A method for identifying the variety and grade of fish maw based on nuclear magnetic resonance technology

Abstract

A method for identifying the variety and grade of fish maw based on nuclear magnetic resonance technology, belonging to the field of analytical technology. Collect samples of different varieties of fish maw; enzymatically hydrolyze the samples with a mixture of phosphate buffer, trypsin, and alkaline protease; perform nuclear magnetic resonance detection on the samples and collect the original spectra; preprocess the spectra to obtain two-dimensional integral data; conduct data statistical analysis for the attribution of characteristic peaks, calculate the indexes of different fish maw contents using the characteristic peaks, and establish a database of thresholds for different fish maw components and contents; use the unified standard of the fish maw database to set the thresholds for component and content indexes, and design an expert system to directly process the two-dimensional integral data to identify the variety and grade of fish maw. For a single sample, the variety or grade of the fish maw can be identified, and at the same time, the area of the characteristic peak, the chemical shift corresponding to the characteristic peak, and the corresponding threshold are given; for multiple samples, the identification result, that is, the variety or grade of the fish maw, is directly given; at the same time, the normalized data of the fish maw will be displayed in the form of a spectrum.

Description

Technical Field

[0001] The invention belongs to the technical field of analysis, and in particular relates to a method for identifying fish maw varieties and grades based on nuclear magnetic resonance technology, which can quickly identify fish maw varieties and grades. Background Art

[0002] Fish maw, also known as fish maw, fish bubble, fish belly, pressed cell, etc., is a dried fish bladder product. It is a nutritious food rich in protein and a precious Chinese medicinal material, as famous as shark fin, sea cucumber and bird's nest. In the past, most fish maws came from kingfish, but with the expansion of the demand and market for fish maw, other varieties of fish maw have gradually entered the field of vision of consumers. Nowadays, there are very few studies on the identification of fish maw, and there is no unified standard. It only relies on traditional experience to determine the variety and quality. Fish maw in the market is usually identified mainly by shape, and most of them are initially judged by appearance. The prices of fish maws of different varieties and qualities vary greatly, so some unscrupulous vendors sell inferior products as good ones to make huge profits, which seriously infringes on the rights and interests of consumers. Therefore, the identification of the quality of fish maw is of great significance to consumer purchases and the supervision of the fish maw market, and it also provides reference value for the identification research of other similar foods.

[0003] Nuclear magnetic resonance technology is non-destructive, rapid, accurate, and has high resolution. It is mainly used in new research on molecular structure and reaction processes in the biochemical field, and is also widely used in foodomics. According to its technical characteristics, nuclear magnetic resonance detection can mainly study substances such as water, fatty acids (esters), proteins, and amino acids. Using high-resolution nuclear magnetic resonance technology to detect food samples can obtain a wealth of large and multidimensional information about food macromolecules or small molecules. Using foodomics based on nuclear magnetic resonance hydrogen spectrum technology to detect the grade and variety of fish glue can provide the fish glue market with a scientific and convenient technical method for identifying the quality of fish glue. Summary of the invention

[0004] The purpose of the present invention is to provide a method for identifying fish glue varieties and grades based on nuclear magnetic resonance technology. A threshold database of components and content parameters of ten common fish glues is established. The method of the present invention can be used to quickly and efficiently identify fish glues of different varieties and grades, which is economical, convenient and fast. At the same time, it can also improve the efficiency of relevant regulatory departments, protect the rights and interests of consumers, and facilitate promotion and use.

[0005] The present invention comprises the following steps:

[0006] 1) Collect samples: Collect fish maw samples of different species and store them at room temperature after collection.

[0007] 2) Sample enzymatic hydrolysis: The fish gelatin samples were enzymatically hydrolyzed with a mixture of phosphate buffer, trypsin and alkaline protease;

[0008] 3) Nuclear magnetic resonance spectrum determination: Nuclear magnetic resonance detection is performed on each fish maw sample to obtain the original spectrum.

[0009] 4) Processing and analyzing the original spectrum: The original spectrum is preprocessed to obtain two-dimensional integral data, and data analysis is carried out to obtain the attribution of characteristic peaks. According to the nuclear magnetic resonance characteristic peaks and differential metabolites of different varieties of fish maw, the parameter thresholds of the content of each component of fish maw are calculated, so as to establish a database of fish maw component content thresholds.

[0010] 5) Designing a fish maw variety identification and grade assessment system: Using the fish maw component content threshold database to uniformly set the component and content index thresholds, the MATLAB program software is used to design a fish maw variety identification and grade assessment system, which directly processes the two-dimensional integral data matrix in Excel format for food omics analysis of identifying the variety and grade of fish maw, and the accuracy of the system is verified through new fish maw sample data.

[0011] In step 2), the specific steps of the sample enzymolysis can be as follows: The fish maw is first quickly frozen with liquid nitrogen and then ground into powder or blocks by a pulverizer. Take 100 mg of fish maw, 15 mg of trypsin, 48 mg of alkaline protease and 3 mL of ultrapure water, and heat in a water bath at 55 °C for 4 h for enzymolysis of the fish maw; after the water bath, cool to room temperature, centrifuge at 10,000 g for 10 min at 4 °C, take 300 μL of the supernatant, add 300 μL of buffer (containing 0.05% TSP), shake the solution until uniform, then centrifuge at 10,000 g for 10 min at 4 °C, take out 500 μL and transfer it into a 5 mm nuclear magnetic resonance tube, seal it, and place it in a 277 K refrigerator and let it stand for 12 h waiting for the experiment.

[0012] In step 3), the specific steps of the nuclear magnetic resonance spectrum determination can be as follows: It is completed on a Bruker 600 MHz nuclear magnetic resonance spectrometer equipped with a cryogenic probe, pulse sequence, zg30; number of scans and accumulations, 64 times; relaxation delay, 4 s; single acquisition time, 1.93 s; number of acquisition points, 32K; spectral width, 6613.8 Hz; the experimental environment is controlled at 298 K by the spectrometer; all samples are selected for detection using the zg30 sequence to collect the high-resolution one-dimensional nuclear magnetic resonance hydrogen spectrum of fish maw.

[0013] In step 4), the specific steps for the processing and analysis of the original spectrum may be as follows: The original spectrum of the collected fish maw sample is preprocessed using MestReNova software. After the preprocessing of the spectrum, sectional integration and full-spectrum normalization are performed to obtain a two-dimensional matrix with each row being an analysis sample and each column being micro-component information. Finally, the substance attribution of the spectral peaks is carried out; the integral values in the integral intervals where the characteristic peaks are located are superimposed, and the threshold is determined through the relative quantitative results of the fish maw; the preprocessing includes Fourier transform, adding a 1 Hz window function to improve the signal-to-noise ratio, phase adjustment, baseline correction, calibration and other preprocessing.

[0014] In step 5), the logic for designing the fish maw variety identification and grade identification system may be as follows: It is compiled based on MATLAB software and can directly read the integral data information exported by MestRenova. Then, the integral area is calculated according to the chemical shifts corresponding to the previously screened characteristic metabolites and characteristic peaks, and then compared and analyzed with the previous data to classify the variety and grade of the fish maw; finally, based on these principles, MATLAB is used to write the code, and the code is mainly divided into three parts, namely the code for the main interface and the code for realizing variety identification and grade identification. Finally, it is interfaced through the graphical user interface (GUI) function in MATLAB; the accuracy rates for identifying the variety and grade of the fish maw using the designed identification system reach 92.6% and 90.0%.

[0015] The present invention first performs enzymatic pretreatment on the fish maw sample to meet the requirements of nuclear magnetic resonance experiments; the pretreated fish maw sample to be detected is subjected to nuclear magnetic resonance hydrogen spectrum measurement to obtain the original fingerprint spectrum of the fish maw sample to be detected; these original fingerprint spectra are subjected to spectrum preprocessing to obtain a two-dimensional data matrix containing fish maw small molecule information; these data matrices are imported into the fish maw analysis models of different grades and varieties for identifying the grade and variety of the sample to be detected. In practical applications, according to the existing composition and content parameters, a MATLAB program expert system is designed. Just read the Excel table of the two-dimensional data matrix with fish maw information into the expert system, and the system will automatically analyze and identify the information on the grade or variety of the fish maw according to the requirements.

[0016] The food sample of the present invention is a common fish maw on the market. Its collection and processing process is simple and convenient, which effectively improves the identification efficiency and accuracy of fish maw variety and grade, reduces the detection cost of the regulatory department, and is of great significance for improving the quality supervision of the fish maw industry, protecting the consumer market and the rights and interests of consumers. It has important economic and social benefits and is convenient for popularization and application. At the same time, the present invention is also the first to use nuclear magnetic resonance technology for identifying the grade and variety of fish maw, and the method is novel and unique. Description of the Drawings

[0017] Figure 1 For a typical fish maw 1H-NMR spectrum.

[0018] Figure 2 PCA and PLS-DA score plots for different varieties of fish maw.

[0019] Figure 3 PCA and PLS-DA score plots for different varieties and grades of fish maw.

[0020] Figure 4 Reference diagram for the operation interface of the expert system and the display of variety identification and grade identification results. Detailed implementation mode

[0021] The following embodiments will further illustrate the present invention in conjunction with the accompanying drawings.

[0022] 1. Research object

[0023] In this embodiment, 10 varieties of fish maw in the first batch are collected, namely yellow fish maw (Yel), egg fish maw (Egg), red chicken nose fish maw (Mou), arowana fish maw (Aro), Beihai fish maw (Nor), butterfly fish maw (But), Arctic cod fish maw (Arc), red fish maw (Red), stone fish maw (Sto), and saltwater spider fish maw (Sal). Five samples are prepared for each variety, a total of 50 fish maw samples are used to establish an expert system for fish maw grades. Six varieties of fish maw in the second batch are collected, namely egg fish maw, red fish maw, butterfly fish maw, yellow fish maw, cod fish maw, and Beihai male fish maw. Ten samples are prepared for each variety, a total of 60 fish maw samples are used to verify the expert system for fish maw varieties. Subsequently, during the verification of the expert system, 20 samples will be prepared for fish maw grades and 54 samples will be prepared for fish maw varieties to test the accuracy of the system.

[0024] 2. Reagents and equipment

[0025] The experimental reagents include a mixed solution of phosphate buffer, trypsin, and alkaline protease.

[0026] The experimental consumables include liquid nitrogen, 5 mm NMR tubes, 50 mL EP tubes, marker pens, latex gloves, and masks.

[0027] The experimental equipment includes a powder mill, 100 μL, 200 μL, 500 μL microinjectors, a centrifuge, an oscillator, and a Bruker 600 MHz nuclear magnetic resonance spectrometer.

[0028] 3. Collection and pretreatment of samples

[0029] The fish maw is first quickly frozen with liquid nitrogen and then ground into powder or blocks by a pulverizer. Take 100 mg of fish maw, 15 mg of trypsin, 48 mg of alkaline protease and 3 mL of ultrapure water, and carry out enzymatic hydrolysis of the fish maw by heating in a water bath at 55 °C for 4 h. After the water bath, cool to room temperature, centrifuge at 10,000 g for 10 min at 4 °C, take 300 μL of the supernatant, add 300 μL of buffer (containing 0.05% TSP), shake the solution until uniform, then centrifuge at 10,000 g for 10 min at 4 °C, take out 500 μL and transfer it into a 5 mm NMR tube, seal it, and put it in a 277 K refrigerator and let it stand for 12 h waiting for the experiment.

[0030] 4. Nuclear magnetic resonance spectrum determination

[0031] The nuclear magnetic resonance experiment was completed on a Bruker 600 MHz nuclear magnetic resonance spectrometer equipped with a cryoprobe. The experimental temperature was set at 298 K, and the resonance frequency of hydrogen was 600.13 MHz. The zg30 sequence was selected for the detection of the fish maw sample, and the high-resolution one-dimensional nuclear magnetic resonance hydrogen spectrum of the fish maw was collected. The sampling parameters are as follows: number of scans: 64 times; relaxation delay: 4 s; sampling time: 1.93 s; number of sampling points: 32 K; spectral width: 6613.8 Hz.

[0032] 5. Pretreatment of nuclear magnetic resonance spectrum

[0033] The original fingerprint spectrum of the collected fish maw sample was preprocessed using MestReNova v.9.0.1 software, including Fourier transform, phase correction, baseline correction, spectral peak calibration, removal of residual water peaks, etc. After integration and full-spectrum normalization of the processed spectrum, a two-dimensional matrix with each row being the analysis sample and each column being the small molecule information of the fish maw components was obtained. The attribution of characteristic spectral peaks refers to the databases of KEGG and HMDB and the published literature. The information of the attributed characteristic peaks is shown in Figure 1 .

[0034] 6. Multivariate and univariate statistical analysis

[0035] The data matrix was imported into SIMCA 14.0 software for multivariate statistical analysis, mainly including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). From Figure 2 and 3 the PCA and PLS-DA score plots, it can be seen that the components of the same type or the same grade of fish maw are similar and can be well aggregated together, while the fish maws with different components are well separated in the two principal component directions. The greater the component difference, the more separated the samples are, indicating that different varieties or grades of fish maw can be distinguished by their component substances.

[0036] 7. Calculation of fish maw component content and identification standard of fish maw.

[0037] According to the one-dimensional nuclear magnetic resonance hydrogen spectrum and the screened differential metabolites, the area of the interval corresponding to each component is accumulated to obtain the content of each component, and then the content parameter threshold of the differential metabolites is calculated. The discrimination criteria for fish maw varieties are shown in Table 1, and the discrimination criteria for fish maw grades are shown in Table 2.

[0038] Table 1. Thresholds for Judging Fish Maw Varieties

[0039]

[0040] Note: Red: Red fish maw, Yel: Yellow flower fish maw, But: Butterfly fish maw, Arc: Cod fish maw, Nor: North Sea male fish maw, Egg: Egg fish maw; s: represents the integral area of the characteristic peak of the corresponding component. (s1 = 0.96; s2 = 1.09; s3 = 1.14; s4 = 1.48; s5 = 2.12; s6 = 2.65; s8 = 3.69; s9 = 3.76; s10 = 4.65; s11 = 7.43; s12 = 7.54; s13 = 7.88; s14 = 2.44; s15 = 6.53; s16 = 5.20; s17 = 7.13; s18 = 0.99; s19 = 1.16; s20 = 1.17; s21 = 1.20; s22 = 1.73; s23 = 2.31; s24 = 2.74; s25 = 2.04; s26 = 2.10; s27 = 2.36; s28 = 3.25; s29 = 2.72; s30 = 3.30; s31 = 3.46; s32 = 3.78; s33 = 2.25; s34 = 3.57; s44 = 5.40)

[0041] Table 2. Thresholds for Judging Fish Maw Grades

[0042]

[0043] Note: First grade (Red Chicken Red Beak, Stone Belly Fish Maw); Second grade (Beihai Fish Maw, Yellow Flower Fish Maw, Arctic Cod Fish Maw, Golden Dragon Fish Maw, Saltwater Spider Fish Maw); Third grade (Red Fish Maw, Butterfly Fish Maw, Egg Fish Maw); s: represents the integrated area of the characteristic peak of the corresponding component. (s1 = 0.96; s2 = 1.09; s3 = 1.14; s4 = 1.48; s5 = 2.12; s6 = 2.65; s8 = 3.69; s9 = 3.76; s10 = 4.65; s11 = 7.43; s12 = 7.54; s13 = 7.88; s14 = 2.44; s15 = 6.53; s16 = 5.20; s17 = 7.13; s18 = 0.99; s19 = 1.16; s20 = 1.17; s21 = 1.20; s22 = 1.73; s23 = 2.31; s24 = 2.74; s25 = 2.04; s26 = 2.10; s27 = 2.36; s28 = 3.25; s29 = 2.72; s30 = 3.30; s31 = 3.46; s32 = 3.78; s33 = 2.25; s34 = 3.57; s44 = 5.40)

[0044] 8. Determination Results of MATLAB Software Expert System

[0045] Reference Figure 4 , the MATLAB software expert system can be compiled by itself using MATLAB software. Its code logic is to first directly read the integrated data information exported by MestRenova, then calculate the integrated area according to the chemical shifts corresponding to the previously selected characteristic metabolites and characteristic peaks, and then compare and analyze with the previous data to classify the variety and grade of fish maw; finally, based on these principles, MATLAB is used to write the code, and the code is mainly divided into three parts, namely the code of the main interface and the code for realizing variety identification and grade identification. Finally, it is interfaced through the graphical user interface (GUI) function in MATLAB; the accuracy rates of identifying the variety and grade of fish maw using the designed identification system reach 92.6% and 90.0% respectively.

[0046] The nuclear magnetic data of 20 fish maw samples and 54 fish maw samples are imported respectively to verify the fish maw grade and variety expert system. The final results show that the correct rate of the fish maw grade expert system is 90.0%, and the correct rate of the fish maw variety expert system is 92.6%.

[0047] 9. Conclusion

[0048] As can be seen from the above results, the expert system designed by the method for identifying the grade and variety of fish maw based on nuclear magnetic resonance technology described in the present invention has a good identification effect. At the same time, for a single sample, information such as the area of the identification characteristic peak, the corresponding chemical shift, and the discrimination standard can be reflected in the results. At the same time, the identification results can be saved to the local folder with one key. The accuracy rates of the measured and simulated data also reach ideal effects and can be used for the rapid identification of the varieties and grades of fish maw in the market.

[0049] The method for identifying the grade and variety of fish maw based on nuclear magnetic resonance technology described in the present invention comprises the following steps:

[0050] (1) Pretreat the fish maw sample to be detected according to step 3 above to meet the requirements of nuclear magnetic resonance experiments;

[0051] (2) Measure the nuclear magnetic resonance spectrum of the pretreated fish maw sample to be detected according to step 4 above to obtain the original fingerprint spectrum of the fish maw sample to be detected;

[0052] (3) Pretreat these original fingerprint spectra according to step 5 above to obtain a two-dimensional data matrix containing fish maw molecular information;

[0053] (4) Import this data matrix into the designed expert system in the form of Excel according to steps 6, 7, and 8 above to identify the grade and variety of the fish maw to be detected.

[0054] The present invention is the first to use nuclear magnetic resonance technology to establish fish maw index data and design a program to identify fish maw. Using the method of the present invention, the varieties and grades of different fish maw can be identified quickly and efficiently, providing a scientific identification technology for the supervision of the fish maw market.

[0055] The above is a description rather than a limitation of the present invention patent. Other embodiments based on the idea of the present invention patent are within the protection scope of the present invention.

Claims

1. A method for identifying the variety and grade of fish maw based on nuclear magnetic resonance technology, characterized in that It includes the following steps: 1) Sample collection: Collect fish maw samples of different varieties. After all samples are collected, store them at room temperature. 2) Sample enzymatic hydrolysis: Hydrolyze the fish maw samples with a mixed solution of phosphate buffer, trypsin, and alkaline protease. 3) Nuclear magnetic resonance spectrum determination: Perform nuclear magnetic resonance detection on each fish maw sample to obtain the original spectrum. 4) Process and analyze the original spectrum: Preprocess the original spectrum to obtain two-dimensional integral data, perform data analysis to obtain the attribution of characteristic peaks, and calculate the parameter thresholds of the content of each component of the fish maw based on the nuclear magnetic resonance characteristic peaks and differential metabolites of different varieties of fish maw, thereby establishing a database of fish maw component content thresholds. 5) Design a fish maw variety identification and grade assessment system: Use the fish maw component content threshold database to uniformly set the component and content index thresholds, and use MATLAB program software to design a fish maw variety identification and grade assessment system to directly process the two-dimensional integral data matrix in Excel format for foodomics analysis of identifying the variety and grade of fish maw, and verify the accuracy of the system through new fish maw sample data.

2. The identification method of fish maw varieties and grades based on nuclear magnetic resonance technology according to claim 1, characterized in that In step 2), the specific steps of the sample enzymatic hydrolysis are as follows: The fish maw is first quickly frozen in liquid nitrogen and then ground into powder or blocks by a pulverizer. Take 100 mg of fish maw, 15 mg of trypsin, 48 mg of alkaline protease, and 3 mL of ultrapure water, and heat in a water bath at 55 °C for 4 h for enzymatic hydrolysis of the fish maw. After the water bath, cool to room temperature, centrifuge at 10,000 g for 10 min at 4 °C, take 300 μL of the supernatant, add 300 μL of buffer containing 0.05% TSP, shake the solution until uniform, then centrifuge at 10,000 g for 10 min at 4 °C, take out 500 μL and transfer it into a 5 mm nuclear magnetic resonance tube, seal it, and place it in a 277 K refrigerator and let it stand for 12 h for the experiment.

3. The identification method of fish maw variety and grade based on nuclear magnetic resonance technology according to claim 1, characterized in that In step 3), the specific steps of the nuclear magnetic resonance spectrum determination are as follows: It is completed on a Bruker 600 MHz nuclear magnetic resonance spectrometer equipped with a cryoprobe. Pulse sequence: zg30; Scanning and stacking times: 64 times; Relaxation delay: 4 s; Single acquisition time: 1.93 s; Acquisition points: 32 K; Spectral width: 6613.8 Hz; The experimental environment is controlled at 298 K by the spectrometer; All samples are detected using the zg30 sequence to collect the high-resolution one-dimensional nuclear magnetic resonance hydrogen spectrum of the fish maw.

4. The identification method of fish maw variety and grade based on nuclear magnetic resonance technology according to claim 1, characterized in that In step 4), the specific steps of processing and analyzing the original spectrum are as follows: The original spectrum of the fish maw sample obtained by collection is preprocessed using MestReNova software. After preprocessing the spectrum, perform sectional integration and full-spectrum normalization to obtain a two-dimensional matrix with each row being the analysis sample and each column being the micro-component information, and finally perform substance attribution on the spectral peaks. Overlay the integral values in the integral interval where the characteristic peaks are located, and determine the threshold through the relative quantitative results of the fish maw; The preprocessing includes Fourier transform, adding a 1 Hz window function to improve the signal-to-noise ratio, phase adjustment, baseline correction, and calibration preprocessing.

5. The identification method of fish maw variety and grade based on nuclear magnetic resonance technology according to claim 1, wherein In step 5), the logic of the designed fish maw variety identification and grade evaluation system is as follows: Compiled based on MATLAB software, it directly reads the integral data information exported by MestRenova, then calculates the integral area according to the chemical shifts corresponding to the previously screened characteristic metabolites and characteristic peaks, and then conducts comparative analysis with the previous data to classify the variety and grade of fish maw; Finally, based on these principles, MATLAB is used to write the code, which is mainly divided into three parts, namely the code for the main interface and the code for realizing variety identification and grade evaluation. Finally, it is interfaced through the graphical user interface function in MATLAB; The accuracy rates of using the designed identification system to identify the variety and grade of fish maw reach 92.6% and 90.0% respectively.

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