Method for evaluating quality stability of cigarette product based on multi-dimensional space chemical fingerprint spectrum

Abstract

The invention discloses a method for evaluating cigarette product quality stability based on a multi-dimensional space chemical fingerprint spectrum, and relates to the field of cigarette quality evaluation.The method comprises the following steps that near-infrared diffuse reflection spectrum data of a sample is collected, optimization processing is conducted, and a model is established; establishing a near-infrared correction model by taking a control sample; the method comprises the following steps: collecting gas chromatography fingerprint data, carrying out optimization treatment to establish a model, calculating the dispersion degree of a collected sample and a control sample through a mahalanobis distance, respectively calculating the distribution condition of each type of samples, and evaluating the stability; introducing the collected sample into a contrast model for calculation, determining indexes of the selected cigarette sample, calculating the average content and relative standard deviation of conventional indexes in the sample and the contrast sample, and comparing the similarities of various indexes in the sample and the contrast sample; the method comprises the following steps: collecting sample fingerprints, retrieving the fingerprints through a computer spectrum library, calculating the relative content of each peak, detecting the inherent volatile components of each type of samples, and calculating the deviation.

Description

Technical Field

[0001] The invention relates to the field of cigarette quality evaluation, and in particular to a method for evaluating the quality stability of cigarette products based on multidimensional space chemical fingerprints. Background Art

[0002] The quality of cigarette smoking and the formation of style characteristics are the result of the synergistic effect of the internal chemical components of cigarettes (including leaf group formula and added flavors and fragrances). To judge the product quality of cigarettes, direct sensory evaluation or testing of chemical indicators is usually used.

[0003] The chemical composition of cigarettes is complex, with more than 3,000 chemical components reported. Existing conventional chemical indicators and data processing methods are often difficult to achieve a comprehensive system analysis due to the relatively discrete indicators and insufficient information, and it is difficult to obtain satisfactory results. Especially for cigarette products produced on different production lines of the same brand, the differences are subtle. Existing methods usually use HPLC, LC-MS, GC-MS and other analytical methods to detect one or some of its chemical components, and use the detected chemical components to evaluate its quality.

[0004] However, nearly 100 parameter indicators stipulated in the national standard "Cigarette" (GB / T5606.1-2004, 5606.2~5606.6-2005) are both interrelated and mutually restrictive. It is unscientific to simply select one or a group of parameter indicators to examine and evaluate the stability of product quality. It is difficult to fully evaluate the quality of cigarettes by using a single indicator or a few indicators to evaluate the quality of cigarettes. Moreover, since cigarettes are a smoking product, it is difficult to judge them by a single indicator or a few indicators under the interaction of multiple components in tobacco itself and various artificially added flavors and fragrances. It is difficult to distinguish the importance of multiple indicators simply by increasing the number of indicators, and it is difficult to judge them. Therefore, the chemical detection method has limitations.

[0005] When evaluating the quality of cigarettes based on the specific circumstances of smoking evaluation, it is difficult to objectively reflect the intrinsic quality of cigarettes because smoking evaluation is subject to greater subjective influences; the differences between cigarettes in the same series or the same product on different production lines are small, especially low-content substances in cigarettes are difficult to accurately detect using a single analytical method, and are even more difficult to evaluate and distinguish.

[0006] For the actual production of enterprises, the smoking quality and style characteristics of the same product in different periods, different batches of the same series, and different production lines should be kept stable. However, since the raw materials in cigarettes may be affected by seasonal climate, the various natural components of tobacco itself are different at different stages, and process adjustments are needed to keep the smoking quality and style characteristics stable as much as possible. Therefore, stability testing between the same product is essential. Summary of the invention

[0007] In order to solve a series of problems in actual production, such as the difficulty in judging the stability of the same product by using a single or several chemical detection indicators, the great subjective influence of sensory evaluation, and the difficulty in evaluating and distinguishing, the present invention provides a method for evaluating the quality stability of cigarette products based on a multidimensional space chemical fingerprint.

[0008] The present invention is implemented by the following technologies: The present invention provides a method for evaluating the quality stability of cigarette products based on a multidimensional space chemical fingerprint, comprising the following steps: a. Build a model Collect near-infrared diffuse reflectance spectral data of the samples. Specifically, prepare the samples according to GB / T31-1996, crush them with a cyclone mill, pass them through a 60-mesh sieve, put them into a sealed bag for later use, preheat the spectrometer at room temperature for 2 hours, put the samples into a 5 cm quartz cup with more than half the capacity, press them lightly to make them flat, place them on a rotating table, and collect the near-infrared diffuse reflectance spectral data of the samples.

[0009] After optimization processing, a near-infrared discrimination and classification model was established. The optimization processing included signal noise reduction, outlier detection, spectral preprocessing, modeling spectral region selection and determination of the maximum number of principal components.

[0010] Take the control sample to establish the near-infrared calibration model, import the collected sample into the near-infrared calibration model of the control sample for calculation, and predict and analyze the chemical composition content of the comparative sample and the control sample.

[0011] The gas chromatography fingerprint data of the samples were collected. Specifically, the tobacco shreds of 100 cigarettes were mixed thoroughly and evenly, and then balanced at constant temperature and humidity for 48 hours and stored for use. 0.500 g of the sample was weighed into a headspace bottle, and 1 μL of 25 mg / mL naphthaleneethanol solution was added as an internal standard. The headspace bottle was placed in a headspace injector. The setting conditions of the headspace injector were as follows: heating temperature of 130°C, heating time of 30 min, injection time of 1 min, injection volume of 3 mL; injection loop temperature of 140°C; transmission line temperature of 150°C, automatic injection, and GC / MS analysis; a gas chromatography discrimination and classification model was established after optimization processing, and the optimization processing included signal denoising processing, outlier test, spectral preprocessing, modeling spectral region selection and determination of the maximum number of principal components.

[0012] The samples were classified into different batches and divided into four categories according to production time, namely the original production line, the second quarter of the new production line, the third quarter of the new production line and the fourth quarter of the new production line. The principal component analysis method was applied to calculate the discrete degree of the collected samples and the control samples by Mahalanobis distance. The distribution of samples in each category was calculated and the principal component score diagram was drawn. Stability assessment was performed and quality control diagram was drawn.

[0013] b. Index detection The indicators of the selected cigarette samples were measured, the average content and relative standard deviation of the samples and the control samples were calculated, and the similarity of various indicators in the samples and the control samples was compared.

[0014] The specific steps include: bb, routine chemical index determination and analysis; bc, determination and analysis of volatile components by headspace-gas chromatography; bd, ash and pH value determination and analysis.

[0015] c. Deviation calculation The fingerprints of samples of each category were collected by headspace-gas chromatography. The obtained spectra were searched through a computer spectral library, and the relative content of each peak was calculated by internal standard correction normalization method. The intrinsic volatile components of each category of samples were detected and the deviations were calculated.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The method designed by the present invention is a method for evaluating the quality stability of cigarette products based on multidimensional spatial chemical fingerprints. The method adopts near-infrared spectrometer and headspace-gas chromatography-mass spectrometry technology to collect overall spectrum information of samples, and analyzes the stability and uniformity of the sample production process through pattern recognition technology. The method can effectively examine the quality stability and uniformity of the same product on different production lines, and can study the distribution and stability of samples of various categories. The method has fast and accurate detection conclusions, simple operation and processing, and high cost-effectiveness. The method is suitable for enterprises to maintain the sensory stability of product quality when changing industrial lines or adjusting raw materials, explore the quality differences of new original line products and the characteristic chemical indicators that cause the differences, and provide strong technical support for the research on product quality characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It represents the principal component score diagram of the original production line samples in Example 1.

[0018] Figure 2 The figure shows the quality control diagram of the samples of the original production line in Example 1.

[0019] Figure 3 It represents the principal component score graph of the samples in the second quarter of the new production line in Example 1.

[0020] Figure 4 It represents the quality control chart of samples in the second quarter of the new production line in Example 1.

[0021] Figure 5 It represents the principal component score graph of the samples of the new production line in the third quarter in Example 1.

[0022] Figure 6 It represents the quality control chart of samples in the third quarter of the new production line in Example 1.

[0023] Figure 7 It represents the principal component score graph of the samples of the new production line in the fourth quarter in Example 1.

[0024] Figure 8 It represents the quality control chart of samples in the fourth quarter of the new production line in Example 1.

[0025] Fig. 9 The near-infrared diffuse reflectance spectrum in Example 1 is shown.

[0026] Fig.10 The score distribution diagram of the spectral class A model in Example 1 on PC1 and PC2 is shown.

[0027] Fig.11 The figure shows the classification effect of the spectral model A in Example 1.

[0028] Fig.12 It represents the analysis of the conventional index contents of the samples of the original production line in Example 1.

[0029] Fig.13 It represents the analysis of routine index contents of samples from the new production line in the second quarter in Example 1.

[0030] Fig.14 It represents the routine index content analysis of the samples of the new production line in the third quarter in Example 1.

[0031] Fig.15 It represents the analysis of conventional index contents of the new production line in the fourth quarter in Example 1.

[0032] Fig.16 It shows the comparative analysis of the polyphenol compound contents in different production lines and different seasons in Example 1.

[0033] Fig.17 The figure shows the comparative analysis of ash content and pH value in different production lines and different seasons in Example 1.

[0034] Fig.18 The diagram shows the classification effect of the sample chromatogram model in Example 1.

[0035] Fig.19 The analysis of the original production line and the new production line in different quarters Z1 to 12 in Example 1 is shown.

[0036] Fig. 20 The analysis of the original production line and the new production line in different quarters Z13~24 in Example 1 is shown.

[0037] Fig.21 The analysis of Z25~35 in different quarters of the original production line and the new production line in Example 1 is shown.

[0038] Fig. 22 It represents the principal component score diagram of the samples of the original production line in Example 2.

[0039] Fig.23 The sample quality control diagram of the original production line in Example 2 is shown.

[0040] Fig.24 It represents the principal component score graph of the samples in the second quarter of the new production line in Example 2.

[0041] Fig.25 It represents the quality control chart of samples in the second quarter of the new production line in Example 2.

[0042] Fig.26 It represents the principal component score graph of the samples in the third quarter of the new production line in Example 2.

[0043] Fig. 27 It represents the quality control chart of samples in the third quarter of the new production line in Example 2.

[0044] Fig.28It represents the principal component score graph of the samples in the fourth quarter of the new production line in Example 2.

[0045] Fig.29 It represents the quality control chart of samples in the fourth quarter of the new production line in Example 2.

[0046] Fig.30 It shows the near infrared diffuse reflectance spectrum in Example 2.

[0047] Fig.31 The score distribution diagram of the B spectral model in Example 2 on PC1 and PC2 is shown.

[0048] Fig.32 The diagram shows the classification effect of the B spectral model in Example 2.

[0049] Fig.33 It represents the analysis of the conventional index contents of the samples of the original production line in Example 2.

[0050] Fig.34 It represents the analysis of routine index contents of samples from the new production line in the second quarter in Example 2.

[0051] Fig.35 It represents the analysis of routine index contents of samples from the new production line in the third quarter in Example 2.

[0052] Fig.36 It represents the analysis of conventional index contents of the new production line in the fourth quarter in Example 2.

[0053] Fig.37 It shows the comparative analysis of the polyphenol compound contents in different production lines and different seasons in Example 2.

[0054] Fig.38 It shows the comparative analysis of ash content and pH value in different production lines and different seasons in Example 2.

[0055] Fig.39 The diagram shows the classification effect of the sample chromatogram model in Example 2.

[0056] Fig.40 The figure shows the analysis of different quarters Z1 to 14 of the original production line and the new production line in Example 2.

[0057] Fig.41 The analysis of Z15-28 in different quarters of the original production line and the new production line in Example 2 is shown. DETAILED DESCRIPTION

[0058] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. Example 1

[0059] A method for evaluating the quality stability of cigarette products based on multidimensional spatial chemical fingerprints, taking cigarette sample A as an example, comprises the following steps: a. Build a model aa, establish near infrared discrimination classification model Collect the near-infrared diffuse reflectance spectral data of the samples, and use near-infrared spectrometer and headspace-gas chromatography-mass spectrometry technology to collect the overall spectrum information of the samples. Specifically, prepare the sample according to GB / T31-1996, then crush it with a cyclone mill, pass it through a 60-mesh sieve, and put it into a sealed bag for use. Preheat the FT-NIR spectrometer at room temperature for 2 hours, put the sample into a 5 cm quartz cup with more than half of the capacity, press it lightly to flatness, and place it on a rotating table. Call RESULTTM integrated software to collect the near-infrared diffuse reflectance spectral data of the sample, and set the parameters as follows: the spectral range is 10000-4000 cm -1 , resolution 8cm -1 ; Scanned 78 times; Spectral format is *.spc, near infrared diffuse reflectance spectrum is as follows Fig. 9 As shown; The spectrum was optimized to achieve the best state. The basic parameters were set as follows: the maximum number of principal components was determined to be 10, and the modeling spectrum region was selected to be 10000-4000cm -1 The spectral preprocessing was standardized, the signal noise reduction was performed by the second-order derivative + Norris filter (7, 5), the modeling algorithm used was the PCA-MD method, and the near-infrared discrimination and classification model was established after optimization.

[0060] ab, establish near infrared correction model The products produced by the original production line were taken as control samples. In this embodiment, the samples were aged tobacco leaves stored in the raw material warehouse, and near-infrared calibration models of 12 kinds of aged tobacco leaves were established. The calibration model optimization parameters are shown in Table 1. The collected near-infrared diffuse reflectance spectrum of cigarette sample A was imported into the near-infrared correction model of the control sample for calculation, the chemical composition content of the sample and the control sample was predicted and analyzed, and the product stability of cigarette sample A produced by the new production line and the original production line was evaluated.

[0061]

[0062] ac, establish gas chromatography discrimination classification model The gas chromatography fingerprint data of the sample were collected. Specifically, shredded tobacco from 100 cigarettes was mixed thoroughly and evenly, placed in a constant temperature and humidity chamber for 48 hours, and then placed in a brown ground-mouth sample bottle for use. 0.500 g of sample was weighed into a 20 mL headspace bottle, and 1 μL of 25 mg / mL naphthalene ethanol solution was added as an internal standard. The headspace bottle was placed in a headspace injector. The setting conditions of the headspace injector were heating temperature 130°C, heating time 30 min, injection time 1 min, injection volume 3 mL; injection loop temperature 140°C; transfer line temperature 150°C, automatic injection, and GC / MS analysis. The conditions of GC / MS were injection port temperature 260°C, carrier gas He, flow rate 1 mL / min, split ratio 10:1, GC / MS interface temperature 280°C, ion source EI source, electron energy 70 eV, and scanning range 35-455 amu, the furnace temperature rising conditions are 50℃ (1min)-3℃ / min-150℃ (1min)-10℃ / min-250℃ (1min), that is, keep at 50℃ for 1min, heat to 150℃ at a rate of 3℃ / min, and keep at 150℃ for 1min, then heat to 250℃ at a rate of 10℃ / min, and keep at 250℃ for 1min; the standard spectral library is NIST05 and WILEY275 spectral library; optimization processing is performed, the maximum number of principal components is determined to be 10, the data preprocessing is standardized, the PCA-MD method is used as the modeling algorithm, and a gas chromatography discrimination and classification model is established.

[0063] ad, stability assessment The samples were classified according to different batches and divided into four categories according to production time: original production line (i.e., control samples), second quarter of new production line, third quarter of new production line, and fourth quarter of new production line. The principal component analysis method was used to calculate the degree of dispersion between the collected samples and the control samples through the Mahalanobis distance. The results of the Mahalanobis distance within the class model are shown in Tables 2 and 3. The distribution of samples in each category was calculated, as shown in Tables 2 and 3. Figure 1 , 3 , 5, and 7 are shown in the main component score diagram. The sample distribution diagram of the first and second principal component scores and the classification effect diagram of the class model are shown in Fig.10 , 11 , 18, and conduct stability assessment, such as Figure 2 , 4 , 6, and 8, draw the quality control chart.

[0064]

[0065]

[0066] For samples produced by new production lines and original production lines, such as Figures 1 to 8As shown in the figure, the samples are evenly distributed in the first and second principal component spaces, and only a few samples are outside the control range. This shows that the sample quality is stable and evenly distributed during the production process. It can also be seen from the quality control chart that the A cigarette sample fluctuates slightly around the target value, but does not exceed the 3σ control range.

[0067] from Fig.10 It was found that the A cigarette samples produced in different time periods of the original production line and the new production line were clustered in the first and second principal component score distributions. The results showed that the A cigarette samples produced on the same production line had good similarity and relatively stable quality.

[0068] from Fig.11 It is concluded that the A classification effects of the original production line and the new production line are obviously divided into two areas, indicating that there are differences in the products of the two production lines. The A products produced by the new production line in the three quarters are cross-distributed, indicating that the quality of the new production line products has a smooth transition.

[0069] As shown in Tables 2 and 3, the coefficient of variation of the original production line is small, indicating that the product quality of the original production line is relatively stable; the coefficient of variation of sample A produced by the new production line in the second quarter is large, indicating that the product quality has certain fluctuations after the new line is put into production; as production progresses, the coefficient of variation in the third and fourth quarters gradually decreases, indicating that after adjustment, the quality of the new production line tends to be stable.

[0070] b. Index detection The conventional chemical indexes, volatile component indexes, ash content and pH value of the selected cigarette samples were measured, the average content and relative standard deviation of each index in the samples and the control samples were calculated, and the similarity of each index in the samples and the control samples was compared. The specific steps include the following: ba, conventional chemical index determination The conventional chemical indices of the selected cigarette samples were measured. The indices of the samples in each category were measured, the average was taken and the relative standard deviation was calculated. The stability of different old and new production lines as well as new production lines in different periods was evaluated. The measured values ​​of conventional chemical indices are described in Table 4, and the color scale distribution table is shown in Table 5.

[0071]

[0072]

[0073] From Tables 4 and 5, it can be seen that there are certain differences in the 10 conventional chemical composition indicators in cigarette A products. In the original production line, the relative standard deviations of potassium-chlorine ratio and sugar-alkali ratio are relatively large, 6.89% and 6.40% respectively; in different quarters of the new production line, the relative standard deviations of chloride ion and potassium-chlorine ratio vary greatly, among which the RSD values ​​in the second quarter are 5.73% and 7.31%, in the third quarter are 5.26% and 7.30%, and in the fourth quarter are 7.57% and 7.85%. Among the above 10 indicators, the average content of reducing sugar, sugar-nitrogen ratio, K2O and chlorine in the original production line is slightly higher than that in the new production line in each quarter, the average content of nicotine, total sugar and potassium-chlorine ratio is slightly lower than that in the new production line in each quarter, and the average content of total sugar, sugar-alkali ratio and Schmuck value in the original production line and the new production line is not much different. The average content and standard deviation of each indicator in the original and new production lines did not change significantly, indicating that the above 10 conventional indicators are relatively stable in different production lines and different quarters.

[0074] The changes and stability of the above 10 indicators in different quarters of the original production line and the new production line of A cigarette are analyzed by drawing a graph, see Figures 12 to 15 .

[0075] Depend on Figures 12 to 15 It can be seen that the overall average contents of the above 10 conventional chemical indicators in the original production line and the new production line of brand A in each quarter are slightly different, but there are certain fluctuations in each indicator in the original production line and the new production line in each quarter. Among them, the stability of the original production line is better than that of the new production line, and there are large differences in the standard deviation of the new production line between the quarters.

[0076] bb, Determination of volatile components The indicators of the volatile components of the selected A cigarette sample, in this example, are polyphenol indicators, including chlorogenic acid, rutin, scopoletin and the total amount of polyphenols. The description of the polyphenol indicator measurement values ​​is shown in Table 6.

[0077]

[0078] As can be seen from Table 6, in cigarette brand A, the average content of chlorogenic acid and total polyphenols of the original production line was higher than that of the new production line in each quarter; the average content of rutin and scopoletin of the original production line was lower than that of the new production line in each quarter; among the various indicators of polyphenols, the RSD of rutin changed greatly, new production line in the third quarter > original production line > new production line in the second quarter > new production line in the fourth quarter; chlorogenic acid RSD new production line in the second quarter > new production line in the third quarter > new production line in the fourth quarter > original production line; scopoletin RSD new production line in the third quarter > new production line in the fourth quarter > new production line in the second quarter > original production line; total polyphenols RSD new production line in the third quarter > new production line in the second quarter > original production line > new production line in the fourth quarter.

[0079] The changes and stability of the above four polyphenol indicators in different quarters of the original production line and the new production line of Brand A were analyzed by drawing a graph, see Fig.16 .

[0080] Depend on Fig.16 It can be seen that the four indicators of brand A cigarettes vary greatly. From the average content, it can be seen that the total amount of polyphenols is the highest in the original production line and the new production line in each quarter, and the scopoletin content is the lowest; among them, the average values ​​of total polyphenols and chlorogenic acid in the original production line> the fourth quarter of the new line> the second quarter> the third quarter, the content of rutin in the original production line is lower than that of the new production line in each quarter, and the content of scopoletin in the original production line is lower than that of the new production line in each quarter. From the standard deviation, it can be seen that the new production line has a large change in the second and third quarters, and the original and new production lines have a small change in the fourth quarter, indicating that the polyphenol indicators of the new production line have gradually stabilized after production adjustments.

[0081] bc, ash content and pH value determination The ash content and pH value of the selected A cigarette sample are described in Table 7.

[0082]

[0083] It can be seen from Table 7 that the pH value and average ash content of the original production line and the new production line in each quarter did not change significantly. From the relative standard deviation RSD value, it can be seen that the pH value of the original production line> the third quarter of the new production line> the fourth quarter of the new production line> the second quarter of the new production line; the ash content of the new production line in the third quarter> the second quarter of the new production line> the fourth quarter of the new production line> the original production line.

[0084] The changes and stability of the above two indicators of ash content and pH value in different quarters of the original production line and the new production line of brand A are analyzed by drawing a graph, see Fig.17 .

[0085] Depend on Fig.17 It can be seen that the average ash content in different production lines and quarters of A has a certain change over time, but the change is not obvious. The average pH content and standard deviation do not fluctuate significantly in different production lines and quarters of A. Among them, the stability of the original production line is better than that of the new production line. The average ash content in the original production line is higher than that of the new production line in each quarter. The standard deviation of the new production line in the third quarter fluctuates > the new production line in the second quarter > the new production line in the fourth quarter > the original production line. The analysis results show that the ash and pH indexes of the new production line gradually stabilize after production adjustment.

[0086] c. Deviation calculation Headspace-gas chromatography was used to collect fingerprints of samples of each category. 60 samples from the original production line and 192 samples from the new production line were taken, including 56 samples from the second quarter, 60 samples from the third quarter, and 76 samples from the fourth quarter. The obtained spectra were searched by computer spectral library (NIST05, Wiley275). The retrieved chemical component indicators are shown in Table 8 below. The chemical component indicators were numbered, and the relative content of each peak was calculated by internal standard correction normalization method. The intrinsic volatile components of each category of samples were detected and the deviation was calculated.

[0087]

[0088] Comparing the volatile component index measurement data of cigarette A from different production lines and different quarters, the RSD results of different production lines are listed in Table 9. Figures 19 to 21 The average values ​​and standard deviations of the intrinsic chemical composition indicators of the original and new production lines of cigarette A in different quarters are listed.

[0089]

[0090] As can be seen from Table 9, the volatile component indicators of the original production line and the new production line of cigarette A in different quarters have a certain degree of stability. Since the content of volatile components is relatively low, the RSD values ​​of the coefficient of variation of each volatile component between cigarette A samples fluctuate greatly, indicating that there are large differences in the volatile components in A samples from different production lines.

[0091] Depend on Figures 19 to 21 It can be seen that the intrinsic chemical composition indicators of cigarette A with average contents greater than 1.000 in different quarters between the original production line and the new production line are isobutyraldehyde, formic acid, acetic acid, 1,3-propylene glycol, pyridine, hexanal, dihydro-3-methyl-3 (2H)-furanone, furfural, furfuryl alcohol, 4-cyclopentene-1,3-dione, 1-(3-furanyl)-ethanone, benzaldehyde, 6-methyl-3-furfural, 6-methyl-6-heptene-ethanone, phenylacetaldehyde, 3-acetylpyrrole, 3-3-dihydro-3,6-dihydroxy-6-methyl-4H-pyran-4-one, 6-hydroxymethyl-furfural, nicotine, tricyclic olefins (tricyclic terpenes), and neophytadiene, among which the average contents and standard deviations of nicotine, acetic acid, and 1,3-propylene glycol are relatively high. The standard deviations of the remaining indicators between the original production line and the new production line in different quarters are all less than 10%, indicating that the detection accuracy between samples from the original production line and the new production line in different quarters is good. From Figure 1-19 From the changes in the standard deviations of the chemical composition indicators of the original production line and the new production line in different quarters, it can be seen that the stability of the samples tested by different production lines in the second and third quarters of the new production line is better than that of the original production line and the new production line in the fourth quarter. This may be due to the fluctuations in its intrinsic chemical composition indicators, and may also be related to the number of samples tested.

[0092] In summary, in this embodiment, the stability of product A from different production lines is evaluated by using spectral and chromatographic fingerprints, and the stability of the main chemical composition indicators and volatile component content of the products is analyzed. The results show that the quality stability of the original production line is good, and there is a certain fluctuation in the product quality of the new production line in different quarters, among which the stability of the new production line in the fourth quarter is better than that in the second and third quarters. The spectral and chromatographic fingerprints keep consistent with the stability analysis results of the original production line and the new production line in different quarters. Both spectral and chromatographic fingerprints can be used to evaluate the quality stability of product A, but near-infrared spectroscopy is more suitable for rapid determination of product quality stability than chromatographic fingerprints due to its advantages such as rapidity, pollution-free and low cost. Example 2

[0093] A method for evaluating the quality stability of cigarette products based on multidimensional spatial chemical fingerprints. Taking cigarette sample B as an example, the operation method and parameter settings are consistent with those in Example 1.

[0094] a. Build a model According to the near infrared diffuse reflectance spectrum Fig.30 As shown in Figure 1, the principal component analysis method is used to calculate the discreteness of the collected samples and the control samples through the Mahalanobis distance. The results of the Mahalanobis distance within the class model are shown in Tables 10 and 11. The distribution of samples in each category is calculated respectively, as shown in Tables 10 and 11. Fig. 22 , 24 , 26, and 28, respectively, and the principal component score diagrams are drawn. The sample first and second principal component score distribution diagrams and the class model classification effect diagram are shown in Fig.31 , 32 , 39, and conduct stability assessment, such as Fig.23 , 25 , 27, and 29, draw the quality control chart.

[0095]

[0096]

[0097] Depend on Figures 22 to 29 It can be seen that for samples B produced in different time periods, the samples are evenly distributed in the first and second principal component spaces, and only a few samples are distributed outside the control range. This shows that during the production process, the sample quality is stable and evenly distributed. It can also be seen from the quality control chart that sample B fluctuates around the target value and does not exceed the 3σ control range.

[0098] from Fig.31 It can be seen that the score distribution of B produced at different times on the original production line and the new production line on principal component 1 and principal component 2 is clustered, indicating that the B cigarette samples produced on the same production line have good similarity and stable quality.

[0099] from Fig.32 It can be seen that the B classification effects of the original production line and the new production line are obviously divided into two areas, indicating that there are differences in the products of the two production lines. The B products produced by the new production line in the three quarters are cross-distributed, indicating that the quality of the new production line products has a smooth transition.

[0100] from Fig.39 It can be seen that the chromatographic classification effect diagrams of B produced by the original production line and the new production line are clearly divided into two areas, indicating that there are differences in the products of the two production lines. The B products produced by the new production line in the three quarters are cross-distributed, indicating that the quality of the products of the new production line has a smooth transition.

[0101] From Tables 10 and 11, we can see that the coefficient of variation of the original production line is small, indicating that the product quality of the original production line is relatively stable; the coefficient of variation of sample B produced by the new production line in the second quarter is large, indicating that the product quality has fluctuated to a certain extent after the new line is put into production; but as production progresses, the coefficient of variation in the third and fourth quarters gradually decreases, indicating that after adjustment, the quality of the new production line tends to be stable.

[0102] b. Index detection ba, conventional chemical index determination The stability of different old and new production lines and new production lines at different times, and the measured values ​​of conventional chemical indicators are described in Table 12.

[0103]

[0104] From Table 12, it can be concluded that there are certain differences in the 10 indicators of conventional chemical composition in cigarette B sample, as follows: Nicotine, total sugar, reducing sugar, Schmuck's value and chlorine content in the original production line were lower than those in each quarter of the new production line. Sugar-nitrogen ratio, K2O content and potassium-chlorine ratio in the original production line were higher than those in each quarter of the new production line. The relative standard deviation (RSD%) of potassium-chlorine ratio was the largest in cigarette B sample, which was 8.07% in the original production line, 7.47% in the second quarter of the new production line, 10.79% in the third quarter of the new production line, and 8.84% in the fourth quarter of the new production line.

[0105] The coefficient of variation (RSD%) of reducing sugar content of the above 10 indicators in each quarter of the original production line and the new production line changed little. The RSD% of the original production line was 2.72%, the RSD% of the new production line in the second quarter was 2.23%, the RSD% of the new production line in the third quarter was 1.99%, and the RSD% of the new production line in the fourth quarter was 2.28%.

[0106] The changes and stability of the above 10 indicators in different quarters of the original production line and the new production line of Brand B are analyzed. Figures 33-36 .

[0107] Depend on Figures 33-36It can be seen that the overall average values ​​of the above 10 conventional indicators in the original production line and the new production line of brand B in each quarter are slightly different, but there are certain fluctuations in each indicator in the original production line and the new production line in each quarter. Among them, the stability of the original production line is better than that of the new production line, and there are large differences in the standard deviation of the new production line in each quarter. This may be related to the changes in the samples of the new production line tested in each quarter, or it may be related to the adjustments of the tested cigarette samples during the production process.

[0108] bb, Determination of volatile components The description of the measured values ​​of polyphenol indicators for the volatile components of the selected B cigarette sample is shown in Table 13.

[0109]

[0110] As can be seen from Table 13, in cigarette brand B, the average content of rutin and total polyphenols in the original production line is higher than that in each quarter of the new production line; the average content of chlorogenic acid changes, the new production line fourth quarter> third quarter> original production line> second quarter; and the average content of scopoletin is the highest in the second quarter of the new production line and the lowest in the original production line. From the relative standard deviation RSD value, it can be seen that in the fourth quarter of the new production line, the maximum change of rutin is 16.18%, and the minimum change of total polyphenols is 7.12%.

[0111] The changes and stability of the above four polyphenol indicators in the original production line and the new production line of Brand B in different quarters are analyzed as follows: Fig.37 .

[0112] Depend on Fig.37 It can be seen that the four indicators of brand B cigarettes have great differences. The total amount of polyphenols is the highest in the original production line and the new production line in each quarter, and the content of scopoletin is the lowest. The average value of the total amount of polyphenols is in the original production line> the fourth quarter of the new production line> the second quarter> the third quarter. The content of chlorogenic acid gradually increases with time. The content of rutin in the original production line is higher than that in the new production line in each quarter, while the content of scopoletin in the original production line is lower than that in the new production line in each quarter. The analysis results show that there are certain differences in the various indicators of polyphenols between the original production line and the new production line in each quarter, but the differences are not significant, and the various indicators of polyphenols in the new production line tend to be stable in general between each quarter.

[0113] bc, ash content and pH value determination The ash content and pH value of the selected A cigarette sample are described in Table 14.

[0114]

[0115] As can be seen from Table 14, with the change of time, the average content of ash and pH value gradually increased, the new production line in the fourth quarter> the third quarter> the second quarter> the original production line; the standard deviation RSD value of ash content in each quarter changed in the following order: the new production line in the third quarter> the second quarter> the original production line> the fourth quarter, with the values ​​being 2.42%, 2.35%, 2.25% and 1.74% respectively. The standard deviation RSD value of pH value in each quarter changed in the following order: the new production line in the third quarter> the original production line> the second quarter> the fourth quarter, with the values ​​being 1.25%, 1.01%, 1.00% and 0.85% respectively.

[0116] The changes and stability of the above two indicators of ash content and pH value in different quarters of the original production line and the new production line of Brand B are analyzed as follows: Fig.38 .

[0117] Depend on Fig.38 It can be seen that the average ash content in different production lines and quarters of B gradually increases with the change of time, and the average pH value does not fluctuate significantly in different production lines and quarters of B; among them, the stability of the original production line is better than that of the new production line, and there are certain differences in the standard deviation of the new production line between quarters, but the overall difference is not significant, and the ash content and pH value of the new production line gradually tend to be stable with the change of production time.

[0118] c. Deviation calculation 51 samples from the original production line and 401 samples from the new production line were taken, including 173 samples from the second quarter, 87 samples from the third quarter, and 141 samples from the fourth quarter. The retrieved chemical composition indicators are shown in Table 15. The chemical composition indicators were numbered, and the relative content of each peak was calculated using the internal standard correction normalization method. The intrinsic volatile components of each category of samples were detected and the deviations were calculated.

[0119]

[0120] Comparing the volatile component index measurement data of different production lines and different quarters of cigarette B, the RSD results of different production lines are listed in Table 16. Figures 40-41 The average values ​​and standard deviations of the intrinsic chemical composition indicators of the original and new production lines of cigarette B in different quarters are listed.

[0121]

[0122] In summary, in this embodiment, the main chemical components and volatile components fluctuate to a certain extent in each quarter of the original production line and the new production line, among which the stability of the original production line is better than that of the new production line, and there are certain differences in the stability of the new production line between quarters. However, as production continues, the product quality of the new production line tends to be stable.

[0123] The quality of tobacco is determined by its internal chemical composition. There are many types of internal chemical composition indicators that affect tobacco quality. The above indicators only represent part of the information of tobacco quality. The stability of its internal chemical composition indicators only reflects the partial changes of the components in different production lines. Spectral and chromatographic fingerprints can characterize the overall physical and chemical information of substances and are better used for quality stability assessment.

[0124] Both spectral and chromatographic fingerprints can be used to evaluate the quality stability of product B, and the evaluation results are consistent.

[0125] In conclusion, both spectral and chromatographic fingerprints can be used to evaluate product quality stability. However, near-infrared spectroscopy is more suitable for rapid determination of product quality stability than chromatographic fingerprints due to its advantages of rapidity, pollution-free and low cost.

[0126] The scope of protection claimed by the present invention is not limited to the above specific embodiments. For those skilled in the art, the present invention may have various deformations and changes. Any modifications, improvements and equivalent substitutions made within the concept and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for evaluating the quality stability of cigarette products based on multidimensional spatial chemical fingerprints, characterized in that: The following steps are involved: a. Build a model Collect the near-infrared diffuse reflectance spectrum data of the samples, and establish a near-infrared discrimination and classification model after optimization processing; Taking a control sample to establish a near infrared calibration model; importing the collected sample into the near infrared calibration model of the control sample for calculation, predicting and analyzing the chemical composition content of the comparative sample and the control sample; Collect the sample gas chromatography fingerprint data, and establish the gas chromatography discrimination classification model after optimization processing; The samples were classified into different batches, and the principal component analysis method was applied to calculate the discrete degree of the collected samples and the control samples through the Mahalanobis distance. The distribution of samples in each category was calculated and the principal component score graph was drawn. Stability evaluation was performed and quality control graphs were drawn. b. Index detection Determine the indicators of the selected cigarette samples, calculate the average content and relative standard deviation of the samples and the control samples, and compare the similarity of various indicators in the samples and the control samples; c. Deviation calculation The fingerprints of samples of each category were collected by headspace-gas chromatography. The obtained spectra were searched through a computer spectral library, and the relative content of each peak was calculated by internal standard correction normalization method. The intrinsic volatile components of each category of samples were detected and the deviations were calculated.

2. The method for evaluating the quality stability of cigarette products based on multidimensional spatial chemical fingerprints according to claim 1, characterized in that: The optimization process in step a includes signal noise reduction, outlier detection, spectrum preprocessing, modeling spectral region selection and determination of the maximum number of principal components.

3. The method for evaluating the quality stability of cigarette products based on multidimensional chemical fingerprints according to claim 1, characterized in that: In step a, the production time is divided into four categories: original production line, second quarter of new production line, third quarter of new production line and fourth quarter of new production line.

4. The method for evaluating the quality stability of cigarette products based on multidimensional chemical fingerprints according to claim 1, characterized in that: In step b, the following steps are included: ba, routine chemical index determination and analysis; bb, determination and analysis of volatile components by headspace-gas chromatography; bc, ash content and pH value determination and analysis.

5. The method for evaluating the quality stability of cigarette products based on multidimensional spatial chemical fingerprints according to claim 1, characterized in that: In step a, collecting sample near-infrared diffuse reflectance spectrum data includes the following steps: The prepared sample was crushed, passed through a 60-mesh sieve, and placed in a sealed bag for later use. The spectrometer was preheated at room temperature for 2 hours. The sample was placed in a 5 cm quartz cup with more than half the capacity, gently pressed to be flat, and placed on a rotating table to collect the near-infrared diffuse reflectance spectrum data of the sample.

6. The method for evaluating the quality stability of cigarette products based on multidimensional chemical fingerprints according to claim 1, characterized in that: In step a, collecting sample gas chromatography fingerprint data includes the following steps: Take shredded tobacco from 100 cigarettes and mix them thoroughly. After balancing at a constant temperature and humidity for 48 hours, store them for use. Weigh 0.500 g of sample into a headspace bottle, add 1 μL of 25 mg / mL naphthaleneethanol solution as an internal standard, place the headspace bottle in a headspace sampler, automatically inject the sample, and perform GC / MS analysis. The setting conditions of the headspace sampler are as follows: heating temperature 130°C, heating time 30 min, injection time 1 min, injection volume 3 mL; injection loop temperature 140°C; transmission line temperature 150°C.

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