99 results about "Non targeted metabolomics" patented technology

The invention discloses a research method for researching an influence mechanism of high-fat diet on old-age health by using a mouse model, which comprises the following steps: S1, constructing a high-fat diet-induced mouse model: from normal diet feeding of mice to an old mouse stage, dividing the mice into two groups, and respectively performing high-fat diet feeding and normal diet feeding; s2, detecting physiological and functional indexes of the mice in the high-fat diet feeding group and the normal diet feeding group; s3, microbial diversity analysis: performing 16S rRNA sequencing to analyze the community structure of the intestinal microbiota of the mouse; s4, carrying out non-targeted metabonomics analysis: S4.1, carrying out intestinal tract non-targeted metabonomics sequencing; s4.2, carrying out non-targeted metabonomics sequencing on the liver tissues; s5, carrying out transcriptomics analysis; s6, performing conjoint analysis: performing the conjoint analysis on every two results of transcriptomics analysis, non-targeted metabonomics analysis and microbial diversity analysis; and S7, evaluating the influence of the high-fat diet on the health of the old rats by using the data obtained through analysis.

The invention discloses a metabolic reaction pair prediction method based on deep learning and non-targeted metabonomics, and belongs to the technical field of bioinformatics, and the method comprises the steps: (1) enabling reactants and products to form a metabolite reaction pair, and converting the metabolite reaction pair into a molecular fingerprint pair, thereby constructing a metabolic reaction pair data set; (2) establishing a deep learning model of which the input is molecular fingerprints and the output is a metabolic reaction pair prediction result, and training the model by utilizing the established metabolic reaction pair data set to realize accurate modeling; and (3) inputting a molecular fingerprint pair of a compound to be predicted or a metabolic characteristic into the trained model to generate a prediction result of whether a reaction pair is formed or not. The method can predict whether a metabolic reaction pair is formed between a compound pair or a metabolic characteristic pair represented by non-targeted metabonomics secondary mass spectrometry data. According to the method, the information contained in the non-targeted metabonomics data can be fully mined, the utilization efficiency of the metabonomics data is remarkably improved, and the prediction precision is high.

The invention discloses an optimized 3-nitrophenylhydrazine derivatization method and an application of the optimized 3-nitrophenylhydrazine derivatization method. According to the optimized method disclosed by the invention, the alpha-keto acid metabolite is subjected to direct derivatization reaction by adopting 3-nitrophenylhydrazine; the method comprises the following steps: carrying out a 3-nitrophenylhydrazine / 1-ethyl-(3-dimethylaminopropyl) carbodiimide / pyridine derivatization reaction on a metabolite only containing carboxyl; a 3-nitrophenylhydrazine derivatization method or a 3-nitrophenylhydrazine / 1-ethyl-(3-dimethylaminopropyl) carbodiimide / pyridine derivatization method or a non-derivatization method is adopted for metabolites only containing carbonyl; a non-derivatization method is adopted for other metabolites. According to the method disclosed by the invention, a specific reaction with carbonyl and carboxyl can be carried out, so that non-targeted metabonomics analysis is enhanced, and metabolites in a biological sample can be more comprehensively analyzed.

The invention discloses a screening method of mango pulp processing mode markers and an identification method of mango pulp processing modes, and relates to the technical field of analytical chemistry. According to the method, non-targeted metabonomics is combined with quasi-targeted metabonomics and targeted quantitative analysis, so that the characteristic markers capable of accurately distinguishing mango pulp in different processing modes are systematically screened and verified for the first time. The feature marker screened by the method can be used for constructing a discrimination model of the mango pulp processing mode, high-precision and high-reliability processing mode discrimination is realized based on the established multi-model fusion discrimination method, the discrimination efficiency and accuracy of the mango pulp processing mode are greatly improved, and the method is suitable for popularization and application. And a new technical means and theoretical basis are provided for product quality control and market supervision.

The invention relates to an oxidized fatty acid large-scale qualitative method based on non-targeted metabonomics. The method comprises the following steps: firstly, enriching oxidized fatty acid in a biological sample to obtain non-targeted metabonomics data; secondly, constructing a self-built database which comprises an oxidized fatty acid mass spectrum library, an oxidized fatty acid structure library and an in silico oxidized fatty acid virtual structure library; and then, carrying out self-established virtual database search on data of an oxidized fatty acid extraction sample, and carrying out large-scale identification on the oxidized fatty acid by comparing a first-level ion mass-to-charge ratio, a second-level mass spectrum and / or second-level ion characteristic fragments, retention time and an in-source cracking mode. And finally, co-constructing a molecular network by the secondary mass spectra recorded in all the databases, the identified secondary mass spectra in the samples and the secondary mass spectra of all the unknown metabolites in the samples based on secondary similarity, and carrying out expansion annotation on the unknown oxidized fatty acid through a metabolic reaction relationship.

The invention discloses a method and a device for monitoring the stability of an anaerobic digestion system in real time. The method comprises the following steps: 1, carrying out non-targeted metabonomics analysis on the anaerobic digestion mVOCs category by adopting headspace solid-phase microextraction in combination with gas chromatography-high resolution mass spectrometry; 2, screening differential mVOCs markers based on principal component analysis and orthogonal partial least squares discriminant analysis; and 3, constructing a system stability real-time monitoring system by using an electronic nose sensor array, and when the difference mVOCs marker concentration deviates from a threshold value or difference categories are continuously accumulated, triggering early warning and recommending an optimization strategy by the system. The method and the device can monitor the state of the anaerobic reactor in real time, early warn the methane production inhibition risk in advance compared with traditional parameters (pH, volatile fatty acid and the like), improve the biogas yield and reduce the operation and maintenance cost, are suitable for intelligent management of anaerobic digestion scenes of kitchen garbage, municipal sludge, agricultural waste and the like, and can also be expanded to other anaerobic biological fermentation fields.

The invention discloses a method for analyzing component difference between Maotai-flavor yeast and yeast-making wheat by using non-targeted metabonomics. The method comprises the following steps: S1, pre-treating a sample; s2, detecting each sample by adopting the combination of an ultra-high performance liquid chromatograph and a quadrupole time-of-flight mass spectrometer; s3, acquiring original data, performing peak extraction, peak alignment and normalization by adopting metabonomics data analysis software, and evaluating the overall difference and intra-group variation among sample groups by adopting principal component analysis; constructing a grouping discrimination model through discriminant analysis of an orthogonal partial least square method; determining differential metabolites of the two groups of samples by taking variable projection importance (VIP) greater than 1 and statistical significance P less than 0.01 as a primary screening standard and combining a secondary screening standard that the difference multiple log2FC greater than 2 is up-regulated and the difference multiple log2FC less than-2 is down-regulated; s4, performing compound structure and category annotation on the screened differential metabolites through a metabolome database; and performing pathway enrichment analysis by adopting a statistical test method, and determining a core metabolic pathway enriched by the differential metabolites.

The invention discloses a tea leaf quality grade evaluation method based on a fingerprint chromatogram. The method comprises the following steps: S1, establishing a tea leaf harvesting season discrimination model based on three-dimensional chromatograms and metabolic fingerprints of tea leaves in different harvesting seasons; s2, establishing a tea extraction mode discrimination model based on the multi-grade three-dimensional chromatograms and fingerprints under different tea extraction modes; and S3, based on the tea harvesting season discrimination model and the tea extraction mode discrimination model, establishing a discrimination model for distinguishing four different tea grades by combining a non-targeted metabonomics technology and a chemical mode recognition method, and obtaining, identifying and classifying differential chemical components. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

The invention discloses an application of astaxanthin in preparation of a regulator for improving the gentisic acid level. On the basis of non-targeted metabonomics analysis, it is found for the first time that in the SAP pathogenesis process, the level of a flora metabolite, namely gentisic acid (GA), in intestinal contents is remarkably reduced, and in-vivo gentisic acid level can be remarkably callback through astaxanthin intervention. Further research proves that exogenous supplement of gentisic acid can effectively simulate the protection effect of astaxanthin, including improvement of pancreatic tissue pathological damage, reduction of serum lipase and amylase activity, and inhibition of systemic inflammatory response (down-regulation of IL-1beta, IL-6 and TNF-alpha and up-regulation of IL-10). The invention not only discloses a new mechanism that astaxanthin plays a therapeutic role through an'intestinal flora-metabolite 'axis, but also determines that gentisic acid is used as a novel drug target for treating SAP, and constructs a new method for screening drugs for treating SAP according to the novel drug target.

The invention belongs to the technical field of compound analysis and identification, and discloses application of a metabonomics analysis technology to identification of lycium seed oil. According to the metabonomics analysis technology, the characteristic components of the lycium seed oil are identified through Log2 Fold Channel and VIP values, and the screening conditions for identifying the characteristic components of the lycium seed oil are | Log2 Fold Channel | > 1.5, and VIP < gt >; the method specifically comprises the steps of sample preparation, non-targeted metabonomics analysis, extraction and identification of characteristic peaks, similarity analysis, difference analysis and screening of differential markers. According to the method, a non-targeted metabonomics analysis method is adopted, various metabolic components in the lycium barbarum seed oil can be undiscriminately covered, the limitation that a traditional identification technology only aims at a few indexes is broken through, the unique characteristic components of the lycium barbarum seed oil can be accurately screened out, and the accuracy and specificity of an identification result are greatly improved.

The invention belongs to the technical field of drug detection, and particularly relates to a method for detecting lycium barbarum glycopeptide metabolites in rat plasma and tissues based on non-targeted metabonomics. According to the method, after rat lycium barbarum glycopeptides are administered through intragastric administration, blood, liver, intestine, lung and brain tissue samples after administration are collected. A UPLC-Q-TOF-MS (Ultra Performance Liquid Chromatography-Quadrupole Time of Flight Mass Spectrometry) analysis method is combined with a statistical analysis method comprising principal component analysis (PCA) and orthogonal-partial least squares discriminant analysis (OPLS-DA) to carry out in-vivo metabonomics analysis on the wolfberry glycopeptide. Then, based on a human metabolome database (HMDB), lycium barbarum glycopeptide differential metabolite is searched, so that the pharmacological action mechanism of the lycium barbarum glycopeptide is clarified from the perspective of metabolism, and a foundation is laid for deep research and development of the medicinal value of the lycium barbarum glycopeptide in future.

The invention discloses an excrement metabolism marker for predicting spontaneous premature delivery based on UHPLC-MS (Ultra High Performance Liquid Chromatography-Mass Spectrometry) and application, and belongs to the technical field of biological medicines. According to the premature pregnant woman excrement metabolite specific biomarker and the application thereof, the premature pregnant woman excrement metabolite specific biomarker is screened out through a non-targeted metabonomics technology, and the marker is 1-hydroxyl vitamine d2, Mirtazapine n-oxide, Tetraethene glycol or a combination of the 1-hydroxyl vitamine d2, the Mirtazapine n-oxide and the Tetraethene glycol. A premature delivery prediction model established by using the marker is high in sensitivity and good in specificity, and a reference basis is provided for early prediction and accurate prevention of spontaneous premature delivery.

The present invention discloses a method for tracing the origin of dried ginger based on characteristic metabolites. The present invention is based on non-targeted metabolomics analysis technology, and uses ultra-high performance liquid chromatography tandem mass spectrometry to detect metabolites in dried ginger from different origins, obtains overall metabolite data of dried ginger from different origins, and further applies chemometrics and machine learning algorithms to construct a classification model for tracing the origin of dried ginger, and screens characteristic metabolites that affect the distinction of dried ginger from different origins, so as to achieve accurate tracing of the origin of dried ginger. The method for tracing the origin of dried ginger based on characteristic metabolites of the present invention has the advantages of accurate and reliable experimental results, stable tracing models, etc., and can provide a new idea for identifying the origin of dried ginger.

The invention belongs to the technical field of traditional Chinese medicine, and discloses a method for constructing a neuroinflammation intervention animal model and screening effective components in turmeric dregs, and the method comprises the following steps: S1, preparing an experimental medicament for an animal model; s2, constructing a mouse animal model of the A beta 1-42 induced neuroinflammation; and S3, ascertaining the mechanism and effect of the curcuma longa dregs in intervening the neuroinflammation of the mouse. The invention further provides a method for screening the effective components in the rhizoma curcumae longae dregs, and the effective components for intervening neuroinflammation in the rhizoma curcumae longae dregs are screened by adopting a neuroinflammation intervention animal model. According to the invention, a specially constructed animal model, UPLC-MS / MS and HPLC are combined with a non-targeted metabonomics technology to research the mechanism, effect and effective components of the curcuma longa dreg intervened neuroinflammation mouse, and the method can be widely applied to deep development of curcuma longa traditional Chinese medicine dregs, effectively utilizes curcuma longa dreg resources, and provides support for prevention and treatment of neuroinflammation related diseases.

The invention provides application of acetylcholine and 1-carboxyl-6-hydroxy-3, 4-dihydro-beta-carboline (the code in the attached drawing of the abstract is CHEMBL461889) in early pregnancy diagnosis, non-targeted metabonomics detection and analysis are carried out on the urine of pregnant and non-pregnant sows by virtue of a liquid chromatography-mass spectrometry technology; the two metabolic markers capable of being used for early pregnancy diagnosis of the sows are screened out by introducing two deep learning models for joint analysis, the AUC values of ROC curves of the two metabolic markers are 1, the accuracy is very high, and the pregnancy state of the sows can be accurately judged only 18 days after the sows are bred. The invention provides a novel effective sow early pregnancy diagnosis method for the pig breeding industry, and has important significance for reducing the breeding cost.

The invention provides a biomarker and a system for predicting whether an ACLF patient is dead or not and application of the biomarker and the system, ACLF related samples are analyzed through non-targeted metabonomics, a series of biomarkers capable of predicting the short-term death risk of the ACLF patient are found, a biomarker combination is further screened out from the biomarkers, a short-term post-death risk prediction model of the ACLF patient is constructed, and the prediction model is used for predicting the short-term post-death risk of the ACLF patient. The method achieves convenient and efficient prediction, masters the golden window period of intervention for high-risk people, reduces the economic burden for low-risk patients, and meets the clinical needs.

The present invention discloses a biological age prediction method and device based on non-targeted metabolomics, which relates to the field of biological data processing, including: constructing a biological age prediction model based on stacking integration and training it to obtain a trained biological age prediction model; processing and feature selection of mass spectrometry data of each metabolite in the blood sample of the person to be analyzed to obtain key metabolic features; inputting the key metabolic features corresponding to the person to be tested into the trained biological age prediction model, first passing through each base model in the base model unit to obtain a number of first ages, forming a first age vector with the first ages and inputting them into each secondary learner in the secondary learner unit respectively to obtain a number of second ages, forming a second age vector with the second ages and inputting them into the final learner to obtain the predicted age. The present invention solves the problems of low accuracy, lack of comprehensiveness, complexity and high cost of existing biological age assessment.

The invention provides an application of metabolites in sow vaginal secretion in early pregnancy diagnosis, and finds that three metabolic molecules can be used as markers for the early pregnancy diagnosis of sows for the first time on the basis of non-targeted metabonomics and machine learning conjoint analysis. According to the present invention, the metabolic markers are respectively INDOLEPROPIONIC ACID (indolepropionic acid), cis-Amonitate (cis-Amonitate) and 4-p-Coumaroylquinic acid (4-p-coumaroylquinic acid), the ROC curve AUC values of the metabolic markers are respectively 0.88, 0.92 and 0.77, and the metabolic markers have excellent diagnosis performance during the combination diagnosis. The pregnancy diagnosis is carried out by adopting the vaginal secretions of the sows 18 days after hybridization, the effective pregnancy diagnosis time is advanced by 5-8 days on the basis of a wave diagnosis method, and the method has important significance for reducing cost and increasing ultrasonic efficiency in live pig feeding.

The invention relates to the technical field of food identification, in particular to a method for identifying sheep milk and milk based on metabonomics, and aims to solve the industrial pain point that cheap milk is doped into current high-end sheep milk products and is difficult to detect through conventional physicochemical indexes, and the problems that existing protein, DNA and other identification methods are insufficient in sensitivity, complex in operation and the like. According to the invention, a non-targeted metabonomics analysis strategy is innovatively adopted. The saliency difference metabolites between the sheep milk and the milk are systematically screened from the omics level, and rapid, high-sensitivity and high-specificity identification on the authenticity of the sheep milk and whether the sheep milk is mixed with the milk is realized. According to the invention, a key technical basis and a material core are provided for developing corresponding detection products and market supervision methods.

The present invention belongs to the field of bioinformatics, and specifically relates to a mass spectrometry acquisition method, device, equipment and computer program product, the method comprising: performing biological sample processing on a provided biological sample; performing liquid chromatography separation on the treated biological sample; performing mass spectrometry analysis on the biological sample after liquid chromatography separation; after the mass spectrometry analysis is completed, using the AIE-MS automatic exclusion method to identify and exclude interference signals in the mass spectrometry data, and iteratively acquiring secondary mass spectrometry data. The depth of metabolite identification in the prior art is still at a relatively low level compared to other omics. The mass spectrometry acquisition method, device, equipment and computer program product provided by the present invention can significantly increase the number of compound identifications, aiming to solve the core problem of low metabolite identification depth in existing LC-MS non-targeted metabolomics analysis, and has great potential in improving LC-MS metabolomics identification flux and improving data quality.

The invention relates to the technical field of health assessment, in particular to a dynamic metabolism difference modeling method for a premature infant and a full-term infant, a marker and application, and the method comprises the following steps: obtaining a data set; the data set comprises neonatal plasma samples of a to-be-detected group and a control group; preprocessing the plasma sample in the data set; carrying out non-targeted metabonomics detection on the pretreated plasma sample by adopting ultra-high performance liquid chromatography-time-of-flight mass spectrometry to obtain mass spectrometry original data; preprocessing the mass spectrum original data, performing multivariate statistical analysis on the preprocessed mass spectrum data, and constructing a plasma metabolite discrimination model; and based on the plasma metabolite discrimination model, evaluating the metabolic disorder degree and health risk of the premature infant. According to the method, the metabolic disorder degree and the health risk of the premature infant are evaluated by constructing the plasma metabolite judgment model and based on the differential metabolite characteristics in the plasma metabolite judgment model, and the early screening of the health condition of the premature infant is realized.

The invention provides a metabolic marker combination for identifying okra related species, an identification method and application of the metabolic marker combination, and belongs to the technical field of drug detection. The identification method provided by the invention comprises the following steps: carrying out non-targeted metabonomics detection on dry powder samples of Abelmoschus manihot (L.) Medik., Abelmoschus moschus (L.) Meduli, Abelmoschus sagittifolius (L.) Merr. And Abelmoschus esculentus (L.) Moench by adopting ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS), so as to obtain metabolite data, and carrying out quantitative analysis on the metabolite data, so as to obtain a sample of the Abelmoschus manihot (L.) Meduli, the sample of the Abelmoschus moschus (L.) Merr. And carrying out mass spectrum qualitative and quantitative analysis on metabolite data, and screening out common differential metabolites among the four plants as characteristic metabolites to realize variety identification of okra related species. The method can effectively distinguish okra species with similar morphologies, and has application value in traditional Chinese medicinal material original identification, germplasm resource screening and variety purity detection in a seedling raising stage.

The invention discloses an application of intestinal microbial flora in preparation of a microecological preparation for treating gas explosion brain injury, and the neuroprotective effect of fecal microbial transplantation on GE-induced TBI rats, including neurological dysfunction, neuropathological change and neuroinflammation, is researched by adjusting a microflora-intestinal tract-brain axis. According to the invention, a model of intestinal flora imbalance and brain injury of male rats is established by using a fecal microbial transplantation technology, and then non-targeted metabonomics and microbial diversity analysis are carried out to explore bacteria with potential functions. Meanwhile, the intestinal microbiota composition, the intestinal and brain barrier functions and the number of regulatory T cells of the brain are evaluated. A potential anti-inflammatory mechanism determined by integrating microbiome and metabolome analysis relates to a peripheral immune pathway, and a promising strategy is provided for relieving GE-induced TBI.

The application discloses a metabolic marker for distinguishing metabolic syndrome and simple obesity and application thereof. The marker comprises eight metabolites, i.e. glycocholic acid, lithocholic acid, linoleic acid, valine, leucine, alanine, serine and glycine. The application performs non-targeted metabolomics analysis on serum of metabolic syndrome and simple obesity patients by liquid chromatography mass spectrometry, finds eight metabolites with significant difference between the two, and finds that the eight metabolic markers have high AUC when used for diagnosing and distinguishing metabolic syndrome patients or simple obesity patients alone or in combination.

The application belongs to the technical field of biological medicine, and discloses application of NP in preparation of a preparation for regulating metabolism of the intestines of senile type 2 diabetes. The NP is formed by hydrogen bond combination of a peptide from soft-shelled turtle eggs with naringenin in a mass ratio of 100:1, and the mass of the peptide is less than 3 kDa; the preparation is used for regulating phenotype-related metabolic modules and intestinal micro-ecological-related metabolic modules, remodeling the intestinal metabolic regulation network taking differential metabolites as a bridge, and thus systemically improving metabolic disorders related to senile type 2 diabetes. It is found that NP intervention can obviously correct abnormal total metabolic profiles of feces of senile type 2 diabetes mice, and make the disease type metabolic profile shift to a new steady state of intervention type. Non-targeted metabolomics analysis by LC-MS / MS shows that the overall metabolic profile of the high-dose NP group is closer to that of the classic hypoglycemic drug metformin group, indicating that NP has a significant metabolic remodeling capacity.

The invention discloses an application of intestinal flora metabolite salicylic acid as a predictive marker of Escherichia coli calf diarrhea. The obviously up-regulated metabolite salicylic acid is screened out from the diarrhea calf feces through a non-targeted metabonomics technology, and in a group of new independent samples, it is verified through an ELISA test that the salicylic acid concentration of the diarrhea calf feces is obviously higher than that of a healthy group. ROC curve analysis shows that the area under curve (AUC) of SA for predicting escherichia coli calf diarrhea is 0.8148, and the method has good prediction capability. The invention provides a non-invasive, cheap, rapid and high-specificity prediction method, can be used for early warning and auxiliary diagnosis of Escherichia coli calf diarrhea, and has important significance for guiding clinical precise medication and reducing the death rate of calves.

The invention relates to a composition for preventing and treating alcoholic liver injury and application thereof. The composition mainly comprises the following active ingredients: wheat peptide, soft-shelled turtle peptide and kudzuvine root powder. According to the invention, zebra fish is used as a model organism, ethanol exposure treatment is utilized to induce and establish an alcoholic liver injury model, and the alcoholic liver injury is identified by means of apparent histomorphology observation, biochemical index detection, gene expression quantity analysis and the like. Two bioactive peptides (wheat peptides and soft-shelled turtle peptides) screened from six bioactive peptides and five plant extracts are compounded with one plant extract (radix puerariae powder), and the effects of dispelling the effects of alcohol and protecting the liver of the compound are explored from the aspects of enteric microbiomics and non-targeted metabonomics. Researches show that the intervention of the compound significantly improves the hepatomegaly induced by alcohol, and has a synergistic effect in the aspects of preventing hepatocyte injury (improving ALDH activity) and oxidative injury (improving SOD activity) caused by alcohol.

The invention relates to the field of biological medicines, and discloses application of piperidine acid in preparation of a medicine for preventing and / or treating non-alcoholic steatohepatitis. The invention discovers that the piperidine acid can prevent and / or treat the non-alcoholic steatohepatitis for the first time. Specifically, non-targeted metabonomics detection finds that the content of the piperidine acid in an excrement sample of an NASH patient and a mouse model is reduced, and in-vivo experiments show that supplement of the piperidine acid can reduce the levels of ALT and AST in serum of an NASH mouse, reduce the content of triglyceride in the liver, and improve the expression of the NASH mouse. And the fat change, liver cell balloon-like change, inflammatory cell infiltration and fibrosis degree in the liver are improved in histology. In-vitro experiments show that piperidine acid can improve lipid deposition, inflammatory cell proliferation and fibrosis of liver organs. Moreover, the piperidine acid is an intestinal flora metabolite originally existing in a living body, and has no obvious toxicity to the living body; the oral administration is convenient and fast; commercial reagents are available and can be conveniently obtained.

The invention relates to the technical field of food detection, and particularly discloses a method for identifying Luzhou-flavor liquor, which comprises the following steps: acquiring a plurality of different types of Luzhou-flavor liquor samples as a training set; carrying out non-targeted metabonomics analysis on the training set samples by adopting ultra-high performance liquid chromatography-tandem mass spectrometry, detecting non-volatile compounds in the training set samples, and obtaining composition and relative content data of the training set samples; based on the training set sample information and the non-volatile compound detection result, establishing a qualitative identification model of the Luzhou-flavor liquor by using an orthogonal partial least square discriminant analysis method; and performing non-targeted metabonomics analysis on a to-be-detected Luzhou-flavor liquor sample, and inputting obtained data into the qualitative identification model of the Luzhou-flavor liquor to obtain category information of the Luzhou-flavor liquor. According to the method provided by the invention, the established qualitative identification model of the Luzhou-flavor liquor based on different alcoholic strengths, producing areas and brands is reliable and good in predictability, and a new method can be provided for the qualitative identification of the Luzhou-flavor liquor.

The invention discloses a method for analyzing differential metabolites in different fermentation periods of streptomyces fradiae. The method comprises the following steps: collecting and quenching streptomyces fradiae cells, extracting intracellular metabolites, performing LC-MS (Liquid Chromatography-Mass Spectrometry) detection, and processing and analyzing data. According to the method, samples of streptomyces fradiae in different fermentation periods are studied through a non-targeted metabonomics method, the types, abundance and metabolic pathways of main differential metabolites in the cell fermentation process of streptomyces fradiae are analyzed, and the regulation mechanism of the differential metabolites influencing the cell fermentation of streptomyces fradiae is clarified; therefore, the metabonomics and metabolic engineering research of streptomyces fradiae is promoted, and the industrial production application value of streptomyces fradiae is further improved.