Method for detecting fragrance of flower fragrance type cold-brewed white tea

Abstract

The invention discloses a method for detecting the fragrance of flower fragrance type cold-brewed white tea, which realizes rapid and nondestructive evaluation of the fragrance of white tea through a bimodal detection technology combining electronic nose preliminary screening with GC-MS accurate verification. The method comprises the following steps: firstly, acquiring a sensor response value of headspace gas of tea soup by using an electronic nose, synchronously measuring the content of key aroma components through GC-MS, and establishing a quantitative correlation model between the two; the method can efficiently distinguish the aroma difference of white tea treated by different baking processes, and is especially suitable for predicting key aroma components such as linalool and methyl salicylate, and the detection time is shortened compared with that of traditional GC-MS (Gas Chromatography-Mass Spectrometry).

Description

Technical Field

[0001] The present invention relates to the technical field of tea processing, and more particularly to a method for detecting the aroma of floral cold-brewed white tea. The method is particularly suitable for quality control of white tea products with floral aromas such as jasmine and rose. Background Art

[0002] Tea, an important beverage, is widely consumed worldwide. Tea aroma is formed through a series of complex biochemical reactions during the processing of fresh tea leaves. As a key factor in evaluating tea quality, the accurate identification of tea aroma compounds is crucial. The composition and concentration of aroma are closely related to factors such as tea plant variety, growing environment, processing techniques, and storage time and temperature. The aroma of each tea leaf is a combination of various compounds at varying concentrations, and the interactions between these compounds significantly influence the overall aroma profile. Traditionally, tea aroma is assessed using sensory techniques. Floral aroma scoring relies on subjective judgment by experts, which is highly subjective and has poor reproducibility. GC-MS, currently one of the primary methods for tea aroma compound identification, is accurate but time-consuming and costly, making it inadequate for real-time production line monitoring. Existing electronic nose detection technologies are susceptible to temperature and humidity interference (error rates >20%) and lack the ability to distinguish complex tea aroma characteristics, such as floral from fruity white tea. Summary of the Invention

[0003] This invention provides a method for detecting the aroma of floral cold-brewed white tea, enabling rapid, non-destructive testing of the aroma quality of cold-brewed white tea. It is particularly suitable for evaluating the aroma quality of Baihao Yinzhen tea treated with different roasting and aroma-enhancing processes. It can quickly distinguish the aroma characteristics of different treatment groups, providing real-time feedback for optimizing tea processing. This method offers the advantages of simple operation, rapid detection, and accurate results.

[0004] To achieve the above object, the present invention adopts the following technical solution: a method for detecting the aroma of floral cold-brewed white tea, comprising the following steps: (1) Using an electronic nose to detect tea aroma and obtain conductivity data from the array sensor; (2) Detection of key aroma components in tea by gas chromatography-mass spectrometry (GC-MS); (3) Establish a corresponding relationship model between electronic nose data and GC-MS detection results; (4) Rapidly predict the aroma quality of tea through electronic nose detection data.

[0005] Furthermore, the electronic nose detection adopts the tea soup method. 3g of tea sample is weighed and placed in a 50ml conical flask, and then 150ml of boiling water is poured in. After standing for 30 minutes, the tea soup headspace gas is formed for detection. The electronic nose detection uses a metal oxide sensor as an array sensor to detect the tea soup headspace gas, and collects the stable conductivity ratio data of the W1C, W5S, W3C, and W2S sensors.

[0006] Furthermore, GC-MS detection adopted the headspace solid phase microextraction method, in which 1.00 g of tea sample was added into a 20 mL headspace vial, 1.00 µg of ethyl caprate was added as an internal standard, 6 mL of boiling water was added, and extraction was performed at 60 °C for 30 min using a 65 μm PDMS / DVB extraction head, and the key aroma components were determined by GC-MS.

[0007] Furthermore, the electronic nose array sensor includes W1C, W3C, and W5C sensors that are sensitive to aromatic components, a W2S sensor that is sensitive to alcohols / aldehydes and ketones, and a W1W sensor that is sensitive to sulfides.

[0008] Further, the GC-MS detection conditions are: Chromatographic column: DB-WAX, specifications: 30m×0.25mm×0.25µm; Temperature program: 50°C for 5 min, then increase to 230°C at 6°C / min and hold for 7 min; Mass spectrometer interface temperature 280°C, ion source temperature 230°C Furthermore, the key aroma components include linalool, methyl salicylate, geraniol, α-terpinene, and d-limonene, and their content thresholds are quantitatively calibrated by GC-MS.

[0009] Furthermore, in step (3), principal component analysis (PCA) and partial least squares regression (PLSR) were used to establish a quantitative relationship model between the electronic nose signal and the key aroma components. The association model was established by performing multivariate statistical analysis on the electronic nose sensor response values ​​and the key aroma component contents detected by GC-MS, constructing an aroma quality prediction model, and rapidly outputting the key aroma component contents by inputting the electronic nose data into the model.

[0010] The present invention achieves rapid and non-destructive evaluation of white tea aroma through dual-modal detection technology that combines preliminary screening with electronic nose and precise verification with GC-MS. First, the electronic nose is used to collect sensor response values ​​of the headspace gas of the tea soup, and the content of key aroma components is simultaneously measured by GC-MS to establish a quantitative correlation model between the two. This method can efficiently distinguish the aroma differences of white tea treated with different baking processes, and is particularly suitable for predicting key aroma components such as linalool and methyl salicylate. The detection time is shortened by more than 80% compared with traditional GC-MS. Compared with the existing technology, the present invention has the following beneficial effects: 1) Efficient and convenient: The electronic nose can complete preliminary screening within 5 minutes, effectively replacing some of the cumbersome GC-MS detection processes; 2) Non-destructive testing: Only a small amount of tea is needed for testing, which will not damage the sample and is very suitable for real-time monitoring on the production line; 3) Accurate correlation: The model converts the electronic nose signal into the specific component content, effectively solving the problem of qualitative ambiguity of the electronic nose. DETAILED DESCRIPTION

[0011] The following is a detailed description of a method for detecting the aroma of floral cold-brewed white tea according to the present invention.

[0012] A method for detecting the aroma of floral cold-brewed white tea comprises the following steps: (1) Using an electronic nose to detect tea aroma and obtain conductivity data from the array sensor; (2) Detection of key aroma components in tea by gas chromatography-mass spectrometry (GC-MS); (3) Establish a corresponding relationship model between electronic nose data and GC-MS detection results; (4) Rapidly predict the aroma quality of tea through electronic nose detection data.

[0013] Preferably, in this embodiment, the electronic nose detection adopts the tea soup method. 3 g of tea sample is weighed and placed in a 50 ml conical flask, and then 150 ml of boiling water is poured into it. After standing for 30 minutes, the tea soup headspace gas is formed for detection. The electronic nose detection uses a metal oxide sensor as an array sensor to detect the tea soup headspace gas, and collects the stable conductivity ratio data of the W1C, W5S, W3C, and W2S sensors.

[0014] In this embodiment, preferably, GC-MS detection adopts headspace solid phase microextraction, taking 1.00g of tea sample and adding it to a 20mL headspace bottle, adding 1.00µg of ethyl caproate as an internal standard, adding 6mL of boiling water, and extracting at 60℃ for 30min using a 65μm PDMS / DVB extraction head, and determining the key aroma components by GC-MS.

[0015] In this embodiment, the electronic nose array sensor preferably includes W1C, W3C, and W5C sensors that are sensitive to aromatic components, a W2S sensor that is sensitive to alcohols / aldehydes and ketones, and a W1W sensor that is sensitive to sulfides.

[0016] In this embodiment, preferably, the GC-MS detection conditions are: Chromatographic column: DB-WAX, specifications: 30m×0.25mm×0.25µm; Temperature program: 50°C for 5 min, then increase to 230°C at 6°C / min and hold for 7 min; Mass spectrometer interface temperature 280°C, ion source temperature 230°C In this embodiment, the key aroma components preferably include linalool, methyl salicylate, geraniol, α-terpinene, and d-limonene, and their content thresholds are quantitatively calibrated by GC-MS.

[0017] In this embodiment, preferably, in step (3), principal component analysis (PCA) and partial least squares regression (PLSR) are used to establish a quantitative relationship model between the electronic nose signal and the key aroma components. Establishing the association model specifically involves performing multivariate statistical analysis on the electronic nose sensor response values ​​and the key aroma component contents detected by GC-MS, constructing an aroma quality prediction model, and rapidly outputting the key aroma component contents by inputting the electronic nose data into the model.

[0018] This paper establishes a correlation model between electronic nose sensor data and GC-MS quantitative results to quickly predict the content of key aroma components (such as linalool and methyl salicylate). The core calculation model and formula are as follows: 1. Data Preprocessing Standardization of electronic nose sensor response values ​​(eliminating baseline drift):

[0019] R i : Real-time conductivity value of sensor i (such as W1C, W5S, etc.), R 0,i : Baseline conductivity of sensor i in clean air, Output the normalized response matrix X n×p (n is the number of samples, p is the number of sensors).

[0020] GC-MS data normalization (internal standard method):

[0021] C j : Concentration of aroma component j (such as linalool) (μg / g) A j : Peak area of ​​component j A 内标 : Peak area of ​​ethyl decanoate (internal standard) C 内标 : Internal standard concentration (1.00 μg / g) Output concentration matrix Y n×m (m is the number of key aroma components).

[0022] 2. Feature extraction and dimensionality reduction (PCA) Perform principal component analysis (PCA) on the electronic nose sensor response matrix X (n×p, n number of samples, p number of sensors): T=XP.

[0023] T: principal component score matrix (retain the first k principal components, with cumulative variance contribution rate ≥ 85%); P: Loading matrix (obtained by eigenvalue decomposition).

[0024] 3. Partial Least Squares Regression (PLSR) Model Establish a quantitative relationship between the main component of the electronic nose (T) and the concentration of the key component (Y) of GC-MS: Y=TB+E Y: aroma component concentration matrix measured by GC-MS (n × m, m is the number of components); B: Regression coefficient matrix (solved by minimizing the residual E).

[0025] Specific regression equation (taking linalool as an example): Linalool = b0 + b1 * PC1 + b2 * PC2 + ⋯ + b k *PC k ; b0,b1,…,b k : PLSR model coefficients (calibrated by training set); PC1,PC2,…,PC k : Principal component scores of electronic nose data.

[0026] 4. Model Validation and Optimization Cross-validation: leave one out method (LOO-CV) was used to calculate the coefficient of determination (R 2 ) and root mean square error (RMSE): , ,

[0027] R 2 >0.9, and RMSE<10%, indicating that the model prediction reliability is high.

[0028] Specific example applications: The sensor response value of the electronic nose detecting a white tea sample was obtained after PCA: PC1=2.3,PC2=−0.8,PC3=1.1 The prediction coefficient of linalool calibrated by the PLSR model is: b0=5.2,b1=1.8,b2=−0.5,b3=0.3 The predicted concentration is: Linalool = 5.2 + 1.8 × 2.3 + (−0.5) × (−0.8) + 0.3 × 1.1 = 10.37 μg / g The error with the actual value measured by GC-MS (10.52 μg / g) is only 1.4%, which verifies the effectiveness of the model.

[0029] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for detecting the aroma of floral cold-brewed white tea, characterized in that: The following steps are involved: (1) Using an electronic nose to detect tea aroma and obtain conductivity data from the array sensor; (2) Detection of key aroma components in tea by gas chromatography-mass spectrometry (GC-MS); (3) Establish a corresponding relationship model between electronic nose data and GC-MS detection results; (4) Rapidly predict the aroma quality of tea through electronic nose detection data.

2. The method for detecting the aroma of floral cold-brewed white tea according to claim 1, wherein: The electronic nose detection adopts the tea soup method. 3g of tea sample is weighed and placed in a 50ml conical flask, followed by pouring 150ml of boiling water. After standing for 30 minutes, the tea soup headspace gas is formed for detection. The electronic nose detection uses metal oxide sensors as array sensors to detect the tea soup headspace gas, and collects the stable conductivity ratio data of the W1C, W5S, W3C, and W2S sensors.

3. The method for detecting the aroma of floral cold-brewed white tea according to claim 1, wherein: GC-MS detection uses headspace solid phase microextraction method. Take 1.00g tea sample and add it to 20mL headspace bottle. Add 1.00µg ethyl caprate as internal standard, add 6mL boiling water, and extract at 60℃ for 30min using 65μm PDMS / DVB extraction head. The key aroma components are determined by GC-MS.

4. The method for detecting the aroma of floral cold-brewed white tea according to claim 1, wherein: The electronic nose array sensor includes W1C, W3C, and W5C sensors that are sensitive to aromatic components, W2S sensor that is sensitive to alcohols / aldehydes and ketones, and W1W sensor that is sensitive to sulfides.

5. The method for detecting the aroma of floral cold-brewed white tea according to claim 1, wherein: The GC-MS detection conditions are: Chromatographic column: DB-WAX, specifications: 30m×0.25mm×0.25µm; Temperature program: 50°C for 5 min, then increase to 230°C at 6°C / min and hold for 7 min; The mass spectrometer interface temperature was 280°C, and the ion source temperature was 230°C.

6. The method for detecting the aroma of floral cold-brewed white tea according to claim 1, wherein: The key aroma components include linalool, methyl salicylate, geraniol, α-terpinene, and d-limonene, and their content thresholds are quantitatively calibrated by GC-MS.

7. The method for detecting the aroma of floral cold-brewed white tea according to claim 1, wherein: In step (3), principal component analysis (PCA) and partial least squares regression (PLSR) are used to establish a quantitative relationship model between electronic nose signals and key aroma components.

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