CN115541526A - Method for detecting content of caffeine and catechins in Pu-Er ripe tea based on near infrared - Google Patents
Method for detecting content of caffeine and catechins in Pu-Er ripe tea based on near infrared Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 63
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- LPHGQDQBBGAPDZ-UHFFFAOYSA-N Isocaffeine Natural products CN1C(=O)N(C)C(=O)C2=C1N(C)C=N2 LPHGQDQBBGAPDZ-UHFFFAOYSA-N 0.000 title claims abstract description 50
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Abstract
The invention discloses a method for detecting the content of caffeine and catechin in Pu-Er ripe tea based on near infrared, belonging to the technical field of spectral detection and analysis. The method for detecting the content of caffeine and catechin in Pu-Er ripe tea based on near infrared comprises the steps of collecting near infrared spectrum absorbance information of a Pu-Er ripe tea sample and the content value of caffeine and catechin in Pu-Er ripe tea measured by a high performance liquid chromatography-mass spectrometry combined instrument, respectively constructing a PLSR quantitative prediction model according to the near infrared spectrum absorbance information of the Pu-Er ripe tea and the content value of caffeine and catechin, and rapidly predicting the content of caffeine and catechin in the Pu-Er ripe tea sample to be detected according to the PLSR quantitative prediction model and the near infrared spectrum information of the sample to be detected. The method can be used for accurately and quantitatively predicting the low-content catechin substances in the Pu-Er ripe tea, and can provide a rapid, green and nondestructive real-time detection method for the market.
Description
Technical Field
The invention relates to a method for detecting the content of caffeine and catechin in Pu-Er ripe tea based on near infrared, belonging to the technical field of spectral analysis and detection.
Background
Tea is one of the most popular beverages in the world. Yunnan is an important tea production area in China because of unique geographic environment and climate conditions. Pu' er ripe tea in Yunnan is widely popular among consumers due to unique flavor and some health care effects. Caffeine and catechin components are important quality components in tea leaves, and play an important role in the quality and flavor of the tea leaves. In the production and sale process of tea, the quantitative detection of quality components generally depends on the traditional wet chemical detection technology. The traditional detection method has the disadvantages of complex operation, high price, long time consumption, sample damage and difficulty in meeting the real-time and rapid detection requirements in the quality monitoring process.
In recent years, near infrared spectroscopy is widely used for quality detection of products due to its characteristics of being green, fast, lossless, simple to operate and the like. Wang Yi et al, by optimizing a partial least squares regression model using a pretreatment and interval partial least squares method, analyze the near infrared spectrum of green tea, and establish a quantitative model of tea polyphenols (9.03-35.07%) in green tea, wherein the correlation coefficient of the prediction set of the model is 0.92. 5363 the partial least squares regression model established by Lu Li et al can realize the prediction of the content of tea polyphenol (6.51-19.88%) and caffeine (3.05-5.43%) in the small black tea, and the decision coefficient of the prediction set of the model is more than 0.95. The model is modeled by Zhaoying adoption of a nonlinear model SVM, the content of epicatechin (16.19-34.63 mg/g), epigallocatechin (18.95-60.07 mg/g), epicatechin gallate (13.35-51.00 mg/g) and epigallocatechin gallate (43.02-138.38 mg/g) in green tea is predicted, and the prediction set determination coefficient of the model is more than 0.97. Previous research shows that the near infrared spectrum and a chemometric method are combined to realize the quantification of the related quality components of the tea.
However, substances measured by near infrared in the prior art mainly focus on the substances with higher content in tea such as caffeine and tea polyphenol. Catechins are contained in green tea and other unfermented teas in high content, and a quantitative prediction model established for specific catechins in green tea is also studied. The catechins of the tea leaves produced by fermentation, such as black tea and Pu-Er ripe tea, can be greatly reduced after fermentation. Due to the low sensitivity of the near infrared spectrum, the spectral response value of substances with low content is also low. The difficulty of establishing a near-infrared prediction model aiming at low-content substances is high, so that a research object applying near-infrared spectroscopy to quantify the fermented tea does not aim at specific catechin substances, but aims at substances with high component contents such as total catechin and tea polyphenol in the fermented tea. Pu-Er ripe tea belongs to fermented tea, and the content of catechins is generally reduced in the fermentation process.
The current research does not report that a quantitative prediction model is established aiming at the catechin substances with lower content in the Pu-Er ripe tea. The method realizes quantitative prediction of catechin with low content in Pu-Er ripe tea by optimizing the model, can provide a rapid, green and nondestructive real-time detection method for the market, and enhances quality control in the production and sale processes of tea.
Disclosure of Invention
Aiming at the defects and shortcomings in the prior art, the invention aims to provide the method for detecting the content of caffeine and catechin in the Pu-Er ripe tea based on near infrared, compared with the traditional detection and analysis technology on the market, the method has the advantages of rapidness, greenness, no damage and the like, can better meet the quality detection requirement of the tea in the production and sale processes, saves time and cost for manufacturers, and has wide application prospect as the external verification result of a model shows that the method can be applied to component detection of the Pu-Er ripe tea.
In the modeling process, the invention adopts a preprocessing method to reduce the influence of the state of the sample, stray light, light scattering, instrument response and external environment factor interference, reduce errors and improve the precision of the model. In order to eliminate irrelevant variables and improve the model operation speed and the model precision, a variable screening method is combined on the basis of the model after pretreatment and optimization.
The invention aims to provide a method for detecting the content of caffeine and catechins in Pu-Er ripe tea based on near infrared, which comprises the following steps: collecting near infrared spectrum information of a Pu-Er ripe tea sample and caffeine and catechin content values in the Pu-Er ripe tea measured by a high performance liquid chromatography-mass spectrometer, respectively constructing a PLSR quantitative prediction model according to the near infrared spectrum information and the caffeine and catechin content values, and rapidly predicting the content of caffeine and catechin in the Pu-Er ripe tea of a sample to be detected according to the PLSR quantitative prediction model and the near infrared spectrum information of the sample to be detected; the near infrared spectrum information is the spectral absorbance information of the Pu-Er ripe tea in the wave band of 1000-1800 nm.
In one embodiment, the catechins include catechin, catechin gallate, gallocatechin gallate, epicatechin gallate, epigallocatechin gallate.
In one embodiment, the process of establishing the quantitative prediction model includes the following steps:
(1) Introducing the content values of caffeine and catechin in the Pu-Er ripe tea respectively measured by spectral absorbance data of the Pu-Er ripe tea sample and a high performance liquid chromatography-mass spectrometer into ChemDataSolution software, and dividing the sample into a correction set and a prediction set by adopting a KS algorithm;
(2) Preprocessing the spectral absorbance data of the Pu-Er ripe tea sample and screening variables, and respectively establishing a partial least squares regression PLSR optimal quantitative prediction model of caffeine and catechin.
In one embodiment, the spectral data of the Pu-Er ripe tea sample in the step (1) is obtained by putting Pu-Er ripe tea into a sample tray of a near-infrared spectrometer, naturally flattening tea powder to ensure light-proof property, and scanning the near-infrared spectrum of the sample after reference; the near infrared spectrum conditions are as follows: the scanning mode is diffuse reflection, the scanning wavelength is 1000-1800nm, the environmental temperature is 20-25 ℃, the humidity is 45-50%, each sample is repeatedly scanned for three times, and an average spectrum is taken as the spectrum data of the Pu-Er ripe tea sample.
In one embodiment, the chromatographic conditions of the high performance liquid chromatography-mass spectrometer used in step (1) for determining the content of caffeine and catechins in the Pu-Er ripe tea are as follows: 0.1% (v/v) formic acid water (phase A) and acetonitrile (phase B) are adopted as mobile phases, a chromatographic column model is Syncronis C18 (100 mm multiplied by 2.1mm multiplied by 1.7 mu m, waters), the flow rate is 0.2mL/min, the temperature of a column incubator is 35 ℃, and the sample injection volume is 1 mu L;
a gradient elution mode was used: 0 to 1.5min,10% by weight; 1.5-3min,10-40% by weight B;3-5min,40-80% by weight of B;5-6min,80-10% by weight B;6-10min,10% B.
In one embodiment, the mass spectrometric conditions for determining the content of caffeine and catechins in the Pu-Er ripe tea by the high performance liquid chromatography-mass spectrometer in the step (1) are as follows: an electrospray ionization (ESI) is adopted, a mass spectrum scanning mode is a multiple reaction monitoring mode (MRM), caffeine carries out data acquisition in a positive ion mode, catechin compounds carry out data acquisition in a negative ion mode, the flow rate of purge gas is 3mL/min, the flow rate of dry gas is 15mL/min, the temperature of an ion transmission tube is 250 ℃, the temperature of a heating block is 400 ℃, and the interface voltage is 4.5kV.
In one embodiment, the preprocessing in step (2) includes one or two of first derivative (1D), centering (Center), normalization (normaize), standard normal transformation (SNV), deTrending (DeTrending), SG Smoothing (Savitzky-solar Smoothing), and Multivariate Scatter Correction (MSC).
In one embodiment, the variable screening in step (2) is performed by a variable screening method including one of variable projection importance (VIP), competitive adaptive re-weighted sampling (CARS), random frog leap (RF), and non-information variable culling (UVE).
In one embodiment, the evaluation parameters in the model building process in step (2) include a correction set Root Mean Square Error (RMSEC), a prediction set Root Mean Square Error (RMSEP), a correction set correlation coefficient (Rc), a verification set correlation coefficient (Rp), and a residual prediction Residual (RPD).
In one embodiment, when the object is caffeine in step (2), the optimization method adopted is a preprocessing method 1D + DeTrending and a variable screening method RF; when the object is catechin, the optimization mode adopted is a pretreatment method normaize and a variable screening method CARS; when the object is catechin gallate, the optimization mode adopted is a pretreatment method SNV + MSC and a variable screening method CARS; when the object is gallocatechin, the optimization mode adopted is a pretreatment method MSC +1D and a variable screening method RF; when the object is gallocatechin gallate, the adopted optimization mode is a pretreatment method Center and a variable screening method CARS; when the object is epicatechin, the adopted optimization mode is a pretreatment method MSC and a variable screening method CARS; when the object is epicatechin gallate, the adopted optimization mode is a pretreatment method SNV + Detrending and a variable screening method RF; when the object is epigallocatechin, the adopted optimization mode is a pretreatment method Center + Detrends and a variable screening method CARS; when the object is epigallocatechin gallate, the optimization mode adopted is preprocessing method norm + MSC and variable screening method RF.
In one embodiment, the calibration set is 75% of the samples and the prediction set is 25% of the samples.
The invention also aims to provide an application of the method in detection and analysis of tea samples.
The invention has the beneficial effects that:
(1) The method has simple sample pretreatment, and can quickly predict the content of caffeine and catechins in the Pu-Er ripe tea of the sample to be detected after the model is established;
(2) The invention is green, fast, lossless and low in cost, and can save a large amount of manpower and material resources for manufacturers;
(3) The method can realize the prediction of the content of the catechins with lower content, and can provide a rapid detection means for further exploring the content change of the catechins in the fermentation process of the Pu-Er ripe tea.
Drawings
FIG. 1 is a schematic flow chart of example 1 of the present invention;
FIG. 2 is a near-infrared collected spectrum of a Pu-Er ripe tea sample according to example 1 of the present invention;
fig. 3 is a scattergram of the predicted value and the measured value of the optimal caffeine prediction model in example 1 of the present invention.
Detailed Description
In order to better understand the present invention, the following examples are further described, but the scope of the present invention is not limited to the examples.
The operation of the present invention is described in detail below.
In the present example, 100 Pu-Er ripe tea samples were all commercially available and purchased from Hada tea City of Kunming, yunnan province;
both caffeine and catechin standards were purchased from macelin reagent.
Example 1
1. Collecting near infrared spectral information of a sample
S1, preparation of Pu-Er ripe tea sample
Pulverizing cooked Pu-Er tea sample by a pulverizing machine, sieving with 60 mesh sieve, packaging the sieved sample powder into food sealed bag, and placing into a refrigerator at-4 deg.C for use, wherein the total amount of 100 samples (seeds) is obtained.
S2, near infrared spectrum measurement and collection
And (2) respectively taking 50g of the 100 tea leaf samples obtained in the step (S1), adding the 100 tea leaf samples into a sample tray, enabling the samples to be naturally paved and light-tight, detecting by using an EXPEC 1330 near infrared spectrometer preheated for 20min in advance, wherein the detection mode is a diffuse reflection mode, the scanning wavelength is 1000-1800nm, the environmental temperature is 20-25 ℃, the environmental humidity is 45-50%, each sample is repeatedly scanned for three times, and the average spectrum is taken as the near infrared acquisition spectrum of the sample.
2. High performance liquid chromatography-mass spectrometer for measuring content of caffeine and catechin in Pu-Er ripe tea
S1, pu-Er ripe tea sample pretreatment
Taking 0.1g of tea powder into 5mL centrifuge tubes respectively for 100 samples, adding 1mL of 70% (v/v) methanol solution, performing ultrasonic extraction at 40 ℃ for 15min, centrifuging at 10000r/min for 5min, and collecting supernatant; washing and precipitating for 2 times by taking 0.5mL70% (v/v) methanol solution, centrifuging, collecting supernate, putting the supernate into a 2mL volumetric flask, fixing the volume, and filtering by a 0.22 mu m filter membrane to obtain a solution to be detected;
s2, preparing a mixed standard solution
Respectively weighing a series of caffeine and catechin standard substances with mass (0.2-0.8 mg), adding into a centrifuge tube for dissolving, transferring into a 2ml volumetric flask, and performing constant volume with 70% (v/v) methanol solution to obtain a series of standard substance stock solutions with concentration; preparing a mixed standard substance by respectively taking a certain volume of caffeine and catechin standard substance stock solution, diluting the mixed standard substance by using 70% (v/v) methanol solution to obtain a mixed standard solution of 0.125-2 mg/mL of caffeine and 0.00005-0.0625 mg/mL of catechin, and using the mixed standard solution to make a standard curve.
S3, determining the content of caffeine and catechins in the tea sample
Respectively measuring the solution to be measured in the step S1 and the mixed standard solution in the step S2 by adopting a high performance liquid chromatography-mass spectrometer; constructing a quantitative relation model according to the concentration of the mixed standard solution and the corresponding peak area; calculating the content of caffeine and catechin in the sample to be detected according to the quantitative relation model and the peak area of the sample to be detected;
the liquid chromatography conditions were: using 0.1% (v/v) formic acid water (phase A) and acetonitrile (phase B) as mobile phase, and using chromatographic column of ACQUITY type
HSS C18 (100 mm. Times.2.1 mm,1.8 μm, waters), flow rate 0.2mL/min, column oven temperature 35 ℃, injection volume 1 μ L.
The gradient elution mode is adopted, and the specific gradient is as follows:
0-1.5min,10％B；1.5-3min,10-40％B；3-5min,40-80％B；5-6min,80-10％B；6-10mim,10％B。
the mass spectrum conditions are as follows: the mass spectrometry scan mode is a multiple reaction monitoring mode (MRM) using electrospray ionization (ESI).
The caffeine carries out data acquisition in a positive ion mode, and the catechin compounds carry out data acquisition in a negative ion mode;
the specific parameters of mass spectrometric detection are as follows: the flow rate of the purge air is 3mL/min, the flow rate of the drying air is 15mL/min, the temperature of the ion transmission tube is 250 ℃, the temperature of the heating block is 400 ℃, and the interface voltage is 4.5kV.
3. Near-infrared prediction model construction of caffeine and catechins in Pu-Er ripe tea
(1) Partitioning of sample sets
Introducing the spectral absorbance data of 100 tea samples collected in the step S2 in the step 1 and the content values of caffeine and catechin of 100 tea samples correspondingly measured by a high performance liquid chromatography-mass spectrometer in the step 2 into ChemDataSolution software (Daeven Shuichou Informational technologies Co., ltd.), selecting 75 correction set samples by using a KS algorithm, and 25 prediction set samples, wherein specific information of the samples is shown in Table 1;
TABLE 1 diversity statistics for each physicochemical index
(2) Model optimization and evaluation
The preprocessing method is adopted to reduce the influence of the state of the sample, stray light, light scattering, instrument response, external environment factor interference and the like, reduce errors and improve the precision of the model. In order to eliminate irrelevant variables and improve the model operation speed and the model precision, a variable screening method is combined on the basis of the model after pretreatment and optimization. And substituting the spectral data of the prediction set into the optimized prediction model to obtain the predicted value of the sample, and calculating by using the predicted value and the chemical measurement value to obtain the relevant parameters of the model so as to evaluate the model.
The model evaluation parameters are as follows: RMSEC (corrected set root mean square error), RMSEP (predicted set root mean square error), rc (corrected set correlation coefficient), rp (verified set correlation coefficient), RPD (residual prediction residual).
RMSEC represents the error magnitude of the model prediction correction set samples, and RMSEP represents the prediction error magnitude of the model building on the prediction set samples. The smaller the two error values, the higher the accuracy of the established model; rc and Rp are respectively correlation coefficients of the correction set and the prediction set and represent the magnitude of the model in explaining the sample, and the larger the value is, the more the established model can explain the relationship between the spectrum and the chemical value; the RPD is obtained by the ratio of the standard deviation of the chemical value of the prediction set to the RMSEP, represents the prediction capability of the model to an unknown sample, and the larger the value is, the stronger the generalization capability of the model is.
The results of the pretreatment optimization model are shown in tables 2 and 3, and caffeine and gallocatechin are taken as examples;
TABLE 2 Pre-treatment optimization of caffeine quantitative prediction model results
TABLE 3 pretreatment optimization of gallocatechin quantitative prediction model results
Wherein RAW models the initial spectrum; 1D: a first derivative; MSC: correcting the multivariate scattering; detrending: trend removing; the Center: centralizing; SG: savitzky-solar smoothening; SNV: standard normal transformation; normaize: normalization; MSEC: correcting the root mean square error of the set; MSEP: predicting a set root mean square error; rc: correction set correlation coefficients; and Rp: verifying the set correlation coefficient; RPD: the prediction residual remains.
The result of the pretreatment optimization model shows that the optimal result after the two pretreatment methods are combined is sometimes better than that of one pretreatment method; after the caffeine prediction model is processed through 1D + Detrends, rc is increased from 0.873 to 0.947, RMSEC is decreased from 0.154 to 0.101, rp is increased from 0.657 to 0.874, RMSEP is decreased from 0.290 to 0.216, RPD is increased from 1.359 to 1.394; therefore, 1D + DeTrending is used as the optimal pretreatment method; after the gallocatechin prediction model is processed by MSC +1D, rc is increased from 0.868 to 0.944, RMSEC is decreased from 0.090 to 0.059, rp is increased from 0.881 to 0.910, RMSEP is decreased from 0.087 to 0.074, RPD is increased from 2.102 to 2.475, and therefore MSC +1D is used as an optimal pretreatment method for the gallocatechin model. And further optimizing the model by combining a variable screening method. The results of the optimization model screening based on the variables after the optimal pretreatment mode treatment are shown in tables 4 and 5, and caffeine and gallocatechin are taken as examples.
TABLE 4 results of quantitative prediction model for caffeine optimization by variable screening
TABLE 5 quantitative prediction model results for optimization of gallocatechin by variable screening
(3) Results of model optimization
Taking a caffeine quantitative prediction model as a sample, establishing an optimized catechin PLSR prediction model, wherein the optimal model optimization and results are shown in Table 6;
TABLE 6 best prediction model for different substances
The optimized models have higher Rc, rp and RPD and smaller RMSEC and RMSEP, which shows that the models have high precision and strong prediction capability and can be used for predicting the content of caffeine and catechin in the Pu-Er ripe tea.
4. Rapid determination of caffeine and catechin content in unknown sample
Pulverizing unknown ripe Pu-Er tea sample, sieving with 60 mesh sieve, collecting 50g of sample powder, collecting near infrared spectrum of unknown tea sample by near infrared determination method in step S2, treating by pretreatment and variable screening method identical to the optimal model determined in step 3, substituting the treated near infrared spectrum value into the established near infrared prediction model, and performing model operation to obtain caffeine and catechin content in unknown ripe Pu-Er tea sample, wherein Table 7 shows the unknown ripe Pu-Er tea prediction result.
TABLE 7 prediction of unknown samples
Although the present invention has been described with reference to the preferred embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A method for detecting the content of caffeine and catechins in Pu-Er ripe tea based on near infrared is characterized by comprising the following steps: collecting near infrared spectrum absorbance information of a Pu-Er ripe tea sample and caffeine and catechin content values in Pu-Er ripe tea measured by a high performance liquid chromatography-mass spectrometer, respectively constructing a PLSR quantitative prediction model according to the near infrared spectrum absorbance information of the Pu-Er ripe tea and the caffeine and catechin content values, and rapidly predicting the content of caffeine and catechin in the Pu-Er ripe tea to be measured according to the PLSR quantitative prediction model and the near infrared spectrum absorbance information of the sample to be measured; the near infrared spectrum absorbance information is the spectrum absorbance information of the Pu-Er ripe tea in the wave band of 1000-1800 nm.
2. The method of claim 1, wherein the catechins comprise catechin, catechin gallate, gallocatechin gallate, epicatechin gallate, epigallocatechin gallate.
3. The method of claim 1, wherein the PLSR quantitative prediction model is built by:
(1) Introducing the content values of caffeine and catechin in the Pu-Er ripe tea respectively measured by spectral absorbance data of the Pu-Er ripe tea sample and a high performance liquid chromatography-mass spectrometer into ChemDataSolution software, and dividing the sample into a correction set and a prediction set by adopting a KS algorithm;
(2) Preprocessing the spectral absorbance data of the Pu-Er ripe tea sample and screening variables, and respectively establishing a partial least squares regression PLSR optimal quantitative prediction model of caffeine and catechin.
4. The method according to claim 3, wherein the condition for acquiring the near infrared spectrum information in the step (1) is as follows: the scanning mode is diffuse reflection, the ambient temperature is 20-25 ℃, and the humidity is 45-50%.
5. The method according to claim 3, wherein the preprocessing in step (2) is performed by one or two of first derivative 1D, centering Center, normalized normaize, standard normal transform SNV, deTrending, SG Smoothing Savitzky-Solay smoothening, and multivariate scatter correction MSC.
6. The method as claimed in claim 3, wherein the variable screening in step (2) comprises one of variable projection importance VIP, competitive adaptive re-weighted sampling CARS, random frog-leap RF, and non-information variable rejection UVE.
7. The method according to claim 3, wherein the evaluation parameters in the model building process in step (2) comprise a correction set root mean square error RMSEC, a prediction set root mean square error RMSEP, a correction set correlation coefficient Rc, a verification set correlation coefficient Rp and a residual prediction residual RPD.
8. The method of claim 3, wherein the calibration set of step (1) is 75% samples and the prediction set is 25% samples.
9. The method according to any one of claims 3 to 8, characterized in that, when the object is caffeine in step (2), the optimization modes adopted are pretreatment method 1D + DeTrending and variable screening method RF; when the object is catechin, the optimization mode adopted is a pretreatment method normaize and a variable screening method CARS; when the object is catechin gallate, the optimization mode adopted is a pretreatment method SNV + MSC and a variable screening method CARS; when the object is gallocatechin, the adopted optimization mode is a pretreatment method MSC +1D and a variable screening method RF; when the object is gallocatechin gallate, the adopted optimization mode is a pretreatment method Center and a variable screening method CARS; when the object is epicatechin, the adopted optimization mode is a pretreatment method MSC and a variable screening method CARS; when the object is epicatechin gallate, the adopted optimization mode is a pretreatment method SNV + Detrending and a variable screening method RF; when the object is epigallocatechin, the adopted optimization mode is a pretreatment method Center + Detrends and a variable screening method CARS; when the object is epigallocatechin gallate, the optimization modes adopted are a pretreatment method normaize + MSC and a variable screening method RF.
10. Use of a method according to any one of claims 1 to 9 in the detection and analysis of a tea sample.
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