Method for detecting quality of ampelopsis grossedentata based on nuclear magnetic resonance spectrum
Through nuclear magnetic resonance spectroscopy technology and stoichiometric classification methods, a rattan tea quality identification model was constructed, which solved the problem that the existing technology was difficult to identify rattan tea of different quality, and achieved rapid and accurate quality distinction and identification.
Patent Information
- Application Number
- CN202311776533.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-22
- Publication Date
- 2025-06-24
AI Technical Summary
The existing technology is difficult to effectively identify different quality rattan tea, resulting in uneven quality products on e-commerce platforms, affecting the development of the rattan tea industry.
Nuclear magnetic resonance spectroscopy technology combined with stoichiometric classification method, and 1H NMR spectrum collection and data processing of rattan tea samples was constructed to identify rattan tea quality to achieve the identification of rattan tea of different quality.
It realizes fast and accurate distinction between the quality of vine tea, simple operation, short detection time, good reproducibility of the result, low sample loss, and reliable quality identification method.
Smart Images
Figure CN120195210A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of food detection, and particularly relates to a method for determining the quality of Ampelopsis grossedentata by nuclear magnetic resonance spectroscopy. Background Art
[0002] Ampelopsis grossedentata, also known as Berry Tea, Fairy Grass, Dragon Beard Tea, Ganoderma Tea, etc., is processed from the tender stems and leaves of Ampelopsis grossedentata ( Ampelopsis grossedentata ) which grows in the areas south of the Yangtze River.
[0003] According to the different collection parts, Ampelopsis grossedentata products can be divided into three types: dragon beard, tender leaves and old leaves. The flavors and components of Ampelopsis grossedentata in different parts are different, and the quality is also different. The selling price of Ampelopsis grossedentata is closely related to its quality. Generally, it is considered that the dragon beard Ampelopsis grossedentata made from tender buds is of the highest quality, with the best taste and flavor, and the selling price is relatively high. The tender leaf Ampelopsis grossedentata ranks second, and the old leaf Ampelopsis grossedentata has the lowest quality. At present, the selling prices of Ampelopsis grossedentata on e-commerce platforms vary greatly, and the products are uneven. The development of the Ampelopsis grossedentata industry is still in its infancy, and there is still a huge growth space in the Ampelopsis grossedentata consumption market. Therefore, a method for identifying the quality of Ampelopsis grossedentata needs to be developed urgently.
[0004] As a commonly used detection tool, nuclear magnetic resonance is widely used in the fields of biology, chemistry, food science, medicine, materials, etc. Compared with other detection technologies, nuclear magnetic resonance has the characteristics of simple pretreatment, non-destructive detection, short time consumption, good reproducibility, etc. In the field of food science, it has been applied to component quantification and authenticity identification of oils, honey, dairy products, fruit juices, wines, condiments, etc.
[0005] PCA, OPLS-DA, etc. are commonly used chemometric methods in food authenticity analysis. They can not only directly use commercial built-in software, but also call common data science languages, and perform personalized programming extraction, processing, organization and analysis of data according to needs during the research process. Summary of the Invention
[0006] The purpose of the present invention is to establish a method for determining the quality of Ampelopsis grossedentata to solve the blank of existing detection technologies. This method uses nuclear magnetic resonance spectroscopy technology and combines chemometric classification methods for modeling, and then identifies the quality of Ampelopsis grossedentata.
[0007] The technical solution adopted by the present invention is as follows: The present invention provides a method for determining the quality of Ampelopsis grossedentata, which comprises the following steps: After grinding the Ampelopsis grossedentata sample into powder, adding heavy water, heating for extraction, centrifuging to take the supernatant, adding TSP-d4 solution and buffer solution to obtain the Ampelopsis grossedentata sample solution to be measured; Selecting a suitable pulse sequence, setting technical parameters, establishing a nuclear magnetic resonance spectroscopy method, and collecting the 1 1H NMR spectrum of the Ampelopsis grossedentata sample by a nuclear magnetic resonance spectrometer; Adjusting the phase and correcting the baseline for 1The ¹H NMR spectrum is segmented and integrated. After normalization, the spectrum is saved as a data file and imported into analysis software for multivariate statistical analysis to construct a quality discrimination model for Ampelopsis grossedentata. The prediction data set is imported into the discrimination model and clustered with the data in the model, and then it is predicted to be the corresponding quality grade or picking quarter, so as to realize the discrimination of Ampelopsis grossedentata samples with different qualities.
[0008] Further, after the Ampelopsis grossedentata sample is ground into powder, 50 mg of the sample powder is weighed, 800 μL of heavy water is added, and it is soaked and extracted at 80 °C and 1400 rpm for 1 h. After cooling to room temperature, it is centrifuged at 14000 rpm for 10 min. 420 μL of the supernatant is taken, 60 μL of TSP-d4 solution and 120 μL of phosphate buffer solution are added. After vortex mixing, 550 μL of the sample solution is taken and placed in a 5 mm NMR tube. The NMR tube is sealed and waiting to be measured.
[0009] Further, the pulse sequence is the NOESY1D pulse sequence; the nuclear magnetic resonance spectrometer is a high-field nuclear magnetic resonance spectrometer.
[0010] Further, the 1 ¹H NMR spectrum is obtained by Fourier transform. The number of Fourier transform times is 64 K, the line width factor is 0.3 Hz, the phase is automatically adjusted, and the 0.00 ppm signal peak of TSP-d4 is set as the chemical shift origin; the baseline is manually corrected, and the spectral data with a chemical shift range of 0.63 - 8.48 ppm (except for the water peak at 4.73 - 4.98 ppm) is selected for segmented integration. The integration interval is set to 0.05 ppm, the integration area is normalized, and the 1 ¹H NMR spectrum is converted into a data list; the data is imported into analysis software and analyzed using models such as PCA or OPLS-DA to classify the quality grade and picking quarter of Ampelopsis grossedentata.
[0011] Even further, the concentration of the TSP-d4 solution is 500 mg / L. TSP-d4 and sodium azide are dissolved in heavy water to obtain the TSP-d4 solution, which is stored at room temperature.
[0012] Even further, the concentration of the phosphate buffer solution is 1 mol / L. Potassium dihydrogen phosphate is dissolved in pure water, and the pH is adjusted to 7.0 with sodium hydroxide, and it is stored at room temperature.
[0013] Even further, the specific technical parameters are: the test temperature is set to 25 °C, heavy water is used for field locking, the pulse angle is 90°, the pulse width is 10.7 μs, the mixing time is 100 ms, the relaxation delay time is 4 s, the spectral width is 20 ppm, the number of sampling points is 16K, the number of scans is 32, pre-saturation is used to suppress the water peak, and the applied power is about 50 Hz, and the duration (including the delay time) is 2 s.
[0014] The present invention fills the gap in the existing detection technology, and its beneficial effects are as follows: The present invention provides a method for determining the quality of Ampelopsis grossedentata, which uses nuclear magnetic resonance spectroscopy technology and combines data science or chemometrics methods for analysis, and can identify the quality of Ampelopsis grossedentata.
[0015] The present invention establishes a new detection method, which is simple to operate, has a short detection time, good result reproducibility, and low sample loss, and is a reliable quality identification method.
[0016] The detection method of the present invention can quickly and accurately distinguish the quality grade and picking quarter of Ampelopsis grossedentata, and can provide technical guarantee for the quality identification of Ampelopsis grossedentata. Description of the Drawings
[0017] Figure 1 are representative 1 1H NMR spectra of Ampelopsis grossedentata with different quality grades.
[0018] Figure 2 are score plots of PCA and OPLS-DA for Ampelopsis grossedentata with different quality grades.
[0019] Figure 3 is the score plot of OPLS-DA for Ampelopsis grossedentata in different picking quarters.
[0020] Figure 4 is the result of OPLS-DA for pre-judging the quality grades of 10 Ampelopsis grossedentata samples. Detailed Embodiments
[0021] The following further clarifies the present invention in conjunction with specific embodiments and the drawings. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. After reading the present invention, various equivalent modifications made by those skilled in the art fall within the scope defined by the appended claims of this application.
[0022] Example 1 Identification of Ampelopsis grossedentata Samples with Different Qualities (I) Materials and Reagents
[0023] Heavy water (D2O, 99.9%) and sodium 2,2,3,3-d4-trimethylsilylpropionate (TSP-d4, 98%) (Cambridge Isotope Laboratories, Andover, MA, USA), sodium azide (analytical grade, MYM Biotechnology Co., Ltd., Beijing, China), potassium dihydrogen phosphate (analytical grade, Fuchen Chemical Reagent Co., Ltd., Tianjin, China), sodium hydroxide (analytical grade, Shanghai ANPEL Laboratory Technologies Inc., Shanghai, China). 71 samples (dragon beard, young leaves and old leaves) were collected from Laifeng, Jianshi and Badong counties in Enshi Tujia and Miao Autonomous Prefecture, Hubei Province, China. The collection time was the second (May and June) and third (July, August and September) quarters. The specific information is shown in Table 1. All samples were made into Ampelopsis grossedentata tea according to the traditional processing steps and stored at room temperature.
[0024] Table 1 Sample information and grouping (2) Instruments and equipment
[0025] Agilent DD2 600 MHz nuclear magnetic resonance spectrometer (5 mm PFG OneNMR probe, with 7510 autosampler) (Agilent, USA), Merck Milli-Q ultrapure water system (USA), KQ-500E ultrasonic cleaner (Kunshan Ultrasonic Instrument Co., Ltd., Jiangsu, China), FiveEasy Plus pH meter, XS104 balance (Mettler-Toledo, Shanghai, China), BioShake iQ thermostatic shaker (Germany), Beckman Coulter Microfuge 22R centrifuge (USA), AS ONETRIO TM-1N vortex mixer (Japan), 20 - 200 μL and 100 - 1000 μL adjustable pipettes (Eppendorf, Hamburg, Germany), 5 mm NMR tubes (Norell, USA). (3) Solution preparation
[0026] TSP-d4 solution (500 mg / L): Weigh 50 mg of TSP-d4 and 100 mg of sodium azide, dissolve them in 100 mL of heavy water, mix well and store at room temperature.
[0027] Phosphate buffer solution (1 mol / L, pH 7.0): Weigh 27.22 g of potassium dihydrogen phosphate, dissolve it in 100 mL of pure water, mix well, adjust the pH to 7.0 with sodium hydroxide and store at room temperature. (4) Sample pretreatment
[0028] After grinding the tengcha sample into powder, weigh 50 mg of the sample powder into a 2 mL centrifuge tube, add 800 μL of heavy water, soak and extract at 80 °C and 1400 rpm for 1 h. After cooling to room temperature, centrifuge at 14000 rpm for 10 min. Take 420 μL of the supernatant, add 60 μL of TSP-d4 solution and 120 μL of phosphate buffer. After vortexing and mixing evenly, take 550 μL of the sample solution into a 5 mm NMR tube, seal the NMR tube, and wait for measurement. (V) Instrument Conditions
[0029] Select the NOESY1D pulse sequence, set the test temperature to 25 °C, lock the field with heavy water, the pulse angle is 90°, the pulse width is 10.7 μs, the mixing time is 100 ms, the relaxation delay time is 4 s, the spectral width is 20 ppm, the number of sampling points is 16 K, the number of scans is 32, use pre-saturation to suppress the water peak, and the applied power is about 50 Hz, and the duration (including the delay time) is 2 s. (VI) Data Processing
[0030] Tengcha sample 1 The 1H NMR spectrum is obtained by Fourier transform. The number of Fourier transform times is 64 K, the line width factor is 0.3 Hz, automatically adjust the phase, and set the 0.00 ppm signal peak of TSP-d4 as the chemical shift origin. Manually correct the baseline, select the spectral data with the chemical shift range of 0.63 - 8.48 ppm (except for the water peak at 4.73 - 4.98 ppm) for segmented integration, set the integration interval to 0.05 ppm, normalize the integration area, and 1 Convert the 1H NMR spectrum into a data list. Import the data into the analysis software, and use two models, PCA and OPLS-DA, for analysis to classify the quality grade and picking quarter of tengcha. (VII) Result Analysis Identification of Quality Grade
[0031] Figure 1 For the representative 1 1H NMR spectra of tengcha with different quality grades, it can be found that the components of tengcha with different quality grades are different. The amino acid content in the long whiskers is higher than that in the young leaves and old leaves, while the carbohydrate content in the young leaves and old leaves is significantly higher than that in the long whiskers.
[0032] Select PCA as the tengcha quality grade discrimination model, and the score plot is as Figure 2As shown in Figure A, the dragon whiskers are located on the left side of the vertical axis and are clearly separated from the young leaves and old leaves on the right side of the vertical axis, indicating that there are significant differences between the dragon whisker samples and the young leaf and old leaf samples, and the composition of the dragon whiskers is different from that of the young leaves and old leaves. Compared with the old leaves, most of the young leaves are located in the upper part of the horizontal axis, and most of the old leaves are located in the lower part. However, 5 old leaf samples picked in May are mixed in the young leaf group, and their component contents are similar to those of the young leaves. The reason may be that at the initial stage of plant growth, the conversion rate of the component contents of the old leaves is relatively slow. The scores of the first and second components of the PCA model are 0.423 and 0.148 respectively, indicating that PCA is a good discrimination model for the quality grade of Ampelopsis grossedentata, and the R 2 and Q 2 of the PCA model are 0.897 and 0.755 respectively.
[0033] When OPLS-DA is selected as the discrimination model, different Ampelopsis grossedentata samples are divided into three grades and clustered separately without overlap. As Figure 2 shown in Figure B, there are significant differences between the dragon whiskers of Ampelopsis grossedentata and the young leaves and old leaves, and the young leaf and old leaf samples are distinguished. The R 2 X, R 2 Y and Q 2 of the OPLS-DA model are 0.626, 0.875 and 0.821 respectively. Discrimination of picking quarters
[0034] The OPLS-DA model is applied to analyze the Ampelopsis grossedentata samples of different picking quarters in all samples, dragon whiskers, young leaves and old leaves respectively. The summary of the score results is shown in Figure 3 . As Figure 3 shown in Figure A, the samples picked in the second quarter and the third quarter can be distinguished without overlap. The R 2 X, R 2 Y and Q 2 values are 0.826, 0.935 and 0.912 respectively. The samples of the same quality grade (dragon whiskers, young leaves and old leaves) in different picking quarters are further analyzed. Generally speaking, as Figure 3 shown in Figures B, 3C and 3D, the picking quarters of the samples of different quality grades can be clearly distinguished. The Ampelopsis grossedentata samples picked in the second quarter are on the left side of the vertical axis, and the Ampelopsis grossedentata samples picked in the third quarter are on the right side of the vertical axis, without overlap, and the classification and discrimination of the picking quarters can be achieved. Table 2 shows the R 2 X, R 2 Y and Q 2 values in the OPLS-DA models of dragon whiskers, young leaves and old leaves.
[0035] Table 2 R 2 X, R 2 Y and Q 2 values for OPLS-DA to distinguish different picking quarters of dragon whiskers, young leaves and old leaves (VIII) Identification of actual samples
[0036] Taking quality identification as an example, 10 samples of Ampelopsis grossedentata with different quality grades were randomly selected as the test set, and the remaining 61 samples were used as the training set to evaluate the feasibility of the identification model. The R of the OPLS-DA model for the training set 2 X, R 2 Y and Q 2 were 0.636, 0.872, and 0.789 respectively. The OPLS-DA model was used to perform predictive analysis on the samples, and the test set was imported for prediction. The prediction results are shown in Figure 4 . It can be found that among the 10 Ampelopsis grossedentata samples, 4 are the dragon beard type, and there are 3 young leaves and 3 old leaves respectively, all of which are correctly classified, indicating that this model is feasible for the quality grade identification of actual Ampelopsis grossedentata samples.
[0037] It can be seen that the present invention can quickly and accurately distinguish the quality grade and picking quarter of Ampelopsis grossedentata by using nuclear magnetic resonance spectroscopy in combination with a chemometric model. This method is simple to operate, has a short detection time, good result reproducibility, and low sample loss, and is a reliable quality identification method, which can provide technical guarantee for the quality identification of Ampelopsis grossedentata.
[0038] The above embodiments have described the implementation manners of the present invention in detail. However, the present invention is not limited to the above implementation manners. Within the scope of knowledge possessed by those of ordinary skill in the art, various changes can be made without departing from the gist of the present invention. The above are only the preferred and feasible embodiments of the present invention, and thus do not limit the scope of the rights of the present invention. Any equivalent structural changes made by using the content of the specification of the present invention are included within the scope of the rights of the present invention.
Claims
1. A method for determining the quality of Ampelopsis grossedentata, characterized in that, It includes the following steps: After grinding the Ampelopsis grossedentata sample into powder, deuterated water (heavy water) is added. After heating extraction, the supernatant is taken by centrifugation. Then, 2,2,3,3-deuterated sodium trimethylsilylpropionate (2,2,3,3-D4 sodium-3-trimethylsilylpropionate, TSP-d4) solution and buffer solution are added to obtain the Ampelopsis grossedentata sample solution to be measured; Select an appropriate pulse sequence, set the technical parameters, establish nuclear magnetic resonance (NMR), and collect the 1 1H NMR spectrum of the Ampelopsis grossedentata sample by an NMR spectrometer; Adjust the phase, correct the baseline, and perform segmented integration on the 1 1H NMR spectrum. After normalization, the spectrum is saved as a data file, imported into analysis software for multivariate statistical analysis, and a quality discrimination model for rattan tea is constructed. The prediction data set is imported into the discrimination model and clustered with the data in the model, that is, it is pre-judged as the corresponding quality grade or picking quarter, so as to realize the discrimination of Ampelopsis grossedentata samples of different qualities.
2. The method according to claim 1, wherein After grinding the Ampelopsis grossedentata sample into powder, 50 mg of the sample powder is weighed, 800 μL of deuterated water is added, and it is soaked and extracted at 80 °C and 1400 rpm for 1 h. After cooling to room temperature, it is centrifuged at 14000 rpm for 10 min. 420 μL of the supernatant is taken, 60 μL of TSP-d4 solution and 120 μL of phosphate buffer solution are added. After vortex mixing, 550 μL of the sample solution is taken and placed in a 5 mm nuclear magnetic resonance tube, and the nuclear magnetic resonance tube is sealed for measurement.
3. The method according to claim 1, wherein The pulse sequence is NOESY1D pulse sequence; The technical parameters include temperature, pulse angle, pulse width, mixing time, delay time, spectral width, number of sampling points, number of scans, etc.; The nuclear magnetic resonance spectrometer is a high-field nuclear magnetic resonance spectrometer.
4. The method according to claim 1, characterized in that, The said 1 The 1H NMR spectrum was obtained by Fourier transform with 64 K Fourier transform times, a line width factor of 0.3 Hz, and automatic phase adjustment. The chemical shift origin was set at 0.00 ppm of the TSP-d4 signal peak. Manually correct the baseline, select spectral data with a chemical shift range of 0.63 - 8.48 ppm (excluding the water peak at 4.73 - 4.98 ppm) for segmented integration, set the integration interval to 0.05 ppm, normalize the integration region, and 1 convert the 1H NMR spectrum into a data list; The data is imported into the analysis software, and models such as but not limited to principal component analysis (PCA) or orthogonal partial least squares-discriminant analysis (OPLS-DA) are used to classify the quality grade and picking quarter of Ampelopsis grossedentata.
5. The method according to claim 2, characterized in that, The concentration of the TSP-d4 solution is 500 mg / L. TSP-d4 and sodium azide are dissolved in heavy water to obtain the TSP-d4 solution, which is stored at room temperature.
6. The method according to claim 2, wherein The concentration of the phosphate buffer solution is 1 mol / L. Potassium dihydrogen phosphate is dissolved in pure water and adjusted to pH 7.0 with sodium hydroxide, and it is stored at room temperature.
7. The method according to claim 3, characterized in that The specific technical parameters are as follows: the test temperature is set at 25 °C, heavy water is used for field locking, the pulse angle is 90°, the pulse width is 10.7 μs, the mixing time is 100 ms, the relaxation delay time is 4 s, the spectral width is 20 ppm, the number of sampling points is 16 K, the number of scans is 32, pre-saturation is used to suppress the water peak, and the applied power is about 50 Hz, and the duration (including the delay time) is 2 s.
8. The application of the method, steps and substances described in claims 1-6 and the measurement parameters described in claim 7 in the determination of the quality of Ampelopsis grossedentata.