Cigarette quality stability evaluation method based on tobacco shred mid-infrared spectroscopic analysis

Abstract

The invention relates to a cigarette quality stability evaluation method based on tobacco shred mid-infrared spectroscopic analysis, and belongs to the technical field of tobacco analysis. The method comprises the following steps: collecting tobacco shred samples from different producing areas and batches, drying and crushing the tobacco shred samples, scanning the tobacco shred samples through a mid-infrared spectrometer (400-4000 cm <-1 >), and extracting characteristic wave bands such as 715-726 cm <-1 > and 833-910 cm <-1 > after pretreatment such as smoothing and vector normalization; two-dimensional coordinates are obtained through principal component analysis (PCA), a cut tobacco quality stability index (QSI) is calculated through a formula, and the larger the QSI value is, the poorer the stability is. According to the method, rapid, lossless and objective stability evaluation is achieved through the mid-infrared spectrum technology, and the defects that a traditional method is high in subjectivity, long in consumed time and the like are overcome. Embodiments show that the method has stable evaluation results on the same batch of samples, can effectively distinguish different brands or high-imitation cigarettes, and provides a scientific basis for tobacco processing quality control.

Description

Technical Field

[0001] The invention belongs to the technical field of tobacco analysis, and in particular relates to a cigarette quality stability evaluation method based on tobacco mid-infrared spectroscopy analysis. Background Art

[0002] Cigarette product quality stability is the foundation of cigarette product quality and one of the main indicators for evaluating cigarette product quality. Cigarette production often involves multiple processing points, requires fine-tuning of leaf mixes and excipients between batches, and varies depending on the origin of agricultural products such as tobacco leaves, all of which can have a certain impact on cigarette product stability. Therefore, it is necessary to evaluate cigarette quality stability.

[0003] Currently, methods for evaluating cigarette quality stability include appearance, sensory evaluation, GC-MS, and conventional tobacco index analysis. These methods suffer from drawbacks such as high subjectivity, time-consuming and costly performance, and difficulty in achieving rapid and accurate quality stability assessments. With the development of spectral analysis technology, mid-infrared spectroscopy, due to its rapidity, non-destructive nature, and ability to simultaneously detect multiple components, has shown broad application prospects in tobacco quality evaluation. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for evaluating cigarette quality stability based on mid-infrared spectroscopy analysis of cut tobacco, which can quickly and accurately evaluate cigarette quality stability and provide a scientific basis for quality control in the tobacco processing process.

[0005] The technical solutions of the present invention are as follows:

[0006] A batch stability evaluation method based on mid-infrared analysis of cut tobacco comprises the following steps:

[0007] Step (1) Collection and preparation of tobacco samples:

[0008] Cigarette samples were selected from cigarettes of different origins and batches as control samples. Cut tobacco was taken from the control samples and the test samples, dried at 40°C for 24 hours, and then crushed to a size of 40 mesh or above.

[0009] Step (2) Collection of infrared spectral data

[0010] The sample was scanned by mid-infrared spectrometer with a spectral scanning range of 400 cm -1 -4000cm -1 The number of scans was not less than 32. The infrared spectra were processed using smoothing, vector normalization (SNV), multivariate scatter correction (MSC) and baseline correction (BC) spectral preprocessing methods.

[0011] Step (3) Selection of characteristic bands and PCA analysis

[0012] Extract characteristic bands from the preprocessed spectral data. The selected characteristic bands are: 715-726cm -1 , 833-910cm -1 , 958-1044cm -1 , 1096-1107cm -1 , 1258-1309cm -1 , 1443-1520cm -1 , 1604-1625cm -1 , 1710-1742cm -1 , 2888-2958cm -1 Import the characteristic band data of the control sample and the test sample into the software, perform PCA analysis, and calculate the corresponding two-dimensional coordinates;

[0013] Step (4) Calculation of tobacco quality stability index

[0014] The tobacco quality stability index is calculated according to the following formula:

[0015]

[0016] Where: x i is the X coordinate value of the PCA two-dimensional coordinate of the i-th control sample; y i is the Y coordinate value of the PCA two-dimensional coordinate of the i-th control sample; x0 is the average X coordinate value of the PCA two-dimensional coordinate of all control samples; y0 is the average Y coordinate value of the PCA two-dimensional coordinate of all control samples; a i is the X coordinate value of the PCA two-dimensional coordinate of the i-th sample to be tested; b i is the Y coordinate value of the PCA two-dimensional coordinate of the i-th sample to be tested.

[0017] The QSI value can represent the stability of tobacco. The larger the QSI value, the worse the stability of tobacco.

[0018] Further preferably, the number of control samples in step (1) is not less than 6;

[0019] It is further preferred that the infrared spectrum resolution in step (2) is not less than 0.1 cm -1 ;

[0020] Further preferably, the sum of the two-dimensional variance explanation rates in the PCA analysis in step (3) is not less than 50%;

[0021] Beneficial effects of the present invention:

[0022] 1. The present invention is based on mid-infrared spectroscopy analysis technology, which can quickly and non-destructively obtain the spectral information of tobacco, greatly improving the efficiency of tobacco batch stability evaluation.

[0023] 2. Through the extraction of infrared characteristic bands of tobacco and PCA analysis, an objective and accurate evaluation of tobacco batch stability is achieved, overcoming the subjectivity of traditional sensory evaluation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is the infrared spectrum of the tobacco in the embodiment;

[0025] Figure 2 This is the PCA graph obtained in Example 1. DETAILED DESCRIPTION

[0026] Example 1:

[0027] Six control samples of brand A cigarettes from six batches were selected from three production areas: HH, HZ, and KM. Six random samples of brand A cigarettes were selected from a certain batch of cigarettes to be tested. The samples were crushed into powder with a mesh size of 40 or above. The samples were then analyzed by mid-infrared scanning with a spectral scanning range of 400 cm. -1 -4000cm -1 ; Resolution: 0.1cm -1 The number of scans was not less than 32. The infrared spectra were processed by smoothing, vector normalization (SNV), multivariate scatter correction (MSC) and baseline correction (BC) spectral preprocessing methods. Figure 1 shown.

[0028] Extract characteristic bands from the preprocessed spectral data. The selected characteristic bands are: 715-726cm -1 , 833-910cm -1 , 958-1044cm -1 , 1096-1107cm -1 , 1258-1309cm -1 , 1443-1520cm -1 , 1604-1625cm -1 , 1710-1742cm -1 , 2888-2958cm -1 Import the characteristic band data of the control sample and the sample to be tested into the software, perform PCA analysis, and calculate the corresponding two-dimensional coordinates; the resulting PCA graph is as follows Figure 2 As shown, the variance explanation rate of the first principal component is 0.499, the variance explanation rate of the second principal component is 0.178, and the total variance explanation rate is 0.677. The obtained two-dimensional coordinates are shown in Table 2.

[0029] Table 1 PCA two-dimensional score table

[0030]

[0031]

[0032] The QSI of the tobacco sample was calculated using the tobacco quality stability index QSI to be 4.488.

[0033] Example 2:

[0034] In order to further verify the accuracy and stability of the verification method, the control samples were consistent with those in Example 1. The samples to be tested were DC1 of 10 samples of brand A cigarettes from the same batch of HH factory, DC2 of 8 samples of brand A cigarettes from the same batch as DC1, DC3 of 12 samples of brand A cigarettes from the same batch as DC1, DC4 of 9 samples of brand A cigarettes from the same batch as DC1, as well as 7 samples of high-imitation brand A cigarettes and 9 samples of brand B cigarettes. The QSI was calculated according to the method of Example 1. As a control, the same method as Example 1 was used, except that the infrared spectrum was calculated using the full-band infrared spectrum. The obtained QSI results are shown in Table 2.

[0035] Table 2 QSI calculation results of different methods

[0036] ID characteristic bands Full band DC1 3.562 6.531 DC2 3.471 4.976 DC3 3.536 5.321 DC4 3.422 7.132 Brand A high-quality imitation cigarettes 11.498 5.342 Brand B cigarettes 16.669 10.332

[0037] As shown in Table 2, the characteristic band extraction method exhibits minimal deviation in evaluation results within the same batch, demonstrating good stability and reproducibility. Furthermore, it provides good discrimination between high-quality counterfeit cigarettes and different brands. These results demonstrate the accuracy and stability of this method, making it a reliable method for evaluating the quality stability of cigarettes.

Claims

1. A batch stability evaluation method based on mid-infrared analysis of cut tobacco, characterized in that: include: Step S1: Select cigarette samples from cigarettes of different origins and batches as control samples: Step S2: scanning the control sample and the sample to be tested respectively using a mid-infrared spectrometer, and processing the infrared spectra obtained by the scans to obtain spectral data of the control sample and the sample to be tested; Step S3: extracting characteristic bands from the spectral data of the control sample and the sample to be tested, performing PCA analysis on the characteristic band data of the control sample and the sample to be tested, and calculating the corresponding two-dimensional coordinates; Step S4: Calculate the tobacco quality stability index using the two-dimensional coordinates. A larger tobacco quality stability index value indicates a worse tobacco stability.

2. The method according to claim 1, wherein The step S1 further includes: The control sample and the test sample were dried at 40° C. for 24 h, and then crushed to above 40 meshes to obtain the control cut tobacco sample and the test cut tobacco sample, which were used for scanning by an infrared spectrometer.

3. The method according to claim 1, wherein In step S2, the spectrum scanning range of the infrared spectrometer is 400 cm-1-4000 cm-1; the number of scans is not less than 32 times.

4. The method according to claim 1, wherein In step S3, the selected characteristic bands are: 715-726 cm-1, 833-910 cm-1, 958-1044 cm-1, 1096-1107 cm-1, 1258-1309 cm-1, 1443-1520 cm-1, 1604-1625 cm-1, 1710-1742 cm-1, and 2888-2958 cm-1.

5. The method according to claim 1, wherein In step S4, the tobacco quality stability index is: Wherein: is the X-coordinate value of the PCA two-dimensional coordinate of the i-th control sample; is the Y-coordinate value of the PCA two-dimensional coordinate of the i-th control sample; is the average value of the X-coordinate values of the PCA two-dimensional coordinates of all control samples; is the average value of the Y-coordinate values of the PCA two-dimensional coordinates of all control samples; is the X-coordinate value of the PCA two-dimensional coordinate of the i-th test sample; is the Y-coordinate value of the PCA two-dimensional coordinate of the i-th test sample.

6. The method according to claim 1, wherein In step S1, the number of control samples is no less than 6.

7. The method according to claim 1, wherein In step S2, the infrared spectrum resolution is not less than 0.1 cm-1.

8. The method according to claim 1, wherein In step S3, the sum of the two-dimensional variance explanation rates in the PCA analysis is not less than 50%.

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