A high coverage analysis method for aldehyde and ketone compounds in Baijiu Daqu

Abstract

The present invention detects and analyzes the technical field, and specifically relates to a high coverage analysis method for aldehyde and ketone compounds in liquor Daqu, comprising the following steps: one, obtaining chromatographic and mass spectrometric data information; two, constructing a QSRR prediction model; three, according to the chromatographic and mass spectrometric data information and the QSRR prediction model, analyzing the aldehyde and ketone compounds in the Daqu sample; wherein, the step one includes: ultrasonic extraction, derivatization and collection of chromatographic and mass spectrometric data information of aldehyde and ketone compounds in the Daqu sample by UHPLC-HRMS coupling technology; the reagent of the derivatization reaction includes: 2-hydrazine-4-chloropyridine, 2-hydrazine-3-chloropyridine, 2-hydrazine-5-chloropyridine, at least one of 2-hydrazine-6-chloropyridine. The method has the characteristics of high coverage, high sensitivity, good repeatability, etc. without the need to use expensive heavy isotope labeled derivatization reagents, and has significant advantages in the detection of aldehyde and ketone compounds.

Description

Technical Field

[0001] The invention belongs to the technical field of detection and analysis, and particularly relates to a high coverage analysis method for aldehyde and ketone compounds in liquor Daqu. Background Art

[0002] Maotai-flavor liquor is popular worldwide for its unique taste and aroma. Daqu, as an important saccharifying agent, fermentation agent and aroma agent in the production process of liquor, makes an important contribution to the quality and flavor of liquor. The metabolite composition in Daqu is complex and diverse, among which aldehyde and carbonyl compounds are widely present in Daqu, and aldehyde and carbonyl compounds are closely related to flavor. Therefore, high-coverage detection and annotation of aldehyde and carbonyl compounds in Daqu is conducive to revealing the reasons for the formation of liquor flavor, and can also provide data support and theoretical guidance for the production of high-quality liquor.

[0003] The reported LC-HRMS-based detection of aldehydes and ketones in Daqu usually adopts the method of directly analyzing the metabolite extract. This method is difficult to achieve high coverage analysis of trace aldehydes and ketones in Daqu. Chemical derivatization technology has significant advantages in improving the mass spectrometry response of compounds. A variety of derivatization reagents have been used for the derivatization of aldehydes and ketones, such as ω-bromoacetonylquinoline (BQB), dansylhydrazine (DnsHz) and 2-(2-hydrazinyl-2-oxoethyl)isoquinolin-2-ium bromide (HIQB). However, in most derivatization methods, the sample to be tested is labeled with a pair of light / heavy isotope derivatization reagents, and then a pair of chromatographic peaks with a fixed mass difference, similar retention time and similar peak intensity generated by the light / heavy isotopes are used to screen the derivatized target compounds. The heavy isotope-labeled derivatization reagents used in this method are expensive and difficult to obtain. Summary of the invention

[0004] The present invention aims to provide a high coverage analysis method for aldehyde and ketone compounds in 2-hydrazino-4-chloropyridine liquor Daqu.

[0005] In view of the problem that the analysis methods disclosed in the prior art are difficult to achieve high coverage analysis of trace aldehyde and ketone compounds in Daqu, or the derivatization reagents used are mostly heavy isotope-labeled derivatization reagents, which are relatively expensive and difficult to obtain, the present invention provides a high coverage analysis method for aldehyde and ketone compounds in liquor Daqu based on 2-hydrazino-4-chloropyridine derivatization. Since the used 2-hydrazino-4-chloropyridine itself carries a Cl atom, the derivatized target compound will have a pair of peaks with a mass difference of 1.9971Da, similar retention times, and an intensity ratio of about 1:3. Therefore, the sample does not need to be labeled multiple times, and no expensive heavy isotope derivatization reagents are required.

[0006] In one aspect, the present invention provides a high coverage analysis method for aldehyde and ketone compounds in liquor Daqu, comprising the following steps:

[0007] 1. Obtain chromatographic and mass spectrometric data information;

[0008] 2. Construct QSRR prediction model;

[0009] 3. Analyze the aldehyde and ketone compounds in the Daqu sample according to the chromatographic and mass spectrometric data information and the QSRR prediction model;

[0010] Wherein, the step 1 comprises: ultrasonic extraction and derivatization of the Daqu sample, and collecting chromatographic and mass spectrometric data information of aldehyde and ketone compounds in the Daqu sample by using UHPLC-HRMS coupling technology;

[0011] The reagent for the derivatization reaction includes at least one of 2-hydrazino-4-chloropyridine, 2-hydrazino-3-chloropyridine, 2-hydrazino-5-chloropyridine and 2-hydrazino-6-chloropyridine.

[0012] Compared with some heavy isotope labeled derivatization reagents disclosed in the prior art, the derivatization reagent selected by the technical solution of the present invention contains Cl atoms itself. When screening labeled aldehyde and ketone compounds, the isotope distribution characteristics of the chlorine element can be utilized, that is, the mass difference of the precursor ion is 1.9971Da, the retention time difference is not more than 0.1min, and the intensity ratio of the ion peak pairs is in the range of 0.3-0.4, to screen out potential aldehyde and ketone compounds. That is, the technical solution provided by the present invention can achieve high coverage screening of derivatized aldehyde and ketone compounds without using very expensive heavy isotope derivatization reagents.

[0013] In some embodiments, the ultrasonic extraction comprises the following steps:

[0014] After adding extraction reagent to the Daqu sample, the sample is placed in an ultrasonic instrument for ice-water bath ultrasonic extraction; wherein the extraction reagent is 100% acetonitrile.

[0015] In some embodiments, in mg / μL, the addition ratio of the Daqu sample to the extraction reagent is: 1:5 to 1:10; preferably, the addition ratio of the Daqu sample to the extraction reagent is: 1:5 to 1:7; preferably, the addition ratio of the Daqu sample to the extraction reagent is: 1:5 to 1:6; preferably, the addition ratio of the Daqu sample to the extraction reagent is: 1:5.1 to 1:5.5.

[0016] In some embodiments, in mg / min, the ratio of the weight of the Daqu sample to the time of ultrasonic extraction is: 3:1~1:2: Preferably, the ratio of the weight of the Daqu sample to the time of ultrasonic extraction is: 3:1~2:1; Preferably, the ratio of the weight of the Daqu sample to the time of ultrasonic extraction is: 2.8:1.

[0017] In some embodiments, the ultrasonic extraction time is 10 min-60 min; preferably, the ultrasonic extraction time is 10 min-20 min.

[0018] In some embodiments, the ultrasonic extraction conditions are determined by the following steps:

[0019] 1) The mass of Daqu, the volume ratio of acetonitrile in the extraction solvent, and the range of ultrasonic extraction time were input into the Design Expetr 12 software, and the Daqu samples were subjected to ice-water bath ultrasonic extraction according to the experimental schemes of different ice-water bath ultrasonic extraction conditions given by the software, and then derivatization was performed;

[0020] 2) using UHPLC-HRMS to collect chromatographic and mass spectrometric data information of aldehyde and ketone compounds in the Daqu sample after derivatization in step 1);

[0021] 3) screening out potential aldehyde and ketone compounds based on the collected chromatographic and mass spectrometric data information;

[0022] 4) Based on the relationship between the number of potential aldehyde and ketone compounds obtained under different ultrasonic extraction conditions, an RSM model is established and a 3D curve graph is generated. The obtained model and 3D curve graph are used to determine the optimal extraction conditions.

[0023] In some embodiments, the chromatographic and mass spectrometric data information includes: R , M / Z and MS / MS.

[0024] In some embodiments, in step 3), screening potential aldehyde and ketone compounds includes: screening out the compounds with a mass difference of 1.9971Da±10ppm, t R The compound with a difference of no more than 0.1min and an intensity ratio in the range of 0.3-0.4 is the potential aldehyde and ketone compound.

[0025] In some embodiments, the derivatization comprises the steps of:

[0026] After centrifuging the Daqu sample after ultrasonic extraction, the supernatant is collected; and a derivatization reaction reagent is added to the obtained supernatant to carry out a derivatization reaction.

[0027] In some embodiments, the volume ratio of the reagents for the derivatization reaction to acetonitrile and hydrochloric acid is 14-16:13-15:1-6; preferably, the volume ratio of the reagents for the derivatization reaction to acetonitrile and hydrochloric acid is 14-16:13-15:1-3; preferably, the volume ratio of the reagents for the derivatization reaction to acetonitrile and hydrochloric acid is 15:14:1.

[0028] In some embodiments, the temperature of the derivatization reaction is: 40℃~80℃; the time of the derivatization reaction is: 30~120min; preferably, the temperature of the derivatization reaction is: 60℃~80℃; the time of the derivatization reaction is: 60~90min; preferably, the temperature of the derivatization reaction is: 60℃~70℃, and the time of the derivatization reaction is: 80~90min.

[0029] In some embodiments, the method of collecting chromatographic and mass spectrometric data information of aldehyde and ketone compounds in the derivatized Daqu sample by UHPLC-HRMS comprises the following steps:

[0030] After the derivatization reaction, the Daqu sample was freeze-dried and re-dissolved;

[0031] The reconstituted sample is centrifuged and the supernatant is analyzed by UHPLC-HRMS to collect chromatographic and mass spectrometric data information of aldehyde and ketone compounds in the derivatized Daqu sample; the chromatographic and mass spectrometric data information includes: R , M / Z and MS / MS.

[0032] In some embodiments, the UHPLC-HRMS is an analytical instrument coupled to an Agilent 1290 Infinity liquid chromatography system and an Agilent 6546 Q-ToF.

[0033] In some embodiments, the liquid chromatography conditions of the UHPLC-HRMS are as follows: phase A and phase B are 0.1% formic acid / water and 0.1% formic acid / acetonitrile, respectively; the chromatographic column is an ACQUITY BEH C8 chromatographic column (100 mm×2.1 mm, 1.7 μm, Waters, Milford, MA, USA); the chromatographic column temperature is 50°C; the column flow rate is 0.35 mL / min; the sample injection chamber temperature is set to 4°C, and the sample injection volume is 1 μL; the elution gradient program is: 0-1 min, 5% B; 1-24 min, 5%-100% B; 24-28 min, 100% B; 28-28.1 min, 100%-5% B; 28.1-30 min, 5% B.

[0034] In some embodiments, the mass spectrometry conditions of the UHPLC-HRMS are as follows: the scanning mode is a positive ion mode, in the full scan mode, the scanning range is 100-1000Da, the drying gas flow rate and temperature are 8L / min and 320°C, respectively; the nebulizer pressure is 35psig; the sheath gas temperature and flow rate are 350°C and 11L / min, respectively; the Vcap is 3000V, and the fragmentation voltage is 110V; the secondary scanning range is 30-1200Da, and the secondary collision energies are 10eV, 20eV, and 40eV, respectively.

[0035] In some embodiments, the construction of the QSRR prediction model comprises the following steps:

[0036] The SMILES of multiple aldehyde and ketone compound standard samples after derivatization were collected, and the t R ;

[0037] The collected standard samples were divided into a training set and a test set. The t R And its SMILES were imported into QSRR_Automator software, and the software was used to select effective molecular descriptors and machine learning algorithms to construct the QSRR prediction model of aldehydes and ketones.

[0038] In some embodiments, the allocation of the training set and the test set comprises the following steps: R Sort from small to large, every 4 t R Divide into one group, select the last three compounds in each group as the training set, and the first compound in each group as the test set; preferably, the proportion of the training set is greater than 70%.

[0039] In some embodiments, the construction of the QSRR prediction model further comprises:

[0040] The SMILES of the derivatized products of the standard samples in the test set are introduced into the QSRR prediction model to predict the t corresponding to the derivatized products of the standard samples in the test set. R , get the prediction t R ; The predicted t of the derivatization product of the standard sample in the test set R With real R The comparison was made to verify the performance of the constructed QSRR prediction model.

[0041] In some embodiments, the analysis of aldehyde and ketone compounds in the Daqu sample comprises the following steps:

[0042] Screen out potential aldehyde and ketone compounds according to the chromatographic and mass spectrometric data information obtained in step 1;

[0043] Based on the chromatographic and mass spectrometric data information obtained in step 1 and the QSRR prediction model constructed in step 2, the potential aldehyde and ketone compounds screened out are qualitatively graded and divided into six qualitative grades;

[0044] In some embodiments, the qualitative grade is graded based on indicators including: whether it is verified with the standard sample information of aldehyde and ketone compounds; whether the elemental composition of the potential aldehyde and ketone compounds is known; whether the corresponding candidate structure can be searched in the database according to the primary M / Z of the potential aldehyde and ketone compounds; the prediction of the candidate structure; R With the experiment R Whether the absolute deviation is within 40s; whether the secondary fragments can be manually annotated; whether the similarity between the predicted secondary spectrum of the candidate structure and the actual secondary spectrum is not less than 0.5.

[0045] In some embodiments, screening out potential aldehyde and ketone compounds includes: based on the isotope distribution characteristics of the chlorine element, screening precursor ion peak pairs with a mass difference of 1.9971Da±10ppm, a retention time difference of no more than 0.1min, and an intensity ratio in the range of 0.3-0.4, and the compounds corresponding to the ion peak pairs obtained by screening are potential aldehyde and ketone compounds labeled with 2-hydrazino-4-chloro(35-Cl)pyridine.

[0046] In some embodiments, the qualitative classification of potential aldehyde and ketone compounds comprises the following steps:

[0047] (1) performing standard sample verification on potential aldehyde and ketone compounds and the collected standard sample information of aldehyde and ketone compounds, and the potential aldehyde and ketone compounds in the verification are defined as level 1;

[0048] (2) For the potential aldehyde and ketone compounds that have not been verified with the standard sample information, the candidate structures are searched in the existing database using their corresponding M / Z. The potential aldehyde and ketone compounds for which no candidate structures are found are defined as level 6;

[0049] (3) For the potential aldehyde and ketone compound whose candidate structure is searched in step (2), the QSRR prediction model constructed in step 2 is used to predict the t of the candidate structure. R , if the prediction t of the candidate structure R The actual t of the potential aldehydes and ketones R If the difference is greater than 40s, its qualitative level is defined as level 5;

[0050] (4) For the prediction of the candidate structure described in step (3), R The actual t of the potential aldehydes and ketones RIf the difference is no more than 40s, the qualitative grade of the compound is defined by manually annotating the secondary mass spectrum information of the potential aldehyde and ketone compound. If manual annotation is possible, the qualitative grade is defined as grade 2;

[0051] (5) For the potential aldehyde and ketone compounds that cannot be manually annotated in step (4), the similarity between the predicted MS / MS spectrum and the actual MS / MS spectrum of the potential aldehyde and ketone compounds is compared. If the similarity is not less than 0.5, its qualitative level is defined as level 3; otherwise, its qualitative level is defined as level 4.

[0052] In some embodiments, the standard sample verification in step (1) comprises: R , M / Z and MS / MS with the corresponding aldehyde and ketone compound standards R , M / Z and MS / MS are compared respectively. If the information matches, it indicates that the potential aldehyde and ketone compound is the compound corresponding to the standard sample.

[0053] In some embodiments, the database in step (2) includes at least one or two of: The Food Database and MassBank of North America.

[0054] In some embodiments, the manual annotation in step (4) includes the following cases:

[0055] If there are a series of peaks with a difference of 14.0156Da±10ppm in the secondary spectrum, the structure of the compound may contain a fatty chain; and / or

[0056] If there is a peak of 66.0343 Da ± 10 ppm in the secondary spectrum, the structure of the compound may contain a pyrrole ring; and / or

[0057] If a fragment of 91.0547Da±10ppm appears in the secondary spectrum, the structure of the compound may contain methylenebenzene.

[0058] In some embodiments, the predicted MS / MS spectrum in step (5) is obtained by inputting the SMILES of the candidate structure of the potential aldehyde and ketone compound into CFM-ID software for prediction.

[0059] In some embodiments, the Daqu sample is a quality-controlled Daqu sample obtained by mixing equal masses of yellow, white, and black Daqu.

[0060] On the other hand, the present invention also provides an application of the described method in the field of analysis and detection technology.

[0061] In some embodiments, the technical field of analysis and detection includes: analysis and detection of aldehyde and ketone compounds in Maotai-flavor liquor Daqu.

[0062] In summary, the present application includes at least one of the following beneficial technical effects:

[0063] (1) The present invention discloses a high coverage analysis method for aldehyde and ketone compounds in Baijiu Daqu based on derivatization. The derivatization reagent selected by the method contains Cl atoms. When screening labeled aldehyde and ketone compounds, the isotopic distribution characteristics of the chlorine element can be used to screen out potential aldehyde and ketone compounds. That is, the method achieves high coverage screening of derivatized aldehyde and ketone compounds without using expensive heavy isotope derivatization reagents.

[0064] (2) The method provided by the present invention does not require the use of expensive heavy isotope labeled derivatization reagents, and has the characteristics of high coverage, high sensitivity, simple operation, economy, and rapidity, and has significant advantages in the detection of aldehyde and ketone compounds:

[0065] Among them, high coverage: through the method provided by the present invention, a total of 487 aldehyde and ketone compounds were detected from the QC sample of Baijiu Daqu. Among these 487 aldehyde and ketone compounds, the structures of 314 (64.5%) aldehyde and ketone compounds can be annotated, that is, the method provided by the present invention can achieve high coverage analysis of aldehyde and ketone compounds in Moutai Daqu samples.

[0066] Among them, high sensitivity: the method for analyzing aldehyde and ketone compounds in Baijiu Daqu based on 2-hydrazino-4-chloropyridine derivatization constructed by the present invention has an LOD range of 0.07-0.10nmol and a LOQ range of 0.29-0.4nmol, indicating that the method has high sensitivity.

[0067] Among them, high repeatability: the method constructed by the present invention for analyzing aldehyde and ketone compounds in Baijiu Daqu based on 2-hydrazino-4-chloropyridine derivatization, the intra-day and inter-day RSDs of the four target compounds at low, medium and high concentrations are all less than 10%, indicating that the method has good repeatability. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A diagram of a QSRR prediction model constructed according to an embodiment of the present invention;

[0069] Figure 2 This is a comparative experimental result diagram under different derivatization reaction conditions in the comparative example of the present invention;

[0070] Figure 3 It is a 3D surface diagram of comparative experimental results under different ultrasonic extraction conditions in the comparative example of the present invention;

[0071] Figure 4 This is a flow chart for qualitatively classifying potential aldehyde and ketone compounds according to an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The technical solution of the present invention is further described below by specific embodiments, which do not limit the protection scope of the present invention. Some non-essential modifications and adjustments made by others based on the concept of the present invention still fall within the protection scope of the present invention.

[0073] Example 1 A high coverage analysis method for aldehyde and ketone compounds in liquor Daqu

[0074] 1. Obtaining UHPLC-HRMS information of aldehydes and ketones in Daqu samples

[0075] (1) Ultrasonic extraction: Yellow, white and black Moutai Daqu samples of equal mass were mixed to form a QC mixed sample. 28±0.3 mg of Daqu QC sample was weighed into an EP tube, 150 μL of acetonitrile was added and placed in a 4°C refrigerator overnight. The EP tube was placed in an ultrasonicator filled with ice water for 10 min and then centrifuged (21500 g, 4°C, 10 min) to remove the supernatant.

[0076] (2) Derivatization reaction: 150 μL of 2-hydrazino-4-chloropyridine, 140 μL of acetonitrile and 10 μL of hydrochloric acid were added to the supernatant obtained in step (1) in sequence, mixed and reacted at 60° C. for 90 min; after the reaction was completed, freeze-dried, and the lyophilized material was re-dissolved with 100 μL of 50% acetonitrile, and the re-dissolved sample was centrifuged at 21500 g and 4° C. for 10 min;

[0077] (3) Data acquisition: 80 μL of the supernatant after centrifugation in step (2) was used for UHPLC-HRMS analysis. The liquid chromatography and mass spectrometry information in the supernatant were collected in full scan mode, including t R (retention time), primary M / Z, primary absolute intensity, MS / MS (secondary mass spectrometry) and relative intensity. The detection conditions of UHPLC-HRMS are:

[0078] The data were collected using an Agilent 1290 Infinity liquid chromatography system (Agilent Technologies, Santa Clara, CA, US) and an Agilent 6546 Q-ToF (Agilent Technologies, Santa Clara, CA, US).

[0079] The liquid chromatography conditions were as follows: phase A and phase B were 0.1% formic acid / water (volume ratio) and 0.1% formic acid / acetonitrile (volume ratio), respectively; the chromatographic column was an ACQUITY BEH C8 column (100 mm x 2.1 mm, 1.7 μm, Waters, Milford, MA, USA); the column temperature was 50°C; the column flow rate was 0.35 mL / min; the sample injection chamber temperature was set to 4°C, and the sample injection volume was 1 μL; the elution gradient program was: 0-1 min, 5% B; 1-24 min, 5%-100% B; 24-28 min, 100% B; 28-28.1 min, 100%-5% B; 28.1-30 min, 5% B.

[0080] The mass spectrometry conditions were as follows: the scanning mode was positive ion mode. In full scan mode, the scanning range was 100-1000 Da, the drying gas flow rate and temperature were 8 L / min and 320 °C, respectively; the nebulizer pressure was 35 psig; the sheath gas temperature and flow rate were 350 °C and 11 L / min, respectively; the Vcap was 3000 V, and the fragmentation voltage was 110 V. The secondary scanning range was 30-1200 Da, and the secondary collision energies were 10 eV, 20 eV, and 40 eV, respectively.

[0081] 2. Construction of QSRR model for aldehyde and ketone compounds

[0082] Collect SMILES: Collect SMILES (SMILES, the full name is Simplified Molecular Input Line Entry System, is a linear symbol used to input and represent molecular reactions, and is an ASCII code) of 100 aldehyde and ketone compound standard samples after derivatization; the detailed information of the 100 aldehyde and ketone compound standard samples is shown in Table 1

[0083] Collection R :Using UHPLC-HRMS to collect t of 100 standard sample derivatization products R ,The detection conditions of UHPLC-HRMS were the same as those in the first part;

[0084] Process: R Sort from small to large, every 4 t R The last three compounds in each group were selected, and a total of 75 standard samples were selected as the training set; the true t R The SMILES was imported into QSRR_Automator software, which converted SMILES into 375 molecular descriptors under default parameters. Linear regression, support vector machine and random forest algorithms were used to fit the molecular descriptors and the true t R, and performed 5 internal cross-validations. Finally, 85 molecular descriptors and support vector machine algorithm were selected to establish the QSRR (Quantitative Structure Retention Relationships) model. The obtained QSRR model is shown in Figure 1 As shown, the model scores 0.99.

[0085] In order to verify the performance of the QSRR model, the SMILES of the remaining 25 standard sample derivatization products were imported, and the t values ​​corresponding to the 25 standard sample derivatization products were predicted using the above-established model. R , get the prediction t R , and calculated the predicted t of 25 standard sample derivatization products R and actual t R The average absolute error (AAE) is 36s; a linear model is used to fit the predicted t R and actual t R , and get the R of the linear model 2 In summary, the externally validated AAE < 60s and R 2 >0.9, indicating that the model can be used to predict the t of unknown compounds R . Using the obtained AAE and R 2 Evaluation of the established model (AAE < 60s, R 2 >0.9), the model was used to predict the t of unknown compounds. R ,Since AAE=36s, 40s is finally selected as the threshold for filtering.

[0086] Table 1 Standard sample information for establishing QSRR model

[0087]

[0088]

[0089]

[0090] 3. Qualitative analysis and annotation of aldehyde and ketone compounds

[0091] The specific process of qualitative and annotation is as follows Figure 4 As shown, the details are as follows:

[0092] (1) Screening out potential aldehyde and ketone compounds by the isotopic distribution characteristics of chlorine; Based on the information collected in the first part, using python to screen out precursor ion peak pairs with a mass difference of 1.9971Da±10ppm, a retention time difference of no more than 0.1min, and an intensity ratio in the range of 0.3-0.4, the lighter M / Z obtained by screening is the potential aldehyde and ketone compound labeled with 2-hydrazino-4-chloro(35-Cl)pyridine;

[0093] (2) Standard sample verification is performed on the potential aldehyde and ketone compounds screened out, and the qualitative level of the verification is defined as level 1; the verification steps are as follows: the t R , M / Z and MS / MS and the corresponding standard sample t R , M / Z and MS / MS are compared respectively. If they all match, it indicates that the potential aldehyde and ketone compound is the compound corresponding to the standard sample;

[0094] (3) For the potential aldehyde and ketone compounds that were not verified in step (2), the primary M / Z was used to search for candidate structures in the database (self-built database - aldehyde and ketone compounds reported in the source literature, The Food Database, and MassBank of North America) with a mass deviation of 10 ppm; for the aldehyde and ketone compounds for which the candidate structures were not searched, their theoretical element (C, H, O, N, S, Cl) composition was calculated, and the qualitative level was defined as level 6 (i.e., only the elemental composition of the compound was known);

[0095] (4) For the compounds with candidate structures found in step (3), the QSRR model established in the second part is used to predict the t of the candidate structures. R ; If the candidate structure's t R The real t of the aldehyde and ketone compounds R If the difference is greater than 40s, the qualitative level is defined as level 5 (i.e. the elemental composition of the compound is known and the predicted candidate structure is R With the experiment R Absolute deviation is greater than 40s);

[0096] (5) For the prediction of the candidate structure in step (4), R Experiments with aldehydes and ketones RIf the difference is no more than 40s, the qualitative level of the compound is defined by manually annotating the secondary mass spectrum information of the compound: for example, if there is a series of peaks with a difference of 14.0156Da±10ppm in the secondary spectrum, the structure of the compound may contain a fatty chain; if there is a peak of 66.0343Da±10ppm in the secondary spectrum, the structure of the compound may contain a pyrrole ring; if a fragment of 91.0547Da±10ppm appears in the secondary spectrum, the structure of the compound may contain methylenebenzene, etc.; if it can be manually annotated, its qualitative level is defined as level 2 (that is, the elemental composition of the compound is known, and the prediction of the candidate structure is t R With the experiment R The absolute deviation is within 40s. The corresponding candidate structure can be searched in the database based on the primary accurate mass, and the secondary fragments can be manually annotated);

[0097] (6) In step (5), if manual annotation is not possible, the predicted MS / MS spectrum of the compound (the predicted MS / MS spectrum is obtained by inputting the SMILES of the candidate structure into the CFM-ID software (version number: CFM-ID 4.0) for prediction) is compared with the actual MS / MS spectrum. If the similarity is not less than 0.5, the qualitative level is defined as level 3 (i.e., the elemental composition of the compound is known, and the predicted t R With the experiment R The absolute deviation is within 40s. The corresponding candidate structure can be searched in the database based on the primary accurate mass, and the similarity between the predicted secondary spectrum of the candidate structure and the experimental secondary spectrum is not less than 0.5);

[0098] (7) In step (6), manual annotation is not allowed. Compare the similarity between the predicted MS / MS spectrum of the compound and the actual MS / MS spectrum. If the similarity is less than 0.5, the qualitative level is defined as level 4 (i.e., the elemental composition of the compound is known, and the predicted candidate structure is t R With the experiment R The absolute deviation is within 40s. The corresponding candidate structure can be searched in the database based on the primary accurate mass, and the similarity between the predicted secondary spectrum of the candidate structure and the experimental secondary spectrum is less than 0.5).

[0099] Through the above qualitative steps, the aldehyde and ketone compounds in Daqu are divided into 6 levels, namely:

[0100] Level 1: corresponds to standard sample verification;

[0101] Level 2: Not verified with standard samples, the elemental composition of the compound is known, and the corresponding candidate structure can be searched in the database based on the first-level accurate mass. The prediction of the candidate structure is R With the experiment R The absolute deviation is within 40s, and the secondary fragments can be manually annotated;

[0102] Level 3: The elemental composition of the compound is known, and the corresponding candidate structure can be searched in the database based on the primary accurate mass. The prediction of the candidate structure is R With the experiment R The absolute deviation was within 40 s, the secondary fragments failed to be manually annotated, and the similarity between the predicted secondary spectrum of the candidate structure and the experimental secondary spectrum was not less than 0.5;

[0103] Level 4: The elemental composition of the compound is known, and the corresponding candidate structure can be searched in the database based on the primary accurate mass. The prediction of the candidate structure is R With the experiment R The absolute deviation was within 40 s, the secondary fragments could not be manually annotated, and the similarity between the predicted secondary spectrum of the candidate structure and the experimental secondary spectrum was less than 0.5;

[0104] Level 5: The elemental composition of the compound is known, and the corresponding candidate structure can be searched in the database based on the primary accurate mass. The prediction of the candidate structure is R With the experiment R The absolute deviation is greater than 40s;

[0105] Level 6: The elemental composition of the compound is known, but the corresponding candidate structure cannot be found in the database based on the primary accurate mass;

[0106] By the above qualitative method, a total of 487 aldehyde and ketone compounds were detected from the liquor Daqu QC sample, as shown in Table 2. The grade distribution of the 487 aldehyde and ketone compounds is: 45 are grade 1, 37 are grade 2, 3 are grade 3, 61 are grade 4, 168 are grade 5, and 173 are grade 6. Among these 487 aldehyde and ketone compounds, the structures of 314 (64.5%) aldehyde and ketone compounds can be annotated. The above method can achieve high coverage analysis of aldehyde and ketone compounds in Moutai Daqu samples.

[0107] Table 2. Grade distribution of 487 aldehydes and ketones detected in liquor Daqu samples

[0108]

[0109] Effect Example 1 Methodological Characterization

[0110] Four aldehyde and ketone standard samples, including n-valeraldehyde, 2-heptanone, o-vanillin and hexadecanal, were used as the investigation objects. The 10 different gradients of standard sample concentrations used to investigate the linear range were: n-valeraldehyde concentrations were 0.37nM, 0.56nM, 1.11nM, 2.23nM, 5.56nM, 11.13nM, 173.83nM, 463.54nM, 1390.63nM and 2781.25nM; 2-heptanone concentrations were 0.29nM, 0.44nM, 0.88nM , 1.75nM, 4.83nM, 8.75nM, 136.72nM, 364.58nM, 546.88nM and 1093.75nM; the concentrations of o-vanillin and hexadecanal were 0.42nM, 0.63nM, 1.25nM, 2.5nM, 6.25nM, 12.5nM, 195.31nM, 520.83nM, 781.25nM and 1562.50nM, respectively; the concentration of internal standard (benzaldehyde-D5) was 112.54nM.

[0111] The peak area ratio of the above standard sample to the peak area ratio of the internal standard was used as the ordinate y, and the concentration was used as the abscissa x for linear fitting to obtain the linear range of each compound; the concentrations corresponding to 3 times S / N and 10 times S / N were used as the LOD and LOQ of the four compounds; the low, medium and high concentrations of the four compounds detected in 6 parallel derivatization samples within one day (the low, medium and high concentrations of n-valeraldehyde were 1.11nM, 11.13nM and 695.31nM, respectively; 2-heptanone The intra-day precision was characterized by the relative standard deviation (RSD) of the peak response at the low, medium and high concentration levels of 0.88nM, 8.75nM and 546.88nM respectively; the low, medium and high concentrations of o-vanillin and hexadecanal were 1.25nM, 12.5nM and 781.25nM respectively); the inter-day precision was characterized by the RSD of the peak response at the low, medium and high concentration levels (same intra-day precision) of the four compounds detected within three days in 9 parallel derivatization samples. The methodological characterization results are shown in Table 3 below. The linear range of the four aldehyde and ketone compound standards covers 3 orders of magnitude, and the linear regression coefficient (R 2 ) were better than 0.99; the LOD range of the method was 0.07-0.10 nmol, and the LOQ range was 0.29-0.4 nmol, which met the requirements of metabolomics analysis. The intra-day and inter-day RSDs of the four target aldehydes and ketones at low, medium and high concentrations were all less than 10%, further indicating that the method has good repeatability, robustness and reliability.

[0112] Table 3. Methodology validation results

[0113]

[0114] Comparative Example 1 Optimization of derivatization conditions

[0115] (1) 2-Formylbenzoic acid, dodecyl aldehyde, coniferaldehyde, 2-phenylbutenal and internal standard (furfural-D4) were prepared into standard stock solutions with a concentration of 10 mM, and then mixed in equal volumes. The mixed standard was then diluted with acetonitrile to a concentration of 0.5 mM for each compound;

[0116] (2) 2 μL of the diluted mixed standard sample was subjected to three parallel derivatization reactions at different reaction times of 30 min, 60 min, 90 min, and 120 min, at different reaction temperatures of 10°C, 20°C, 30°C, 60°C, and 80°C, and at different catalyst (hydrochloric acid) volumes of 0 μL, 10 μL, 30 μL, 60 μL, and 150 μL;

[0117] (3) Data collection is performed on the samples derivatized under each condition in step (2), and the ratio of the peak area of ​​the standard sample obtained under each condition to the peak area of ​​the internal standard is used as the dependent variable, and each reaction time or reaction temperature or catalyst volume is used as the independent variable to draw a line graph. The results are as follows: Figure 2 shown.

[0118] To get Figure 2 The line graph shown in the figure was analyzed for change trends. The results showed that the time of the derivatization reaction, the reaction temperature and the volume of the catalyst would have different degrees of influence on the derivatization reaction, and then affect the final relative peak area. Specifically, with the increase of the time of the derivatization reaction, the responses of the four compounds would change. When the reaction time was 90 min, the four compounds had good responses, so 90 min was selected as the optimal reaction temperature. According to the same method, the optimal reaction temperature and the optimal catalyst (hydrochloric acid) volume were selected as 60 ° C and 10 μL.

[0119] Comparative Example 2 Optimization of ultrasonic extraction conditions

[0120] This comparative example explored the extraction conditions of aldehyde and ketone compounds in Daqu samples by RSM, the mass of Daqu, the volume proportion of acetonitrile in the extraction solvent, and the ultrasonic time, as follows:

[0121] (1) First, the yellow, white and black Maotai Daqu samples of equal mass were mixed to form QC mixed samples. Then, according to the preliminary experimental results, the Daqu mass was input into Design Expetr 12 software (Stat-Ease Inc., Minneapolis, USA): 14 mg, 28 mg, 41 mg (14-41 mg), the volume proportion of acetonitrile in the extraction solvent was: 20%, 36%, 60%, 84% (36%-84%), and the ultrasonic time was: 10 min, 20 min, 35 min, 50 min, 60 min (10-100 min). The three-factor central composite experimental design (CCD) was selected, and the QC samples were subjected to ice-water bath ultrasonic extraction according to the experimental scheme given by the software, as shown in Table 4 below:

[0122] Table 4 Parameters for establishing the RSM model

[0123]

[0124]

[0125] (2) After the ultrasonication, the mixture was centrifuged (21500 g, 4° C., 10 min), the supernatant was taken out, and 150 μL of 2-hydrazino-4-chloropyridine, 140 μL of acetonitrile and 10 μL of hydrochloric acid were added in sequence, mixed and reacted at 60° C. for 90 min; after the reaction was completed, the mixture was freeze-dried, and the freeze-dried product was re-dissolved with 100 μL of 50% acetonitrile. The conditions of this step were the same as those in Example 1;

[0126] (3) Using UHPLC-HRMS in full scan mode, we collected the LC and MS information of the Daqu samples derivatized under each extraction condition (including t R , M / Z and MS / MS), the conditions of this step are the same as those in Example 1;

[0127] (4) Based on the t collected in step (3) R , M / Z and MS / MS information to filter out the mass difference of 1.9971Da±10ppm, t R Compounds with a difference of no more than 0.1 min and an intensity ratio in the range of 0.3-0.4 are potential aldehyde and ketone compounds;

[0128] (5) An RSM model was established based on the relationship between ultrasonic extraction conditions and the number of potential aldehyde and ketone compounds obtained, and a 3D curve graph was generated, as shown in the following figure. Figure 3 As shown by Figure 3 It can be seen that the interaction between ultrasonic time and ACN content in the extraction solvent or the interaction between ultrasonic time and Daqu quality has little effect on the extraction efficiency ( Figure 3 A- Figure 3B). The interaction between the quality of Daqu and the ACN content in the extraction solvent has a greater impact on the extraction efficiency ( Figure 3 C) Through the above experimental exploration and result analysis, the optimal ultrasonic extraction conditions for the technical solution of the present invention were finally determined.

[0129] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A high coverage analysis method for aldehyde and ketone compounds in liquor Daqu, characterized in that: The steps include:

1. Obtain chromatographic and mass spectrometric data information; 2. Construct QSRR prediction model; 3. Analyze the aldehyde and ketone compounds in the Daqu sample according to the chromatographic and mass spectrometric data information and the QSRR prediction model; Wherein, the step 1 comprises: ultrasonic extraction and derivatization of the Daqu sample, and using UHPLC-HRMS coupling technology to collect chromatographic and mass spectrometric data information of aldehyde and ketone compounds in the Daqu sample; the reagents of the derivatization reaction include: at least one of 2-hydrazino-4-chloropyridine, 2-hydrazino-3-chloropyridine, 2-hydrazino-5-chloropyridine, and 2-hydrazino-6-chloropyridine; The analysis of aldehyde and ketone compounds in the Daqu sample comprises the following steps: Screening out potential aldehyde and ketone compounds according to the chromatographic and mass spectrometric data information obtained in step 1, including: screening out potential aldehyde and ketone compounds according to the isotopic distribution characteristics of chlorine element; Based on the chromatographic and mass spectrometric data information obtained in step 1 and the QSRR prediction model constructed in step 2, the potential aldehyde and ketone compounds screened out are qualitatively classified, including: (1) Performing standard sample verification on potential aldehyde and ketone compounds and the collected aldehyde and ketone compound standard sample information, and the potential aldehyde and ketone compounds in the verification are defined as level 1; (2) For the potential aldehyde and ketone compounds that have not been verified with the standard sample information, the candidate structures are searched in the existing database using their corresponding M / Z. The potential aldehyde and ketone compounds for which no candidate structures are found are defined as level 6; (3) For the potential aldehyde and ketone compound whose candidate structure is searched in step (2), the QSRR prediction model constructed in step 2 is used to predict the t of the candidate structure. R , if the prediction t of the candidate structure R The actual t of the potential aldehydes and ketones R If the difference is greater than 40s, its qualitative level is defined as level 5; (4) For the prediction of the candidate structure described in step (3), R The actual t of the potential aldehydes and ketones R If the difference is no more than 40s, the qualitative grade of the compound is defined by manually annotating the secondary mass spectrum information of the potential aldehyde and ketone compound. If manual annotation is possible, the qualitative grade is defined as grade 2; (5) For the potential aldehyde and ketone compounds that cannot be manually annotated in step (4), the similarity between the predicted MS / MS spectrum and the actual MS / MS spectrum of the potential aldehyde and ketone compounds is compared. If the similarity is not less than 0.5, its qualitative level is defined as level 3; otherwise, its qualitative level is defined as level 4.

2. The method according to claim 1, characterized in that The ultrasonic extraction comprises the following steps: adding an extraction reagent to the Daqu sample and then placing the sample in an ultrasonic instrument for ice-water bath ultrasonic extraction; wherein the extraction reagent is acetonitrile.

3. The method according to claim 2, characterized in that The extraction reagent is 100% acetonitrile.

4. The method according to claim 2, characterized in that In terms of mg / μL, the addition ratio of the Daqu sample to the extraction reagent is 1:5~1:

10.

5. The method according to claim 2, characterized in that In terms of mg / μL, the addition ratio of the Daqu sample to the extraction reagent is 1:5~1:

6.

6. The method according to claim 2, characterized in that In terms of mg / min, the ratio of the weight of the Daqu sample to the ultrasonic extraction time is 3:1 to 1:

2.

7. The method according to claim 2, characterized in that In terms of mg / min, the ratio of the weight of the Daqu sample to the ultrasonic extraction time is 3:1 to 2:

1.

8. The method according to claim 2, characterized in that The ultrasonic extraction time is 10 min-60 min.

9. The method according to claim 2, characterized in that The ultrasonic extraction time is 10min-20min.

10. The method according to claim 2, characterized in that The conditions of the ultrasonic extraction are determined by the following steps: 1) The mass of Daqu, the volume percentage of acetonitrile in the extraction solvent, and the range of ultrasonic extraction time were input into the Design Expetr 12 software. The Daqu samples were subjected to ice-water bath ultrasonic extraction according to the experimental schemes of different ice-water bath ultrasonic extraction conditions given by the software, and then derivatized. 2) using UHPLC-HRMS to collect chromatographic and mass spectrometric data information of aldehyde and ketone compounds in the derivatized Daqu sample of step 1); 3) Screening out potential aldehyde and ketone compounds based on the collected chromatographic and mass spectrometric data information; 4) Based on the relationship between the number of potential aldehyde and ketone compounds obtained under different ultrasonic extraction conditions, an RSM model was established and a 3D curve graph was generated. The obtained model and 3D curve graph were used to determine the optimal extraction conditions.

11. The method according to claim 10, characterized in that The chromatographic and mass spectrometric data information includes: R , M / Z and MS / MS spectra.

12. The method according to claim 10, characterized in that In the step 3), screening potential aldehyde and ketone compounds includes: screening out the compounds with a mass difference of 1.9971 Da ± 10 ppm, t R The compound with a difference of no more than 0.1 min and an intensity ratio in the range of 0.3-0.4 is the potential aldehyde and ketone compound.

13. The method according to claim 1, characterized in that The derivatization comprises the following steps: After centrifuging the Daqu sample after ultrasonic extraction, the supernatant was taken; A derivatization reagent, acetonitrile and hydrochloric acid are added to the obtained supernatant to carry out a derivatization reaction.

14. The method according to claim 13, characterized in that The added volume ratio of the derivatization reaction reagent to acetonitrile and hydrochloric acid is: 14-16:13-15:1-3.

15. The method according to claim 13, characterized in that The temperature of the derivatization reaction is 60°C to 80°C; the time of the derivatization reaction is 60 to 90 minutes.

16. The method according to claim 1, wherein: The method of collecting chromatographic and mass spectrometric data information of aldehyde and ketone compounds in the derivatized Daqu sample by UHPLC-HRMS comprises the following steps: After the derivatization reaction, the Daqu sample was freeze-dried and re-dissolved; The reconstituted sample was centrifuged and the supernatant was analyzed by UHPLC-HRMS to collect the chromatographic and mass spectrometric data information of aldehyde and ketone compounds in the derivatized Daqu sample; The chromatographic and mass spectrometric data information includes: R , M / Z and MS / MS spectra.

17. The method according to claim 16, characterized in that The UHPLC-HRMS is an analytical instrument combined with an Agilent 1290 Infinity liquid chromatography system and an Agilent 6546 Q-ToF.

18. The method according to claim 16, characterized in that The liquid chromatography conditions of the UHPLC-HRMS are as follows: phase A and phase B are 0.1% formic acid / water and 0.1% formic acid / acetonitrile, respectively; the chromatographic column is an ACQUITY BEH C8 chromatographic column; the chromatographic column temperature is 50°C; the column flow rate is 0.35 mL / min; the sample injection chamber temperature is set to 4°C, and the sample injection volume is 1 μL; the elution gradient program is: 0-1 min, 5% B; 1-24 min, 5%-100% B; 24-28 min, 100% B; 28-28.1 min, 100%-5% B; 28.1-30 min, 5% B.

19. The method according to claim 16, characterized in that The mass spectrometry conditions of the UHPLC-HRMS are as follows: the scanning mode is positive ion mode, in full scan mode, the scanning range is 100-1000 Da, the drying gas flow rate and temperature are 8 L / min and 320 °C, respectively; the nebulizer pressure is 35 psig; the sheath gas temperature and flow rate are 350 °C and 11 L / min, respectively; the Vcap is 3000 V, and the fragmentation voltage is 110 V; the secondary scanning range is 30-1200 Da, and the secondary collision energies are 10 eV, 20 eV, and 40 eV, respectively.

20. The method of claim 1, wherein: The construction of the QSRR prediction model includes the following steps: The SMILES of multiple aldehyde and ketone compound standard samples after derivatization were collected, and the t R ; The collected standard samples were divided into training set and test set, and the t R And its SMILES were imported into QSRR_Automator software, and the software was used to select effective molecular descriptors and machine learning algorithms to construct the QSRR prediction model of aldehydes and ketones.

21. The method of claim 20, wherein: The allocation of the training set and the test set comprises the following steps: R Sort from small to large, every 4 t R Divide them into one group, select the last three compounds in each group as the training set, and the first compound in each group as the test set.

22. The method of claim 20, wherein: The proportion of the training set is greater than 70%.

23. The method of claim 20, wherein: The construction of the QSRR prediction model further includes: importing the SMILES of the derivatized products of the standard samples in the test set into the QSRR prediction model to predict the t corresponding to the derivatized products of the standard samples in the test set. R , get the prediction t R ; The predicted t of the derivatization product of the standard sample in the test set R With real R The comparison was made to verify the performance of the constructed QSRR prediction model.

24. The method of claim 1, wherein: The method of screening out potential aldehyde and ketone compounds according to the chromatographic and mass spectrometric data information obtained in step 1 includes: screening precursor ion peak pairs with a mass difference of 1.9971 Da±10 ppm, a retention time difference of no more than 0.1min, and an intensity ratio in the range of 0.3-0.4 according to the isotopic distribution characteristics of the chlorine element, and the compounds corresponding to the ion peak pairs obtained by screening are potential aldehyde and ketone compounds labeled with a derivatization reagent.

25. The method of claim 1, wherein: The standard sample verification in step (1) comprises: R , M / Z and MS / MS spectra and the t of the corresponding aldehyde and ketone compound standards R , M / Z and MS / MS spectra are compared respectively. If the information matches, it indicates that the potential aldehyde and ketone compound is the compound corresponding to the standard sample.

26. The method of claim 1, wherein: The database in step (2) includes at least one or two of: The Food Database and MassBank of North America.

27. The method of claim 1, wherein: The manual annotation in step (4) includes at least the following cases: If there are a series of peaks with a difference of 14.0156 Da ± 10 ppm in the secondary spectrum, the structure of the compound may contain aliphatic chains; and / or If there is a peak at 66.0343 Da ± 10 ppm in the secondary spectrum, the compound may have a pyrrole ring; and / or If a fragment of 91.0547 Da ± 10 ppm appears in the secondary spectrum, the structure of the compound may contain methylenebenzene.

28. The method of claim 1, wherein: The predicted MS / MS spectrum in step (5) is obtained by inputting the SMILES of the candidate structure of the potential aldehyde and ketone compound into the CFM-ID software for prediction.

29. The method of claim 1, wherein: The Daqu sample is a Daqu sample obtained by mixing yellow, white and black Daqu in equal mass.

30. Use of the method according to any one of claims 1 to 29 in the field of analysis and detection technology.

31. The use according to claim 30, characterized in that The technical field of analysis and detection includes: analysis and detection of aldehyde and ketone compounds in Maotai-flavor liquor Daqu.

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