rinsing, blot the moisture on the sample surface with filter paper, air dry, and then use a JMCT12 rice appearance quality tester to determine the bran retention of the sample.

[0071] Comparative Example 2: Weigh 5 g of whole brown rice (milled for 3 min) and place it in a petri dish. Add ultrapure water to submerge the sample and let it stand for 1 min. After washing away the bran powder, immediately add eosin Y-methylene blue staining agent to submerge the sample and let it stand for 2 min. After discarding the staining solution, immediately add 80% ethanol solution to submerge and wash the sample surface. Repeat the rinsing process 3 times, rinsing for 1 min each time. After rinsing, blot the moisture on the sample surface with filter paper, air dry, and then use a JMCT12 rice appearance quality tester to determine the bran retention rate of the sample.

[0072] Comparative Example 3: Weigh 5 g of whole brown rice (milled for 9 min) and place it in a petri dish. Add ultrapure water to submerge the sample and let it stand for 1 min. After washing away the bran powder, immediately add eosin Y-methylene blue staining agent to submerge the sample and let it stand for 2 min. After discarding the staining solution, immediately add 80% ethanol solution to submerge and wash the sample surface. Repeat the rinsing process 3 times, rinsing for 1 min each time. After rinsing, blot the moisture on the sample surface with filter paper, air dry, and then use a JMCT12 rice appearance quality tester to determine the bran retention rate of the sample.

[0073] The test results of the final skin retention rate of Examples 1 to 9 and Comparative Examples 1 to 3 are shown in Table 1 below.

[0074] The skin retention data measured by the Micro-CT method in Examples 1 to 3 are compared with the measurement results measured by the staining method in Comparative Examples 1 to 3 according to GB / T 5502-2018. (See attached table for details.) Figure 2 .

[0075] The results of the husk retention test of the two types of black rice in Examples 4 to 9, measured by the micro-CT method, are shown below. Figure 3 .

[0076] Table 1 - Results of Skin Retention Test

[0077] Results analysis: Combining Figures 1 to 3 As shown in Table 1, the experimental results of this invention demonstrate that the method for determining the bran retention of colored rice based on Micro-CT, through innovative tomographic scanning technology and a dual grayscale threshold segmentation algorithm, achieves non-destructive and accurate determination of the bran retention of colored rice such as black rice and red rice. This effectively solves the technical problems of large measurement errors and cumbersome operation caused by the rich pigment content in the bran of colored rice in traditional staining and transmission imaging methods. This method not only accurately distinguishes between the bran and endosperm regions of the rice grain, maintaining the integrity of the sample structure, but also has advantages such as simple operation and rapid detection. It provides a reliable technical means for quality control of the processing precision of colored rice, significantly improving the evaluation efficiency and accuracy of colored rice processing quality, and is of great significance for promoting the development of the whole grain food industry. Furthermore, the method provided by this invention not only achieves accurate detection of the entire milling gradient from brown rice to polished rice, but also clearly distinguishes different processing precisions (e.g., milling >1% for 3 min indicates substandard rice, while <0.1% for 9 min meets the fine milling standard), providing a reliable quantitative basis for the optimization of colored rice processing technology and quality control, and has significant practical application value.

[0078] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the patent protection scope of the present invention.

Claims

1. A method for determining the husk retention of colored rice based on Micro-CT, characterized in that, The method includes the following steps: S1. Obtain rice grain samples of colored rice with different milling degrees; S2. Perform Micro-CT tomography on the rice grain sample to obtain tomographic image data of the rice grain sample; S3. Based on the tomographic scan image data, obtain an image sequence containing all cross-sections of the rice grain sample through three-dimensional reconstruction. S4. Based on preset segmentation thresholds and grayscale thresholds, perform batch processing on all cross-sectional images in the image sequence, and calculate the following areas for each cross-section in sequence: (a) For each cross-sectional image, the segmentation threshold is applied to segment the image, the rice grain region is identified from the background, and the cross-sectional area of ​​the rice grain is calculated. (b) For each cross-sectional image, image contrast enhancement processing is first performed to enhance the grayscale difference between the cortex and endosperm, and then the grayscale threshold is applied to perform image segmentation, identify the cortical region, and calculate the cortical area of ​​the rice grain. S5. From the image sequence, select all cross-sections with a cross-sectional area greater than zero for each rice grain as valid cross-sections; The total area of ​​the rice grain cortex is obtained by summing the cortex areas of all effective cross sections, and the total cross-sectional area of ​​the rice grain is obtained by summing the cross-sectional areas of all effective cross sections. S6. Calculate the ratio of the total area of ​​the rice grain peel to the total cross-sectional area of ​​the rice grain to obtain the peel retention degree.

2. The method for determining the husk retention of colored rice based on Micro-CT according to claim 1, characterized in that, In step S2, before the Micro-CT tomographic scan, the sample is further subjected to a process of decoction and removal of impurities.

3. The method for determining the husk retention of colored rice based on Micro-CT according to claim 1, characterized in that, In step S1, the colored rice is one of black rice, red rice, or purple rice.

4. The method for determining the husk retention of colored rice based on Micro-CT according to claim 1, characterized in that, In step S2, the parameters of the Micro-CT tomographic scan include: X-ray source voltage 80kV, current 100μA, scanning mode SQ scan, and acquisition frame rate of 1080-1800FPS.

5. The method for determining the husk retention of colored rice based on Micro-CT according to claim 1, characterized in that, In step S3, the single frame size of the three-dimensional reconstructed cross-sectional image sequence is 350×350 pixels, and the total number of frames is not less than 1000.

6. The method for determining the husk retention of colored rice based on Micro-CT according to claim 1, characterized in that, In step S4: The segmentation threshold mentioned in step (a) has a lower limit of 81 to 116 and an upper limit of 255; And / or, In step S4(b), image contrast enhancement processing is first performed, wherein the grayscale display range of the image is adjusted to a lower limit of 182 to 253 and an upper limit of 184 to 255; then the grayscale threshold is applied to perform image segmentation, wherein the lower limit of the grayscale threshold is 128 and the upper limit is 255.

7. The method for determining the husk retention of colored rice based on Micro-CT according to claim 1, characterized in that, In step S6, the formula for calculating the degree of husk retention is: husk retention (%) = (total area of ​​rice grain husk / total cross-sectional area of ​​rice grain) × 100%.

8. The method for determining the husk retention of colored rice based on Micro-CT according to claim 1, characterized in that, In step S4, the statistical analysis of the rice grain cortex area and the rice grain cross-sectional area is automatically completed by image analysis software.

9. The method for determining the husk retention of colored rice based on Micro-CT according to claim 1, characterized in that, The method for determining the husk retention of colored rice based on Micro-CT is a non-destructive testing method, and the rice grain sample retains its intact structure after testing.

10. The method for determining the husk retention of colored rice based on Micro-CT according to any one of claims 1-9, characterized in that, Following step S6, the following is also included: S7. Compare the calculated husk retention with the preset processing precision level to determine the milling grade of colored rice.

Images