A method for rapid detection of pepper pungency based on specific parenchyma cell thickness and application

Abstract

This invention provides a rapid detection method and application for chili pepper spiciness based on specific thin-walled cell thickness, belonging to the field of chili pepper quality detection technology. The method uses the radial thickness of specific thin-walled cells on the inner side of the bright red chili pepper pericarp as a morphological marker. The thickness is measured through sampling, vibrating sectioning, and microscopic observation, and then substituted into the linear regression equation y = 25.751x - 3078.4, where x is the cell thickness in μm and y is the predicted Scoville Heat Unit (SHU) index, allowing for rapid calculation of spiciness. This invention establishes a direct correlation between chili pepper spiciness and cell morphological parameters for the first time, overcoming the limitations of traditional high-performance liquid chromatography (HPLC) methods (high cost, long cycle, sample destruction) and near-infrared spectroscopy (NIRS) methods (poor model versatility). This invention possesses outstanding advantages such as non-destructive, high efficiency, low cost, and high precision, making it particularly suitable for large-scale rapid screening of processing chili pepper breeding populations, significantly accelerating the breeding process.

Description

Technical Field

[0001] This invention belongs to the field of chili pepper quality detection technology, specifically a rapid detection method and application for chili pepper spiciness based on specific thin-walled cell thickness. Background Technology

[0002] Chili peppers (Capsicum spp.) are one of the most widely cultivated vegetables and condiments in my country, possessing both high economic value and a broad consumer base. Their rich nutritional components and unique flavor have enabled large-scale cultivation and consumption globally. Based on different consumption scenarios, chili peppers can be clearly divided into two main categories: fresh-eating and processed. In recent years, with the development of facility agriculture technology, fresh chili peppers have achieved a balanced supply throughout the year, and market demand for their quality continues to upgrade. At the same time, the chili pepper processing industry is showing a diversified development trend, with processed products now covering more than ten categories, including chili powder, chili sauce, chili oil, pickled chili peppers, roasted chili sauce, and chopped chili sauce. Furthermore, the personalized customization needs of the catering industry are becoming increasingly prominent. Taking hot pot restaurants as an example, chili products alone require at least four types of processing-specific chili peppers, depending on their intended use: chili peppers for simmering (focusing on appearance), chili peppers for the broth base (adjusting spiciness and color), chili oil (enhancing aroma), and chili paste (suitable for dipping sauces). This trend is driving increasingly refined quality requirements for processing-specific chili peppers, with stricter selection standards for fruit appearance, color, taste, flavor, and internal nutritional characteristics. Therefore, conducting precise research and efficient screening of quality traits for processing chili peppers has become a core requirement in the breeding field.

[0003] Score, as one of the most critical quality traits of processing chili peppers, directly determines their processing uses and product value. Rapidly and accurately screening for spiciness-specific materials suitable for different processing needs is a core step in improving the efficiency of chili pepper breeding. Currently, conventional methods for detecting chili pepper spiciness mainly rely on chemical detection techniques such as high-performance liquid chromatography (HPLC), calculating the Scoville Heat Unit (SHU) index by measuring capsaicin and dihydrocapsaicin content to evaluate spiciness. However, these methods suffer from drawbacks such as complex operation, long detection cycles (more than 2 hours for a single sample), high cost, and strong sample destructiveness, making it difficult to meet the needs of rapid screening of large-scale populations during the breeding process.

[0004] Furthermore, existing technologies lack morphological markers that can directly correlate with spiciness. Breeders often rely on experience or subsequent chemical testing to screen materials for spiciness, resulting in low screening efficiency and insufficient accuracy. This severely hinders the breeding process of selecting chili peppers with optimal spiciness for processing. Therefore, developing a rapid spiciness detection technology based on morphological markers to achieve efficient, accurate, and low-cost screening of the spiciness of bright red fruits in processing chili pepper breeding populations has become an urgent technical problem to be solved. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a morphological marker for identifying the spiciness of chili peppers, wherein the morphological marker is the specific thin-walled cell thickness on the inner side of the pericarp in bright red chili peppers.

[0006] Another objective of this invention is to provide a rapid method for detecting the spiciness of chili peppers, which can achieve rapid detection of chili pepper spiciness efficiently and at low cost.

[0007] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0008] This invention provides a morphological marker for identifying the spiciness of chili peppers, wherein the morphological marker is the specific thickness of thin-walled cells on the inner side of the pericarp in bright red chili pepper fruits.

[0009] Preferably, the specific thin-walled cell thickness is obtained by measuring the radial distance of the cell.

[0010] This invention also provides a rapid method for detecting the spiciness of chili peppers, comprising the following steps: cutting a 1-1.5 cm thick transverse segment 2 cm below the fruit stalk of the chili pepper sample to be tested; preparing the transverse segment into a live section using a vibrating slicer; acquiring a microscopic image of the live section, measuring the thickness of specific thin-walled cells, and obtaining a thickness measurement value; substituting the thickness measurement value into the regression equation y=25.751x-3078.4 to calculate the Scoville index, and rapidly assessing the spiciness level of the chili pepper sample to be tested based on the spiciness gradient corresponding to the Scoville index.

[0011] Preferably, the cross-sectional thickness of the live tissue slice is 10-50 μm.

[0012] Preferably, the specific thin-walled cell thickness refers to the radial distance of the specific thin-walled cells perpendicular to the surface of the pericarp.

[0013] Preferably, the method is applicable to processed red chili peppers with a spiciness range of 1,000 to 30,000 SHU.

[0014] Preferably, cell microscopic images are acquired under a 10× or 5× objective lens.

[0015] Preferably, the thickness measurement value is obtained by averaging the thickness of multiple specific thin-walled cells of a single chili variety.

[0016] The present invention also provides an application of the morphological markers or the rapid detection method described herein in pepper breeding screening.

[0017] The present invention also provides an application of the morphological markers or the rapid detection method described herein in the quality grading of raw materials for chili processing.

[0018] Compared with the prior art, the present invention has the following advantages:

[0019] This invention is the first to discover a strong linear correlation between the thickness of specific thin-walled cells in chili pepper fruits and their spiciness, and based on this, a novel morphological detection method has been developed. Compared to traditional high-performance liquid chromatography (HPLC), this invention shortens the single-sample detection cycle from 2-3 hours to 15-20 minutes, increasing efficiency by over 90%. Simultaneously, because it eliminates the need for complex chemical reagents and expensive chromatographic consumables, the cost per sample is reduced from 80-120 yuan to 2-5 yuan, a reduction of over 95%. More importantly, this method only requires cutting a small segment of the peel, and the fruit can be safely used for seed production after testing, achieving non-destructive spiciness screening of breeding materials and resolving a fundamental contradiction in the application of HPLC technology in breeding. The linear regression model constructed based on a large number of samples in this invention has a high coefficient of determination (R²). 2 The relative error in predicting unknown varieties can be controlled within 5% (>0.991), fully meeting the accuracy requirements for breeding screening. Compared with near-infrared analysis, this invention does not require a large number of standard samples for preliminary modeling, avoiding the predicament of model failure due to changes in variety, origin, and instrument. It achieves immediate results and has stronger universality. In addition, the method has a fixed operation procedure, requiring only basic slicing and microscopic observation skills, without the need for professional personnel or operational qualifications, making it easy to standardize and promote in grassroots breeding units. By transforming spiciness detection from complex chemical analysis to intuitive morphological observation, this invention not only provides a high-efficiency, low-cost, and non-destructive screening tool for processing pepper breeding, greatly accelerating the screening and breeding process of high-spiciness germplasm resources, but also provides a brand-new technical solution for the rapid grading of raw material quality in the pepper processing industry, possessing extremely high industrial application value. Attached Figure Description

[0020] Figure 1 This is a cross-sectional view of a pepper fruit under a 5× objective lens, stained with Safranin-Fast Green. In the image, Sp (Special parenchyma) represents special parenchyma cells, Pa (Parendnyma) represents parenchyma tissue, Co (Collenchyma) represents collenchyma tissue, and Ep (Epidermis) represents epidermis. The scale bar is 1 mm.

[0021] Figure 2This is a cross-sectional view of a pepper fruit stained with Safranin-Fast Green under a 10× objective lens. In the image, Sp (Special parenchyma) represents special parenchyma cells, Pa (Parendnyma) represents parenchyma tissue, Co (Collenchyma) represents collenchyma tissue, Ep (Epidermis) represents epidermis, Ie (Inner epidermal) represents endodermis, and Vb represents (Vascular bundle). The scale bar is 500 μm.

[0022] Figure 3 This is an image of a live section of a pepper fruit obtained using a vibratory slicer. In the image, Sp (Special parenchyma) represents a specific parenchyma cell, and the scale bar is 200 μm.

[0023] Figure 4 Histogram of specific thin-walled cell thickness frequencies from 115 samples of fresh red pepper material;

[0024] Figure 5 Scoville Heat Unit (SHU) frequency histogram for 115 samples of fresh red peppers;

[0025] Figure 6 The regression equation for pepper-specific thin-walled cell thickness and Scoville index (SHU) was used for 115 pepper samples.

[0026] Figure 7 This is a microscopic view of specific thin-walled cells in a test sample for Example 2, with a scale bar of 200 μm.

[0027] Figure 8 Pearson correlation matrix for each key variable in 15 chili pepper samples. Detailed Implementation

[0028] This invention provides a morphological marker for identifying the spiciness of chili peppers. The morphological marker is the thickness of specific parenchyma cells on the inner side of the pericarp in bright red chili pepper fruits. The specific parenchyma cells mentioned in this invention refer to a type of parenchyma cell located adjacent to the inner epidermis of the chili pepper fruit cavity, arranged in a single continuous layer, and significantly larger in volume than other parenchyma tissue cells; their cross-sectional area is typically 1.5 × 10⁻⁶. 4 μm 2 -1.5×10 6 μm 2 Its structure is shown in Figure 1 and Figure 2In this invention, the thickness of the specific thin-walled cells is preferably obtained by measuring the radial distance of the cells. The radial distance referred to in this invention refers to the maximum dimension of the specific thin-walled cells in a cross-section of a chili pepper fruit, perpendicular to the surface of the pericarp or the outer surface of the fruit. This direction is parallel to the radius of the fruit and best reflects the degree of specific development of the cells during fruit enlargement, and shows the strongest correlation with the accumulation of total capsaicin.

[0029] In this invention, as an optional implementation, the preferred measurement range for the specific thin-walled cell thickness is 130 μm to 1500 μm, which covers the vast majority of processing chili varieties to which the method of this invention is applicable. When the measured value is lower or higher than the measurement range of this invention, it indicates that the sample spiciness is too low or too high, exceeding the optimal application range of the linear prediction model of this invention.

[0030] This invention also provides a rapid method for detecting the spiciness of chili peppers. The method preferably includes the following steps: cutting a 1-1.5 cm thick transverse segment 2 cm below the fruit stalk of the chili pepper sample to be tested; preparing the transverse segment into a live section using a vibrating slicer; acquiring a microscopic image of the live section, measuring the thickness of specific thin-walled cells, and obtaining a thickness measurement value; substituting the thickness measurement value into the regression equation y=25.751x-3078.4 to calculate the Scoville index; and evaluating the spiciness level of the chili pepper sample to be tested based on the spiciness gradient corresponding to the Scoville index.

[0031] In this invention, by fixing the sampling site, errors introduced by differences in cell development in different parts of the fruit are eliminated. The area 2 cm below the fruit stalk avoids morphologically abrupt changes such as the fruit shoulder, and the placenta in this area is fully developed and has active material transport. The developmental state of its specific thin-walled cells best represents the overall spiciness level of the fruit.

[0032] In this invention, a vibratory microtome is preferably used for slicing. Compared with traditional paraffin microtome or freehand slicing, it can prepare thin-layer live tissue sections with uniform thickness and natural cell structure without chemical fixation and dehydration, thus preserving the original morphology and moisture of the cells to the maximum extent and ensuring the authenticity of the measured values.

[0033] In this invention, the cross-sectional thickness of the live cell section is preferably 10-50 μm. This thickness range ensures that the section has sufficient structural integrity for easy handling and observation, while also ensuring that light can penetrate to obtain a clear, monolayer cell image under an optical microscope, thereby accurately measuring the thickness of the target cells.

[0034] In this invention, image analysis software compatible with a microscope is used, and a linear measurement tool is selected. The vertical distance from the inner wall of the cell (near the fruit cavity) to the outer wall (near ordinary parenchyma tissue) is measured strictly along the radial direction of the cell (i.e., perpendicular to the pericarp surface). To ensure statistical representativeness of the data, 150-360 complete, specific parenchyma cells should be randomly measured for each variety / sample. Finally, the arithmetic mean of all measurements is taken as the final thickness value for that sample.

[0035] In this invention, the method is applicable to processed chili peppers with a spiciness range of 1000 to 30000 SHU. Processed chili peppers, as used in this invention, refer to varieties commonly used in the production of chili sauce, chili powder, hot pot base, and seasoning oils, where spiciness is a core quality indicator. This differs significantly from fresh-eating chili peppers, which are primarily consumed as fresh fruits and vegetables, in terms of breeding objectives and quality requirements. "Fresh red fruit" refers to fruit that has reached physiological maturity and whose peel has fully turned red. The spiciness range of 1000-30000 SHU corresponds to the specific thin-walled cell thickness range of approximately 130 μm to 1500 μm described in this invention. Within this range, the linear regression model exhibits extremely high goodness of fit and prediction accuracy.

[0036] In this invention, the slides are preferably placed under a conventional optical microscope, using a 5× or 10× objective lens for observation and image acquisition. This magnification is sufficient to clearly distinguish the complete outline of specific thin-walled cells, while ensuring that each field of view can accommodate a sufficient number of cells, thus improving measurement efficiency. Images are recommended to be saved in a format that preserves metadata and layer information, such as CZI format.

[0037] The present invention also provides an application of the morphological markers or the rapid detection method described herein in pepper breeding screening.

[0038] This invention also provides an application of the aforementioned morphological markers or rapid detection methods in the quality grading of chili processing raw materials. The technical solutions provided by this invention are described in detail below with reference to embodiments, but these should not be construed as limiting the scope of protection of this invention.

[0039] Unless otherwise specified, the experimental methods used in the following embodiments are conventional methods. Unless otherwise specified, the experimental materials used in the following embodiments are commercially available products.

[0040] Example 1: Correlation Analysis and Predictive Model Construction of Pepper-Specific Thin-walled Cell Thickness and Scoville Hue Index (SHU)

[0041] 1. Selection of chili pepper samples and determination of spiciness value (SHU)

[0042] (1) Sample selection criteria: To ensure the universality and coverage of the model, bright red chili peppers covering three spiciness levels (low, medium, and high spiciness) were selected (very low spiciness SHU < 1000 for sweet peppers and very high spiciness SHU ≥ 30000 for industrial chili peppers, excluding processed chili peppers), specifically as follows: low spiciness (1000 ≤ SHU < 5000), medium spiciness (5000 ≤ SHU < 15000), and high spiciness (15000 ≤ SHU < 30000). All chili pepper materials were selected from bright red fruits with consistent physiological maturity (the peel was completely red and the stem was slightly green), and water and fertilizer management was uniform to exclude interference from environmental factors. In this example, 115 representative chili pepper materials in terms of genetic type were selected from the chili pepper planting base of Xindu Modern Agricultural Demonstration Park of Sichuan Academy of Agricultural Sciences, including inbred lines, hybrid combinations, and DH lines, as shown in Table 1.

[0043] (2) Biological replicates: For each chili pepper material, ≥10-15 biological replicates were set up. That is, 5-6 or more healthy plants of the same material and the same batch were selected. For each plant, the fruit at the 4th-6th node of the fruiting main branch was selected as a sample. Three fruits of uniform size (single fruit weight difference ≤10%) and free from pests and diseases were used as test samples. Thus, the sample size of bright red fruit slices for a single chili pepper material was 15-18. 10-20 specific thin-walled cells were measured for each bright red fruit slice. In summary, a total of 150-360 specific thin-walled cells were measured for a single material, and the average value was taken to ensure the statistical reliability of the data.

[0044] (3) SHU value determination method: The total capsaicin content (GB / T 21266-2007) was determined by high performance liquid chromatography (HPLC). According to the formula: SHU=W×0.9×(16.1×10 3 ) + W × 0.1 × (9.3 × 10 3 The Scoville index was calculated by measuring each sample three times in parallel and taking the average value as the final SHU value. The measurement error was controlled within ±3%.

[0045] Table 1. 115 examples of chili pepper materials

[0046]

[0047]

[0048]

[0049] 2. Determination of specific thin-walled cell morphological parameters

[0050] (1) Sample pretreatment: Take a 1-1.5 cm thick transverse section about 2 cm below the stem of each pepper fruit, rinse with distilled water, and prepare 10-50 μm thick transverse sections of live specimens using a vibratory microtome. Figure 3 Seal the slide with distilled water to maintain the humidity of the living cells.

[0051] (2) Selection and determination of morphological parameters: Images of the sections were acquired using an optical microscope (ZEISS Axio Imager A2, 10× objective lens) and imaging system (ZEISS Axiocam 712 color). The images were saved in czi format. The thickness of specific thin-walled cells (unit: μm) was measured using the "line" tool of ZEN lite 3.3 software (Zeiss, Germany). At least 5 cell images were acquired for each bright red fruit section (each image contained approximately 3-5 complete cells), and a total of 10-20 cells were measured. A total of 150-360 specific thin-walled cells were measured for a single material sample, and the average value was taken. Cell thickness: Complete cells in the field of view of each image were selected, and the radial distance of the cells perpendicular to the pericarp surface was measured. The detection results are shown in Table 1.

[0052] 3. Correlation analysis and regression model construction

[0053] (1) Normality test of data

[0054] To meet the data requirements for linear regression analysis, normality tests were performed on specific parenchyma cell thickness and Scoville Heat Unit (SHU) data from 115 chili pepper varieties. The Shapiro-Wilk test, skewness, and kurtosis indices were then used for comprehensive assessment.

[0055] 1) Specific thin-walled cell thickness

[0056] The Shapiro-Wilk test results show that the p-value is 0.071 (>0.05), and the null hypothesis of "following a normal distribution" cannot be rejected; the skewness value is 0.628, with an absolute value <1, showing only a slight right skewness, and the deviation from the normal distribution is very minor; the kurtosis value is 0.215, close to the kurtosis baseline value (0) of the normal distribution, and the steepness of the distribution is not significantly different from that of the normal distribution. Figure 4 In summary, the specific thin-walled cell thickness data conform to a normal distribution, satisfying the basic requirements of linear regression for data distribution.

[0057] 2) Scoville Heat Unit (SHU)

[0058] The Shapiro-Wilk test results showed a p-value of 0.058 (>0.05), which failed to reject the null hypothesis of a normal distribution; the skewness was 0.793, with an absolute value <1, indicating a moderate right skewness but a relatively uniform overall distribution; the kurtosis was 0.102, close to a normal distribution, with no obvious peaks or flat peaks. Figure 5 In summary, the Scoville index data, combined with a sufficient sample size of 115 cases and the robustness of linear regression analysis, conforms to a normal distribution, meeting the basic requirements of linear regression for data distribution, and can be used for subsequent analysis.

[0059] (2) Correlation analysis

[0060] 1) Pearson correlation analysis

[0061] Pearson correlation analysis was performed on specific thin-walled cell thickness and Scoville Heat Unit (SHU) in 115 samples to test the intrinsic association between the two variables. The results showed that the Pearson correlation coefficient (r) was 0.996. According to the correlation strength classification criteria, r ≥ 0.9 indicates an extremely strong positive linear correlation, meaning that the higher the specific thin-walled cell thickness, the higher the SHU almost synchronously. The significance test p-value was < 0.001, far below the 0.05 significance level, indicating that the correlation was highly statistically significant and random error interference could be ruled out.

[0062] 2) Supplementary verification of KS test

[0063] The Kolmogorov-Smirnov (KS) test results showed that the asymptotic significance (two-sided) of the specific thin-walled cell thickness was 0.162 (>0.05), further confirming that it approximately conforms to a normal distribution; the asymptotic significance (two-sided) of the Scoville index (SHU) was 0.053 (>0.05), which conforms to a normal distribution.

[0064] In summary, there is a real and strong positive linear correlation between specific thin-walled cell thickness and the Scoville Hue index (SHU), providing a solid statistical basis for establishing a regression prediction model.

[0065] (3) Construction of linear regression model

[0066] Based on the confirmed strong correlation, in order to further establish a quantitative relationship that can be used for practical prediction, a simple linear regression analysis was performed with specific thin-walled cell thickness (x) as the independent variable and Scoville index (SHU) (y) as the dependent variable. The core results are as follows:

[0067] 1) Regression equation

[0068] The regression equation obtained after fitting is: y = 25.751x - 3078.4, see... Figure 6 .

[0069] ① Coefficient Interpretation

[0070] Slope (25.751): This indicates that for every 1 μm increase in the thickness of specific thin-walled cells, the Scoville index (SHU) increases by an average of 25.751 units, which intuitively reflects the positive predictive effect of cell thickness on spiciness.

[0071] Intercept (-3078.4): This is only a constant term in the mathematical fitting and has no actual biological significance (because cell thickness cannot be 0, and this value only represents the theoretical prediction of the equation when x=0).

[0072] ② Model fit

[0073] The model's coefficient of determination (R²) 2 The SHU value was 0.9915, indicating that 99.15% of the variation in the SHU index could be explained by specific thin-walled cell thickness variations, demonstrating an excellent model fit (R²). 2 The closer to 1, the higher the fit; after adjustment, R... 2 It is 0.991, compared with R 2 The values ​​are close, indicating that the model has not overfitted due to the introduction of dependent variables and has good stability.

[0074] ③ Model significance test

[0075] The analysis of variance results showed that the F-value of the regression model was 13134.592, and the significance p-value was <0.001, which was far below the 0.05 significance level. This indicates that the linear regression model as a whole has extremely high statistical significance and is not the result of accidental fitting.

[0076] Example 2

[0077] 1. Sample Collection and Processing: Fifteen chili pepper samples not used in the modeling of Example 1 were selected. Each sample weighed at least 500g and was used for total capsaicin content determination. Ten fruits were randomly selected from each 500g sample for sectioning and specific thin-walled cell measurement. The experiment was repeated three times.

[0078] 2. Total capsaicin content detection:

[0079] Total capsaicin was determined using high-performance liquid chromatography (HPLC). According to the national standard GB / T 21266-2007, the determination method for capsaicin content in chili peppers and chili pepper products was used to convert the measured total capsaicin content to the Scoville Heat Unit (SHU) using the following formula: SHU = W × 0.9 × (16.1 × 10⁻⁶) / (16.1 × 10⁻⁶) 3 ) + W × 0.1 × (9.3 × 10 3 W represents the total capsaicin content.

[0080] 3. Specific thin-walled cell measurement:

[0081] Ten fresh red chili peppers were randomly selected from the sample and their tissue sections were prepared using a vibratory slicer.

[0082] (1) Sample pretreatment: Cut the tissue about 2 cm below the fruit stalk into segments of about 1-1.5 cm, ensuring that the cut surface is flat.

[0083] (2) Fix the sample: Use 502 glue or other fixatives to firmly fix the cut tissue segments onto the sample stage, ensuring they are tightly fitted and securely fixed to the sample stage surface to prevent displacement during cutting.

[0084] (3) Install the sample stage: Securely install the fixed cutting sample stage onto the sample rack of the vibratory slicer, adjust the position so that the tissue to be cut is parallel to the blade, and leave a suitable cutting gap.

[0085] (4) Fine-tuning and fixing: Tighten the sample stage again using the device knob to ensure that the tissue does not loosen during vibration cutting, thus ensuring uniform slice thickness and moderate speed. In this embodiment, the slice thickness is maintained at 30μm. Place the cut fresh red fruit slices on a glass slide, moisten with water, cover with a coverslip, and wait for instrumental analysis.

[0086] Images of the sections were acquired using an optical microscope (ZEISS Axio Imager A2) and an imaging system (ZEISS Axiocam 712color) with a 10× objective lens. Images were saved in CZI format. The morphological parameters of the fruit cells (in μm) were measured using ZEN lite 3.3 software (Zeiss, Germany). The thickness of specific thin-walled cells was measured using the "line" tool. Ten tissue sections were prepared from each fruit sample, and the morphological parameters of each section were measured 30 times. Specific thin-walled cells appeared as shown under the microscope. Figure 7 As shown.

[0087] Substituting the thickness values ​​of the specific thin-walled cells measured above into the regression equation y=25.751x-3078.4 obtained in Example 1, the predicted value SHU was calculated. Simultaneously, the actual SHU values ​​of these materials were determined using HPLC, and the relative error between the predicted and measured values ​​was calculated. A total of 15 test materials were prepared for this example; detailed information on the fresh red pepper fruit samples and testing details is shown in Table 2.

[0088] Table 2 15 chili pepper samples

[0089]

[0090] As shown in Table 2, this embodiment, through validation on 15 independent samples, demonstrates that the prediction model based on specific thin-walled cell thickness has excellent accuracy and wide applicability. The average relative error between the predicted and measured values ​​is only 4.93%, far below the acceptable error range in breeding practice, fully demonstrating that the method of this invention can replace traditional HPLC detection for rapid and accurate screening of the spiciness of processing pepper breeding materials.

[0091] 4. Pearson correlation analysis

[0092] Pearson correlation analysis was performed on the results in Table 2 using SPSS. The results are as follows: Figure 8 As shown, this matrix, generated based on data from 15 independent validation samples, was used to analyze the relationship between total capsaicin content, specific thin-walled cell thickness, and the measured (detected) Scoville Heat Unit (SHU) values ​​and predicted values. The Pearson correlation coefficients (r) between all variables were above 0.997 and significant at the 0.01 level. Crucially, the morphological marker (cell thickness) showed a very strong correlation of 0.997 with the measured chemical SHU value, fully demonstrating the reliability of specific thin-walled cell thickness as a marker for spiciness assessment, and showcasing the high accuracy and predictive ability of the rapid detection method of this invention.

[0093] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A rapid method for detecting the spiciness of chili peppers, characterized in that, The procedure includes the following steps: A 1-1.5 cm thick transverse segment is cut 2 cm below the fruit stalk of the chili pepper sample to be tested; the transverse segment is prepared into a live section using a vibrating slicer; microscopic images of the live section are acquired, and the thickness of specific thin-walled cells is measured to obtain a thickness measurement value; the thickness measurement value is substituted into the regression equation y=25.751x-3078.4 to calculate the Scoville index; based on the Scoville index and the corresponding spiciness gradient, the spiciness level of the chili pepper sample to be tested is quickly assessed. The specific parenchyma cells mentioned refer to a type of parenchyma cells located adjacent to the inner epidermis of the pepper fruit cavity, arranged in a single continuous layer, and larger in volume than other parenchyma tissue cells, with a cross-sectional area of ​​1.5 × 10⁻⁶. 4 μm 2 -1.5×10 6 μm 2 .

2. The rapid detection method according to claim 1, characterized in that, The cross-sectional thickness of the live tissue slices is 10-50 μm.

3. The rapid detection method according to claim 1, characterized in that, The specific thin-walled cell thickness refers to the radial distance of the specific thin-walled cells perpendicular to the surface of the pericarp.

4. The rapid detection method according to claim 1, characterized in that, The method is applicable to processed red chili peppers with a spiciness range of 1,000 to 30,000 SHU.

5. The rapid detection method according to claim 1, characterized in that, Cell microscopic images were acquired under a 10× or 5× objective lens.

6. The rapid detection method according to claim 1, characterized in that, The thickness measurement value is obtained by averaging the thickness of multiple specific thin-walled cells of a single chili variety.

7. The application of the rapid detection method according to any one of claims 1-6 in pepper breeding screening.

8. The application of the rapid detection method according to any one of claims 1-6 in the quality grading of raw materials for chili processing.

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