Space-time quantitative evaluation method for browning quality of fresh-cut Chinese yam

Abstract

The invention belongs to the technical field of food processing monitoring, and particularly relates to a time-space quantitative evaluation method for browning quality of fresh-cut Chinese yams. The method mainly comprises three steps of image acquisition and processing, quantitative evaluation of a browning time dimension and quantitative evaluation of a browning space dimension. The browning condition of the fresh-cut Chinese yam in the actual storage and processing process can be reflected more comprehensively and accurately by combining the image acquisition and processing technology and the time-space dimension quantitative evaluation; besides, the time dimension and space information are fused, a space-time quantitative model of the Chinese yam browning quality is constructed, the browning diffusion degree, the browning progress speed and the expansion trend of the browning diffusion degree and the browning progress speed in different areas of a sample section can be tracked and evaluated, and the subtle change and dynamic evolution process of browning can be accurately captured. Compared with a traditional method only paying attention to the overall trend of color changes, the method can reveal the local change difference and the evolution mechanism of the local change difference, and technical support is provided for accurate monitoring and intelligent regulation and control in the food processing process.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of food processing monitoring, and specifically relates to a method for spatiotemporal quantitative evaluation of browning quality of fresh-cut yam. BACKGROUND

[0002] During the processing of fresh-cut yam, browning easily occurs on the cutting surface due to mechanical damage, which directly shortens its shelf life. Browning not only affects the appearance of yam, making it change from white or light yellow to brown or even dark brown, reducing visual appeal, and further affecting the purchasing intention of consumers. The main reason for browning is that the cell structure is damaged during cutting, leading to the exudation of cell juice and the release of a large amount of polyphenolic substances. After these substances come into contact with polyphenol oxidase (PPO) and peroxidase (POD), rapid enzymatic reactions occur, generating quinone compounds, which then polymerize into dark brown high molecular pigments. In addition, high temperature or dry environment can also exacerbate the Maillard reaction, and the oxidation of ascorbic acid further accelerates the browning process. Over time, browning not only affects the appearance of yam, but also leads to the loss of key nutrients such as antioxidants, especially total phenols and ascorbic acid. At the same time, the increase in malondialdehyde (MDA) content reflects the intensification of lipid peroxidation, further accelerating the deterioration of quality.

[0003] For the browning problem of fresh-cut yam, there has been some progress in related preservation technology, but most studies still rely on traditional colorimeters or chemical analysis methods to determine the degree of browning. Although the colorimeter detection method is simple, it requires manual sampling and can damage the sample. In addition, it can only reflect the surface color of a single point and cannot comprehensively evaluate the degree of browning, making it difficult to accurately reflect the overall quality of fresh-cut yam. The chemical analysis method extracts tissue fluid and uses spectrophotometry to determine PPO activity or polyphenol content. Although this method is relatively accurate, it is complex, time-consuming, and consumes a large amount of reagents, making it difficult to meet the needs of large-scale detection on the production line. Since enzymatic browning usually occurs in the early stage after cutting and the reaction is rapid, traditional methods often fail to capture the dynamic changes of browning due to insufficient sensitivity.

[0004] The browning phenomenon of fresh-cut yam has obvious spatiotemporal dynamic characteristics. As time goes on, browning gradually spreads from the cutting surface to the interior, and the rate and degree of browning differ significantly at different locations. However, current detection techniques cannot comprehensively and accurately evaluate this spatiotemporal evolution process, and there is a lack of methods to quantitatively characterize the spatiotemporal variation characteristics of fresh-cut yam browning. Therefore, it is difficult to meet the continuous monitoring needs of the production line for the browning process. Traditional methods fail to fully explore and grasp the dynamic changes in the time dimension and the distribution differences in the spatial dimension of browning, which not only restricts the precise regulation of process parameters in processing technology, but also hinders the targeted optimization of preservation technology based on browning characteristics, making it difficult to meet the needs of the fresh-cut yam industry for quality improvement and control.

[0005] Therefore, it is essential to design a method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yams in order to better guide production and storage and improve the quality stability of fresh-cut yams. Summary of the Invention

[0006] To overcome the limitations of existing technologies, this invention provides a method for the spatiotemporal quantitative evaluation of browning quality in fresh-cut yam. In the temporal dimension, dynamic indicators such as the uniformity of browning distribution, the degree of browning diffusion, and the rate of browning progression are analyzed and constructed to quantify the evolution of browning from initial local unevenness to overall convergence in the later stage. In the spatial dimension, the common patterns of browning gradient diffusion and varietal differences are revealed through concentric ring region division and comparison of different parts.

[0007] To achieve the above technical objectives, the present invention adopts the following solution:

[0008] A method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yam, the specific steps of which are as follows:

[0009] Step one, image processing includes the following procedures:

[0010] Step 1: Image Acquisition and Preprocessing

[0011] Select yam samples and slice them to obtain sliced ​​samples; continuously sample the sliced ​​samples to obtain images of the sliced ​​samples; preprocess the acquired images to obtain preprocessed images; the preprocessing includes image grayscale conversion, noise reduction, removal of reflective areas, and background removal;

[0012] Step 2, Browning region feature extraction:

[0013] Browning region feature extraction is performed on the preprocessed image in Process 1. The extraction steps are as follows: First, a grayscale value range needs to be set according to the grayscale features of the image to determine the range of the browning region; then, a grayscale range mask is generated; then, based on the grayscale range mask, a bitwise AND operation is performed between the mask and the original image to initially extract the corresponding browning region; finally, global threshold segmentation is performed to obtain the accurate segmentation result and output it to form a standardized image, thus completing the feature extraction of the entire browning region.

[0014] Step two, quantitative evaluation of browning time dimension, includes the following process:

[0015] Step 1: Based on the standardized images obtained in Steps 1 and 2, calculate the browning distribution uniformity (σ). The calculation formula is as follows: in, G is the average gray value of the image, N is the total number of pixels in the image, and G is the average gray value of the image. i Let be the grayscale value of the i-th pixel, where i is a positive integer;

[0016] Step 2: Compare the uniformity of browning distribution of sliced ​​samples under different resting times. The larger the σ value, the more obvious the browning in the area, indicating that it is in the early stage of browning diffusion, indicating that the browning area has not fully expanded and its spatial non-uniformity is higher. Conversely, if the σ value is smaller, it indicates that the browning area in the image is gradually increasing and the browning distribution tends to be more uniform.

[0017] Step 3: Calculate the degree of browning diffusion (R). The calculation formula is as follows: Where, p i As an indicator function, if the pixel belongs to the browning region, then p i =1, otherwise p i =0; N is the total number of pixels in the image, G i Let be the grayscale value of the i-th pixel, where i is a positive integer.

[0018] Step four: Compare the degree of browning diffusion in the sliced ​​samples at different resting times. The larger the R value, the greater the extent of browning diffusion in the image, indicating a higher degree of browning severity; conversely, the smaller the R value, the less browning the sample.

[0019] Step 5: Calculate the browning progression rate (V). The calculation formula is as follows: Where B1 and B2 are the percentages of browning area measured at time points t1 and t2, respectively, and t1 and t2 are the corresponding time points;

[0020] Step 6: Compare the browning progression rate of sliced ​​samples under different resting times. The larger the V value, the faster the browning spreads. If the V value increases rapidly in a short period of time, it indicates that the fresh-cut yam has undergone a large degree of oxidation. Conversely, the smaller the V value, the slower the browning spreads.

[0021] Step 3, quantitative evaluation of browning spatial dimensions, includes the following process:

[0022] Step 1: Based on the standardized image obtained in Steps 1 and 2, the sample image is divided into three concentric ring regions, denoted as L. i Each ring region corresponds to a different radius range; where i takes the values ​​1, 2, and 3.

[0023] Step 2: Calculate the pixel percentage (P) of the browning region within each ring. The calculation formula is as follows: Where M i For L i The number of pixels within the browning region; N i For L i The total number of pixels in the region, where i takes values ​​of 1, 2, or 3;

[0024] Step 3: A heatmap is drawn based on the pixel proportion of the browning region within each ring. The darker the color of the heatmap, the lower the degree of browning in that region; conversely, the lighter the color, the higher the degree of browning. Multiple heatmaps are generated from images at different time points. By comparing the color changes between the various heatmaps, the spatial expansion pattern of browning is analyzed.

[0025] Step 4: Calculate the pixel percentage (P) of the browning region within each ring of different parts of the yam sample, and draw curves and heat maps based on the P values ​​to show the trend of the pixel percentage of the browning region in each part of the yam changing with storage time and the browning distribution of each part at key time nodes; by comparing and analyzing the curves and heat maps, the differences in browning diffusion in different parts can be determined.

[0026] Preferably, the thickness of the sliced ​​sample in step one of process one is 3-5 mm.

[0027] Preferably, the camera used for continuous sampling in step one is a CMOS (Complementary Metal-Oxide-Semiconductor) camera; during sampling, the sliced ​​sample is placed 10-12 cm directly below the camera.

[0028] Preferably, in step one, the frequency of continuous sampling of the sample is to automatically capture an image every 20 seconds.

[0029] Preferably, the image of the sliced ​​sample in step one of process one is a BMP format RGB image with a resolution of 2448×2048.

[0030] Preferably, the grayscale value range in step one, process two, is [40, 90].

[0031] Preferably, the method for generating the grayscale range mask in step one, process two, is to use the cv2.inRange function in OpenCV.

[0032] Preferably, the global threshold segmentation method described in step one, process two, is the global threshold (Otus) algorithm.

[0033] Preferably, the concentric ring division in step three, process one, is as follows: the core inner ring region is denoted as L1: its radius is 30.00% of the total image radius, reflecting the browning status of the central region of the sample; the transitional middle ring region is denoted as L2: covering a radius range of 30.00%-70.00%, reflecting the intermediate transition zone from the center to the periphery; the outer epidermal ring region is denoted as L3: occupying the remaining part, reflecting the browning diffusion characteristics of the edge region.

[0034] Preferably, the different parts of the yam sample mentioned in step three, process four, are the head, middle, and tail of the yam.

[0035] Beneficial effects:

[0036] Compared with existing technologies, the method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yam provided by this invention has the following advantages:

[0037] (1) Compared with traditional sensory observation or colorimeter measurement for browning evaluation, this invention uses image acquisition and processing technology combined with spatiotemporal dimension quantitative evaluation to more comprehensively and accurately reflect the browning changes of fresh-cut yam during actual storage and processing. This method not only avoids human subjective error, but also accurately captures the subtle changes and dynamic evolution of browning.

[0038] (2) This invention integrates temporal and spatial information to construct a spatiotemporal quantitative model for the browning quality of fresh-cut yam, enabling the tracking and evaluation of the degree of browning diffusion, the rate of browning progression, and its expansion trend in different regions of the sample cross-section. Compared to traditional methods that only focus on the overall trend of color change, this method can reveal the differences in local changes and their evolution mechanisms, providing technical support for precise monitoring and intelligent control in food processing.

[0039] (3) This invention has good versatility and ease of operation. Its image acquisition and processing process is highly standardized and applicable to yam samples of different batches, different cuts and different processing conditions. The method is easy to integrate into intelligent detection systems and has good repeatability and scalability. It can be widely used in multiple application scenarios such as quality control of fresh-cut yam, browning mechanism research and preservation technology evaluation. Attached Figure Description

[0040] Figure 1 The degree of variation in the uniformity of browning distribution among the three types of yam.

[0041] Figure 2 The image shows a heat map of the browning diffusion degree of three types of yam.

[0042] Figure 3 This is a dynamic change diagram of the browning diffusion degree of three types of yam at L1, L2, and L3.

[0043] Figure 4 This is a diagram showing the degree of browning diffusion in the first concentric ring regions (L1, L2, L3) of three types of yam. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail with the help of the following embodiments.

[0045] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art.

[0046] While only preferred methods and materials have been described in this invention, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe the methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0047] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be readily apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0048] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0049] Example 1:

[0050] Step one, yam image processing includes the following steps:

[0051] Step 1: Image Acquisition and Preprocessing

[0052] Three different varieties of yam were selected: Chinese yam, Huai yam, and glutinous yam. The yams were divided into three parts: the head, the middle, and the tail. First, the yams were sliced ​​to a thickness of 3mm. The slices were placed 10cm directly below a CMOS (Complementary Metal-Oxide-Semiconductor) camera. After the camera was started, the head, middle, and tail slices of the yam were continuously sampled at a frequency of 20 seconds per frame (total monitoring time of 10 minutes) to obtain images of the yam slices. The images were then processed by grayscale conversion, noise reduction, removal of reflective areas, and background removal to obtain pre-processed images of fresh-cut yams.

[0053] Step 2, Browning region feature extraction:

[0054] First, a suitable grayscale value range needs to be set based on the grayscale features of the image to determine the extent of the browning region. Grayscale histogram analysis of multiple sets of fresh-cut yam sample images showed that the grayscale values ​​of most browning regions clustered within the range of [40, 90]. This range exhibited high stability and robustness across different samples, effectively capturing the grayscale features of the target region. Therefore, the grayscale value range was set to [40, 90]. Next, the OpenCV function `cv2.inRange` was used to generate a grayscale range mask. This function retains pixels falling within the [40, 90] range and sets other pixels to zero, thus obtaining a preliminary binary mask. Following this, based on the grayscale range mask, the `cv2.bitwise_and` operation was used to perform a bitwise AND operation between the mask and the original image, initially extracting the corresponding browning region. Finally, the Otsu algorithm was used for global threshold segmentation to obtain accurate segmentation results, which were then output as a standardized image, completing the entire browning region extraction process.

[0055] Step two, quantitative evaluation of browning time dimension, includes the following process:

[0056] Step 1: Based on the images obtained in Step 1 and Step 2, calculate the browning distribution uniformity (σ). The calculation formula is as follows: in, G is the average gray value of the image, N is the total number of pixels in the image, and G is the average gray value of the image. i Let be the grayscale value of the i-th pixel.

[0057] Step 2: Compare the uniformity of browning distribution of sliced ​​samples under different resting times. The larger the σ value, the more obvious the browning in the area, which may be in the early stage of browning diffusion, indicating that the browning area has not fully expanded and its spatial non-uniformity is higher. Conversely, it indicates that the browning area in the image is gradually increasing and the browning distribution tends to be uniform.

[0058] As shown in Table 1, the uniformity of browning distribution in different parts of the three yam varieties all exhibited a common trend of "high value in the initial stage - gradual decrease - stabilization". In the initial stage (sampling time 1), the uniformity of browning distribution at the head of glutinous rice yam, Chinese yam, and vegetable yam were 28.67, 32.42, and 20.86, respectively, reflecting the color dispersion caused by differences in local enzyme activity and substrate distribution. At the 5th sampling time (100 seconds), the uniformity of browning distribution at the head of glutinous rice yam dropped to 16.24 (a decrease of 43.3%), indicating a violent browning reaction, while Chinese yam only dropped to 26.04 (a decrease of 19.7%), indicating a moderate reaction, and vegetable yam dropped to 14.92 (a decrease of 28.5%), showing significant initial fluctuations.

[0059] As the reaction progressed (200-600 seconds, 10-30 sampling times), the uniformity of browning distribution at the head of the glutinous yam stabilized at 14.48-16.24, with a coefficient of variation of only 4.3%, indicating that the browning homogenization was completed rapidly. The uniformity of browning distribution at the head of the Chinese yam slowly decreased from 23.68 to 20.83 (a decrease of 11.2%), but remained higher than that of the glutinous yam at the same time, indicating a slow browning process and a low homogenization rate. The uniformity of browning distribution at the head of the Chinese yam fluctuated between 14.36-13.03, and then slightly rebounded to 13.03 in the later stage, reflecting the complexity of the reaction caused by its tissue heterogeneity.

[0060] Table 1. Dynamic changes in the uniformity of browning distribution in different parts of three yam varieties.

[0061]

[0062] Figure 1 (a), (b), and (c) represent the degree of variation in the uniformity of browning distribution in the head, middle, and tail regions of each yam variety, respectively; for example... Figure 1 As shown, the uniformity of browning distribution of various yam varieties in different regions exhibited a trend of initially high uniformity, followed by a gradual decrease and eventual stabilization. In the initial stage, the high uniformity of browning distribution reflected color dispersion caused by differences in local reaction rates, enzyme activity, and microenvironment. As the reaction continued, local browning hotspots gradually diffused and merged, the chemical reaction tended towards equilibrium, and the color distribution changed from uneven to uniform, leading to a decrease and stabilization of the uniformity of browning distribution. Regardless of whether it was the beginning, middle, or end of the storage process, glutinous rice yam showed a significant decreasing trend in the uniformity of browning distribution in the initial stage, subsequently stabilizing. In contrast, the uniformity of browning distribution in Huai yam changed relatively slowly throughout the storage process, remaining at a high level with small fluctuations and a slight decreasing trend, indicating a slower and more stable browning process. Vegetable yam, on the other hand, showed significant fluctuations in the initial stage, but its uniformity of browning distribution gradually stabilized over time, with the later browning process becoming more uniform.

[0063] Step 3: Calculate the degree of browning diffusion (R). The calculation formula is as follows: Where, p i As an indicator function, if the pixel belongs to the browning region, then p i =1, otherwise p i =0; N is the total number of pixels in the image.

[0064] Step 4: Compare the degree of browning diffusion of sliced ​​samples under different resting times. The larger the R value, the greater the extent of browning diffusion in the image, indicating a higher degree of browning severity; conversely, the smaller the R value, the less browning the sample.

[0065] The browning process of different parts of three types of yam was quantitatively analyzed by 20 seconds per cycle (total duration 10 minutes). Key data are shown in Table 2. The browning diffusion degree (R) of the glutinous yam's apical part rapidly increased from 8.23% to 58.72% (600s), reaching 19.23% at 100s, an increase of 133.6%, exhibiting a "rapid initial start-up followed by a slower growth rate," indicating that the apical tissue is highly sensitive to browning. The browning diffusion degree of the Huai yam's apical part fluctuated between 8.21% and 8.63%, with a maximum increase of only 4.9%, showing almost no significant expansion. The browning diffusion degree of the common yam's apical part increased from 8.19% to 12.33%, an increase of 50.5%, with an initial growth rate faster than Huai yam but much lower than glutinous yam.

[0066] The browning diffusion rate in the central part of glutinous yam increased from 9.62% to 18.01%, reaching 14.56% at 200s (an increase of 51.3%), after which the rate of increase slowed down, increasing by only 23.7% from 200 to 600s, indicating that the central reaction quickly entered a plateau period after its initiation. The browning diffusion rate in the central part of Chinese yam fluctuated between 10.86% and 12.63%, with a maximum increase of 14.5% followed by a brief decline (10.86% at 300s). The browning diffusion rate in the central part of common yam increased from 10.03% to 17.92%, stabilizing at 17.32%–17.92% in the later stages.

[0067] The browning diffusion at the tail of glutinous yam showed an "explosive growth," increasing from 12.06% to 25.31% (an increase of 109.8%) at 100s, reaching 59.71% (an increase of 395.0%) at 200s, and stabilizing at 79.63% at 600s, the highest value among all parts, confirming that the tail tissue has active metabolism and that browning expansion is almost unrestricted. The browning diffusion at the tail of Chinese yam increased slightly from 6.32% to 6.53%, with an overall increase of only 3.3%, showing almost no change, indicating its extremely strong resistance to browning. The browning diffusion at the tail of Chinese yam was 6.51% in the early stage (100s), similar to that of Chinese yam, but accelerated to 39.06% after 400s, reaching 51.73% at 600s, an increase of 715.0%, with a significantly increased growth rate in the later stages.

[0068] Table 2. Dynamic changes in the degree of browning diffusion in different parts of three yam varieties.

[0069]

[0070]

[0071] The proportion of browned areas in different parts of various yam varieties showed varying degrees of increase. Specifically, the browning spread was fastest in the tail region, followed by the head region, while the middle region showed a relatively slow rate of development. This phenomenon is closely related to the environmental conditions, physical structure, and physiological characteristics of different parts of the yam.

[0072] To more intuitively reveal the browning characteristics of fresh-cut yam at different parts over time, a heat map was further used to analyze the degree of browning diffusion (see...). Figure 2 );from Figure 2 As can be observed, the heatmap visually presents the spatial distribution pattern of browning in fresh-cut yams. Browning is most pronounced in the tail area, followed by the head, while browning in the middle is milder and unevenly distributed. In the glutinous rice yam sample, the browning area shows a relatively uniform and extensive expansion; the Huai yam shows milder browning, with its heatmap indicating a low proportion and limited range of browning in the sample; the common yam exhibits significant browning in the tail area, while browning in other parts is relatively weak.

[0073] Step 5: Calculate the browning progression rate (V). The calculation formula is as follows: Wherein, B1 and B2 are the percentages of browning area measured at time points t1 and t2, respectively, and t1 and t2 are the corresponding time points.

[0074] Step 6: Compare the browning progression rate of sliced ​​samples under different resting times. The larger the V value, the faster the browning spreads. If the V value increases rapidly in a short period of time, it indicates that the fresh-cut yam has undergone a large degree of oxidation. Conversely, the smaller the V value, the slower the browning spreads. As shown in Table 3, the browning progression rate of different parts of the three types of yam all show a common trend of "rapid decline in the early stage and stabilization in the later stage", but there are significant differences between varieties. The initial oxidation rate of glutinous yam dropped sharply from 17.04% / min to 6.68% / min, a decrease of 60.8%, and stabilized at 4.93% / min-3.55% / min after 200s, indicating that phenolic substances were rapidly oxidized in the early stage and tended to reach equilibrium due to substrate consumption in the later stage. The initial oxidation rate of Chinese yam was always <8.83% / min, and dropped to 2.26% / min after 200s, which was only 35.8% of that of glutinous yam at the same time. The initial oxidation rate of Chinese yam was close to that of Chinese yam (8.85% / min in the early stage), but the decrease was slightly larger in the later stage (0.24% / min at 600s), reflecting its moderate oxidation activity.

[0075] The browning rate in the central part of glutinous yam decreased from 1.63% / min to 0.28% / min, with a decrease of 66.3% in the early stage (0-200s), indicating a rapid decline after the browning of the central tissue was initiated. The central part of Chinese yam showed a rare negative value (-0.01% / min at 100s), and the rate approached 0 in the later stage (0.02% / min at 600s), indicating that the browning reaction basically stopped. The central part of Chinese yam had a similar rate to that of glutinous yam (1.61% / min in the early stage), but the decline was slower in the later stage (0.29% / min at 600s), maintaining a low-intensity continuous reaction.

[0076] The tail of the glutinous yam showed an "explosive decline," with the speed dropping sharply from 14.52% / min to 11.89% / min at 100s (a decrease of 18.1%), and stabilizing at 3.01% / min-6.93% / min after 200s, corresponding to the explosive increase in the degree of browning diffusion in the early stage of the tail. The tail speed of the Chinese yam was always <0.84% / min, and only 0.02% / min at 600s, with almost no reaction. The tail of the Chinese yam was the only part with an increased speed in the later stage, increasing from 1.23% / min to 2.86% / min after 400s (an increase of 132.5%), with a significant increase in speed in the later stage.

[0077] Table 3. Dynamic changes in the rate of browning progression in different parts of three yam varieties.

[0078]

[0079] Overall, regardless of whether it was the first, middle, or last part of the yam, the rate of browning progression in fresh-cut yam showed a trend of first rapidly decreasing and then stabilizing. Among them, the browning progression rate of glutinous rice yam showed the most significant change, followed by regular yam, while Huai yam showed the lowest rate and the smallest fluctuation.

[0080] Step 3, quantitative evaluation of browning spatial dimensions, includes the following process:

[0081] Step one: Based on the images obtained in steps one and two, the sample image is divided into three concentric ring regions, each with a different radius range. This method uses the geometric center of the image as a reference to divide the image into three concentric ring regions, each with a different radius range.

[0082] The inner core ring L1 region has a radius of 30.00% of the total image radius and mainly reflects the browning status of the central region of the sample; the intermediate transition ring L2 region covers 40.00% of the radius and reflects the intermediate transition zone that diffuses from the center to the periphery; the outer epidermal ring L3 region occupies the remaining part and reflects the browning diffusion characteristics of the edge region.

[0083] Step 2: Calculate the pixel percentage (P) of the browning region within each ring. The calculation formula is as follows: Where M i For L i The number of pixels within the browning region; N i For L i The total number of pixels in the region.

[0084] Step 3 involves creating a heatmap based on the pixel percentage of browned areas within each ring. Darker colors in the heatmap indicate lower levels of browning in that area, while lighter colors indicate higher levels of browning. Multiple heatmaps are generated from images at different time points (100s, 200s, 300s, 400s, 500s, and 600s). By comparing the color changes between these heatmaps, the spatial expansion pattern of browning is analyzed.

[0085] The graph illustrates the change over time in the browning diffusion degree of three varieties of glutinous yam, Chinese yam, and vegetable yam within the concentric ring regions L1, L2, and L3. Figure 3 ) and heat map ( Figure 4 Observation reveals that the browning characteristics in their spatial distribution have both commonalities and obvious varietal differences.

[0086] From a common perspective, all three types of yam exhibited a gradually increasing trend in browning percentage across regions L1, L2, and L3 during storage. Notably, the browning percentage in region L3 consistently exceeded that in L1 and L2, indicating that the external tissues of the yam are more susceptible to environmental factors such as oxygen, temperature, and humidity, thus accelerating enzymatic oxidation. Thermographic maps show that region L3 exhibited the earliest and most pronounced color gradient change, followed by region L2, while the color change in region L1 was the most delayed, further illustrating that the browning reaction initiated in region L3 and gradually spread to region L1.

[0087] In terms of varietal differences, the browning reaction of glutinous yam is more intense. Specifically, the proportion of browning in its L1 region gradually increased from approximately 1.18% initially to nearly 25.00%, while the L2 region rapidly increased from less than 1% to over 52.00%, and the L3 region increased from approximately 9.80% to approximately 51.91%. This change indicates that glutinous yam is extremely sensitive to browning, exhibiting obvious browning diffusion both internally and externally. In contrast, the browning process of Chinese yam is more moderate, with the proportion in its L1 region fluctuating only between approximately 2.02% and 3.64%, the L2 region varying between approximately 3.91% and 4.94%, and while the L3 region shows an upward trend, its final value is only about 14.00%. The browning rate of Chinese yam in the L1 region remained relatively stable at around 2.00%, while the rate in the L2 region increased slightly (from about 1.77% to 4.18%). The rate in the L3 region, however, rose rapidly from about 10.82% initially to about 21.27%, indicating that browning of Chinese yam is mainly concentrated in the epidermal area.

[0088] In summary, all three types of yam showed the most obvious common characteristics of L3 browning reaction in terms of spatial distribution of browning areas. However, the browning diffusion rate of glutinous rice yam was faster, the reaction of Huai yam was mild and stable, while that of vegetable yam showed obvious external browning and limited internal diffusion.

[0089] Step four involves calculating the pixel percentage (P) of the browning region within each ring at different locations on the yam, and plotting curves and heatmaps based on the P values. These plots illustrate the changing trend of the pixel percentage of the browning region in each part of the yam with storage time, and the browning distribution of L1, L2, and L3 at key time points. By comparing and analyzing the curves and heatmaps, the differences in browning diffusion at different locations are identified.

[0090] This study analyzes the changes in the proportion of browned areas in the head, middle, and tail sections of glutinous yam over time using curve graphs, revealing the commonalities and differences in the browning diffusion process of each part. Although all three parts of glutinous yam show the pattern of highest L3 browning rate and lowest L1 browning rate, the head section exhibits a higher external reaction in the early stage, followed by a diffusion pattern where the inner and outer layers gradually converge. The middle section shows a relatively gradual change overall. In contrast, the tail section not only has a higher initial browning level, but all ring areas also rapidly reach a high level, indicating that the browning reaction in the tail section is more intense.

[0091] This study analyzes the changes in the proportion of browned areas in the head, middle, and tail sections of Chinese yam within the L1, L2, and L3 regions over storage time using curve graphs, and visually displays the distribution of each region at each time point using heat maps. The head, middle, and tail sections of Chinese yam all exhibit a low-level, slowly increasing browning trend. Although the browning rate in L3 is slightly higher than in L1, the overall reaction is relatively mild and not significant.

[0092] This study investigated the browning diffusion characteristics of different parts of Chinese yam. Curve graphs were used to depict the changing trends of the proportion of L1, L2, and L3 browning areas in each part with storage time, and heat maps were used to visually display the browning distribution of each ring at six time points. All parts of the Chinese yam followed the pattern of highest browning rate at L3 and lowest at L1, but the browning diffusion reaction in the head and middle parts was less significant than that in the tail part.

[0093] Experiments on three varieties of yam—glutinous rice yam, Chinese yam, and vegetable yam—have demonstrated that this method can effectively capture the spatiotemporal dynamic characteristics of browning in each variety, providing a scientific basis for the quality assessment and preservation process optimization of fresh-cut yam.

[0094] Note: The above embodiments are only used to illustrate the present invention and are not intended to limit the technical solutions described in the present invention. Therefore, although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention. All technical solutions and improvements that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yam, characterized in that, Includes the following steps: Step one, image processing includes the following procedures: Step 1: Image Acquisition and Preprocessing Select yam samples and slice them to obtain sliced ​​samples; then continuously sample the sliced ​​samples to obtain images of the sliced ​​samples; The acquired images are preprocessed to obtain preprocessed images; the preprocessing includes grayscale conversion, noise reduction, removal of reflective areas, and background removal. Step 2, Browning region feature extraction: Browning region feature extraction is performed on the preprocessed image in Process 1. The extraction steps are as follows: First, a grayscale value range needs to be set according to the grayscale features of the image to determine the range of the browning region; then, a grayscale range mask is generated; then, based on the grayscale range mask, a bitwise AND operation is performed between the mask and the original image to initially extract the corresponding browning region; finally, global threshold segmentation is performed to obtain the accurate segmentation result and output it to form a standardized image, thus completing the feature extraction of the entire browning region. Step two, quantitative evaluation of browning time dimension, includes the following process: Step 1: Based on the standardized images obtained in Steps 1 and 2, calculate the browning distribution uniformity σ using the following formula: in, G is the average gray value of the image, N is the total number of pixels in the image, and G is the average gray value of the image. i Let be the grayscale value of the i-th pixel, where i is a positive integer; Step 2: Compare the uniformity of browning distribution of sliced ​​samples under different resting times. The larger the σ value, the more obvious the browning in the area, indicating that it is in the early stage of browning diffusion, indicating that the browning area has not fully expanded and its spatial non-uniformity is higher. Conversely, if the σ value is smaller, it indicates that the browning area in the image is gradually increasing and the browning distribution tends to be more uniform. Step 3: Calculate the degree of browning diffusion R. The calculation formula is as follows: Where, p i As an indicator function, if the pixel belongs to the browning region, then p i =1, otherwise p i =0; N is the total number of pixels in the image, G i Let be the grayscale value of the i-th pixel, where i is a positive integer; Step four: Compare the degree of browning diffusion in the sliced ​​samples at different resting times. The larger the R value, the greater the extent of browning diffusion in the image, indicating a higher degree of browning severity; conversely, the smaller the R value, the less browning the sample. Step 5: Calculate the browning progression rate V. The calculation formula is: Where B1 and B2 are the percentages of browning area measured at time points t1 and t2, respectively, and t1 and t2 are the corresponding time points; Step 6: Compare the browning progression rate of sliced ​​samples under different resting times. The larger the V value, the faster the browning spreads. If the V value increases rapidly in a short period of time, it indicates that the fresh-cut yam has undergone a large degree of oxidation. Conversely, the smaller the V value, the slower the browning spreads. Step 3, quantitative evaluation of browning spatial dimensions, includes the following process: Step 1: Based on the standardized image obtained in Steps 1 and 2, the sample image is divided into three concentric ring regions, denoted as L. i Each ring region corresponds to a different radius range; where i takes the values ​​1, 2, and 3. Step 2: Calculate the pixel percentage P of the browning region within each ring. The calculation formula is as follows: Where M i For L i The number of pixels within the browning region; N i For L i Total number of pixels in the region; Step 3: Based on the pixel proportion of the browning region within each ring, a heat map is drawn. The darker the color of the heat map, the lower the degree of browning in that region; conversely, the lighter the color, the higher the degree of browning. Multiple heat maps are generated from images at different time points. By comparing the color changes between the various heat maps, the spatial expansion pattern of browning is analyzed. Step 4: Calculate the pixel percentage P of the browning region within each ring of different parts of the yam sample, and draw curves and heat maps based on the P value to show the trend of the pixel percentage of the browning region in each part of the yam changing with storage time and the browning distribution of each part at key time nodes; by comparing and analyzing the curves and heat maps, the differences in the diffusion of browning in different parts can be determined.

2. The method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yam according to claim 1, characterized in that, The thickness of the sliced ​​sample mentioned in step one of process one is 3-5 mm.

3. The method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yam according to claim 1, characterized in that, The camera used for continuous sampling in Step 1 is a CMOS camera; during sampling, the sliced ​​sample is placed 10-12 cm directly below the camera.

4. The method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yam according to claim 3, characterized in that, In step one of the process, the frequency of continuous sampling of the sample is to automatically capture an image every 20 seconds.

5. The method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yam according to claim 1, characterized in that, The image of the sliced ​​sample mentioned in Step 1 is a BMP format RGB image with a resolution of 2448×2048.

6. The method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yam according to claim 1, characterized in that, The grayscale value range mentioned in step one of process two is [40, 90].

7. The method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yam according to claim 1, characterized in that, The method for generating the grayscale range mask described in step one, process two, uses the cv2.inRange function in OpenCV.

8. The method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yam according to claim 1, characterized in that, The global threshold segmentation method described in step one, process two, is a global threshold algorithm.

9. The method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yam according to claim 1, characterized in that, The concentric ring division described in Step 3 of Process 1 is as follows: the core inner ring region is denoted as L1: its radius is 30.00% of the total image radius, reflecting the browning status of the central region of the sample; the transition middle ring region is denoted as L2: it covers a radius range of 30.00%-70.00%, reflecting the intermediate transition zone of diffusion from the center to the periphery; the outer ring region is denoted as L3: it occupies the remaining part, reflecting the browning diffusion characteristics of the edge region.

10. The method for spatiotemporal quantitative evaluation of browning quality in fresh-cut yam according to claim 1, characterized in that, The different parts of the yam sample mentioned in step three, process four, are the head, middle and tail of the yam.

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