CN113484272B - Method for rapidly predicting oil content in fresh tobacco leaves by adopting similarity analysis technology based on near infrared spectrum - Google Patents
Method for rapidly predicting oil content in fresh tobacco leaves by adopting similarity analysis technology based on near infrared spectrum Download PDFInfo
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Abstract
The invention discloses a method for rapidly predicting oil in fresh tobacco leaves by adopting a similarity analysis technology based on near infrared spectrum, which comprises the following steps: step (1), collecting and pretreating a sample; step (2), near-infrared scanning; step (3), selecting a characteristic wave band; step (4), preprocessing infrared data; step (5), oil content grading treatment; step (6), similarity analysis; step (7), establishing a near infrared-similarity prediction model; and (8) infrared prediction of the oil content of the fresh tobacco sample. The invention utilizes infrared spectrum technology and combines with a spectrum similarity analysis method to construct a tobacco oil content rapid analysis model and an analysis method, and is used for rapid detection and evaluation of tobacco oil content. In the process of establishing the prediction model, the pretreatment process is inspected, the characteristic wave band related to oil content is determined, the model established according to the data is more accurate, and the prediction accuracy is further ensured.
Description
Technical Field
The invention relates to a method for quickly predicting chemical components of tobacco leaves, in particular to a method for quickly predicting oil in fresh tobacco leaves based on a near infrared spectrum-similarity analysis technology.
Background
Tobacco leaves are the basis of the cigarette industry, and the quality of the tobacco leaves directly influences the quality of cigarette products. The chemical components of the tobacco leaves are internal factors for determining the quality of smoking evaluation, and the two factors have close relationship. The flue-cured tobacco oil content is closely related to the toughness, elasticity, hygroscopicity, maturity, physicochemical characteristics and smoke quality of tobacco leaves, is a comprehensive quality factor and is an important index in tobacco leaf classification. The oil content has a profound influence on the sensory evaluation quality of tobacco leaves, the tobacco leaves have more oil content, the corresponding aroma quality is better, the miscellaneous gas content is less, and the aftertaste comfort is improved.
The components of the oil are complex, and the tobacco oil mainly comprises higher fatty acid, ester substances, thuja triene substances, glycosides and the like according to related literature reports, so that the concept of the tobacco oil is not accurately defined at present. The current flue-cured tobacco grading standard GB2635-1992 mainly determines different grades of oil content according to observation, handfeel and other related appearance characteristics of tobacco grading workers, and has certain subjectivity.
The currently more common oil content measurement method is an ultrasonic-assisted Soxhlet extraction method. The method has objective and accurate results, but has the defects of complicated process, long detection time and difficult high-flux rapid evaluation. In addition, the new oil content analysis method comprises a tensile testing method, a visual picture analysis method and the like, and the methods are novel and can also be primarily used for analyzing the oil content of the tobacco leaves, but the methods are not mature at present and are less in application.
The present invention has been made to solve the above problems.
Disclosure of Invention
The invention utilizes infrared spectrum technology and combines with a spectrum similarity analysis method to construct a tobacco oil content rapid analysis model and an analysis method, and is used for rapid detection and evaluation of tobacco oil content.
The invention provides a method for rapidly predicting oil in fresh tobacco leaves by adopting a similarity analysis technology based on near infrared spectrum, which comprises the following steps:
step (1), collecting and pre-treating sample
Selecting a certain amount of tobacco plant samples, collecting one or more pieces of tobacco leaves, removing leaf veins, taking leaves, freezing and drying half, crushing and grinding the dried leaves into fine powder with the granularity of more than 40 meshes, and grading tobacco leaf oil in half;
step (2), near infrared scanning
Scanning fresh tobacco powder by adopting a near-infrared diffuse reflection spectrum;
step (3) selecting characteristic wave band
Analyzing the molecular structure of oil in fresh tobacco leaves and the near infrared spectrum thereof to determine the main characteristic peak wave band of protein;
step (4), infrared data preprocessing
Processing the infrared spectrum obtained in the step (3) by adopting a spectrum preprocessing method of smoothing (Smooth), vector normalization (SNV), Multivariate Scattering Correction (MSC), second derivative (D2) and Baseline Correction (BC);
step (5), oil content grading treatment
Randomly extracting 3 pieces of tobacco leaves from each tobacco plant sample, evaluating the oil content of the tobacco leaves piece by piece according to the standard of GB2635-1992 flue-cured tobacco, quantitatively scoring the tobacco leaves according to the standard shown in Table 1, asking a plurality of tobacco leaf experts to score, evaluating each sample for three times, and taking an average value;
TABLE 1 tobacco leaf oil content quantitative scoring standard
Step (6), similarity analysis
After the near infrared data obtained in the step (4) correspond to the oil content data obtained in the step (5), performing similarity analysis by using a sample with a high oil content, namely a score of 8-10 as a female parent and adopting TQ analysis software to obtain a similarity value;
step (7) of establishing a near infrared-similarity prediction model
After the near infrared data obtained in the step (4) correspond to the sample similarity data obtained in the step (6), constructing a linear regression function relation Y between the near infrared similarity and the oil content, wherein the linear regression function relation Y is 0.26127X-17.20868;
step (8) infrared prediction of fresh tobacco sample oil content
And (3) processing the fresh tobacco leaf samples to be detected according to the steps (1), (2) and (3), and introducing the processed fresh tobacco leaf samples into the model established in the step (7), so as to obtain the infrared predicted value of the oil content.
Preferably, in the step (1), the number of the samples is not less than 50, and the mesh number of the sample particles after the crushing and grinding is not less than 80.
Preferably, in the step (2), the near-infrared scanning condition is that the uniformly mixed solid powder sample is placed into a sample cup, a sample presser is used for lightly pressing and flattening, and the thickness of the sample is more than or equal to 10 mm; the scanning times are 64 times, the resolution is 1nm, the scanning range is 4000-12000 nm, and the error is eliminated by averaging in 3 times of tests; and sequentially carrying out absorbance, automatic baseline correction and normalization processing on the obtained original spectrum to obtain a corresponding standardized spectrum.
Preferably, in step (3), the protein is characterized by a peak wavelength: 4015 to 5450 and 6098 to 8502.
Preferably, in step (5), the number of the smoking experts is not less than 5.
Compared with the prior art, the invention has the following beneficial effects:
1. the infrared spectrum is a molecular oscillation spectrum, can obtain the whole chemical information of a sample, and is widely applied to quality control and analysis evaluation of traditional Chinese medicines, agricultural products, wines and the like due to the characteristics of rapidness, no damage, simplicity and the like. The near infrared spectrum region is consistent with the complex frequency of the vibration of the hydrogen-containing group (O-H, N-H, C-H) in the organic molecule and the absorption region of each level of frequency doubling, and the characteristic information of the hydrogen-containing group of the organic molecule in the sample can be obtained by scanning the near infrared spectrum of the sample.
Spectral similarity measurement (spectral similarity) plays an important role in the field of spectral data analysis. The main purposes of the technology are: and analyzing the similarity between the test spectrum (unknown class spectrum) and the control spectrum (known class spectrum) by using a specific spectrum similarity measurement function, and dividing the class attribute of the test spectrum according to the magnitude of the similarity value. To a certain extent, the main research fields of hyperspectral data analysis, such as ground feature classification, abnormal target detection, mixed pixel decomposition, etc., are all based on spectral similarity.
The invention utilizes the infrared spectrum technology and combines the spectrum similarity analysis method to construct a rapid analysis model and an analysis method of the tobacco oil content, and the rapid analysis model and the analysis method are used for rapid detection and evaluation of the tobacco oil content.
2. The method has short operation time, and can quickly obtain the oil content of the fresh tobacco leaves in only a few minutes.
3. Compared with a sensory evaluation method used by national standards, the data obtained by the method is more objective and accurate, and the standard of a prediction result is ensured.
4. The preprocessing process is considered, and the infrared data preprocessing method is determined to be that the infrared spectrum is processed by adopting a spectrum preprocessing method of smoothing (Smooth), vector normalization (SNV), Multivariate Scattering Correction (MSC), second derivative (D2) and Baseline Correction (BC), so that a model established according to the data is more accurate, and the accuracy of prediction is further ensured.
5. The invention adopts the characteristic wave band related to oil content to determine that the main characteristic peak wave band of protein is as follows: 4015-5450 and 6098-8502, so that the accuracy and precision of the established model are obviously improved.
6. The method has high accuracy and can be used for high-throughput evaluation and screening of the oil content of the gene editing tobacco material. The similarity of the tobacco leaves has certain correlation with the oil content, and can be used for predicting the trend of the oil content of the gene editing material, so that the accurate prediction of the oil content is still to be improved. The method is particularly suitable for tobacco samples with large source difference, gene editing and rapid prediction analysis of oil in transgenic tobacco samples.
Drawings
FIG. 1 is a schematic diagram of a spectral modeling process;
FIG. 2 is a scatter diagram of the true values of the oil component and predicted values of different pre-processed near-infrared models;
FIG. 3 is a scatter diagram of true values of oil content and predicted values of near-infrared models of different wave bands;
FIG. 4 is a graph of a near infrared similarity-oil true value fit;
Detailed Description
The present invention will be described below with reference to specific examples, but the embodiments of the present invention are not limited thereto. The experimental methods not specified in the examples are generally commercially available according to the conventional conditions and the conditions described in the manual, or according to the general-purpose equipment, materials, reagents and the like used under the conditions recommended by the manufacturer, unless otherwise specified. The starting materials required in the following examples and comparative examples are all commercially available.
In order to construct a tobacco leaf oil spectrum model, pretreatment, band selection and other aspects in the spectral construction process are mainly optimized (as shown in fig. 1).
1. Influence of pretreatment
In order to eliminate various interferences and noises such as baseline drift, light scattering and the like in the spectrum signal, effective characteristic information contained in the spectrum is fully extracted, the prediction precision of a correction model is improved, and necessary preprocessing is carried out on the spectrum. In the experiment, the spectrum of the flue-cured tobacco leaves is preprocessed by respectively adopting a first derivative and a second derivative in combination with Savitzky.Golay (SG) smoothing filtering (segment length 7; segment spacing 3) and Norris derivative filtering (segment length 5; segment spacing 5), and the results are shown in the following table 2 (figure 2). The method shows that different preprocessing methods have certain influence on the prediction result of the model. Wherein the near infrared second derivative + norris smoothing filter is relatively good.
TABLE 2 selection of spectral pretreatment method for flue-cured tobacco leaf model
2. Selection of wavelength ranges
The prediction effects of models built in different wavelength ranges are different, and when the full-wavelength spectrum is adopted for building the models, the models contain instrument noise and certain spectral regions with weak information, so that the prediction performance of the models is poor. In order to better extract the oil content information in the tobacco leaves, the characteristic near infrared 4015-5450 and 6098-8502 cm shown in Table 3 are obtained according to the main components of the oil content, such as higher fatty acid, ester substances, thujaplicin substances, glycoside and the like -1 。
TABLE 3 selection of characteristic waves for flue-cured tobacco leaf models
The continuous projection algorithm (SPA) can fully search a variable group containing minimum redundant information from the spectral information, so that the collinearity among variables is minimized, the number of variables used for modeling can be greatly reduced, and the modeling speed and efficiency are improved. The extraction wavelengths are shown in table 3 (fig. 3). And modeling the wave bands and the wavelengths according to a full spectrum modeling method.
The parameters of the established model are shown in table 3: the correlation coefficient between the actual value and the predicted value of the oil content in the model is more than 0.8, which shows that the predicted value and the actual value of the model have good correlation, and also shows that the established model can well predict the oil content of the flue-cured tobacco sample. And the correlation coefficient of the characteristic waveband spectrum is maximum, and RMSECV is minimum, so that the method can be used for near infrared spectrum modeling of oil components.
As can also be seen from Table 3, the selected characteristic wavelength is mostly at 4000-5000cm -1 ，6000～8000cm -1 Insofar this is mainly caused by stretching vibrations of the C-H bond, and O-H bond, which are mainly associated with esters, fatty acids and glycosides in tobacco oil.
3. Similarity fit
Spectral similarity (spectral similarity) plays an important role in the field of spectral data analysis. The main purposes of the technology are: and analyzing the similarity between the test spectrum (unknown class spectrum) and the control spectrum (known class spectrum) by using a specific spectrum similarity measurement function, and dividing the class attribute of the test spectrum according to the magnitude of the similarity value. To some extent, the main research fields of hyperspectral data analysis, such as ground object classification, abnormal target detection, mixed pixel decomposition, and the like, are all based on spectral similarity.
And (3) taking a sample with high oil content (namely a score value of 8-10) as a female parent, and calculating the similarity between a single sample and the female parent by adopting the obtained pretreatment method and the characteristic wave band. Linear fitting is carried out on the similarity of the samples and the oil content, and a regression equation is calculated and is shown in figure 4, wherein the near infrared linear regression is represented by Y-0.26127X-17.20868, R 2 0.81. The graph shows that the near infrared similarity is between 0.74 and 0.99, and the slope of the regression curve is positive, which indicates that the similarity is positively correlated with the oil content, i.e. the more similar the sample is to the parent with a large content of oil, the higher the oil content is. The correlation coefficient is 0.8178, which shows that the oil content has better correlation with the calculated near infrared similarity, and can be used for modeling and calculating the oil content of the flue-cured tobacco.
4. Model prediction accuracy prediction
Taking 10 unknown flue-cured tobacco leaf samples, carrying out near infrared spectrum scanning, predicting the oil content values by using the similarity prediction model of the invention, giving out the leaf oil content assignment of each sample according to GB 2635-92 flue-cured tobacco and table 1, and comparing the predicted value with the manual assignment, wherein the result is shown in table 4. since the oil content of the tobacco leaf is a qualitative description of the appearance quality of the tobacco leaf, the value is given according to human sensory evaluation, certain error is certainly existed, while 10% of error in the tobacco leaf grading is regarded as accurate, the relative deviation of the invention is 16.96%, which shows that the prediction accuracy of the method of the invention is better.
TABLE 4 flue-cured tobacco oil content model prediction data ID
Claims (3)
1. A method for rapidly predicting oil content in fresh tobacco leaves by adopting a similarity analysis technology based on near infrared spectrum is characterized by comprising the following steps: the method comprises the following steps:
step 1, sample collection and pretreatment
Selecting a certain amount of tobacco plant samples, collecting one or more pieces of tobacco leaves, removing leaf veins and taking leaves, freezing and drying half of the tobacco leaves, crushing and grinding the dried tobacco leaves into fine powder with the particle size of more than 40 meshes, and grading the oil content of the tobacco leaves on half of the tobacco leaves;
step 2, near infrared scanning
Scanning fresh tobacco powder by adopting a near-infrared diffuse reflection spectrum;
step 3, selecting characteristic wave bands
Analyzing the molecular structure and the near infrared spectrum of the oil in the fresh tobacco leaves to determine the main characteristic peak wave band of the protein;
step 4, infrared data preprocessing
Processing the infrared spectrum obtained in the step 3 by adopting a spectrum preprocessing method of smoothing, vector normalization, multivariate scattering correction, second derivative and baseline correction;
step 5, oil content grading treatment
Randomly extracting 3 pieces of tobacco leaves from each tobacco plant sample, evaluating the oil content of the tobacco leaves piece by piece according to the standard of GB2635-1992 flue-cured tobacco, quantitatively scoring the tobacco leaves according to the standard shown in Table 1, asking a plurality of tobacco leaf experts to score, evaluating each sample for three times, and taking an average value;
TABLE 1 tobacco leaf oil content quantitative scoring standard
Step 6, similarity analysis
After the near infrared data obtained in the step 4 corresponds to the oil content data obtained in the step 5, performing similarity analysis by using a sample with high oil content, namely a sample with a score value of 8-10 as a female parent and adopting TQ analysis software to obtain a similarity value;
step 7, establishing a near infrared-similarity prediction model
After the near infrared data obtained in the step 4 correspond to the sample similarity data obtained in the step 6, constructing a linear regression function relation Y between the near infrared similarity and the oil content, wherein the Y is 0.26127X-17.20868; wherein X is similarity, and Y is oil content;
step 8, infrared prediction of oil content of fresh tobacco sample
And (4) processing fresh tobacco samples to be detected according to the steps 1, 2, 3, 4, 5 and 6, and then introducing the processed fresh tobacco samples into the model built in the step 7 to obtain the infrared predicted value of the oil content.
2. The method according to claim 1, wherein in step 1, the number of the samples is not less than 50, and the mesh number of the sample particles after the pulverization and grinding is not less than 80 mesh.
3. The method of claim 1, wherein in step 3, the protein has major characteristic peak bands of: 4015 to 5450 and 6098 to 8502cm -1 。
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