Method and system for detecting hydroxytyrosol content for food quality inspection
By combining the AMPD and BEADS algorithms, the accuracy problem of detecting hydroxytyrosol in olive oil samples was solved, enabling reliable judgment of hydroxytyrosol content and improving the accuracy and reliability of detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- BEIJING DAMILI TECH CO LTD
- Filing Date
- 2025-12-04
- Publication Date
- 2026-07-07
AI Technical Summary
During the quality inspection of olive oil products, the presence of unstable components such as schizocyclic ethers, flavonoids, and phenolic acids in the samples reduces the accuracy of hydroxytyrosol detection. Furthermore, impurities such as heavy metals interfere with the detection, causing baseline drift and noise interference in chromatographic data.
The AMPD automatic multi-scale peak detection algorithm is used to obtain peak points and chromatographic peak sequences. By analyzing the noise fitting deviation coefficient, anti-interference stability coefficient and chromatographic peak shape interference coefficient, baseline correction is performed in combination with the BEADS algorithm. Adaptive asymmetric penalty coefficient is used to correct chromatographic data to improve detection accuracy.
This method improves the accuracy and reliability of detecting hydroxytyrosol content in olive oil food samples, avoids over- or under-correction, and ensures the credibility of the test results.
Smart Images
Figure CN121540829B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of spectral analysis technology, specifically to a method and system for detecting hydroxytyrosol content in food quality inspection. Background Technology
[0002] Hydroxytyrosol is a key active ingredient in extra virgin olive oil, and its content decreases with shelf life, making it a core criterion for identifying genuine olive oil. However, during the quality inspection of olive oil products, samples contain various components such as sepsis ethers, flavonoids, and phenolic acids. Furthermore, oleuropein accounts for approximately 50% of the total phenols, and its unstable molecular structure is easily hydrolyzed or acidified to form hydroxytyrosol. Simultaneously, in olive oil samples, residual oil, moisture, or heavy metals (such as lead and arsenic) may not be separated, potentially obscuring the target peak or competing with hydroxytyrosol for binding sites. This results in numerous discrete noise points in liquid chromatography, causing baseline drift in the chromatographic data and reducing the accuracy of hydroxytyrosol detection in olive oil samples. Summary of the Invention
[0003] To address the aforementioned technical problems, the purpose of this application is to provide a method and system for detecting hydroxytyrosol content in food quality inspection. The specific technical solution adopted is as follows:
[0004] In a first aspect, embodiments of this application provide a method for detecting the hydroxytyrosol content in food quality inspection, the method comprising the following steps:
[0005] S1, collect spectral data of olive oil food samples and obtain liquid chromatographic data of food samples;
[0006] S2.1, obtain the peak points, chromatographic peak sequences, and half-sequences of chromatographic peaks based on the liquid chromatography data of olive oil food samples; obtain the noise fitting deviation coefficient and anti-interference stability coefficient based on the chromatographic peak sequences and half-sequences of chromatographic peaks; obtain the chromatographic peak shape interference coefficient based on the anti-interference stability coefficient and the noise fitting deviation coefficient.
[0007] S2.2, obtain the peak shape symmetry tilt coefficient based on the chromatographic peak shape interference coefficient and the difference before and after the peak point in the half sequence of the chromatographic peak; obtain the adaptive asymmetric penalty coefficient based on the peak shape symmetry tilt coefficient of all chromatographic peak sequences;
[0008] S3. Based on the adaptive asymmetric penalty coefficient and the liquid chromatography data of olive oil food samples, obtain the corrected chromatographic data of olive oil food samples to determine whether the content of hydroxytyrosol in olive oil food samples meets the standard.
[0009] Furthermore, the step of obtaining peak points, peak sequences, and half-sequences of chromatographic peaks based on liquid chromatography data of olive oil food samples includes:
[0010] The liquid chromatography data of olive oil food samples are used as input to the AMPD automatic multi-scale peak detection algorithm, which outputs all peak points in the liquid chromatography data of olive oil food samples.
[0011] The midpoint between each peak point and the next adjacent peak point in the liquid chromatography data of olive oil food samples is used as a dividing point to divide the liquid chromatography data of olive oil food samples into multiple chromatographic peak sequences.
[0012] The minimum distance between the start point, end point and peak point of each chromatographic peak sequence is taken as the optimal half length of each chromatographic peak sequence. With the peak point of each chromatographic peak sequence as the center, a half-sequence of chromatographic peak with a length of (2r+1) is constructed, where r is the optimal half length of the chromatographic peak sequence.
[0013] Furthermore, the acquisition of the noise fitting deviation coefficient and the anti-interference stability coefficient includes:
[0014] Curve fitting is performed on the half-sequence of chromatographic peaks, and the chromatographic peak fitting curve is output. The absolute values of the differences between each data point in the chromatographic peak sequence and the corresponding data point in the chromatographic peak fitting curve are arranged in ascending order according to time, and the chromatographic fitting residual sequence is constructed.
[0015] The sum of the information entropy of the chromatographic fitting residual sequence and the standard deviation after standardization is multiplied by the average value of the chromatographic fitting residual sequence to obtain the noise fitting deviation coefficient.
[0016] The minimum value of the first and last data points in the chromatographic peak sequence is taken as the minimum chromatographic peak intensity. The sum of the minimum chromatographic peak intensity and the preset denominator adjustment parameter is taken as the adjusted minimum peak intensity. The ratio of the maximum value of all data points in the chromatographic peak sequence to the adjusted minimum peak intensity is taken as the anti-interference stability coefficient.
[0017] Further, obtaining the chromatographic peak shape interference coefficient includes:
[0018] The sum of the number 1 and the anti-interference stability coefficient is used as the adjustment anti-interference coefficient, and the logarithmic function with the number 2 as the base and the adjustment anti-interference coefficient as the argument is used as the chromatographic anti-interference index.
[0019] The function of the number 2 is to adaptively adjust the range of values for the chromatographic peak shape interference coefficient, and the purpose of the number 1 is to prevent the denominator from being 0, which would render the chromatographic peak shape interference coefficient meaningless.
[0020] The ratio of the noise fitting deviation coefficient to the chromatographic anti-interference index is used as the chromatographic peak shape interference coefficient.
[0021] Further, obtaining the peak symmetry tilt coefficient includes:
[0022] Using the peak point in the chromatographic peak half-sequence as the dividing point, the chromatographic peak half-sequence is divided into the front sub-band sequence and the back sub-band sequence;
[0023] The chromatographic symmetry deviation coefficient is obtained based on the difference in integrals and minimum values between the front and rear sub-band sequences, and the chromatographic trend disorder coefficient is obtained based on the difference in trends between the front and rear sub-band sequences.
[0024] The sum of the chromatographic symmetry deviation coefficient and the chromatographic trend disorder coefficient is multiplied by the chromatographic peak shape interference coefficient to obtain the peak shape symmetry tilt coefficient.
[0025] Further, obtaining the chromatographic symmetry deviation coefficient includes:
[0026] The starting point to the peak point of the chromatographic peak half sequence is taken as the chromatographic pre-interval, the peak point to the ending point of the chromatographic peak half sequence is taken as the chromatographic post-interval, and the absolute value of the difference between the integral of the chromatographic peak half sequence in the chromatographic pre-interval and the integral in the chromatographic post-interval is taken as the chromatographic integral difference on both sides of the peak.
[0027] Calculate the absolute value of the difference between the minimum values of the preceding sub-band sequence and the following sub-band sequence, and calculate an exponential function with the natural constant as the base and the opposite of the absolute value as the exponent;
[0028] The ratio of the difference in chromatographic integrals on both sides of the peak to the calculated result of the exponential function is used as the chromatographic symmetry deviation coefficient.
[0029] Further, obtaining the chromatographic trend disorder coefficient includes:
[0030] The absolute value of the difference in trend intensity between the preceding and following sub-band sequences is taken as the chromatographic trend difference on both sides of the peak. The DTW distance between the preceding and following sub-band sequences is multiplied by the chromatographic trend difference on both sides of the peak and taken as the chromatographic trend disorder coefficient.
[0031] Furthermore, the adaptive asymmetric penalty coefficient is the average of the peak shape symmetry tilt coefficients of all chromatographic peak sequences.
[0032] Furthermore, the step of obtaining chromatographic data of calibrated olive oil food samples and determining whether the content of hydroxytyrosol in the olive oil food samples meets the standard includes:
[0033] The adaptive asymmetric penalty coefficient is used as the asymmetric penalty factor of the BEADS algorithm. The BEADS algorithm is used to perform baseline correction on the liquid chromatography data of olive oil food samples and output the corrected chromatographic data of olive oil food samples.
[0034] The sequence similarity between the chromatographic data of the calibrated olive oil food sample and the standard hydroxytyrosol liquid chromatographic data in the database is used as the criterion for compliance. When the absolute value of the sequence similarity is greater than or equal to the preset compliance threshold, the hydroxytyrosol content in the olive oil food sample is deemed to meet the standard; otherwise, the hydroxytyrosol content in the olive oil food sample is deemed to fail to meet the standard.
[0035] Secondly, embodiments of this application also provide a hydroxytyrosol content detection system for food quality inspection, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.
[0036] This application has at least the following beneficial effects: It obtains peak points, chromatographic peak sequences, and half-sequences of chromatographic peaks from liquid chromatographic data of olive oil food samples; obtains chromatographic fitting residual sequences based on the difference between each half-sequence and its fitting result; analyzes the values and fluctuations of the chromatographic fitting residual sequences to obtain noise fitting deviation coefficients, which are used to measure the degree of interference from impurities and noise; analyzes the differences in the maximum values of the half-sequences of chromatographic peaks to obtain anti-interference stability coefficients, which are used to measure the impact of impurities and noise on the liquid chromatographic data of olive oil food samples; and determines the chromatographic peak shape interference coefficient by combining the noise fitting deviation coefficient. By comprehensively considering the degree of interference from impurities and noise, as well as the anti-interference degree of the liquid chromatographic data of olive oil food samples, it improves the protection against interference from impurities and noise in the liquid chromatographic data of olive oil food samples. The accuracy of the degree assessment is improved by analyzing the differences in minimum values, trends, and integrals before and after the peak points in the half-sequence of chromatographic peaks to obtain chromatographic symmetry deviation coefficients and chromatographic trend disorder coefficients. Combined with the chromatographic peak shape interference coefficient, the peak shape symmetry tilt coefficient is determined, and the chromatographic peak shape interference coefficient is corrected, thus improving the reliability of the assessment of asymmetry caused by impurities and noise interference in the liquid chromatographic data of olive oil food samples. Based on the peak shape symmetry tilt coefficients of all chromatographic peak sequences, an adaptive asymmetry penalty coefficient is determined, and the BEADS algorithm is used to correct the liquid chromatographic data of olive oil food samples, obtaining corrected olive oil food sample chromatographic data. This avoids the problems of over-correction and under-correction of the liquid chromatographic data of olive oil food samples, thereby improving the reliability of judging whether the content of hydroxytyrosol in olive oil food samples meets the standards. Attached Figure Description
[0037] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0038] Figure 1 A flowchart illustrating the steps of a method for detecting hydroxytyrosol content in food quality inspection, provided in one embodiment of this application;
[0039] Figure 2 This is a schematic diagram illustrating the acquisition of the adaptive asymmetric penalty coefficient.
[0040] Figure 3 Liquid chromatography data for olive oil food samples;
[0041] Figure 4 These are the peak points in the liquid chromatography data of olive oil food samples.
[0042] Figure 5 The chromatographic peak sequence;
[0043] Figure 6 This is a half-sequence of chromatographic peaks;
[0044] Figure 7 For fitting curves of chromatographic peaks;
[0045] Figure 8 For chromatographic fitting of residual sequences;
[0046] Figure 9 This is a schematic diagram of the STL decomposition of the preceding subband sequence;
[0047] Figure 10 To calibrate the chromatographic data of olive oil food samples. Detailed Implementation
[0048] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the hydroxytyrosol content detection method and system for food quality inspection proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.
[0050] The following, in conjunction with the accompanying drawings, details the specific scheme of the hydroxytyrosol content detection method and system provided in this application for food quality inspection.
[0051] Please see Figure 1 The diagram illustrates a flowchart of a method for detecting hydroxytyrosol content in food quality inspection according to an embodiment of this application. The method includes the following steps:
[0052] Step S001: Collect spectral data of olive oil food samples and obtain liquid chromatographic data of the food samples.
[0053] In some embodiments of this application, the process for collecting liquid chromatography data of olive oil food samples is as follows:
[0054] To analyze the hydroxytyrosol content in olive oil food samples, high-performance liquid chromatography (HPLC) was used. The chromatographic column was a ZORBAX-SB-C18 column, the mobile phase was 10% methanol-water solution, the detection wavelength was 280 nm, the column temperature was 30℃, the flow rate was 1 mL / min, the injection volume was 10 µL, and the retention time was set to 1 h. Olive oil food samples were then sampled at equal intervals within the retention time, with a sampling time of 1 ms, to obtain the HPLC data.
[0055] In one embodiment of this application, the liquid chromatography data of the olive oil food sample are as follows: Figure 3 As shown.
[0056] It should be noted that the selection of the chromatographic column and the settings of methanol-water solution concentration, detection wavelength, column temperature, flow rate, injection volume, retention time and sampling time are optional and can be chosen by the implementer as part of other implementation methods. This embodiment does not impose any special restrictions on these settings.
[0057] Step S002: Obtain the peak points, chromatographic peak sequences, and half-sequences of chromatographic peaks based on the liquid chromatography data of the olive oil food sample.
[0058] During the extraction process of olive oil food samples, the active molecules contained within the olive oil food samples are usually unstable and easily hydrolyzed or acidified. This can result in incompletely removed solvent impurities, buffer crystals, or other residues in the mobile phase of the olive oil food samples. Consequently, there are many baselines in the liquid chromatography data of olive oil food samples, requiring baseline correction to reduce interference from impurities when detecting the hydroxytyrosol content in olive oil food samples.
[0059] It should be noted that multiple chromatographic peaks exist in the liquid chromatography data of olive oil food samples. Each peak represents the concentration and content of a component in the olive oil food sample extract. Ideally, the chromatographic peaks in the liquid chromatography data of olive oil food samples should be bell-shaped curves with a near-Gaussian distribution. However, due to the instability of active molecules in olive oil food samples, impurities in the extract, and background noise, there is significant baseline drift in the liquid chromatography, causing the response intensity to not reflect the true mobile phase response intensity. Furthermore, peak shape variations can lead to anomalies such as tailing peaks, leading peaks, and split peaks, thus affecting the identification of hydroxytyrosol content in olive oil food samples.
[0060] To obtain information about individual chromatographic peaks in the liquid chromatography data of olive oil food samples, the liquid chromatography data of the olive oil food samples was used as input to the AMPD automatic multi-scale peak detection algorithm, which outputs all peak points in the liquid chromatography data of olive oil food samples. The midpoint between each peak point and the next adjacent peak point in the liquid chromatography data of olive oil food samples was used as a dividing point, dividing the liquid chromatography data of olive oil food samples into multiple chromatographic peak sequences, each containing one peak point.
[0061] In the liquid chromatography data of olive oil food samples, the chromatographic peaks do not appear at equal intervals, resulting in a long leading or trailing delay in the chromatographic peak sequence. Preferably, as an embodiment of this application, the method for obtaining the half-sequence of chromatographic peaks is as follows: for each chromatographic peak sequence, the minimum distance between the start point, end point and peak point of each chromatographic peak sequence is taken as the optimal half-length of each chromatographic peak sequence. With the peak point of each chromatographic peak sequence as the center, a half-sequence of chromatographic peaks with a length of (2r+1) is constructed, where r is the optimal half-length of the chromatographic peak sequence.
[0062] In one embodiment of this application, the peak points in the liquid chromatography data of olive oil food samples are as follows: Figure 4 As shown, the chromatographic peak sequence is as follows Figure 5 As shown, the chromatographic peaks correspond to the semi-sequence as follows: Figure 6 As shown.
[0063] Step S003: Obtain the chromatographic peak shape interference coefficient based on the chromatographic peak sequence and the half-sequence of the chromatographic peak.
[0064] Ideally, the chromatographic peak half-sequence is relatively smooth, exhibiting a Gaussian distribution with a bell-shaped curve. However, when liquid chromatography data is severely affected by impurities, there will be a lot of noise, making the chromatographic peak half-sequence uneven. To measure the overall interference of the chromatographic peak sequence, curve fitting is performed on the chromatographic peak half-sequence, and the chromatographic peak fitting curve is output. The chromatographic fitting residual sequence is constructed using the absolute value of the difference between the chromatographic peak sequence and the chromatographic peak fitting curve at corresponding sampling times. Preferably, as an embodiment of this application, a nonlinear least squares method is used to curve fit the chromatographic peak half-sequence. The implementer may also choose other curve fitting methods, and this embodiment does not make any special provisions for them.
[0065] In one embodiment of this application, the chromatographic peak fitting curve is as follows: Figure 7 As shown, the chromatographic fitting residual sequence is as follows: Figure 8 As shown.
[0066] Therefore, by combining the chromatographic peak sequence, the chromatographic peak fitting curve, and the chromatographic fitting residual sequence, the chromatographic peak shape interference coefficient is obtained:
[0067]
[0068]
[0069]
[0070] In the formula, This represents the noise fitting deviation coefficient of the m-th chromatographic peak sequence in the liquid chromatography data of an olive oil food sample. , Let represent the m-th chromatographic peak sequence and the m-th chromatographic fit residual sequence in the liquid chromatography data of the olive oil food sample, respectively. This represents the calculation of information entropy. This represents the standard deviation after standardization (Z-Score) mapping. This indicates the calculation of the average value. This represents the anti-interference stability coefficient of the m-th chromatographic peak sequence in the liquid chromatography data of olive oil food samples. , These represent the maximum value function and the minimum value function, respectively. This represents the length of the m-th chromatographic peak sequence in the liquid chromatography data of an olive oil food sample. , These represent the values of the first and last data points in the m-th chromatographic peak sequence of the olive oil food sample, respectively. This represents a denominator adjustment parameter greater than 0, intended to prevent the denominator from being 0. In this embodiment... The value is 1. This represents the peak shape interference coefficient of the m-th peak sequence in the liquid chromatography data of an olive oil food sample. This represents the logarithmic function with base 2.
[0071] It should be noted that the number 2 is used to adaptively adjust the range of values for the chromatographic peak shape interference coefficient. The purpose of adding 1 to the anti-interference stability coefficient is to prevent the denominator from being zero, rendering the chromatographic peak shape interference coefficient meaningless. When the data point values within the chromatographic fitting residual sequence are larger, and the information entropy and standard deviation of the chromatographic fitting residual sequence are larger, it indicates a larger difference between the data points in the chromatographic peak sequence and the chromatographic peak fitting curve, with greater fluctuations and more complex fluctuations. This means the liquid chromatographic data of olive oil food samples is more likely to be affected by impurities and noise, resulting in more noise in the chromatographic peak sequence, making it difficult to fit the liquid chromatographic data of olive oil food samples, and the value of the noise fitting deviation coefficient is larger. Simultaneously, when the ratio of the maximum to the minimum value of the chromatographic peak sequence is smaller, it indicates a weaker response intensity of the chromatographic peak, making it more susceptible to noise interference, and the value of the anti-interference stability coefficient is smaller. When the noise fitting deviation coefficient is larger and the anti-interference stability coefficient is smaller, it indicates that the chromatographic peak sequence is more susceptible to impurities and noise interference, and the degree of interference from impurities and noise is greater, resulting in a larger value of the chromatographic peak shape interference coefficient.
[0072] Step S004: Obtain the peak shape symmetry tilt coefficient based on the chromatographic peak shape interference coefficient and the difference before and after the peak point in the half sequence of the chromatographic peak pair.
[0073] It should be noted that the peak shape interference coefficient can reflect the degree of interference from impurities and noise on the liquid chromatographic data of olive oil food samples. Impurities and noise not only cause more noise in the chromatographic peaks, but also cause the peak shape to change, resulting in a shift in the peak shape. As a result, the peak time of the liquid chromatographic data of olive oil food samples does not correspond to that of the standard hydroxytyrosol liquid chromatographic data in the database, affecting the determination of the hydroxytyrosol content in olive oil food samples.
[0074] To measure the shift in chromatographic peak shape, each chromatographic peak half-sequence is divided into a preceding sub-band sequence and a following sub-band sequence, using the peak point in each half-sequence as the dividing point. The preceding and following sub-band sequences are then used as inputs to the STL decomposition algorithm to calculate the trend intensity of the preceding and following sub-band sequences. The STL decomposition algorithm and the calculation of trend intensity are well-known techniques and will not be elaborated upon in this embodiment.
[0075] It should be noted that the STL decomposition algorithm can decompose a sequence into a smoothing trend component, a seasonal component, and a remainder component. In one embodiment of this application, a schematic diagram of the STL decomposition of the first subband sequence is shown below. Figure 9 As shown.
[0076] This yields the peak-shaped symmetry tilt coefficient:
[0077]
[0078]
[0079]
[0080] In the formula, This represents the chromatographic symmetry deviation coefficient of the m-th chromatographic peak sequence in the liquid chromatography data of an olive oil food sample. , , These represent the start point, peak point, and end point of the half-sequence of the m-th chromatographic peak in the liquid chromatography data of the olive oil food sample, respectively. This represents the half-sequence of the m-th chromatographic peak in the liquid chromatography data of an olive oil food sample. This represents the logarithmic function with the natural constant as the base. This is an empirical constant, and in this embodiment, it is set to 0.1 to avoid erroneous values in the calculation of the peak symmetry tilt coefficient. This represents the function that takes the minimum value. This represents the chromatographic trend disorder coefficient of the m-th chromatographic peak sequence in the liquid chromatography data of an olive oil food sample. This indicates the strength of the trend in the calculated sequence. This indicates the calculation of the DTW distance between two sequences. , These represent the preceding and following sub-band sequences of the m-th chromatographic peak in the liquid chromatography data of the olive oil food sample, respectively. This represents the peak shape symmetry tilt coefficient of the m-th chromatographic peak sequence in the liquid chromatography data of an olive oil food sample. This represents the peak shape interference coefficient of the m-th chromatographic peak sequence in the liquid chromatography data of an olive oil food sample.
[0081] If the liquid chromatography data of olive oil food samples are severely affected by impurities and noise, causing the chromatographic peak shape to tilt, the symmetry axis of the chromatographic peak will shift to a certain extent. This leads to a larger difference in the minimum values and integrals between the preceding and following sub-band sequences, resulting in a larger chromatographic symmetry deviation coefficient. Simultaneously, the tilted peak shape causes a significant difference in the trends and fluctuations between the preceding and following sub-band sequences, increasing the DTW distance and resulting in a larger chromatographic trend disorder coefficient. Ultimately, this leads to a larger peak tilt symmetry coefficient.
[0082] Step S005: Obtain the adaptive asymmetric penalty coefficient based on the peak shape symmetry tilt coefficient of all chromatographic peak sequences, obtain the chromatographic data of the calibrated olive oil food sample, and determine whether the content of hydroxytyrosol in the olive oil food sample meets the standard.
[0083] Calculate the peak shape symmetry tilt coefficient of all chromatographic peak sequences in the liquid chromatographic data of olive oil food samples to reflect the interference of the overall liquid chromatographic data of olive oil food samples.
[0084] Preferably, as an embodiment of this application, the method for obtaining the adaptive asymmetric penalty coefficient is as follows: the average value of the peak shape symmetry tilt coefficients of all chromatographic peak sequences is used as the adaptive asymmetric penalty coefficient.
[0085] In one embodiment of this application, a schematic diagram of obtaining the adaptive asymmetric penalty coefficient is shown below. Figure 2 As shown.
[0086] It should be noted that when the HPLC data of olive oil food samples are severely affected by impurities and noise, a larger adaptive asymmetric penalty coefficient should be set to fully correct baseline drift and asymmetry in the HPLC data. When the HPLC data of olive oil food samples are only slightly affected by impurities and noise, and the overall distribution of the HPLC data is good, a smaller adaptive asymmetric penalty coefficient should be set to avoid overcorrection of the HPLC data.
[0087] Preferably, as an embodiment of this application, the correction process for the chromatographic data of olive oil food samples is as follows:
[0088] An extremum-based operation is used to map the adaptive asymmetric penalty coefficient to the [0-1] interval, and the mapped result is used as the asymmetric penalty factor for the BEADS algorithm. The BEADS algorithm is then used to perform baseline correction on the liquid chromatography data of olive oil food samples, and the corrected chromatographic data of the olive oil food samples are output. The BEADS algorithm is a well-known technique and will not be described in detail in this embodiment.
[0089] In one embodiment of this application, the chromatographic data of the olive oil food sample are corrected as follows: Figure 10 As shown.
[0090] The sequence similarity between the chromatographic data of the calibrated olive oil food sample and the standard hydroxytyrosol liquid chromatographic data in the database is used as the compliance criterion. When the absolute value of the sequence similarity is greater than or equal to the compliance threshold, the hydroxytyrosol content in the olive oil food sample is deemed to meet the standard; otherwise, the hydroxytyrosol content in the olive oil food sample is deemed not to meet the standard. In this embodiment, the compliance threshold is set to 0.6, and cosine similarity is used as the sequence similarity calculation method. Implementers may also use Euclidean distance, DTW distance, etc.
[0091] Based on the same inventive concept as the above methods, this application also provides a hydroxytyrosol content detection system for food quality inspection, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described methods for detecting hydroxytyrosol content in food quality inspection.
[0092] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.
[0093] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
[0094] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.
Claims
1. A method for detecting hydroxytyrosol content in food quality inspection, characterized in that, The method includes the following steps: S1, collect spectral data of olive oil food samples and obtain liquid chromatographic data of food samples; S2.1, obtain the peak points, chromatographic peak sequences, and half-sequences of chromatographic peaks based on the liquid chromatography data of olive oil food samples; obtain the noise fitting deviation coefficient and anti-interference stability coefficient based on the chromatographic peak sequences and half-sequences of chromatographic peaks; obtain the chromatographic peak shape interference coefficient based on the anti-interference stability coefficient and the noise fitting deviation coefficient. S2.2, obtain the peak shape symmetry tilt coefficient based on the chromatographic peak shape interference coefficient and the difference before and after the peak point in the half sequence of the chromatographic peak; obtain the adaptive asymmetric penalty coefficient based on the peak shape symmetry tilt coefficient of all chromatographic peak sequences; S3. Based on the adaptive asymmetric penalty coefficient and the liquid chromatography data of olive oil food samples, obtain the corrected chromatographic data of olive oil food samples to determine whether the content of hydroxytyrosol in olive oil food samples meets the standard. The acquisition of noise fitting deviation coefficient and anti-interference stability coefficient includes: Curve fitting is performed on the half-sequence of chromatographic peaks, and the chromatographic peak fitting curve is output. The absolute values of the differences between each data point in the chromatographic peak sequence and the corresponding data point in the chromatographic peak fitting curve are arranged in ascending order according to time, and the chromatographic fitting residual sequence is constructed. The sum of the information entropy of the chromatographic fitting residual sequence and the standard deviation after standardization is multiplied by the average value of the chromatographic fitting residual sequence to obtain the noise fitting deviation coefficient. The minimum value of the first and last data points of the chromatographic peak sequence is taken as the minimum chromatographic peak intensity. The sum of the minimum chromatographic peak intensity and the preset denominator adjustment parameter is taken as the adjusted minimum peak intensity. The ratio of the maximum value of all data points in the chromatographic peak sequence to the adjusted minimum peak intensity is taken as the anti-interference stability coefficient. The acquisition of the chromatographic peak shape interference coefficient includes: The sum of the number 1 and the anti-interference stability coefficient is used as the adjustment anti-interference coefficient, and the logarithmic function with the number 2 as the base and the adjustment anti-interference coefficient as the argument is used as the chromatographic anti-interference index. The function of the number 2 is to adaptively adjust the range of values for the chromatographic peak shape interference coefficient, and the purpose of the number 1 is to prevent the denominator from being 0, which would render the chromatographic peak shape interference coefficient meaningless. The ratio of the noise fitting deviation coefficient to the chromatographic anti-interference index is used as the chromatographic peak shape interference coefficient. The process of obtaining the peak symmetry tilt coefficient includes: Using the peak point in the chromatographic peak half-sequence as the dividing point, the chromatographic peak half-sequence is divided into the front sub-band sequence and the back sub-band sequence; The chromatographic symmetry deviation coefficient is obtained based on the difference in integrals and minimum values between the front and rear sub-band sequences, and the chromatographic trend disorder coefficient is obtained based on the difference in trends between the front and rear sub-band sequences. The sum of the chromatographic symmetry deviation coefficient and the chromatographic trend disorder coefficient is multiplied by the chromatographic peak shape interference coefficient to obtain the peak shape symmetry tilt coefficient.
2. The method for detecting hydroxytyrosol content in food quality inspection as described in claim 1, characterized in that, The process of obtaining peak points, peak sequences, and half-sequences of chromatographic peaks based on liquid chromatography data of olive oil food samples includes: The liquid chromatography data of olive oil food samples are used as input to the AMPD automatic multi-scale peak detection algorithm, which outputs all peak points in the liquid chromatography data of olive oil food samples. The midpoint between each peak point and the next adjacent peak point in the liquid chromatography data of olive oil food samples is used as a dividing point to divide the liquid chromatography data of olive oil food samples into multiple chromatographic peak sequences. The minimum distance between the start point, end point and peak point of each chromatographic peak sequence is taken as the optimal half length of each chromatographic peak sequence. With the peak point of each chromatographic peak sequence as the center, a half-sequence of chromatographic peak with a length of (2r+1) is constructed, where r is the optimal half length of the chromatographic peak sequence.
3. The method for detecting hydroxytyrosol content in food quality inspection as described in claim 1, characterized in that, The acquisition of the chromatographic symmetry deviation coefficient includes: The starting point to the peak point of the chromatographic peak half sequence is taken as the chromatographic pre-interval, the peak point to the ending point of the chromatographic peak half sequence is taken as the chromatographic post-interval, and the absolute value of the difference between the integral of the chromatographic peak half sequence in the chromatographic pre-interval and the integral in the chromatographic post-interval is taken as the chromatographic integral difference on both sides of the peak. Calculate the absolute value of the difference between the minimum values of the preceding sub-band sequence and the following sub-band sequence, and calculate an exponential function with the natural constant as the base and the opposite of the absolute value as the exponent; The ratio of the difference in chromatographic integrals on both sides of the peak to the calculated result of the exponential function is used as the chromatographic symmetry deviation coefficient.
4. The method for detecting hydroxytyrosol content in food quality inspection as described in claim 1, characterized in that, The acquisition of the chromatographic trend disorder coefficient includes: The absolute value of the difference in trend intensity between the preceding and following sub-band sequences is taken as the chromatographic trend difference on both sides of the peak. The DTW distance between the preceding and following sub-band sequences is multiplied by the chromatographic trend difference on both sides of the peak and taken as the chromatographic trend disorder coefficient.
5. The method for detecting hydroxytyrosol content in food quality inspection as described in claim 1, characterized in that, The adaptive asymmetric penalty coefficient is the average of the peak shape symmetry tilt coefficients of all chromatographic peak sequences.
6. The method for detecting hydroxytyrosol content in food quality inspection as described in claim 1, characterized in that, The process of obtaining chromatographic data of calibrated olive oil food samples and determining whether the content of hydroxytyrosol in the olive oil food samples meets the standards includes: The adaptive asymmetric penalty coefficient is used as the asymmetric penalty factor of the BEADS algorithm. The BEADS algorithm is used to perform baseline correction on the liquid chromatography data of olive oil food samples and output the corrected chromatographic data of olive oil food samples. The sequence similarity between the chromatographic data of the calibrated olive oil food sample and the standard hydroxytyrosol liquid chromatographic data in the database is used as the criterion for compliance. When the absolute value of the sequence similarity is greater than or equal to the preset compliance threshold, the hydroxytyrosol content in the olive oil food sample is deemed to meet the standard; otherwise, the hydroxytyrosol content in the olive oil food sample is deemed to fail to meet the standard.
7. A hydroxytyrosol content detection system for food quality inspection, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-6.