Method for identifying vinegar type by using LC-HRMS fingerprint technology
By combining non-targeted LC-HRMS fingerprint technology with multivariate statistical analysis, the problem of vinegar type identification was solved, rapid and accurate vinegar classification was achieved, the reliability and reproducibility of the analysis results were ensured, and the interests of consumers were protected.
Patent Information
- Application Number
- CN202410261542.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-07
- Publication Date
- 2025-09-09
AI Technical Summary
Existing technologies make it difficult to quickly and accurately distinguish and identify different fermentation types of vinegar, especially when liquid fermented vinegar is masquerading as solid fermented vinegar. Conventional detection methods are unable to meet market accuracy and consumer protection needs.
Non-targeted LC-HRMS (Orbitrap) fingerprint technology combined with multivariate statistical analysis was used to conduct a comprehensive analysis of vinegar samples, establish a classification model, use quality control samples to monitor instrument stability, and adopt dilution method and data integration to achieve differential identification of solid and liquid brewed vinegar.
It achieves fast and accurate vinegar classification, ensures the reliability and reproducibility of analysis results, can identify counterfeit products, protect consumer interests, and promote the healthy development of the vinegar industry.
Smart Images
Figure CN120609919A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of food technology detection, and particularly relates to a method for classifying and studying edible vinegars of different fermentation types by utilizing non-targeted LC-HRMS (Orbitrap) fingerprint technology. Background Art
[0002] Vinegar, a global acidic condiment, holds a crucial place in the culinary culture of various countries. Depending on its fermentation type, vinegar can be divided into two categories: solid-state fermented vinegar and liquid-state fermented vinegar. Solid-state fermented vinegar, due to its unique production process and fermentation procedure, possesses distinct characteristics compared to liquid-state fermented vinegar.
[0003] First, the fermentation cycle of solid-state fermented vinegar is relatively long, usually taking several months or even longer, which is related to its special fermentation process and raw materials. Secondly, the basic substances of solid-state fermented vinegar are richer, including a variety of cereals, beans, etc., which provides it with more nutrients and flavor substances, and also increases its unique taste. In addition, the production intensity of solid-state fermented vinegar is relatively high, the production process is relatively complex, and more manpower and material resources are required, but it also produces a richer and unique flavor. During the fermentation process, solid-state fermented vinegar produces some functional active substances, such as chuanxiongzine, which not only gives vinegar special nutritional value, but also adds health functions to it.
[0004] However, compared to liquid-fermented vinegar, solid-state fermented vinegar has higher production costs, primarily due to its lengthy fermentation process, complex manufacturing techniques, and the wide variety of raw materials required. Consequently, the price of solid-state fermented vinegar is often higher, significantly different from that of liquid-fermented vinegar. This price difference creates the risk of liquid-fermented vinegar being passed off as solid-state fermented vinegar, leading to market confusion.
[0005] On the other hand, liquid fermented vinegar can be adjusted during the production process through blending and seasoning to make its flavor more similar to solid fermented vinegar. This makes it difficult to distinguish the flavors of different fermentation types, and conventional testing methods often cannot accurately determine their true type.
[0006] Therefore, to address this issue, it is crucial to develop a rapid and accurate non-targeted fingerprint analysis method for classifying different fermentation types of vinegar. This method utilizes advanced instrumentation, such as non-targeted LC-HRMS (Orbitrap), and multivariate statistical analysis to comprehensively analyze vinegar samples, accurately distinguishing different vinegar types and identifying potential counterfeit products. This approach not only helps protect consumer rights but also promotes the healthy development of the vinegar industry.
[0007] Liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS) is an advanced analytical technique with many advantages, including high sensitivity, high resolution, a wide analytical range, and short analysis times. In targeted analysis, LC-HRMS is primarily used for the detection of pesticides, veterinary drugs, and food additives. Its high sensitivity and high resolution enable the accurate identification and quantification of target compounds.
[0008] In terms of non-targeted analysis, LC-HRMS can be applied to fingerprint analysis, a method that has been widely used in various fields. In recent years, research on the use of LC-HRMS for non-targeted fingerprint analysis has continued to emerge. For example, José Raúl Belmonte-Sánchez et al. successfully classified different types of gold rum using LC-HRMS (Orbitrap) fingerprinting technology combined with principal component analysis and partial least squares discriminant analysis. Their research demonstrated that the model achieved near 100% classification accuracy, demonstrating the strong potential of LC-HRMS for non-targeted analysis.
[0009] In addition, García-Seval et al. also used non-targeted LC-HRMS fingerprint technology, combined with principal component analysis and partial least squares discriminant analysis, to successfully characterize and classify different Spanish honeys.
[0010] Their results showed that LC-HRMS fingerprinting technology can quickly and accurately classify and identify complex samples in non-targeted analysis.
[0011] These studies demonstrate the enormous potential of nontargeted LC-HRMS fingerprinting technology as a powerful tool for authenticating vinegars of different fermentation types. This technology allows for rapid and accurate classification of vinegars, providing more effective assurance for food quality and safety. Therefore, further research into nontargeted LC-HRMS fingerprinting methods will provide a deeper understanding of food composition and properties, promoting the development and progress of the food industry. Summary of the Invention
[0012] The present invention proposes a method for classifying and studying edible vinegar of different fermentation types by utilizing non-targeted LC-HRMS (Orbitrap) fingerprint technology. The method has good reproducibility, short analysis time and simple preparation method.
[0013] This paper proposes a method for classifying and analyzing vinegars of different fermentation types using non-targeted LC-HRMS fingerprinting technology. During the measurement process, quality control (QC) samples and pure water blanks are injected at the beginning of the sample and after every ten injections. Multivariate statistical analysis of the integrated area of the fingerprints allows for a clearer analysis of the differences between solid-state and liquid-brewed vinegars, and allows for the creation of models for vinegars brewed using two different methods.
[0014] Furthermore, a dilution method is used to dilute the sample by adding pure water to the sample to be tested.
[0015] Furthermore, the test comprises the following steps:
[0016] a) Sample preparation: Take 1 mL of vinegar sample, dilute 100 times with ultrapure water, mix well, filter through a 0.22 μm syringe filter (aqueous phase), and place into a sample bottle for testing.
[0017] b) After injecting the test mixture obtained in step a), the LC-HRMS system was controlled using Xcalibur software version 4.6.0.1 (ThermoFisher Scientific, USA). In addition, quality control samples (QC) and pure water blanks were injected at the beginning of the sample injection and after every ten injections. The QC samples were obtained by adding the same volume of all vinegar samples and mixing them thoroughly.
[0018] c) The raw data obtained after sampling in step b) was integrated into the total ion chromatogram using Thermon Compound Discoverer software. The integrated value was used as the input variable for the subsequent multivariate statistical analysis, and a classification model for different vinegars was established using chemometrics.
[0019] Specifically, the present invention relates to the following technical solutions:
[0020] On the one hand, the present invention provides a method for classifying and studying different fermentation types of vinegar using non-targeted LC-HRMS (Orbitrap) fingerprint technology, clearly distinguishing the differences between solid and liquid brewed vinegar through multivariate statistical analysis, and establishing vinegar models for two different brewing methods, which include:
[0021] a. Integrate the total ion chromatogram using Thermon Compound Discoverer software, and use the integral value as the input variable for multivariate statistical analysis;
[0022] b. Use chemometrics to establish a classification model for different vinegars.
[0023] Preferably, the vinegar sample includes solid brewed raw vinegar and liquid brewed raw vinegar, such as aged vinegar, white vinegar, etc.
[0024] Further preferably, during the determination process, the sample to be tested is diluted 100 times with ultrapure water, mixed and filtered with a 0.22 μm needle filter (aqueous phase), and then placed in a sample bottle.
[0025] More preferably, quality control samples (QC) and pure water blanks are injected at the beginning of the sample and after every ten injections, wherein the QC samples are obtained by adding the same volume of all vinegar samples and mixing them, so as to evaluate the stability of the instrument.
[0026] Another aspect of the present invention provides a method for classifying vinegars of different fermentation types using non-targeted LC-HRMS (Orbitrap) fingerprint technology, characterized in that the differences between solid-state and liquid-brewed vinegars are clearly distinguished through multivariate statistical analysis, and vinegar models of two different brewing methods are established.
[0027] Preferably, the present invention uses Thermon Compound Discoverer software to integrate the total ion chromatogram, and the integrated value is used as an input variable for multivariate statistical analysis.
[0028] Preferably, the present invention uses chemometrics to establish a classification model for different vinegars.
[0029] More preferably, solid brewing raw vinegar and liquid brewing raw vinegar are included in the determination of vinegar samples, such as aged vinegar raw vinegar, white vinegar raw vinegar, etc.
[0030] Furthermore, in the determination process of the present invention, the sample to be tested is diluted 100 times with ultrapure water, mixed and filtered with a 0.22 μm needle filter (aqueous phase), and then placed in a sample bottle.
[0031] Furthermore, quality control samples (QC) and pure water blanks were injected at the beginning of the sample and after every ten injections, where the QC samples were obtained by adding the same volume of all vinegar samples and mixing them to evaluate the stability of the instrument.
[0032] The present invention has the following advantages:
[0033] The present invention proposes a method for classifying and analyzing vinegar of different fermentation types using non-targeted LC-HRMS fingerprinting technology. The method adopts a series of effective steps in the determination process to ensure the accuracy and reliability of the analysis results.
[0034] First, quality control (QC) samples are introduced during the assay process to help assess instrument stability and ensure the reliability of analytical data. The inclusion of QC samples allows monitoring for any potential instrument drift or other technical issues during analysis, thereby increasing the confidence in the experimental results.
[0035] Secondly, the method utilizes fingerprint data generated by LC-HRMS, combined with multivariate statistical analysis, to classify solid-state and liquid-fermented raw vinegar. The integrated area of the fingerprints is used to characterize each sample, which is then classified using multivariate statistical analysis. This analytical strategy, combining LC-HRMS technology with statistical methods, is capable of comprehensively and accurately distinguishing different types of vinegar.
[0036] It is worth noting that this approach has a number of advantages.
[0037] First, this method does not require destructive treatment of the sample and will not affect the original properties of the sample.
[0038] Secondly, pre-processing is simple, requiring only a series of pre-processing steps on the data generated by LC-HRMS, including peak detection, baseline correction, peak alignment, etc. The execution of these steps makes the data more readable and comparable, helping to improve the accuracy and stability of subsequent analysis.
[0039] In addition, the method has good reproducibility and relatively short analysis time, making it suitable for rapid analysis of large-scale samples. By normalizing the data, possible concentration differences between samples can be eliminated, further improving the accuracy and stability of the classification model.
[0040] Therefore, the method proposed in the present invention for classifying and analyzing vinegar of different fermentation types using non-targeted LC-HRMS fingerprint technology has the advantages of simple operation, accuracy and reliability, and can provide a fast and effective quality detection method for the food industry. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.
[0042] Figure 1-4 This is a multivariate statistical analysis chart of solid and liquid brewing vinegar, where:
[0043] Figure 1 Principal component analysis;
[0044] Figure 2 is partial least squares discriminant analysis;
[0045] Figure 3 For gravel map;
[0046] Figure 4 This is the linear discriminant analysis graph. DETAILED DESCRIPTION
[0047] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.
[0048] The present invention will be described in more detail below with the help of the following examples. The following examples are merely illustrative and it should be understood that the present invention is not limited to the following examples.
[0049] The present invention proposes a method for classifying and analyzing vinegar of different fermentation types using non-targeted LC-HRMS fingerprint technology, comprising the following steps:
[0050] Through multivariate statistical analysis, the differences between solid and liquid brewed vinegar can be analyzed more clearly, and vinegar brewed using two different methods can be modeled.
[0051] In one embodiment of the present invention, a dilution method is used [Barnaba C, Dellacassa E, Nicolini G, et al. Identification and quantification of 56 targeted phenols in wines, spirits, and vinegars by online solid-phase extraction-ultrahigh-performance liquid chromatography-quadrupole-orbitrap mass spectrometry [J]. Journal of Chromatography A, 2015, 1423: 124-135. DOI: 10.1016 / j.chroma.2015.10.085.], and the stability of the instrument is tested by QC samples [GARCíA-SEVAL V, SAURINA J, SENTELLAS S, et al. Characterization and Classification of Spanish Honey by Non-Targeted LC–HRMS (Orbitrap) Fingerprinting and Multivariate Chemometric Methods [J]. Molecules, 2022, 27(23).].
[0052] Specifically, the present invention uses a Thermo Scientific Orbitrap Exploris 120 LC-MS (ThermoFisher Scientific Inc) for testing.
[0053] In one embodiment of the present invention, pure water is added to the sample to be tested and mixed evenly.
[0054] In one embodiment of the present invention, the following steps are included:
[0055] a) Sample preparation: Add pure water to the sample to be tested. Further, mix thoroughly, filter through a 0.22 μm syringe filter (aqueous phase), and place into a sample bottle for testing.
[0056] b) After injecting the test mixture obtained in step a), Xcalibur software version 4.6.0.1 (ThermoFisher Scientific, USA) was used to control the LC-HRMS system. In addition, quality control samples (QC) and pure water blanks were injected at the beginning of the sample and after every ten injections;
[0057] c) The raw data obtained after the sampling in step b) was preliminarily analyzed using Thermon Compound Discoverer 3.2 software on the obtained LC-HRMS fingerprint, and the fingerprint data obtained from the analysis was used as the input variable for the subsequent multivariate statistical analysis.
[0058] Furthermore, principal component analysis was used to obtain the natural dispersion of solid-state fermented vinegar and liquid-state fermented vinegar.
[0059] Furthermore, the supervised classification model partial least squares discriminant analysis is used for further analysis of the unsupervised principal components to obtain better classification results.
[0060] Furthermore, the number of principal components of commercially available aged vinegar with added edible alcohol, solid-state fermented vinegar and liquid-fermented vinegar was selected through a scree plot, and then a linear discriminant analysis model was established to better protect pure solid-state fermented vinegar.
[0061] The present invention will be described in detail below with reference to the embodiments.
[0062] Example 1: Classification of different fermentation types of vinegar using non-targeted LC-HRMS fingerprint technology
[0063] 1. Reagents, materials and instruments used
[0064] 1.1 Reagents used: Vinegar samples were raw vinegar from the factory; formic acid (mass spectrometry grade) was purchased from Thermo Fisher Scientific (China) Co., Ltd.; acetonitrile (chromatographic grade) was purchased from Meker, Germany; and ultrapure water was prepared using a 250 L / H ultrapure water machine.
[0065] 1.2 Instruments used: Thermo Scientific Orbitrap Exploris 120 LC-MS (ThermoFisherScientific Inc.); Hypersil GOLD C18 chromatographic column (1.9 μm / 2.1 × 50 mm, ThemoFisherScientific Inc., USA); 250 L / H ultrapure water machine (Shanxi Guohuan Power Environmental Protection Technology Co., Ltd.); TPS1000 heated lid constant temperature mixer (Hangzhou Ruicheng Instrument Co., Ltd.).
[0066] 2 Sample preparation and testing steps
[0067] 2.1 Sample preparation: Take 1 mL of vinegar sample, dilute it 100 times with ultrapure water, mix well, filter it through a 0.22 μm syringe filter (aqueous phase), and then put it into a sample bottle for testing.
[0068] 2.2 Chromatographic conditions: chromatographic column Hypersil GOLD C18 (1.9 μm / 2.1×50 mm), column temperature 35°C; flow rate 0.3 mL / min; injection volume 1 μL; 0.1% formic acid aqueous solution A and acetonitrile as mobile phase B, gradient elution as shown in Table 1.
[0069]
[0070] 2.3 Mass Spectrometry Conditions: A heated electrospray ionization source (ESI source) was used; spray voltages: 3500 V (+), 2500 V (-); heating temperature: 300°C; sheath gas pressure: 50 Arb; auxiliary gas pressure: 15 Arb; capillary temperature: 320°C. Scan mode: Full MS-ddMS2 (4 scans); scan range: 70–1050 m / z; primary full scan resolution: 60,000 FWHM; data-dependent secondary scan resolution: 30,000 FWHM.
[0071] 3. Measurement results
[0072] Figure 1-4 This is a multivariate statistical analysis chart of solid and liquid brewing vinegar, where:
[0073] Figure 1 Principal component analysis;
[0074] Figure 2 is partial least squares discriminant analysis;
[0075] Figure 3 For gravel map;
[0076] Figure 4 This is the linear discriminant analysis graph.
[0077] 4 Multivariate statistical analysis of vinegar
[0078] A total of 525 features were extracted from the recorded vinegar fingerprints through non-targeted fingerprint analysis. Principal component analysis (PCA) was performed on the data using R data analysis software to assess the natural dispersion of the samples. Two principal components (PCs) were extracted, accounting for 75.7% of the total variance in the dataset, with PC 1 accounting for 68.2% and PC 2 for 7.5%. A plot of PC 1 and PC 2 scores shows that the quality control (QC) profiles are clustered and located very close to the center of the plot, demonstrating the reproducibility and stability of the non-targeted LC-HRMS data and that the chemometric results are unaffected by any sequence drift.
[0079] The supervised classification model PLS-DA was used to further analyze the unsupervised PCA, and better classification results were obtained. PLS-DA analysis can show the component differences to a greater extent. The permutation test of the established PLS-DA model was performed, and R 2 Y is 0.986, indicating that the model is an effective model with good analytical ability; Q 2 It is 0.949, indicating that the model has strong predictive ability and can be used to distinguish different fermentation types of vinegar.
[0080] GB / T 18187-2000, "Brewed Vinegar," stipulates that solid-state fermented vinegar is made from grains and their by-products through the solid-state fermentation of mash. In the test samples, commercially available vinegar with added edible alcohol was found in pure solid-state fermented vinegar, based on the GB 2719-2018, "National Food Safety Standard for Vinegar." An LDA model was developed for vinegar with added edible alcohol, pure solid vinegar, and pure liquid vinegar to protect pure solid-state fermented vinegar. The first 16 PCs from principal component analysis (PCA) covered 98.1% of the variance in the fingerprint dataset. The first 16 independent PCs obtained from the fingerprint data served as input variables for the LDA. The linear discriminant function established using the PCA / LDA model maximized the classification visualization effect for such high-dimensional (multivariate) and small sample size data. A 5-fold cross-validation was performed on the established model, resulting in a model accuracy of 0.822.
[0081] 5 Conclusion
[0082] LC-HRMS technology has the advantages of comprehensive analytical information, high sensitivity, and high accuracy. In the present invention, the pretreatment of vinegar only involves dilution, the pretreatment process is simple, and the originality of the vinegar sample is retained. Using non-targeted LC-HRMS (Orbitrap) fingerprint technology combined with multivariate statistical analysis, a classification model for solid-state fermentation vinegar and liquid-state fermentation vinegar was established. At the same time, LDA analysis was performed on aged vinegar with added edible alcohol, solid-state fermentation vinegar, and liquid-state fermentation vinegar. The results showed that PLS-DA can effectively distinguish between vinegars of different fermentation types, and the validity of the model was verified using the permutation test. The explanation rate parameter R 2 Y=0.986, prediction ability parameter Q 2 =0.949, indicating that the model has strong predictive power and is effective, suitable for distinguishing different fermentation types of vinegar. The establishment of the PCA / LDA model is beneficial for protecting pure solid-state fermentation vinegar. This further demonstrates that LC-HRMS fingerprints can be a highly effective means of distinguishing different fermentation types of vinegar, providing a foundation for subsequent identification of different fermentation types. This will help promote the standardization of the vinegar market and protect consumer interests.
[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be covered by the scope of the claims of the present invention.
Claims
1. A method for classifying different types of fermented vinegar using non-targeted LC-HRMS (Orbitrap) fingerprint technology, clearly distinguishing the differences between solid and liquid brewed vinegar through multivariate statistical analysis, and establishing vinegar models for two different brewing methods, comprising: a. Integrate the total ion chromatogram using Thermon Compound Discoverer software, and use the integral value as the input variable for multivariate statistical analysis; b. Use chemometrics to establish a classification model for different vinegars.
2. The method according to claim 1, characterized in that The vinegar samples include solid brewed raw vinegar and liquid brewed raw vinegar, such as aged vinegar, white vinegar, etc.
3. The method according to claim 1, characterized in that During the determination process, the sample to be tested was diluted 100 times with ultrapure water, mixed, filtered with a 0.22 μm needle filter (aqueous phase), and then placed in a sample bottle.
4. The method according to claim 1, wherein Quality control samples (QC) and pure water blanks were injected at the beginning of the sample and after every ten injections. The QC samples were prepared by adding the same volume of all vinegar samples and mixing them to evaluate the stability of the instrument.
5. A method for classifying different fermentation types of vinegar using non-targeted LC-HRMS (Orbitrap) fingerprint technology, characterized in that: Multivariate statistical analysis was used to clearly distinguish the differences between solid and liquid brewing vinegar, and vinegar models for the two different brewing methods were established.
6. The method according to claim 5, characterized in that Thermon Compound Discoverer software was used to integrate the total ion chromatograms, and the integrated values were used as input variables for multivariate statistical analysis.
7. The method according to claim 5 or 6, characterized in that A classification model for different vinegars was established using chemometrics.
8. The method according to claim 5, characterized in that The determination of vinegar samples includes solid brewing raw vinegar and liquid brewing raw vinegar, such as aged vinegar, white vinegar, etc.
9. The method according to claim 5, characterized in that During the determination process, the sample to be tested was diluted 100 times with ultrapure water, mixed, filtered with a 0.22 μm needle filter (aqueous phase), and then placed in a sample bottle.
10. The method according to claim 5, characterized in that Quality control samples (QC) and pure water blanks were injected at the beginning of the sample and after every ten injections. The QC samples were prepared by adding the same volume of all vinegar samples and mixing them to evaluate the stability of the instrument.