Nondestructive testing method for organic acid content of Korla pear

Abstract

The invention relates to the technical field of Korla bergamot pear detection, and discloses a Korla bergamot pear organic acid content nondestructive detection method, which comprises: collecting a bergamot pear sample with consistent maturity, no damage and complete surface layer; the surface is wiped with absolute ethyl alcohol and then naturally air-dried, only the surface layer is subjected to vacuum freeze drying and smashing, and the integrity of a main body is reserved; according to the method, ICP-MS, HT-IRMS and GC-MS / EMIS are combined with an HS-HPME extraction technology, multi-dimensional detection of mineral elements, isotope contents and ratios and volatile components is carried out, and an isotope and element characteristic database and a regional characteristic volatile substance fingerprint spectrum are constructed; analyzing and screening characteristic indexes through orthogonal partial least squares, and eliminating interference factors; and establishing a linear discrimination model, forming an organic acid content detection and origin identification integrated technical system, and optimizing detection parameters. According to the method, rapid and nondestructive testing and source area traceability are realized, and reliable technical support is provided for quality evaluation of the Korla bergamot pears.

Description

Technical Field

[0001] This invention relates to the field of Korla fragrant pear detection technology, specifically a non-destructive method for detecting the organic acid content of Korla fragrant pears. Background Technology

[0002] Organic acid content is one of the key indicators for measuring the quality of Korla fragrant pears. Its test results not only provide an important basis for the quality evaluation and grading of fragrant pears, but also have great significance in the fields of fruit and vegetable quality traceability and market supervision. Non-destructive testing technology has become an important development direction in the field of fruit and vegetable quality testing because it can avoid damaging the test object.

[0003] In existing technologies for detecting organic acids in fruits and vegetables, the pretreatment stage often requires procedures such as pitting and stem removal, or the drying and pulverizing processes used can damage the main structure of the sample. This approach stems from the design logic that relies on the overall structure of the sample for detection, resulting in the sample failing to maintain its original commercial form and its commercial attributes being compromised. Furthermore, existing detection methods often employ single detection techniques targeting a limited number of indicators, failing to consider the intrinsic relationships between organic acids and mineral elements, stable isotopes, and volatile components. They also fail to integrate organic acid content detection with origin identification, resulting in a lack of comprehensive analysis of multi-dimensional characteristics during the detection process. This limits the relevance and accuracy of the test results and hinders the efficient synergy between quality indicator detection and origin traceability. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a non-destructive testing method for the organic acid content of Korla fragrant pears. This method solves the problems mentioned in the background art, such as the inability of the tested samples to maintain their original commercial form, the destruction of commercial attributes, the lack of comprehensive analysis of multi-dimensional characteristics in the testing process, the limitation of the pertinence and accuracy of the test results, and the difficulty in achieving efficient synergy between quality indicator testing and origin traceability.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a non-destructive method for detecting the organic acid content of Korla fragrant pears, comprising the following steps:

[0006] S1 Sample Collection

[0007] Select Korla fragrant pears that are uniformly ripe and undamaged, remove impurities attached to the surface, and ensure that the surface structure of the samples is intact;

[0008] S2 Non-destructive Preprocessing

[0009] After wiping the sample surface with anhydrous ethanol, the sample is air-dried naturally without removing the core or stem. The sample surface is then freeze-dried directly using a vacuum freeze dryer. The freeze-dried surface material is then pulverized into a uniform powder, packaged, and sealed for later use. The integrity of the sample is maintained throughout the process to avoid damaging the commercial properties of the pear.

[0010] S3 Multidimensional Detection

[0011] S301 Isotope Detection

[0012] ICP-MS was used to determine the mineral elements and O and H isotope contents in freeze-dried samples, and a database of isotope and elemental characteristics of Korla fragrant pears from different production areas was established simultaneously.

[0013] S302 isotope ratio detection

[0014] The δH and δ¹⁸O isotope ratios in freeze-dried samples were determined using HT-IRMS technology.

[0015] S303 Volatile Component Detection

[0016] The composition of volatile components in freeze-dried samples was determined by combining GC-MS / EMIS technology with HS-HPME extraction method, and a regionally characteristic fingerprint spectrum of volatile substances was constructed.

[0017] S4 Detection Data Analysis

[0018] Based on the synergistic correlation characteristics between organic acids and mineral elements, stable isotopes and volatile components, orthogonal partial least squares analysis was used to screen out characteristic indicators that can specifically characterize the content of organic acids and eliminate unrelated interfering factors.

[0019] S5 establishes a linear discriminant model

[0020] The screened characteristic index data are input into the model to obtain the organic acid content detection results. At the same time, an isotope and elemental characteristic database and a volatile substance fingerprint spectrum are established to form an integrated technical system for organic acid content detection and origin identification of Korla fragrant pear. The ICP-MS working parameters are optimized according to the detection requirements of mineral elements and isotopes. The GC-MS adopts a capillary column adapted for the separation of volatile components, and the heating program is optimized and adjusted according to the separation efficiency.

[0021] Preferably, in step S2, the vacuum freeze-drying is set according to the requirement of preserving the active ingredients on the sample surface. Only the freeze-dried sample surface is collected and pulverized. After pulverization, it is passed through a nylon sieve and placed in a brown sealed bottle for room temperature equilibration. The equilibration process isolates it from external impurities.

[0022] The vacuum freeze-drying conditions must be carefully designed to match the surface composition characteristics of Korla pears. Precise control of the freeze-drying rate and pressure parameters is crucial to minimize the degradation or loss of active ingredients such as organic acids and volatile substances. The selection of a nylon sieve must match the particle size requirements of the freeze-dried surface material to ensure powder uniformity and repeatability in subsequent analyses. The equilibration process must be conducted in a clean and undisturbed environment. Sealing and isolating the sample from moisture, dust, and volatile impurities prevents secondary changes in sample composition and maintains the original state of the sample surface components. This provides a pure and stable analytical matrix for subsequent multi-dimensional analysis, ensuring the structural integrity and commercial value of the pear throughout the process.

[0023] Preferably, in step S303, the combined technique of ICP-MS and HS-HPME-GC-MS is used. During HS-HPME extraction, the surface lyophilized sample is placed in a headspace vial, a saturated NaCl solution is added, and the equilibrium conditions are set according to the enrichment requirements of volatile components. The activated extraction head is inserted into the headspace vial, and after adsorption for a certain period of time at a suitable distance without contacting the sample, it is desorbed at the GC-MS inlet.

[0024] The core design of this hyphenated technique lies in achieving complementarity and high efficiency in the detection of mineral elements and volatile components. In the HS-HPME extraction stage, the addition of saturated NaCl solution aims to adjust the osmotic pressure of the system, promoting the migration of volatile components from the sample into the headspace region and improving enrichment efficiency. The activation of the extraction head must adhere to the principle of material compatibility with the target components, removing residual impurities through preset temperature and time parameters to ensure adsorption specificity. Controlling the adsorption distance must avoid contamination caused by direct contact between the extraction head and the sample powder, while ensuring the sufficiency of the adsorption process. The desorption stage requires optimization of the GC-MS inlet temperature program to achieve rapid and complete desorption of volatile components. Through a seamless design of the process flow, the hyphenated technique reduces component loss during sample transfer, improving the sensitivity and accuracy of the detection results.

[0025] Preferably, in step S302, during the detection of the stable isotope ratio of δH and δ18O, the non-destructive adaptation parameters of δH and δ18O are optimized using Gas-Bench technology, a suitable temperature is set for the constant temperature sample pan, and a mixture of CO2 and He in a specific ratio is filled for a sufficient time to completely remove the air from the headspace vial, allowing the gas to fully exchange isotopes with the hydrogen and oxygen elements in the sample. Direct contact between the sample and the detection reagent is avoided throughout the process, strictly ensuring the non-destructive nature of the detection.

[0026] The key to optimizing Gas-Bench technology lies in achieving a deep fit between isotope detection and non-destructive testing requirements. The temperature setting of the isothermal sample pan must balance the efficiency of hydrogen and oxygen isotope exchange with the stability of sample composition, avoiding isotope fractionation or changes in sample composition due to improper temperature. The design of the CO2 / He mixture ratio must be based on the thermodynamic characteristics of the isotope exchange reaction. A suitable environment for isotope exchange is provided through gas atmosphere control, while gas purging thoroughly removes interfering air components from the headspace vial, preventing interference from external isotopes on the detection results. The entire process employs an indirect contact detection mode, achieving isotope exchange and signal acquisition through gas-mediated processes. This ensures the accuracy of hydrogen and oxygen isotope ratio detection and fundamentally avoids contact between reagents and the pear substrate, ensuring the sample retains its complete commercial properties after testing, meeting the core requirements of non-destructive testing.

[0027] Preferably, in step S4, the focus is on correlating the correlation between organic acids and mineral elements such as calcium, iron, copper, zinc, potassium, sodium, magnesium, and manganese, as well as characteristic volatile components, and the selected characteristic indicators meet the significance requirements of linear discriminant analysis.

[0028] Correlation analysis requires the use of multidimensional statistical methods to systematically explore the intrinsic relationships between organic acids and target mineral elements and characteristic volatile components, including synergistic patterns and mutual influence mechanisms. The selection of characteristic indicators must establish strict significance criteria, not only meeting the statistical requirements of linear discriminant analysis but also possessing practical characterization significance, i.e., accurately reflecting the differences in organic acid content and excluding spurious correlation factors caused by environmental fluctuations or detection errors. During the selection process, the physiological characteristics of Korla fragrant pears should be considered, focusing on mineral elements and volatile metabolites related to organic acid synthesis and accumulation. This ensures that the selected characteristic indicators are specific, stable, and reproducible, providing reliable core data support for the subsequent construction of linear discriminant models and improving the predictive accuracy and applicability of the models.

[0029] Preferably, in step S5, the process of establishing the linear discriminant model is as follows: collect Korla fragrant pear samples from different production areas and with different organic acid content gradients to obtain complete detection data, use the external standard method for quantitative analysis, combine the correlation data of relevant element content in the soil of the corresponding production area, construct a feature vector containing mineral elements, isotope ratios and volatile components, and input the linear discriminant equation to obtain the predicted value of organic acid content.

[0030] Sample collection must cover soil types, climatic differences, and natural gradients of organic acid content across different production areas to ensure data comprehensiveness and representativeness. External standard quantitative analysis requires optimized procedures to guarantee the accuracy and comparability of quantitative results for mineral elements, isotope ratios, and volatile components. Integration of soil-related element content data aims to uncover the correlation between the production area environment and the organic acid content and characteristic indicators of Korla pears, enhancing the model's regional adaptability. Feature vector construction must adhere to dimensionality optimization principles, comprehensively covering key detection indicators while avoiding redundant information that increases model complexity. The establishment of linear discriminant equations must be based on statistical analysis of large amounts of sample data. Algorithm optimization will improve the equation's predictive ability for organic acid content while also considering the correlation with origin characteristics, laying the core algorithmic foundation for the integrated technology system.

[0031] Preferably, in step S301, there is no need to microwave digest the sample before ICP-MS detection. The surface lyophilized powder sample is directly loaded into the detection container, and quantitative detection is performed using the external standard method. Standard calibration is performed periodically.

[0032] The core of the microwave-free digestion design lies in simplifying the sample pretreatment process, reducing potential elemental loss, contamination risks, and organic acid degradation during digestion, while simultaneously meeting the high-efficiency requirements of non-destructive testing. The selection of the detection container must meet the compatibility standards of ICP-MS detection, avoiding interference from the container material with mineral element and isotope detection results, and ensuring the purity of the detection environment. The application of the external standard method requires standardized operating procedures, including consistency in sample volume and instrument calibration, to ensure the reliability of quantitative results. Regular standard calibration must adhere to the instrument's quality control requirements, promptly correcting detection deviations through comparison with standard substances, maintaining stable instrument performance, and ensuring the comparability and accuracy of detection data from different batches and samples, providing high-quality data for subsequent data analysis and model building.

[0033] Preferably, in step S1, the sample is stored in a refrigerated manner after collection. During the storage process, squeezing, collision and drastic temperature changes are avoided. After refrigeration, there is no need to reheat before testing, and non-destructive pretreatment is performed directly.

[0034] The choice of refrigeration preservation method must be adapted to the physiological characteristics of Korla fragrant pears. Low temperatures inhibit sample respiration and microbial activity, reducing changes in key indicators such as organic acids and volatile components. Comprehensive protective measures are necessary during preservation to avoid surface damage caused by compression or collision, prevent internal component imbalances due to drastic temperature fluctuations, and ensure sample stability from collection to testing. The no-warming design aims to avoid the impact of temperature changes on surface components and isotopic properties. Direct, non-destructive pretreatment shortens the sample processing cycle, reduces external environmental interference, and ensures that the composition of the lyophilized surface remains consistent with its original state during pretreatment. This provides a true and reliable sample matrix for subsequent multi-dimensional analysis, ensuring the accuracy of the test results.

[0035] Preferably, in step S5, the model validation adopts the blind sample validation method, selecting blind samples of Korla fragrant pears from different production areas and at different maturity levels, covering samples from core production areas and non-core production areas, completing the detection of organic acid content and determination of origin association, and comparing it with the actual value measured by authoritative testing methods;

[0036] The design of blind sample validation methods must be comprehensive and objective. The selection of blind samples should consider both the wide distribution of production areas and the gradient of maturity. Coverage of samples from both core and non-core production areas aims to verify the model's adaptability to pears from different regions, while samples at different maturity levels can assess the model's ability to detect dynamic changes in organic acid content. The validation process must strictly adhere to blind sample management regulations to avoid leakage of sample information that could affect the objectivity of the validation results. Comparison with authoritative testing methods requires a scientific evaluation system. By analyzing the degree of agreement between predicted and actual values, the model's detection accuracy, stability, and anti-interference ability can be assessed. The validation results are not only used for model optimization and improvement but also provide empirical support for the reliability of the technical system, ensuring that it can meet the accurate requirements for organic acid content detection and origin determination in practical applications.

[0037] Preferably, in step S5, the isotope and elemental characteristic database and the volatile substance fingerprint spectrum include an organic acid content fingerprint spectrum database. The organic acid characteristic indicators of Korla fragrant pears from different production areas are associated and stored with the isotope, elemental characteristic database and the regional characteristic fingerprint spectrum of volatile substances. The dual functions of rapid detection of organic acid content and traceability of origin are realized through database comparison.

[0038] The database construction must adhere to the principles of structure and standardization. The organic acid content fingerprint database needs to integrate the organic acid characteristic information of Korla pears from different production areas and under different conditions, and establish a multi-dimensional correlation index with isotope, elemental properties, and volatile substance regional characteristic data. The design of the associated storage must ensure the efficiency of data retrieval, and achieve rapid retrieval and comparison of characteristic indicators through technologies such as classification management and keyword matching. The functional design of the database must consider both practicality and scalability. The rapid detection function shortens the detection cycle by comparing the characteristic indicators of the sample to be tested with the standard spectrum in the database in real time; the origin traceability function achieves accurate determination of the production area based on the specific matching of regional characteristic indicators. At the same time, the database must have a dynamic update mechanism to continuously include sample data from new production areas and new batches, and continuously improve the accuracy and coverage of detection and traceability.

[0039] Compared with the prior art, the present invention provides a non-destructive method for detecting the organic acid content of Korla fragrant pears, which has the following beneficial effects:

[0040] This non-destructive method for detecting the organic acid content of Korla fragrant pears selects samples with uniform maturity, no damage, and intact surface structure. It combines wiping with anhydrous ethanol followed by natural air drying, and then vacuum freeze-drying and pulverizing only the surface layer of the sample. This process preserves the integrity of the Korla fragrant pear sample throughout, effectively avoiding damage to the commercial properties of the pear caused by core removal and stem removal. By integrating multi-dimensional detection technologies such as ICP-MS, HT-IRMS, and GC-MS / EMIS with HS-HPME extraction, it achieves comprehensive detection of mineral elements, O and H isotope content, δH and δ18O isotope ratios, and volatile components, simultaneously constructing... A database of isotopes and elemental properties, along with fingerprints of volatile substances with regional characteristics, were established. Orthogonal partial least squares analysis was then used to screen specific indicators characterizing organic acid content and eliminate irrelevant interfering factors, significantly improving the accuracy and relevance of organic acid content detection results. Finally, a linear discriminant model was established, forming an integrated technical system for organic acid content detection and origin identification. Furthermore, optimization of ICP-MS operating parameters, GC-MS capillary columns, and heating programs further ensured the adaptability and efficiency of the detection technology, providing reliable technical support for the rapid, non-destructive detection of organic acid content in Korla fragrant pears and for origin traceability. Attached Figure Description

[0041] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] This invention provides a technical solution: a non-destructive method for detecting the organic acid content of Korla fragrant pears. Please refer to [link / reference]. Figure 1 This includes the following steps:

[0044] S1 Sample Collection

[0045] Select Korla fragrant pears that are uniformly ripe and undamaged, remove impurities attached to the surface, and ensure that the surface structure of the samples is intact;

[0046] It provides a stable and uniform sample basis for subsequent non-destructive preprocessing and multi-dimensional detection, avoiding detection interference caused by sample differences from the source, and ensuring the reliability and comparability of the detection results.

[0047] S2 Non-destructive Preprocessing

[0048] After wiping the sample surface with anhydrous ethanol, the sample is air-dried naturally without removing the core or stem. The sample surface is then freeze-dried directly using a vacuum freeze dryer. The freeze-dried surface material is then pulverized into a uniform powder, packaged, and sealed for later use. The integrity of the sample is maintained throughout the process to avoid damaging the commercial properties of the pear.

[0049] This process preserves the original state of the characteristic components related to organic acids on the sample surface to the greatest extent possible, while avoiding structural damage to the main body of the sample, thus maintaining the good commercial value of the pear. At the same time, the freeze-drying and pulverizing process creates favorable conditions for the efficient application of subsequent multi-dimensional detection technologies.

[0050] S3 Multidimensional Detection

[0051] S301 Isotope Detection

[0052] ICP-MS was used to determine the mineral elements and O and H isotope contents in freeze-dried samples, and a database of isotope and elemental characteristics of Korla fragrant pears from different production areas was established simultaneously.

[0053] It has achieved precise capture of characteristic elements and isotope information related to organic acid content. The established database provides core data support for subsequent characteristic index screening and origin identification, laying the foundation for an integrated technical system.

[0054] S302 isotope ratio detection

[0055] The δH and δ¹⁸O isotope ratios in freeze-dried samples were determined using HT-IRMS technology.

[0056] This further enriches the detection information in the isotope dimension, complements the mineral element and O and H isotope content data, strengthens the synergistic correlation between organic acid content and isotope characteristics, and provides more effective information in multiple dimensions for the accurate screening of subsequent characteristic indicators.

[0057] S303 Volatile Component Detection

[0058] The composition of volatile components in freeze-dried samples was determined by combining GC-MS / EMIS technology with HS-HPME extraction method, and a regionally characteristic fingerprint spectrum of volatile substances was constructed.

[0059] It has achieved comprehensive capture of regionally specific volatile components. The constructed fingerprint spectrum not only provides characteristic basis for the characterization of organic acid content, but also provides unique regional identification marks for the identification of place of origin, which helps to improve the integrated technology system.

[0060] S4 Detection Data Analysis

[0061] Based on the synergistic correlation characteristics between organic acids and mineral elements, stable isotopes and volatile components, orthogonal partial least squares analysis was used to screen out characteristic indicators that can specifically characterize the content of organic acids and eliminate unrelated interfering factors.

[0062] The intrinsic relationship between each detection dimension and organic acid content was fully explored. Through scientific analysis methods, the effective information was accurately focused, the interference of irrelevant factors on the detection results was significantly reduced, and the specificity of organic acid content characterization and the accuracy of detection results were further improved.

[0063] S5 establishes a linear discriminant model

[0064] The screened characteristic index data are input into the model to obtain the organic acid content detection results. At the same time, an isotope and elemental characteristic database and a volatile substance fingerprint spectrum are established to form an integrated technical system for organic acid content detection and origin identification of Korla fragrant pear. The ICP-MS working parameters are optimized according to the detection requirements of mineral elements and isotopes. The GC-MS adopts a capillary column adapted for the separation of volatile components, and the heating program is optimized and adjusted according to the separation efficiency.

[0065] This technology achieves the organic integration of rapid detection of organic acid content and accurate identification of origin, constructing a fully functional integrated technical system. Optimization of detection equipment parameters and separation conditions ensures the adaptability and operational efficiency of various detection technologies, providing a strong guarantee for the large-scale application of the technology.

[0066] In step S2, the vacuum freeze-drying is performed under conditions that are set according to the requirements of preserving the active ingredients on the sample surface. Only the freeze-dried sample surface is collected and pulverized. After pulverization, the sample is passed through a nylon sieve and placed in a brown sealed bottle for room temperature equilibration. During the equilibration process, external impurities are isolated from contamination.

[0067] The vacuum freeze-drying conditions must be carefully designed to match the surface composition characteristics of Korla pears. Precise control of the freeze-drying rate and pressure parameters is crucial to minimize the degradation or loss of active ingredients such as organic acids and volatile substances. The selection of a nylon sieve must match the particle size requirements of the freeze-dried surface material to ensure powder uniformity and repeatability in subsequent analyses. The equilibration process must be conducted in a clean and undisturbed environment. Sealing and isolating the sample from moisture, dust, and volatile impurities prevents secondary changes in sample composition and maintains the original state of the sample surface components. This provides a pure and stable analytical matrix for subsequent multi-dimensional analysis, ensuring the structural integrity and commercial value of the pear throughout the process.

[0068] In step S303, the combined technique of ICP-MS and HS-HPME-GC-MS is used. During HS-HPME extraction, the surface lyophilized sample is placed in a headspace vial, saturated NaCl solution is added, and equilibrium conditions are set according to the enrichment requirements of volatile components. The activated extraction head is inserted into the headspace vial, and after adsorption for a certain period of time at a suitable distance without contacting the sample, it is desorbed at the GC-MS injection port.

[0069] The core design of this hyphenated technique lies in achieving complementarity and high efficiency in the detection of mineral elements and volatile components. In the HS-HPME extraction stage, the addition of saturated NaCl solution aims to adjust the osmotic pressure of the system, promoting the migration of volatile components from the sample into the headspace region and improving enrichment efficiency. The activation of the extraction head must adhere to the principle of material compatibility with the target components, removing residual impurities through preset temperature and time parameters to ensure adsorption specificity. Controlling the adsorption distance must avoid contamination caused by direct contact between the extraction head and the sample powder, while ensuring the sufficiency of the adsorption process. The desorption stage requires optimization of the GC-MS inlet temperature program to achieve rapid and complete desorption of volatile components. Through a seamless design of the process flow, the hyphenated technique reduces component loss during sample transfer, improving the sensitivity and accuracy of the detection results.

[0070] In step S302, during the detection of the stable isotope ratio of δH and δ18O, the non-destructive adaptation parameters of δH and δ18O are optimized using Gas-Bench technology, a suitable temperature is set for the constant temperature sample pan, and a mixture of CO2 and He in a specific ratio is filled for a sufficient time to completely remove the air from the headspace vial, allowing the gas to fully exchange isotopes with the hydrogen and oxygen elements in the sample. Direct contact between the sample and the detection reagent is avoided throughout the process, strictly ensuring the non-destructive nature of the detection.

[0071] The key to optimizing Gas-Bench technology lies in achieving a deep fit between isotope detection and non-destructive testing requirements. The temperature setting of the isothermal sample pan must balance the efficiency of hydrogen and oxygen isotope exchange with the stability of sample composition, avoiding isotope fractionation or changes in sample composition due to improper temperature. The design of the CO2 / He mixture ratio must be based on the thermodynamic characteristics of the isotope exchange reaction. A suitable environment for isotope exchange is provided through gas atmosphere control, while gas purging thoroughly removes interfering air components from the headspace vial, preventing interference from external isotopes on the detection results. The entire process employs an indirect contact detection mode, achieving isotope exchange and signal acquisition through gas-mediated processes. This ensures the accuracy of hydrogen and oxygen isotope ratio detection and fundamentally avoids contact between reagents and the pear substrate, ensuring the sample retains its complete commercial properties after testing, meeting the core requirements of non-destructive testing.

[0072] In step S4, the focus is on the correlation between organic acids and mineral elements such as calcium, iron, copper, zinc, potassium, sodium, magnesium, and manganese, as well as characteristic volatile components. The selected characteristic indicators meet the significance requirements of linear discriminant analysis.

[0073] Correlation analysis requires the use of multidimensional statistical methods to systematically explore the intrinsic relationships between organic acids and target mineral elements and characteristic volatile components, including synergistic patterns and mutual influence mechanisms. The selection of characteristic indicators must establish strict significance criteria, not only meeting the statistical requirements of linear discriminant analysis but also possessing practical characterization significance, i.e., accurately reflecting the differences in organic acid content and excluding spurious correlation factors caused by environmental fluctuations or detection errors. During the selection process, the physiological characteristics of Korla fragrant pears should be considered, focusing on mineral elements and volatile metabolites related to organic acid synthesis and accumulation. This ensures that the selected characteristic indicators are specific, stable, and reproducible, providing reliable core data support for the subsequent construction of linear discriminant models and improving the predictive accuracy and applicability of the models.

[0074] In step S5, the process of establishing the linear discriminant model is as follows: collect Korla fragrant pear samples from different production areas and with different organic acid content gradients to obtain complete detection data, use the external standard method for quantitative analysis, combine the correlation data of relevant element content in the soil of the corresponding production area, construct a feature vector containing mineral elements, isotope ratios and volatile components, and input the linear discriminant equation to obtain the predicted value of organic acid content.

[0075] Sample collection must cover soil types, climatic differences, and natural gradients of organic acid content across different production areas to ensure data comprehensiveness and representativeness. External standard quantitative analysis requires optimized procedures to guarantee the accuracy and comparability of quantitative results for mineral elements, isotope ratios, and volatile components. Integration of soil-related element content data aims to uncover the correlation between the production area environment and the organic acid content and characteristic indicators of Korla pears, enhancing the model's regional adaptability. Feature vector construction must adhere to dimensionality optimization principles, comprehensively covering key detection indicators while avoiding redundant information that increases model complexity. The establishment of linear discriminant equations must be based on statistical analysis of large amounts of sample data. Algorithm optimization will improve the equation's predictive ability for organic acid content while also considering the correlation with origin characteristics, laying the core algorithmic foundation for the integrated technology system.

[0076] In step S301, there is no need to microwave digest the sample before ICP-MS detection. The surface lyophilized powder sample is directly loaded into the detection container, and the external standard method is used for quantitative detection. Standard calibration is performed periodically.

[0077] The core of the microwave-free digestion design lies in simplifying the sample pretreatment process, reducing potential elemental loss, contamination risks, and organic acid degradation during digestion, while simultaneously meeting the high-efficiency requirements of non-destructive testing. The selection of the detection container must meet the compatibility standards of ICP-MS detection, avoiding interference from the container material with mineral element and isotope detection results, and ensuring the purity of the detection environment. The application of the external standard method requires standardized operating procedures, including consistency in sample volume and instrument calibration, to ensure the reliability of quantitative results. Regular standard calibration must adhere to the instrument's quality control requirements, promptly correcting detection deviations through comparison with standard substances, maintaining stable instrument performance, and ensuring the comparability and accuracy of detection data from different batches and samples, providing high-quality data for subsequent data analysis and model building.

[0078] In step S1, the sample is stored in a refrigerated manner after collection. During the storage process, squeezing, collision and drastic temperature changes are avoided. After refrigeration, there is no need to reheat before testing. Non-destructive pretreatment is performed directly.

[0079] The choice of refrigeration preservation method must be adapted to the physiological characteristics of Korla fragrant pears. Low temperatures inhibit sample respiration and microbial activity, reducing changes in key indicators such as organic acids and volatile components. Comprehensive protective measures are necessary during preservation to avoid surface damage caused by compression or collision, prevent internal component imbalances due to drastic temperature fluctuations, and ensure sample stability from collection to testing. The no-warming design aims to avoid the impact of temperature changes on surface components and isotopic properties. Direct, non-destructive pretreatment shortens the sample processing cycle, reduces external environmental interference, and ensures that the composition of the lyophilized surface remains consistent with its original state during pretreatment. This provides a true and reliable sample matrix for subsequent multi-dimensional analysis, ensuring the accuracy of the test results.

[0080] In step S5, the model validation adopts the blind sample validation method, selecting blind samples of Korla fragrant pears from different production areas and at different maturity levels, covering samples from core production areas and non-core production areas, completing the detection of organic acid content and determination of origin association, and comparing it with the actual value measured by authoritative testing methods;

[0081] The design of blind sample validation methods must be comprehensive and objective. The selection of blind samples should consider both the wide distribution of production areas and the gradient of maturity. Coverage of samples from both core and non-core production areas aims to verify the model's adaptability to pears from different regions, while samples at different maturity levels can assess the model's ability to detect dynamic changes in organic acid content. The validation process must strictly adhere to blind sample management regulations to avoid leakage of sample information that could affect the objectivity of the validation results. Comparison with authoritative testing methods requires a scientific evaluation system. By analyzing the degree of agreement between predicted and actual values, the model's detection accuracy, stability, and anti-interference ability can be assessed. The validation results are not only used for model optimization and improvement but also provide empirical support for the reliability of the technical system, ensuring that it can meet the accurate requirements for organic acid content detection and origin determination in practical applications.

[0082] In step S5, the isotope and elemental characteristic database and the volatile substance fingerprint spectrum, including the organic acid content fingerprint spectrum database, are used to associate and store the organic acid characteristic indicators of Korla fragrant pears from different production areas with the isotope, elemental characteristic database and the regional characteristic fingerprint spectrum of volatile substances. Through database comparison, the dual functions of rapid detection of organic acid content and traceability of origin are realized.

[0083] The database construction must adhere to the principles of structure and standardization. The organic acid content fingerprint database needs to integrate the organic acid characteristic information of Korla pears from different production areas and under different conditions, and establish a multi-dimensional correlation index with isotope, elemental properties, and volatile substance regional characteristic data. The design of the associated storage must ensure the efficiency of data retrieval, and achieve rapid retrieval and comparison of characteristic indicators through technologies such as classification management and keyword matching. The functional design of the database must consider both practicality and scalability. The rapid detection function shortens the detection cycle by comparing the characteristic indicators of the sample to be tested with the standard spectrum in the database in real time; the origin traceability function achieves accurate determination of the production area based on the specific matching of regional characteristic indicators. At the same time, the database must have a dynamic update mechanism to continuously include sample data from new production areas and new batches, and continuously improve the accuracy and coverage of detection and traceability.

[0084] The non-destructive testing workflow for organic acid content in Korla fragrant pears is as follows: Sample collection: Select fragrant pears with uniform maturity, no damage, and intact surface structure, remove impurities to provide stable and homogeneous samples for subsequent testing, avoid detection interference, and ensure the reliability and comparability of results; Non-destructive pretreatment: Wipe the surface with anhydrous ethanol and air dry naturally, then freeze-dry, pulverize, and repackage and seal the surface to preserve the integrity of the main body, avoid damaging the commercial attributes, and create conditions for subsequent testing; Multi-dimensional detection: Isotope detection: Use ICP-MS technology to determine the mineral elements and O and H isotope content and establish a database to accurately capture relevant information and provide core data support; Isotope ratio detection: Use HT-IRMS technology to determine δ The ratio of H and δ¹⁸O isotopes enriches isotopic dimensional information and strengthens synergistic correlation; volatile component detection uses specific techniques to determine the composition of volatile components and construct fingerprint spectra, comprehensively capturing region-specific components and providing characteristic evidence and regional identification; detection data analysis, based on synergistic correlation characteristics, employs orthogonal partial least squares analysis to screen characteristic indicators, eliminate interference factors, and improve the specificity and accuracy of organic acid content characterization; a linear discriminant model is established, and the screened data is input to obtain detection results, establishing a database and fingerprint spectra to form an integrated technical system, optimizing detection equipment parameters and separation conditions, realizing the integration of rapid detection of organic acid content and accurate identification of origin, and ensuring the large-scale application of the technology.

[0085] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A non-destructive method for detecting the organic acid content of Korla fragrant pears, characterized in that, Includes the following steps: S1 Sample Collection Select Korla fragrant pears that are uniformly ripe and undamaged, remove impurities attached to the surface, and ensure that the surface structure of the samples is intact; S2 Non-destructive Preprocessing After wiping the sample surface with anhydrous ethanol, the sample is air-dried naturally without removing the core or stem. The sample surface is then freeze-dried directly using a vacuum freeze dryer. The freeze-dried surface material is then pulverized into a uniform powder, packaged, and sealed for later use. The integrity of the sample is maintained throughout the process to avoid damaging the commercial properties of the pear. S3 Multidimensional Detection S301 Isotope Detection ICP-MS was used to determine the mineral elements and O and H isotope contents in freeze-dried samples, and a database of isotope and elemental characteristics of Korla fragrant pears from different production areas was established simultaneously. S302 isotope ratio detection The δH and δ¹⁸O isotope ratios in freeze-dried samples were determined using HT-IRMS technology. S303 Volatile Component Detection The composition of volatile components in freeze-dried samples was determined by combining GC-MS / EMIS technology with HS-HPME extraction method, and a regionally characteristic fingerprint spectrum of volatile substances was constructed. S4 Detection Data Analysis Based on the synergistic correlation characteristics between organic acids and mineral elements, stable isotopes and volatile components, orthogonal partial least squares analysis was used to screen out characteristic indicators that can specifically characterize the content of organic acids and eliminate unrelated interfering factors. S5 establishes a linear discriminant model The screened characteristic index data are input into the model to obtain the organic acid content detection results. At the same time, an isotope and elemental characteristic database and a volatile substance fingerprint spectrum are established to form an integrated technical system for organic acid content detection and origin identification of Korla fragrant pear. The ICP-MS working parameters are optimized according to the detection requirements of mineral elements and isotopes. The GC-MS adopts a capillary column adapted for the separation of volatile components, and the heating program is optimized and adjusted according to the separation efficiency.

2. The non-destructive testing method for organic acid content in Korla fragrant pears according to claim 1, characterized in that: In step S2, the vacuum freeze-drying is performed under conditions that preserve the active ingredients on the sample surface. Only the freeze-dried sample surface is collected, pulverized, and then passed through a nylon sieve. The pulverized sample is then placed in a brown sealed bottle and kept at room temperature for equilibration. The equilibration process isolates the sample from external contaminants.

3. The non-destructive testing method for organic acid content in Korla fragrant pears according to claim 1, characterized in that: In step S303, the combined technique of ICP-MS and HS-HPME-GC-MS is used. During HS-HPME extraction, the surface lyophilized sample is placed in a headspace vial, saturated NaCl solution is added, and equilibrium conditions are set according to the enrichment requirements of volatile components. The activated extraction head is inserted into the headspace vial, and after adsorption for a certain period of time at a suitable distance without contacting the sample, it is desorbed at the GC-MS inlet.

4. The non-destructive testing method for organic acid content in Korla fragrant pears according to claim 1, characterized in that: In step S302, during the detection of the stable isotope ratio of δH to δ18O, the non-destructive adaptation parameters of δH and δ18O are optimized using Gas-Bench technology. A suitable temperature is set for the constant temperature sample pan, and a mixture of CO2 and He in a specific ratio is introduced for a sufficient time to completely remove the air from the headspace vial. This allows the gas to fully exchange isotopes with the hydrogen and oxygen elements in the sample, and avoids direct contact between the sample and the detection reagent throughout the process, strictly ensuring the non-destructive nature of the detection.

5. The non-destructive testing method for organic acid content in Korla fragrant pears according to claim 1, characterized in that: In step S4, the focus is on the correlation between organic acids and mineral elements such as calcium, iron, copper, zinc, potassium, sodium, magnesium, and manganese, as well as characteristic volatile components. The selected characteristic indicators meet the significance requirements of linear discriminant analysis.

6. The non-destructive testing method for organic acid content in Korla fragrant pears according to claim 1, characterized in that: In step S5, the process of establishing the linear discriminant model is as follows: collect Korla fragrant pear samples from different production areas and with different organic acid content gradients to obtain complete detection data, use the external standard method for quantitative analysis, combine the correlation data of relevant element content in the soil of the corresponding production area, construct a feature vector containing mineral elements, isotope ratios and volatile components, and input the linear discriminant equation to obtain the predicted value of organic acid content.

7. The non-destructive testing method for organic acid content in Korla fragrant pears according to claim 1, characterized in that: In step S301, there is no need to microwave digest the sample before ICP-MS detection. The surface lyophilized powder sample is directly loaded into the detection container, and quantitative detection is performed using the external standard method. Standard calibration is performed periodically.

8. The non-destructive testing method for organic acid content in Korla fragrant pears according to claim 1, characterized in that: In step S1, the sample is stored in a refrigerated manner after collection. During the storage process, squeezing, collision and drastic temperature changes are avoided. After refrigeration, there is no need to reheat before testing, and non-destructive pretreatment is performed directly.

9. The non-destructive testing method for organic acid content in Korla fragrant pear according to claim 1, characterized in that: In step S5, the model validation adopts the blind sample validation method, selecting blind samples of Korla fragrant pears from different production areas and at different maturity levels, covering samples from both core and non-core production areas, completing the detection of organic acid content and determination of origin association, and comparing it with the actual values ​​measured by authoritative testing methods.

10. The non-destructive testing method for organic acid content in Korla fragrant pear according to claim 1, characterized in that: In step S5, the isotope and elemental characteristic database and the volatile substance fingerprint spectrum, including the organic acid content fingerprint spectrum database, are used to associate and store the organic acid characteristic indicators of Korla fragrant pears from different production areas with the isotope, elemental characteristic database and the regional characteristic fingerprint spectrum of volatile substances. Through database comparison, the dual functions of rapid detection of organic acid content and traceability of origin are realized.

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