A multivariate near-infrared quantitative model calibrated using a one-measurement-multiple-evaluation method, its construction method, and its application.

Abstract

This invention relates to a multivariate near-infrared quantitative model based on a one-measure-multiple-evaluation method, its construction method, and its application. The construction method includes the following steps: (1) preparing a representative sample set; (2) using chlorogenic acid as an internal reference, obtaining the content of target polyphenolic compounds in each sample in the representative sample set of step (1) using a one-measure-multiple-evaluation method based on liquid chromatography, and collecting near-infrared diffuse reflectance spectral data of each sample in the representative sample set of step (1); (3) correlating the content of target polyphenolic compounds in each sample in the representative sample set of step (2) with the near-infrared diffuse reflectance spectral data, establishing a regression model and solving it, determining the optimal number of principal components, completing the model construction, and performing model validation. The multivariate near-infrared quantitative model provided by this invention can realize the simultaneous and rapid detection of multiple target polyphenolic compounds in tobacco or its products, adapting to the needs of precise laboratory analysis and large-scale industrial screening.

Description

Technical Field

[0001] This invention relates to the field of tobacco component analysis and detection technology, and in particular to a multivariate near-infrared quantitative model based on a one-measure-multiple-evaluation method, its construction method, and its application. Background Technology

[0002] Tobacco and tobacco products have complex chemical compositions, and their intrinsic quality is largely influenced by the content and synergistic effects of various chemical components. Polyphenolic compounds, as important secondary metabolites in tobacco, not only significantly affect the color and sensory flavor of tobacco but are also a key indicator for evaluating the quality of tobacco and its products. Studies have shown that the content and proportion of polyphenolic compounds are closely related to the aroma quality, taste characteristics, and product stability of tobacco.

[0003] It is evident that the quality evaluation of tobacco and tobacco products relies on the simultaneous detection of multiple components. However, the existing technologies have significant bottlenecks: (1) Traditional chemical analysis methods: require the use of various expensive reference standards, resulting in high detection costs, complex sample pretreatment, and a detection cycle of 1-3 hours, which cannot meet the needs of large-scale rapid screening; (2) Quantitative Analysis Method (QAMS): although it can achieve multi-component quantification through a single internal reference and reduce the cost of reference standards, it is still essentially a chemical analysis with low detection efficiency and cannot be adapted to industrial online detection; (3) Near-infrared spectroscopy: has the advantages of being fast and non-destructive, but traditional modeling requires the use of HPLC external standard method to obtain chemical reference values, which still faces the problems of high reference standard costs and cumbersome calibration procedures; (4) Limitations of existing fusion attempts: some studies have only established QAMS and near-infrared quantitative models in parallel, without using QAMS as the calibration source for near-infrared modeling. This is a simple superposition of two independent methods, which has not solved the dual pain points of "high cost" and "low efficiency", and has not achieved the performance optimization brought about by technical synergy.

[0004] Currently, there is a lack of a complete technical solution that balances detection accuracy, economy, and high efficiency. Therefore, developing a multivariate near-infrared quantitative model and detection method based on QAMS technology has become crucial for solving the challenge of rapid detection of multiple components in complex systems. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a multivariate near-infrared quantitative model based on a one-measure-multiple-evaluation method, its construction method, and its application. The multivariate near-infrared quantitative model provided by this invention enables the simultaneous and rapid detection of multiple target polyphenolic compounds in tobacco or its products, adapting to the needs of precise laboratory analysis and large-scale industrial screening.

[0006] To achieve this objective, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for constructing a multivariate near-infrared quantitative model based on a one-measurement-multiple-evaluation calibration method, the method comprising the following steps: (1) Prepare a representative sample set; (2) Using chlorogenic acid as an internal reference, the content of target polyphenolic compounds in each sample in the representative sample set of step (1) was obtained by one measurement and multiple evaluation method based on liquid chromatography, and near-infrared diffuse reflectance spectral data of each sample in the representative sample set of step (1) were collected. (3) Correlate the content of target polyphenolic compounds in each sample in the representative sample set in step (2) with the near-infrared diffuse reflectance spectral data, establish a regression model and solve it, determine the optimal number of principal components, complete the model construction and perform model verification.

[0007] This invention uses QAMS instead of the external standard method, requiring only chlorogenic acid as an internal reference to obtain multi-component chemical reference values, thus solving the core problem of "expensive reference standards and high calibration costs" in near-infrared modeling. By utilizing the improved correlation of chemical indicators through the QAMS method, a multivariate near-infrared quantitative model is constructed, breaking through the limitations of traditional near-infrared univariate modeling and improving detection accuracy. Ultimately, a complete technical system of "sample preparation → QAMS calibration → spectral acquisition → model prediction" is formed, achieving a triple breakthrough of "low cost + speed + accuracy".

[0008] Preferably, the representative sample set in step (1) includes samples from different origins, varieties, grades, or processing techniques.

[0009] Preferably, the number of samples in the representative sample set in step (1) is not less than 100 (e.g., it can be 100, 110, 120, 130, 150, 200, etc.).

[0010] Preferably, the preparation of the representative sample set in step (1) includes drying, crushing, sieving and moisture balancing of each sample.

[0011] Preferably, the target polyphenolic compound in step (2) includes any one or a combination of at least two of neochlorogenic acid, scopolamine, cryptochlorogenic acid, caffeic acid, hyoscyamine, rutin, or kaempferol-3-O-rutin, and more preferably any one or a combination of at least two of neochlorogenic acid, cryptochlorogenic acid, hyoscyamine, or rutin.

[0012] This invention, through multiple calculations, demonstrates that its multivariate near-infrared quantitative model is particularly suitable for the detection of combinations of five substances: chlorogenic acid, neochlorogenic acid, cryptochlorogenic acid, hyoscyamine, and rutin, exhibiting higher detection accuracy.

[0013] Preferably, in step (2), the specific operation of the one-measure-multiple-evaluation method based on liquid chromatography includes: (a) The sample to be tested is mixed with solvent one for extraction to obtain the test solution; the standard substance of the target polyphenol compound is mixed with solvent two to obtain several standard solutions of different concentrations; (b) The test solution and standard solution were detected by liquid chromatography. Chlorogenic acid was used as an internal reference. The relative correction factor between chlorogenic acid and the target polyphenolic compound was calculated. The content of the target polyphenolic compound in each sample in the representative sample set of step (1) was obtained by using the relative correction factor.

[0014] Preferably, the solvent includes any one or a combination of at least two of methanol, water, methyl tert-butyl ether, n-hexane, or cyclohexane.

[0015] Preferably, the first solvent is a combination of methanol, water and n-hexane, or the first solvent is a combination of methanol, water and cyclohexane.

[0016] In this invention, a specific methanol-water-n-hexane / or cyclohexane system is selected for extraction and purification. The supernatant is then subjected to HPLC analysis. The operation is simple and rapid, with high throughput and low solvent consumption. It can simultaneously extract and purify eight polyphenolic compounds in tobacco and tobacco products. Compared with the commonly used analytical methods for polyphenolic compounds in tobacco, it greatly reduces the amount of solvent used (by 80%) and facilitates batch sample analysis.

[0017] In addition, tobacco samples have complex chemical compositions and contain a large number of interfering substances such as pigments, alkaloids, and sugars. Compared with traditional purification adsorbents C18 and GCB, using n-hexane or cyclohexane for purification can reduce experimental costs while achieving a very ideal purification effect, providing a clean solution for use, and reducing the maintenance of the liquid chromatography system during use.

[0018] Preferably, the volume ratio of methanol, water and n-hexane is 1:(0.8-1.2):(1.6-2.4).

[0019] Preferably, the volume ratio of methanol, water and cyclohexane is 1:(0.8-1.2):(1.6-2.4).

[0020] The values ​​0.8-1.2 can be independently 0.8, 0.9, 1, 1.1, 1.2, etc., and the values ​​1.6-2.4 can be independently 1.6, 1.8, 2, 2.2, 2.4, etc.

[0021] Preferably, the ratio of the tobacco or its product to solvent one is 1 mg: (0.3-0.5) mL (e.g., 1 mg: 0.3 mL, 1 mg: 0.35 mL, 1 mg: 0.4 mL, 1 mg: 0.45 mL, 1 mg: 0.5 mL, etc.).

[0022] In this invention, the extraction solvent has a small volume, which saves solvent consumption and makes it easy to achieve high-throughput sample analysis.

[0023] Preferably, the second solvent includes methanol and / or water.

[0024] Preferably, the relative correction factor is calculated using the slope correction method.

[0025] In this invention, the slope correction method has a smaller average relative error compared to the multi-point correction method or the single-point correction method, making it more suitable for the quantitative analysis of polyphenolic compounds in tobacco and tobacco products. Specifically, the average relative error of the multi-point correction method and the external standard method is less than 5% for all components except scopolamine, which has an average relative error of 6.47%. The average relative error of the slope correction method and the external standard method is less than 3.21%. The average relative error of the single-point correction method and the external standard method is larger; for example, the average relative error of scopolamine reaches 8.11%.

[0026] In this invention, the slope correction method exhibits good robustness and reproducibility. The RSD of the RCF for each analyte at different flow rates is less than 2.18%; the RSD of the RCF for each analyte at different column temperatures is less than 2.41%. The RSD of the RCF under different instruments and columns from different manufacturers is less than 2.96%. Therefore, this method can be widely used in different laboratories with different instruments and under different chromatographic conditions.

[0027] Preferably, in the liquid chromatography method of step (2), the mobile phase includes mobile phase A and mobile phase B, wherein mobile phase A includes a combination of water, methanol and acetic acid, and mobile phase B includes a combination of methanol and acetic acid.

[0028] Preferably, the volume ratio of water, methanol and acetic acid in the mobile phase A is 100:(1-3):(1-3).

[0029] Preferably, the volume ratio of methanol to acetic acid in the mobile phase B is 100:(1-3).

[0030] The values ​​1-3 above can each be 1, 1.5, 2, 2.5, 3, etc., independently.

[0031] Preferably, the elution program of the mobile phase in the liquid chromatography method is gradient elution, specifically as follows: From 0 to 15 min, the volume percentage of mobile phase A changes uniformly from 88-92% (e.g., 88%, 89%, 90%, 91%, 92%, etc.) to 68-72% (e.g., 68%, 69%, 70%, 71%, 72%, etc.), and the volume percentage of mobile phase B changes uniformly from 8-12% (e.g., 8%, 9%, 10%, 11%, 12%, etc.) to 28-32% (e.g., 28%, 29%, 30%, 31%, 32%, etc.); from 15 to 20 min... At min 22, the volume percentage of mobile phase A is uniformly changed from 68-72% (e.g., 68%, 69%, 70%, 71%, 72%, etc.) to 8-12% (e.g., 8%, 9%, 10%, 11%, 12%, etc.), and the volume percentage of mobile phase B is uniformly changed from 28-32% (e.g., 28%, 29%, 30%, 31%, 32%, etc.) to 88-92% (e.g., 88%, 89%, 90%, 91%, 92%, etc.), and then maintained constant until min 22; min 22-22.1 For min, the volume percentage of mobile phase A is uniformly changed from 8-12% (e.g., 8%, 9%, 10%, 11%, 12%, etc.) to 88-92% (e.g., 88%, 89%, 90%, 91%, 92%, etc.), and the volume percentage of mobile phase B is uniformly changed from 88-92% (e.g., 88%, 89%, 90%, 91%, 92%, etc.) to 8-12% (e.g., 8%, 9%, 10%, 11%, 12%, etc.), and then kept constant until min 30.

[0032] In this invention, by specifically selecting the mobile phase and elution procedure, the separation of various polyphenolic compounds can be achieved, thereby improving the detection accuracy.

[0033] Preferably, in the liquid chromatography method described in step (2), the chromatographic column packing material is C18 with a particle size of 3-5 μm (e.g., 3 μm, 4 μm, 5 μm, etc.), an inner diameter of 3.0-4.6 mm (e.g., 3.0 μm, 4.0 μm, 4.6 mm, etc.), and a length of 100-250 mm (e.g., 150 mm, 180 mm, 200 mm, 220 mm, 250 mm, etc.).

[0034] In this invention, Waters Symmetry C18, DIKMA Luna 5u C18(2) 100A, and Agilent ZORBAX Eclipse Plus C18 columns are preferred, with 4.6 mm × 250 mm × 5 μm being the most preferred specifications. The separation effect of the above columns is better.

[0035] Preferably, the column temperature of the chromatographic column in the liquid chromatography method described in step (2) is 28-40℃ (for example, it can be 28℃, 30℃, 32℃, 35℃, 38℃, 40℃, etc.).

[0036] Preferably, the wavelength detection in step (2) of the liquid chromatography is segmented detection, specifically: The detection wavelength was 325 nm from 0 to 9 min; 340 nm from 9 to 10.8 min; 325 nm from 10.8 to 17 min; 345 nm from 17 to 20 min; and 350 nm from 20 to 30 min.

[0037] In this invention, if detection is performed using a single wavelength of 340nm, some targets will respond very weakly, resulting in insufficient detection sensitivity. This invention improves the detection sensitivity by employing a segmented wavelength detection method.

[0038] Preferably, the near-infrared diffuse reflectance spectral data acquisition range in step (2) is 10000-4000 cm⁻¹. -1 Spectral resolution of 6-10 cm -1 (For example, it could be 6 cm) -1 7 cm -1 8 cm -1 9 cm -1 10 cm -1 (etc.), the number of scans is 64-108 (for example, it can be 64, 72, 81, 90, 108, etc.).

[0039] Preferably, after acquiring the near-infrared diffuse reflectance spectral data in step (2), the preprocessing steps include sample rejection, scattering correction, smoothing differentiation, feature selection, and normalization.

[0040] Preferably, the sample removal is performed using the principal component-Mahanobis distance method.

[0041] Preferably, the scattering correction employs a multivariate scattering correction method.

[0042] Preferably, the smoothing derivative is achieved using the Savitzky-Golay convolution derivative smoothing method.

[0043] Preferably, the feature selection includes removing features in the 4800-5200 cm range. -1 (For example, it could be 4800 cm) -1 4900 cm -1 5000 cm -1 5200 cm -1 (etc.), 6800-7200 cm-1 (For example, it could be 6800 cm) -1 6900 cm -1 7000 cm -1 7200 cm -1 Absorption peaks at (etc.).

[0044] Preferably, the normalization adopts the standard method.

[0045] In this invention, scattering correction is used to eliminate spectral differences caused by varying scattering levels during spectral measurements (differences arise from variations in measurement location, light intensity, etc.), thereby enhancing the correlation between the spectrum and the data. Smoothing is a simple algorithm to improve the spectral signal-to-noise ratio. Its mathematical principle is based on the assumption that spectral noise is white noise, conforming to a normal distribution with a mean of zero. Smoothing effectively reduces spectral noise. Differentiating the spectrum effectively eliminates baseline and other background interference, distinguishes overlapping peaks, and improves resolution and sensitivity. Since the moisture content of tobacco leaves generally exceeds 10%, the characteristic peaks of water are removed when selecting spectral feature points, retaining the remaining spectrum. The above processing of near-infrared diffuse reflectance spectral data can improve the accuracy of modeling and prediction.

[0046] Preferably, after obtaining the content of the target polyphenolic compound in step (2), a normalization process is further performed, preferably using a standard method for normalization.

[0047] Preferably, the regression model in step (3) is a PLSR2 regression model, and the solution is obtained using the NIPALS method.

[0048] Traditional Partial Least Squares Regression (PLSR) is a single-variable regression (PLSR1), which aims to maximize the covariance between the independent variable x and the strain vector y. When there are multiple dependent variables, PLSR1 is used to model them separately multiple times, providing regression equations for each dependent variable. However, when there is a certain correlation between the dependent variables y, a multivariate PLSR regression method (PLSR2) can be used, where the dependent variable y is a matrix, and its goal is to maximize the covariance between the independent variable x and the dependent variable y matrix. In this invention, the QAMS method improves the correlation between various chemical indicators, making it a natural fit for PLSR2.

[0049] Secondly, the present invention provides a multivariate near-infrared quantitative model constructed according to the construction method of the multivariate near-infrared quantitative model based on the one-measurement-multiple-evaluation method described in the first aspect.

[0050] Thirdly, the present invention provides a method for detecting polyphenolic compounds in tobacco or its products, the method comprising: collecting near-infrared diffuse reflectance spectral data of the sample, inputting the data into the multivariate near-infrared quantitative model described in the second aspect, and obtaining the content of polyphenolic compounds.

[0051] Fourthly, the present invention provides a detection system for polyphenolic compounds in tobacco or its products, the detection system comprising a sample preparation unit, a near-infrared diffuse reflectance spectroscopy acquisition unit, a model analysis unit, and a result output unit; the model analysis unit comprises the multivariate near-infrared quantitative model described in the second aspect.

[0052] Preferably, the sample preparation unit includes a drying device, a pulverizing device, a sieving device, and a moisture balancing device.

[0053] Preferably, the near-infrared diffuse reflectance spectroscopy acquisition unit includes a near-infrared spectrometer, an integrating sphere, an optical fiber probe, and a sample cell.

[0054] Preferably, the model analysis unit includes a data processing chip and a storage medium.

[0055] Preferably, the result output unit includes a display screen and a data interface.

[0056] Compared with the prior art, the present invention has at least the following beneficial effects: (1) Significantly reduced detection costs: The QAMS method only requires a single internal reference (chlorogenic acid) to complete the quantitative determination of multiple target components, eliminating the need to purchase multiple expensive reference standards, thus reducing the detection cost to less than 20% of the traditional external standard method, while also having high detection precision, accuracy, repeatability and stability. (2) Significantly improved detection efficiency: The detection time of near-infrared spectroscopy is only "seconds". Compared with the detection cycle of traditional HPLC, the efficiency is improved by hundreds of times, which perfectly meets the needs of large-scale rapid screening in industrial applications. (3) High detection accuracy: Traditional PLSR1 is applicable because the external standard method has independent indicators, while the application of PLSR2 requires that the indicators have correlation. This adaptation logic needs to be based on the characteristics of QAMS. In the preferred scheme, this invention optimizes the spectral preprocessing, models the PLSR2 algorithm, and conducts rigorous methodological verification. The model's coefficient of determination (R²) is high. 2 The root mean square error (RMSEP) of the prediction is greater than 0.90, and the predicted value is not significantly different from the measured value by the QAMS method (p>0.05). It can also accurately distinguish and quantify structurally similar components such as neochlorogenic acid and cryptochlorogenic acid. (4) Wide range of applications: It is not only applicable to the detection of polyphenolic compounds in tobacco and tobacco products, but can also be extended to the rapid detection of multiple components in other complex systems such as Chinese medicinal materials and food, providing a new technical paradigm for the detection of multiple components in complex systems; (5) Good system stability: By optimizing the spectral acquisition conditions, verifying the durability and reproducibility of the correction factor, and cross-validating and externally validating the model, the system can obtain stable and reliable detection results under different detection environments and different operators, and has the applicability of both laboratory precision analysis and rapid detection on the production site. Attached Figure Description

[0057] Figure 1 The chromatogram provided in Example 1 is (1-neochlorogenic acid, 2-scopolamine, 3-chlorogenic acid, 4-cryptochlorogenic acid, 5-caffeic acid, 6-hyoscyamine, 7-rutin, 8-kaempferol-3-O-rutin).

[0058] Figure 2 The chromatogram provided in Example 2 is (1-neochlorogenic acid, 2-scopolamine, 3-chlorogenic acid, 4-cryptochlorogenic acid, 5-caffeic acid, 6-hyoscyamine, 7-rutin, 8-kaempferol-3-O-rutin).

[0059] Figure 3 The chromatogram provided in Example 3 is (1-neochlorogenic acid, 2-scopolamine, 3-chlorogenic acid, 4-cryptochlorogenic acid, 5-caffeic acid, 6-hyoscyamine, 7-rutin, 8-kaempferol-3-O-rutin).

[0060] Figure 4 The chromatogram provided in Example 4 is (1-neochlorogenic acid, 2-scopolamine, 3-chlorogenic acid, 4-cryptochlorogenic acid, 5-caffeic acid, 6-hyoscyamine, 7-rutin, 8-kaempferol-3-O-rutin).

[0061] Figure 5 The chromatogram provided in Example 5 is (1-neochlorogenic acid, 2-scopolamine, 3-chlorogenic acid, 4-cryptochlorogenic acid, 5-caffeic acid, 6-hyoscyamine, 7-rutin, 8-kaempferol-3-O-rutin).

[0062] Figure 6 This is the chromatogram provided in Example 6.

[0063] Figure 7 This is the chromatogram provided in Example 7.

[0064] Figure 8 This is the chromatogram provided in Example 8.

[0065] Figure 9 This is the chromatogram provided in Example 9.

[0066] Figure 10 This is the chromatogram provided in Example 10.

[0067] Figure 11 This is the modeling flowchart in Application Example 1.

[0068] Figure 12 This is the principal component selection graph from Application Example 1.

[0069] Figure 13 This is the QAMS-PLSR2 regression scatter plot from Application Example 1 (blue dots: modeling set; red dots: test set).

[0070] Figure 14 This is a heatmap showing the correlation between quantitative indicators using the external standard method in Test Example 5.

[0071] Figure 15 This is a heatmap showing the correlation between quantitative indicators using the QAMS method in Test Example 5. Detailed Implementation

[0072] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments. However, the following examples are merely simplified examples of the present invention and do not represent or limit the scope of protection of the present invention. The scope of protection of the present invention is determined by the claims.

[0073] The reagents and instruments used in the following examples are as follows: 122 samples of tobacco leaves from different origins, grades, and parts of the tobacco plant were provided by Zhejiang China Tobacco Industry Co., Ltd.

[0074] Neochlorogenic acid (≥98.0%), chlorogenic acid (≥95.0%), cryptochlorogenic acid (≥98.0%), hyoscyamine (≥99%) (standard, Sigma-Aldrich, USA); scopolamine (97%), caffeic acid (98.0%), rutin (98.0%), kaempferol-3-O-rutin (98.0%) (standard, Trc, Canada); methanol (chromatographic grade, Dima Technology Co., Ltd.); acetic acid (chromatographic grade, Dima Technology Co., Ltd.); Milli-Q ultrapure water (prepared by Millipore ultrapure water system).

[0075] Agilent 1260 high performance liquid chromatograph; Antaris II near-infrared spectrometer (Thermoelectric Corporation, USA); Waters Symmetry C18 (4.6mm×250mm×5μm); DIKMA Luna 5u C18(2) 100A (4.6mm×250mm×5μm); Agilent ZORBAX Eclipse Plus C18 (4.6 mm×250mm×5μm); Agilent Microsorb-MV 100-5 C18 (4.6 mm×250mm×5μm); Agilent ZORBAX SB-Aq (4.6 mm×250mm×5μm); XP205 electronic balance (sensitivity 0.0001 g, Mettler Toledo, Switzerland); 4-16K centrifuge (Sigma, Germany).

[0076] Example 1 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a single-test, multiple-evaluation method, including the following steps: (1) Preparation of standard solutions (1.1) Mixed standard stock solution Accurately weigh 100 mg of chlorogenic acid, 10 mg of caffeic acid, 10 mg of scopolamine and 100 mg of rutin into a 10 mL volumetric flask, accurate to 0.1 mg respectively, and dilute to volume with methanol to prepare mixed standard stock solution 1; store sealed and protected from light at 0-4℃.

[0077] Accurately weigh 10 mg of neochlorogenic acid, scopolamine, cryptochlorogenic acid, and kaempferol-3-O-rutin into a 10 mL volumetric flask, accurate to 0.1 mg, and dilute to volume with methanol to prepare mixed standard stock solution 2; store sealed and protected from light at 0~4℃.

[0078] (1.2) Mixed standard working solutions Accurately transfer 20, 50, 100, 200, 500, and 1000 μL of mixed standard stock solution 1 into different 10 mL volumetric flasks, and then add 12, 30, 60, 120, 300, and 600 μL of mixed standard stock solution 2, respectively. Dilute to the mark with methanol aqueous solution at a volume ratio of 1:1, and these are used as a series of mixed standard solutions 1 to 6.

[0079] (2) Sample processing Accurately weigh 10.0 mg of tobacco or tobacco product sample and place it in a 5 mL centrifuge tube. Accurately add 4 mL of a methanol, water, and n-hexane solution with a volume ratio of 1:1:2 and extract by sonication for 20 min. Collect the supernatant and filter it through a 0.45 μm aqueous filter membrane. Transfer the supernatant to a chromatographic bottle to obtain the test solution.

[0080] (3) High performance liquid chromatography detection High-performance liquid chromatography (HPLC) was used to detect the series of mixed standard solutions 1-6 and the test solution, under the following conditions: Instrument: Agilent 1260 high performance liquid chromatograph; column: Waters Symmetry C18; column temperature: 30℃; injection volume: 10 μL; Mobile phase A: water:methanol:acetic acid = 100:2:2 (volume ratio); Mobile phase B: methanol:acetic acid = 100:2 (volume ratio); Flow rate: 1.0 mL / min; The gradient elution program was as follows (based on a total volume of mobile phases A and B of 100%): 0-15 min, 10-30% B; 15-20 min, 30-90% B; 20-22 min, 90% B; 22-22.1 min, 90-10% B; 22.1-30 min, 10% B. Diode array segmented wavelength detection method: 0-9 min, 325 nm; 9-10.8 min, 340 nm; 10.8-17 min, 325 nm; 17-20 min, 345 nm; 20-30 min, 350 nm; reference wavelength 480 nm.

[0081] (4) Localization of chromatographic peaks of polyphenolic compounds Using chlorogenic acid as an internal reference, the chromatographic peaks were located based on the relative retention times of chlorogenic acid and neochlorogenic acid, scopolamine, cryptochlorogenic acid, caffeic acid, hyoscyamine, rutin, and kaempferol-3-O-rutin in the high performance liquid chromatogram of the mixed standard solution.

[0082] (5) Calculation of relative correction factor Using the high-performance liquid chromatography (HPLC) chromatograms of a series of mixed standard solutions, with chlorogenic acid as an internal reference, the relative correction factor fs / i = as / ai was rapidly calculated based on the ratio of the slopes of the standard curves of chlorogenic acid to those of neochlorogenic acid, scopolamine, cryptochlorogenic acid, caffeic acid, hyoscyamine, rutin, and kaempferol-3-O-rutin. In this formula, ai is the slope of the standard curve of the analyte, and as is the slope of the standard curve of the reference.

[0083] (6) Calculation of the content of the analyte Using relative correction factors, the contents of neochlorogenic acid, scopolamine, cryptochlorogenic acid, caffeic acid, hyoscyamine, rutin, and kaempferol-3-O-rutin in the test solution were calculated. The concentration of the analyte was Ci = Ai / ai = (Ai·fs / i) / (as), where ai is the slope of the standard curve of the analyte, as is the slope of the standard curve of the reference, and Ai is the peak area of ​​the analyte.

[0084] Example 2 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a single-test, multiple-evaluation method. The only difference between this method and Example 1 is that: In step (2), the extraction solvent is a 3 mL solution of methanol, water and n-hexane in a volume ratio of 1:0.8:1.6.

[0085] In step (3), the mobile phase A: water: methanol: acetic acid = 100:1:1 (volume ratio); the mobile phase B: methanol: acetic acid = 100:3 (volume ratio), and the column temperature is 35℃; The gradient elution program was as follows (based on a total volume of mobile phases A and B of 100%): 0-15 min, 8-28% B; 15-20 min, 28-88% B; 20-22 min, 88% B; 22-22.1 min, 88-8% B; 22.1-30 min, 8% B. Other examples are shown in Example 1.

[0086] Example 3 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a single-test, multiple-evaluation method. The only difference between this method and Example 1 is that: In step (2), the extraction solvent is 5 mL of a methanol, water, and cyclohexane solution with a volume ratio of 1:1.2:2.4; In step (3), the mobile phase A: water: methanol: acetic acid = 100:3:3 (volume ratio); the mobile phase B: methanol: acetic acid = 100:1 (volume ratio); and the column temperature is 40℃. The gradient elution program was as follows (based on a total volume of mobile phases A and B of 100%): 0-15 min, 12-32% B; 15-20 min, 32-92% B; 20-22 min, 92% B; 22-22.1 min, 92-12% B; 22.1-30 min, 12% B. Other examples are shown in Example 1.

[0087] Example 4 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a one-test-multiple-evaluation method. The only difference between this method and Example 1 is that in step (3), a DIKMA Luna 5u C18(2) 100A chromatographic column is used, and the rest is the same as in Example 1.

[0088] Example 5 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a one-test-multiple-evaluation method. The only difference between this method and Example 1 is that in step (3), an Agilent ZORBAX Eclipse Plus C18 column is used, and the rest is the same as in Example 1.

[0089] Example 6 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a one-test-multiple-evaluation method. The only difference between this method and Example 1 is that in step (3), an Agilent Microsorb-MV 100-5 C18 column is used, and the rest is the same as in Example 1.

[0090] Example 7 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a one-test-multiple-evaluation method. The only difference between this method and Example 1 is that in step (3), an Agilent ZORBAX SB-Aq chromatographic column is used, and the rest is the same as in Example 1.

[0091] Example 8 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a one-test-multiple-evaluation method. The only difference between this method and Example 1 is that in step (3), the mobile phase A is water:methanol = 100:2, the mobile phase B is methanol, and the other steps are the same as in Example 1.

[0092] Example 9 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a one-test-multiple-evaluation method. The only difference between this method and Example 1 is that in step (3), the gradient elution program is as follows (based on the total volume of mobile phases A and B being 100%): 0-18 min, 10-30% B; 18-20 min, 30-90% B; 20-22 min, 90% B; 22-22.1 min, 90-10% B; 22.1-30 min, 10% B, and the other steps are the same as in Example 1.

[0093] Example 10 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a one-test-multiple-evaluation method. The only difference between this method and Example 1 is that in step (3), the gradient elution program is as follows (based on the total volume of mobile phases A and B being 100%): 0-18 min, 10-30% B; 18-24 min, 30-90% B; 24-26 min, 90% B; 26-26.1 min, 90-10% B; 26.1-35 min, 10% B, and the other steps are the same as in Example 1.

[0094] Example 11 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a single test and multiple evaluation method. The only difference between this method and Example 1 is that in step (3), a single wavelength of 340 nm is used for detection, while the rest is the same as in Example 1.

[0095] Example 12 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a one-test-multiple-evaluation method. The difference between this method and Example 1 is only that in step (5), the calculation method for the relative correction factor is replaced by a multi-point correction method, specifically: The relative correction factor obtained from multiple mass concentration points ( f s / i The average value is used for quantification, calculated according to the formula. f s / i : f s / i =A s × C i / ( A i × C s In the formula, : A s The peak area of ​​the internal reference material; C i and A i The mass concentration (mg / L) and peak area of ​​the analyte are given. C s The concentration of the internal control (mg / L) is the mass concentration of the internal control; other parameters are as described in Example 1.

[0096] Example 13 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a one-test-multiple-evaluation method. The difference between this method and Example 1 is only that in step (5), the calculation method for the relative correction factor is replaced by a single-point correction method, specifically: The relative correction factor is calculated using the external standard method at concentration points, and then calculated according to the formula. f s / i : f s / i = k s / k i In the formula: k s The slope of a single point of the internal reference. k i The slope is the single-point slope of the component to be tested; other parameters are as described in Example 1.

[0097] Example 14 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a one-test-multiple-evaluation method. The only difference between this method and Example 1 is that in step (3), the extraction solvent is an equal volume ratio of methanol, water and methyl tert-butyl ether solution of 1:1:2. Other steps are the same as in Example 1.

[0098] Example 15 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a one-test-multiple-evaluation method. The only difference between this method and Example 1 is that in step (3), the extraction solvent is an equal volume ratio of methanol and aqueous solution of 1:1. Other steps are the same as in Example 1.

[0099] Example 16 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on a one-test-multiple-evaluation method. The only difference between this method and Example 1 is that in step (3), the amount of extraction solvent is adjusted to 2 mL, and the rest is the same as in Example 1.

[0100] Comparative Example 1 This embodiment provides a method for detecting polyphenolic compounds in tobacco or its products based on the external standard method. The only difference between this method and Example 1 is that the one-to-many evaluation method is replaced by the external standard method. Specifically, a series of mixed standard solutions 1 to 6 are taken and analyzed by HPLC. The linear relationship between the peak area and concentration of polyphenolic compounds in each standard working solution is calculated to obtain the regression equation. The concentration of the target compound in the sample is obtained by substituting the peak area obtained from the sample into the regression equation. Other steps are the same as in Example 1.

[0101] Test Example 1 (1) Resolution detection The mixed standard solution 3 was detected using the liquid chromatography method provided in Examples 1-10, and the chromatograms are shown below. Figures 1-10 As shown.

[0102] Figures 1-3 The results show that the chromatographic detection conditions provided in Examples 1-3 have good separation effects on all eight polyphenolic compounds.

[0103] Figure 1 , Figures 4-7 The comparison showed that Waters Symmetry C18 provided in Example 1, DIKMA Luna 5u C18(2) 100A provided in Example 4, and Agilent ZORBAX Eclipse Plus C18 provided in Example 5 could all achieve baseline separation of 8 polyphenol compounds, while Agilent Microsorb-MV 100-5 C18 provided in Example 6 and Agilent ZORBAX SB-Aq provided in Example 7 had poor separation effects.

[0104] Figure 1 , Figure 8 The comparison shows that the separation effect is poor when the methanol-water system of Example 8 is used as the mobile phase because the phenolic hydroxyl groups in polyphenolic compounds are easily oxidized and ionized.

[0105] Figure 1 , Figures 9-10 The comparison shows that the mobile phase elution method provided in Example 1 can quickly complete the analysis of the sample within 30 minutes, improving the detection efficiency; while the mobile phase elution method provided in Example 9 has poor separation effect, and the mobile phase elution method provided in Example 10 has a longer analysis time.

[0106] (2) Response intensity detection The segmented detection time in Example 1 is shown in Table 1. The response results of each target object in Example 1 (segmented detection) and Example 11 (single wavelength detection) are shown in Table 2.

[0107] Table 1 Table 2 Table 2 shows that, by employing a segmented detection method at the maximum absorption wavelength of different target compounds, the response intensity of all polyphenolic compounds except scopolamine and hyoscyamine was significantly enhanced compared to single-wavelength detection. Therefore, the segmented detection method chosen in this invention can improve the detection sensitivity of the method.

[0108] Test Example 2 Methodological Validation (1) Linearity, limit of detection and limit of quantitation The mixed standard working solutions were analyzed using the method provided in Example 1. The linear relationship between the peak area and concentration of polyphenolic compounds in each standard working solution was calculated, and the regression equation and its correlation coefficient were obtained. The mixed standard working solution with the lowest concentration was measured 10 times consecutively. The limit of detection (LOD) was set at three times the standard deviation of the results, and the limit of quantitation (LOQ) was set at ten times the standard deviation of the results. The results are shown in Table 3.

[0109] Table 3 As shown in the table, the eight polyphenolic compounds exhibited good linearity within their respective linear ranges (R0 of the regression equation). 2 All values ​​were greater than 0.999. The limits of detection for the eight polyphenols were between 0.01 and 0.15 mg / L, and the limits of quantitation were between 0.02 and 0.48 mg / L, which met the quantitative analysis requirements of their respective target analytes.

[0110] (2) Precision, repeatability and stability Take 3 mixed standard solutions and perform 6 consecutive injections using the method provided in Example 1 to conduct a precision test; perform 6 parallel determinations of the same sample according to the pretreatment method to conduct a repeatability test; take the same pretreated tobacco sample and place it at room temperature for 0, 4, 8, 12, 16 and 24 h for injection and determination to conduct a stability test. The results are shown in Table 4.

[0111] Table 4 As shown in the table, the precision, repeatability, and stability RSD of the eight polyphenolic compounds are less than 2.28%, indicating that the method has good precision, reproducibility, and stability.

[0112] (3) Spike recovery rate and relative standard deviation In tobacco samples with known contents of 8 polyphenolic compounds, three different concentration levels (high, medium, and low) were added to conduct spiked recovery experiments. At each spiking level, the method of Example 1 was used to determine the recovery rate 6 times in parallel. The recovery rate of each polyphenolic compound in the sample was calculated, and the precision (RSD) of the method was examined. The results are shown in Table 5.

[0113] Table 5 As shown in the table, the average recoveries of the eight polyphenolic compounds at different concentration levels ranged from 92.00% to 102.38%, with relative standard deviations ranging from 1.56% to 4.10%. This indicates that the method has good accuracy and repeatability, and is suitable for the determination of eight polyphenolic compounds in tobacco and tobacco products.

[0114] Test Example 3 Verification using the one-test-multiple-evaluation method (1) Determination of the relative correction factor (RCF) The slope correction method provided in Example 1, the multi-point correction method provided in Example 12, and the single-point correction method provided in Example 13 were used to detect a series of mixed standard solutions. The relative correction factors obtained are shown in Table 6.

[0115] Table 6 The table shows that the RSD of the RCF calculated by the three methods is less than 4.02%.

[0116] (2) Examination of RCF durability To investigate the durability of RCF under different chromatographic conditions, the chromatographic conditions were appropriately varied within a certain range. This invention investigated the effects of changes in flow rate and column temperature on three methods for calculating correction factors.

[0117] The effects of different flow rates and column temperatures on RCF in Example 1 (slope method) are shown in Tables 7 and 8, respectively, and the remaining parameters are the same as in Example 1.

[0118] Table 7 Table 8 The effects of different flow rates and column temperatures on RCF in Example 12 (multi-point correction method) are shown in Tables 9 and 10, respectively, and the other parameters are the same as in Example 12.

[0119] Table 9 Table 10 The effects of different flow rates and column temperatures on RCF in Example 13 (single-point correction method) are shown in Tables 11 and 12, respectively, and the other parameters are the same as in Example 13.

[0120] Table 11 Table 12 The results showed that the slope method had a smaller RSD (less than 2.18%) than the multi-point correction method and the single-point method in terms of the effect of different flow rates on RCF. Similarly, the slope method had a smaller RSD (less than 2.41%) than the multi-point correction method and the single-point method in terms of the effect of different column temperatures on RCF. This indicates that changes in chromatographic conditions have a relatively small impact on the RCF of each analyte and demonstrate good robustness.

[0121] (3) RCF reproducibility test The effects of different instruments and columns from different manufacturers on RCF were investigated. The effects of different instruments and columns from different manufacturers in Example 1 (slope method) are shown in Table 13. The remaining parameters are the same as in Example 1.

[0122] Table 13 The effects of different instruments and chromatographic columns from different manufacturers in Example 12 (multi-point calibration method) are shown in Table 14. The remaining parameters are the same as in Example 12.

[0123] Table 14 The effects of different instruments and columns from different manufacturers in Example 13 (single-point calibration method) are shown in Table 15. The remaining parameters are the same as in Example 13.

[0124] Table 15 The results show that the RSD of RCF calculated by the slope correction method is generally smaller than that calculated by the multi-point correction method and the single-point method, with an RSD of less than 2.96%, indicating that RCF has good reproducibility under different instruments and different chromatographic columns.

[0125] (4) Chromatographic peak localization Using chlorogenic acid as an internal reference, the effects of different instruments and chromatographic columns on the relative retention time (min) of each analyte were investigated. Other parameters were as described in Example 1. The results are shown in Table 16.

[0126] Table 16 The results showed that the RSD of the relative retention times of the seven analytes and the internal reference was less than 5.26%, indicating that the relative retention times of each component fluctuated little when using different chromatographic columns and systems, and could be used for peak localization.

[0127] Test Example 4 Sample content determination (1) Comparison of results between the one-test-multiple-evaluation method and the external standard method Thirty tobacco and tobacco product samples were taken (yp1~yp10 were reconstituted tobacco samples, yp11~yp20 were commercially available cigarette samples, and yp21~yp30 were first-cured tobacco samples). The components other than chlorogenic acid were calculated according to Example 1 (single-test multiple evaluation slope correction method), Example 12 (single-test multiple evaluation multi-point correction method), Example 13 (single-test multiple evaluation single-point correction method), and Comparative Example 1 (external standard method). The results obtained by the single-test multiple evaluation method and the external standard method were compared with the relative deviation as a parameter. The content of each component (mg / g) is shown in Tables 17-19, and the relative deviation (%) of each component is shown in Tables 20-21.

[0128] Table 17 Table 18 Table 19 Table 20 Table 21 The results showed that, except for scopolamine (6.47%), the average relative error of the multi-point correction method and the external standard method was less than 5% for all other components. The average relative error of the slope correction method and the external standard method was less than 3.21%. The average relative error of the single-point correction method and the external standard method was larger; for example, the average relative error of scopolamine reached 8.11%. These results indicate that both the established multi-evaluation method and the external standard method can be used to determine the content of phenolic compounds in tobacco and tobacco products. Further comparison of the three correction factor calculation methods showed that, except for cryptochlorogenic acid, the average relative error of the slope correction method for other components was significantly lower than that of the other two correction methods. This indicates that the slope correction method and the external standard method yielded more consistent results in the determination of polyphenolic compound content in tobacco and tobacco products.

[0129] (2) Comparison of detection limit and quantitation limit results for segmented and unsegmented detection. The detection limit and quantitation limit results were obtained by using the segmented wavelength detection method of Example 1 and the non-segmented wavelength detection method of Example 11, respectively, as shown in Table 22.

[0130] Table 22 The results show that the segmented determination method established in this invention has lower limits of detection and quantitation. This indicates that the method has higher sensitivity.

[0131] The reconstituted tobacco samples yp1~yp10 were tested using the method provided in Example 1, and the results are shown in Table 23.

[0132] Table 23 The polyphenol content in reconstituted tobacco leaves is significantly lower than that in virgin flue-cured tobacco leaves and commercially available cigarette tobacco. As shown in the table, the caffeic acid content in some samples (YP3, YP8, and YP9) is below the limit of quantitation for the non-segmented method. This indicates that the traditional non-segmented method is not suitable for the determination of some reconstituted tobacco leaf samples, while the segmented method can meet the requirements for these samples. Therefore, the segmented method is more suitable for the detection of polyphenols in reconstituted tobacco leaf samples, especially for the determination of low-content polyphenols. (4) Comparison of extraction effects The same tobacco sample was pretreated using the extraction systems of Examples 1-3 and Examples 14-16, respectively. The results (mg / g) of the determination of eight polyphenolic compounds are shown in Table 24.

[0133] Table 24 The results showed that the methanol-water-n-hexane and methanol-water-cyclohexane systems had better extraction and purification effects. The methanol-water-methyl-tert-butyl ether system resulted in the loss of polyphenols such as caffeic acid, rutin, hyoscyamine, and kaempferol-3-O-rutin. The methanol-water system could not achieve effective extraction of the target substances within the same volume, resulting in lower concentrations. The extraction efficiency decreased when the extraction system size was too small.

[0134] Application Example 1 This application example provides a method for constructing a multivariate near-infrared quantitative model based on a one-measurement-multiple-evaluation calibration method (the construction process is as follows). Figure 11 As shown), the construction method includes the following steps: (1) Preparation of representative sample set: 122 tobacco samples to be tested covering different origins, varieties, grades or processing techniques were collected. Each sample was dried at (40±1)℃ for 4 h, then pulverized by cyclone mill, passed through a 40-mesh sieve, sealed and subjected to moisture equilibration treatment for later use. (2) Determination of chemical reference values ​​by QAMS method: Chlorogenic acid was used as an internal reference. The relative correction factor between chlorogenic acid and the target polyphenolic compounds (neochlorogenic acid, cryptochlorogenic acid, hyoscyamine, rutin) was established by the method provided in Example 1. Then the content of the target polyphenolic compounds in each sample in the representative sample set of step (1) was determined and normalized by the standard method (zscore). (3) Near-infrared diffuse reflectance spectral data acquisition: Near-infrared diffuse reflectance spectroscopy technology was used, with an optical fiber probe as the detection component, in the range of 10000-4000 cm⁻¹. -1 Collection range, 8 cm -1 Under spectral resolution conditions, 72 scans were performed. Step (1) was to obtain the near-infrared diffuse reflectance spectral data of each sample in the representative sample set. Then, the principal component-Mahland distance method (PCA-MAHAL) was used to remove abnormal samples. One sample was removed. The multivariate scattering correction method was used for scattering correction. Savitzky-Golay convolutional derivative smoothing (derivSG) was used to complete smoothing and differentiation simultaneously (the window width was selected as 15, the polynomial degree was 5, and the derivative degree was 2). Samples in the 4800-5200 cm⁻¹ range of the near-infrared spectrum were removed. -1 6800-7200 cm -1 The absorption peaks were observed, and the remaining spectra were retained and normalized using the z-score method. (4) Correlate the content (Y) of the target polyphenolic compounds in each sample in the representative sample set in step (2) with the near-infrared diffuse reflectance spectral data (X) after processing in step (3) (there are 101 modeling samples), establish a PLSR2 regression model and solve it using the NIPALS method to determine the optimal number of principal components ( Figure 12Complete the model building and take 20 samples that were not involved in the modeling to validate the model; The solution process is as follows: Unlike directly fitting a model between X and Y, NIPALS first decomposes X and Y into a low-dimensional space of latent variables: Where: T is the principal component analysis (PCA) score matrix of X, P is the PCA loading matrix of X; U is the PCA score matrix of Y, Q is the PCA loading matrix of Y; E and F are the fitting error matrices; Perform least squares regression between T and U, i.e.: Where: G0 is the fitting error matrix; The overall regression model is obtained as follows: Where: G is the fitting error matrix; Repeat the above steps until the second norm of the error matrix F converges to 10. -10 Until then, that is: Where: λ is the 2nd norm of the error matrix F, i.e., λ 2 For matrix E T The largest eigenvalue of E.

[0135] The results of the QAMS-PLSR2 regression modeling in Application Example 1 are shown in Table 25.

[0136] Table 25 In model validation, near-infrared spectroscopy (NIR analysis) and QAMS were used for determination, and the results are shown in Table 26. Paired t-tests were used to validate the results, and the results are shown in Table 27. The QAMS-PLSR2 regression scatter plot (blue dots: modeling set; red dots: test set) is shown below. Figure 13 As shown. (Refer to Tables 26-27 and...) Figure 13 As can be seen, there is no significant difference between the two sets of data, indicating that the model provided in Example 1 has predictive accuracy.

[0137] Table 26 Table 27 Application Example 2 This application example provides a method for constructing a multivariate near-infrared quantitative model based on the one-measure-multiple-evaluation method. The only difference between this method and application example 1 is that the target polyphenolic compounds in step (2) are neochlorogenic acid, scopolamine, cryptochlorogenic acid, caffeic acid, scopolamine and rutin. Other operation steps are the same as in application example 1.

[0138] The QAMS-PLSR2 regression modeling results in Application Example 2 are shown in Table 28.

[0139] Table 28 The modeling results of Application Examples 1 and 2 show that the present invention preferably uses neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, hyoscyamine and rutin as target compounds for modeling, and the resulting model has smaller errors.

[0140] Application Example 3 This application example provides a method for constructing a multivariate near-infrared quantitative model based on the one-measure-multiple-evaluation method. The only difference between this method and application example 1 is that the target polyphenolic compounds in step (2) are neochlorogenic acid, cryptochlorogenic acid and rutin, and the other operation steps are the same as in application example 1.

[0141] The results of the QAMS-PLSR2 regression modeling in Application Example 3 are shown in Table 29.

[0142] Table 29 The modeling results of Application Examples 1 and 3 show that the present invention preferably uses neochlorogenic acid, chlorogenic acid, cryptochlorogenic acid, hyoscyamine and rutin as target compounds for modeling, and the resulting model has smaller errors.

[0143] Comparative Application Example 1 This comparative application example provides a method for constructing a multivariate near-infrared quantitative model based on the one-measurement-multiple-evaluation method. The only difference between this method and application example 1 is that step (4) uses the modeling results of PLSR1, as shown in Table 30. The results show that the modeling effect of PLSR2 is better than that of PLSR1.

[0144] Table 30 Test Example 5 Based on the 122 modeling samples in Application Example 1, correlation heatmaps between quantitative indicators were calculated using both the external standard method and the QAMS method, as follows: Figures 14-15 As shown (1-neochlorogenic acid, 2-scopolamine, 3-chlorogenic acid, 4-cryptochlorogenic acid, 5-caffeic acid, 6-hyoscyamine, 7-rutin, 8-kaempferol-3-O-rutin).

[0145] The results showed that the chemical indicators were independent of each other in the conventional external standard method, while the QAMS method, due to the introduction of correction factors between different target substances, objectively led to a certain correlation between the chemical indicators. The correlation heatmap showed that the correlation coefficients between the indicators were improved by the QAMS method compared to the external standard method. Further paired-samples t-tests indicated that the correlation coefficients of the QAMS method were significantly higher than those of the external standard method (p<0.01), making it more suitable for PLSR2 modeling.

[0146] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for constructing a multivariate near infrared quantitative model based on a calibration by a multi-quantitative method, characterized in that, The construction method comprises the following steps: (1) preparing a representative sample set; (2) taking chlorogenic acid as an internal reference, obtaining the content of the target polyphenolic compound of each sample in the representative sample set of step (1) by using a one-sample-multiple-estimation method based on liquid chromatography, and collecting the near-infrared diffuse reflectance spectrum data of each sample in the representative sample set of step (1); (3) correlating the content of the target polyphenolic compound of each sample in the representative sample set of step (2) with the near-infrared diffuse reflectance spectrum data, establishing a regression model and solving, determining the optimal principal component number, completing model construction and model verification.

2. The construction method of claim 1, wherein, The representative sample set of step (1) comprises samples of different origins, varieties, grades or processing technologies; Preferably, the number of samples in the representative sample set of step (1) is not less than 100; Preferably, the preparation of the representative sample set of step (1) comprises drying, crushing, sieving and moisture balancing treatment of each sample; Preferably, the target polyphenolic compound of step (2) comprises any one or a combination of at least two of neochlorogenic acid, scopolin, cryptochlorogenic acid, caffeic acid, anisatin, rutin or kaempferol-3-O-rutinoside, preferably any one or a combination of at least two of neochlorogenic acid, cryptochlorogenic acid, anisatin or rutin.

3. The construction method according to claim 1 or 2, characterized in that, In step (2), the specific operation of the one-sample-multiple-estimation method based on liquid chromatography comprises: (a) mixing the sample to be tested and solvent one to obtain a test solution; mixing the standard substance of the target polyphenolic compound and solvent two to obtain several standard solutions with different concentrations; (b) detecting the test solution and the standard solution by using liquid chromatography, taking chlorogenic acid as an internal reference, calculating the relative correction factor between chlorogenic acid and the target polyphenolic compound, and obtaining the content of the target polyphenolic compound of each sample in the representative sample set of step (1) by using the relative correction factor; Preferably, the solvent one comprises any one or a combination of at least two of methanol, water, methyl tert-butyl ether, n-hexane or cyclohexane; Preferably, the solvent one is a combination of methanol, water and n-hexane, or the solvent one is a combination of methanol, water and cyclohexane; Preferably, the volume ratio of methanol, water and n-hexane is 1:(0.8-1.2):(1.6-2.4); Preferably, the volume ratio of methanol, water and cyclohexane is 1:(0.8-1.2):(1.6-2. 4); Preferably, the dosage ratio of the tobacco or its product to the solvent one is 1 mg:(0.3-0.5) mL; Preferably, the solvent two comprises methanol and / or water; Preferably, the calculation method of the relative correction factor is the slope correction method.

4. The method according to any one of claims 1 to 3, characterized in that, In the liquid chromatography of step (2), the mobile phase comprises mobile phase A and mobile phase B, the mobile phase A comprises a combination of water, methanol and acetic acid, and the mobile phase B comprises a combination of methanol and acetic acid; Preferably, the volume ratio of water, methanol and acetic acid in the mobile phase A is 100:(1-3):(1-3); Preferably, the volume ratio of methanol and acetic acid in the mobile phase B is 100:(1-3); Preferably, the elution program of the mobile phase in the liquid chromatography is gradient elution, specifically: from 0 to 15 min, the volume percentage of mobile phase A is changed from 88-92% to 68-72% at a constant rate, and the volume percentage of mobile phase B is changed from 8-12% to 28-32% at a constant rate; from 15 to 20 min, the volume percentage of mobile phase A is changed from 68-72% to 8-12% at a constant rate, and the volume percentage of mobile phase B is changed from 28-32% to 88-92% at a constant rate, and then maintained until 22 min; from 22 to 22.1 min, the volume percentage of mobile phase A is changed from 8-12% to 88-92% at a constant rate, and the volume percentage of mobile phase B is changed from 88-92% to 8-12% at a constant rate, and then maintained until 30 min.

5. The method according to any one of claims 1 to 4, characterized in that, The packing material of the chromatographic column in the liquid chromatography of step (2) is C18, the particle size is 3-5 μm, the inner diameter is 3.0-4.6 mm, and the length is 100-250 mm; Preferably, the column temperature of the chromatographic column in the liquid chromatography of step (2) is 28-40℃; Preferably, the wavelength detection of the liquid chromatography of step (2) is segmented detection, specifically: from 0 to 9 min, the detection wavelength is 325 nm; from 9 to 10.8 min, the detection wavelength is 340 nm; from 10.8 to 17 min, the detection wavelength is 325 nm; from 17 to 20 min, the detection wavelength is 345 nm; from 20 to 30 min, the detection wavelength is 350 nm.

6. The method according to any one of claims 1 to 5, characterized in that, The collection range of the near-infrared diffuse reflection spectrum data in step (2) is 10000-4000 cm -1 , the spectral resolution is 6-10 cm -1 , and the scanning times are 64-108 times. Preferably, after the near-infrared diffuse reflectance spectroscopy data collection of step (2), further includes sample rejection, scatter correction, smoothing derivation, feature selection and normalization preprocessing; Preferably, the sample rejection adopts principal component-Mahalanobis distance method; Preferably, the scatter correction adopts multivariate scatter correction method; Preferably, the smoothing derivation adopts Savitzky-Golay convolution derivation smoothing method; Preferably, the feature selection includes rejection of absorption peaks at 4800-5200 cm -1 , 6800-7200 cm -1 . Preferably, the normalization adopts standard method; Preferably, the regression model of step (3) is PLSR2 regression model, and the solution adopts NIPALS method.

7. A multivariate near-infrared quantitative model based on the calibration method of any one of claims 1-6.

8. A method for detecting polyphenolic compounds in tobacco or a product thereof, characterized by, The detection method comprises: collecting near-infrared diffuse reflectance spectroscopy data of the sample, inputting the multivariate near-infrared quantitative model of claim 7, and obtaining the content of the polyphenolic compound.

9. A system for detecting polyphenolic compounds in tobacco or a product thereof, characterized by, The detection system comprises a sample preparation unit, a near-infrared diffuse reflectance spectroscopy acquisition unit, a model analysis unit and a result output unit; the model analysis unit comprises the multivariate near-infrared quantitative model of claim 7.

10. The detection system of claim 9, wherein, The sample preparation unit comprises a drying device, a crushing device, a sieving device and a moisture balance device; Preferably, the near-infrared diffuse reflectance spectroscopy acquisition unit comprises a near-infrared spectrometer, an integrating sphere, an optical fiber probe and a sample cell; Preferably, the model analysis unit comprises a data processing chip and a storage medium; Preferably, the result output unit includes a display screen and a data interface.

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