133 results about "Chemometrics" patented technology

An AI-based euryale ferox processing process quality control system of the present invention belongs to the technical field of food processing and quality control, and comprises a quality perception quantification module used for carrying out real-time nondestructive detection on a material unit entering a processing flow, and determining the quality of the material unit through a pre-established chemometrics model. The system comprises a material unit acquisition module for acquiring original spectrum data, a quality dynamic evolution prediction module for analyzing and quantifying the acquired original spectrum data into an initial starch crystallinity index, and a quality dynamic evolution prediction module for predicting a quality evolution track of the material unit in future processing time through a dynamic evolution prediction model based on the initial starch crystallinity index, a current quality state and real-time process parameters. According to the method, different processing paths can be predictively planned for materials with different characteristics, so that disturbance caused by heterogeneity of the raw materials is effectively overcome.

The invention discloses a method for detecting aspergillus flavus and aflatoxin B1 of crops based on an infrared spectrum. The method comprises the steps of crop sample material collection, rice multi-strain mould infection, spectrum collection, crop sample aflatoxin B1 determination, spectrum pretreatment, correction model construction and model verification. The invention aims at detecting the content of aspergillus flavus infection and toxin B1 thereof in crops by combining a near-infrared spectrometer and a stoichiometric method. The method has the advantages of being simple and easy to implement, high in analysis speed, high in detection precision, low in analysis cost, free of any pollution to the environment and the like, samples do not need any pretreatment, and the method has important practical value for guaranteeing the quality safety of agricultural products and reducing the carcinogenic risk of people.

The invention relates to the field of online analysis of chemical components of materials, and discloses an online monitoring process for components of a lead frame electroplating solution, which comprises the following steps: conventionally monitoring the spectrum of the electroplating solution to calculate the concentration; calculating a spectral entropy index based on the deviation degree of the real-time spectrum and a pre-established statistical baseline model, and comparing the spectral entropy index with a preset failure trigger threshold; a dynamic calibration step is started only when the spectral entropy index exceeds the preset failure trigger threshold, the dynamic calibration step compares a physical true difference spectrum with a model virtual difference spectrum by injecting a standard pulse to generate a model error vector, and the model error vector is utilized to correct the chemometrics model, so that the stoichiometry accuracy is improved. According to the method, on-demand calibration is realized, and the consumption of the standard substance is reduced while the real-time accuracy of the model is guaranteed.

The invention discloses a Raman spectrum joint classification and quantification method based on a prior fusion double-flow network, and belongs to the technical field of spectral analysis and chemometrics. The method comprises the following steps: acquiring Raman spectrum original data, and preprocessing to obtain standardized spectrum data; raman spectrum prior information is extracted, and a prior information double-flow network containing classification and quantitative flow sub-networks is constructed; standardized data and prior information are input, classification preheating-combined fine tuning training is adopted, conflict is relieved by combining dynamic weighting and PCGrad, and samples are expanded through a physical consistency strategy synchronously; processing unknown samples by using the trained network, and outputting categories and component contents; according to the method, weak peak information is mined through double-flow architecture and cross-flow attention, derivative noise is suppressed, classification-quantification performance is balanced through double-task optimization, and small sample overfitting is solved through physical expansion; on a polycyclic aromatic hydrocarbon data set, classification and regression indexes are superior to those of a traditional method, the low-concentration / weak-peak scene precision and robustness are better, and the engineering application prospect is good.

The invention discloses a near infrared spectrum modeling method and system based on merge matrix PCA, and relates to the technical field of near infrared spectrum and the field of chemometrics. The method comprises the following steps: acquiring original near infrared spectrum data and reference data of a to-be-detected sample as independent variable data and corresponding dependent variable data respectively; respectively performing data set division on the independent variable data and the dependent variable data by adopting a sample set division method; and constructing a regression prediction model based on a least square regression method, training and verifying the regression prediction model by using the preprocessed data set, performing correlation analysis on the data set and a prediction target based on a principal component analysis method in the training process of the regression prediction model, and performing principal component extraction. According to the near infrared spectrum modeling method and system based on merging matrix PCA, dimensionality reduction is carried out on high-dimensional data possibly existing in a sample, then a robust prediction model is established, and rapid and accurate quantitative analysis is achieved.

The invention provides a neural network model variable selection method based on an SHAP value, and belongs to the field of spectrum data processing chemometrics variable selection, and the method comprises the steps: S1, carrying out the standardization processing of a sample spectrum, and obtaining a spectrum data set; s2, selecting spectral data to train a neural network model based on the current variable set; s3, calculating an SHAP value of each variable in the current variable set based on K-Fold cross validation to obtain a variable importance vector; s4, sequencing the variable importance vectors, and removing unimportant variables based on an e exponential function to obtain a new variable set; s5, judging that the set number of iterations is reached, if so, selecting the variable set with the minimum cross validation error as the optimal variable set, and if not, returning to S2, and inputting a new variable set into the neural network model for continuous execution; the invention further provides a neural network model variable selection device based on the SHAP value, input variable importance evaluation and variable selection are achieved, and spectrum modeling and detection precision is improved.

The invention discloses a Maotai-flavor liquor process category and authenticity identification model construction method, an identification method, an identification device and equipment. The method comprises the following steps: acquiring data of liquor samples; preprocessing the sample data to obtain preprocessed sample data; the preprocessed sample data are input into an initial identification model constructed based on a graph neural network or a partial least square discrimination method, the initial identification model is trained, target identification models are obtained, and the target identification models comprise a gas chromatography identification model, an ion chromatography identification model and / or a three-dimensional fluorescence spectrum identification model. According to the method, multiple analysis technologies such as GC, IC and EEM and a chemometrics modeling analysis module are adopted, analysis results of different technologies are mutually verified, a target identification model with high prediction precision is constructed, and accurate identification of the process category and authenticity of the Maotai-flavor liquor is realized.

The invention discloses a quick quantitative detection method for Brassica rapa polysaccharide based on near infrared spectroscopy and chemometrics, and belongs to the technical field of polysaccharide detection of agricultural products. According to the method, a near-infrared spectrometer is used for collecting a sample to prepare spectral information, PyCharm software is used for optimizing an original spectrum, a PLS quantitative prediction model between the polysaccharide content and the spectral information is established, and the performance and predictive capacity of the model are evaluated, so that the accurate and rapid detection of the Brassica rapa polysaccharide content by the near-infrared spectroscopy is realized. According to the quantitative regression model established by the method, R2c and R2p are respectively 0.879 and 0.836, the corrected root mean square error RMSEC and the predicted root mean square error RMSEP are respectively 2.560 and 2.974, the predicted relative analysis error RPD is 2.474, each index is ideal, the model fitting degree is good, the prediction effect is better, and the precision and the stability both meet the expected use requirements.

The invention is in the field of generation of chemometric models for spectrometers. It relates to a computer- implemented method for generating a chemometric model for a spectrometer comprising: a) receiving spectra of an object and object data associated with a physical or chemical characteristic of the object, b) augmenting the provided spectra and the associated object data, c) generating at least two different candidate chemometric models comprising d) training the candidate chemometric models using the augmented spectra and the object data, e) determining the prediction accuracy of the trained candidate chemometric models, f) selecting a chemometric model of the trained candidate chemometric models using the prediction accuracy and g) outputting the selected chemometric model.

The invention provides a birch bark quality comprehensive evaluation method based on combination of a fingerprint spectrum, chemometrics and a quantitative analysis of multiple components by single marker, and relates to the technical field of quality control and evaluation of birch bark. Comprising the following five steps: preparing a birch bark sample, setting chromatography and mass spectrometry conditions, generating a fingerprint spectrum and evaluating similarity, analyzing chemometrics data, and carrying out quantitative analysis by using a single-marker multi-evaluation method. Multiple components can be quantified at the same time, and the quality of the birch bark is comprehensively evaluated. The multi-component analysis mode can cover the key effective components in the medicinal materials, so that the quality evaluation deviation caused by neglecting other components is avoided, and the reference can be provided for the quality control and evaluation of the birch bark accurately, simply and conveniently in combination with a chemometrics method through the established birch bark fingerprint spectrum and the multi-index component determination method.

The present invention discloses a method for detecting the aging time of Chinese liquor based on GC-MS and machine learning models, belonging to the technical field of Chinese liquor identification. First, the present invention uses a gas chromatography-mass spectrometry instrument to collect the qualitative and quantitative data sets of compounds in Chinese liquor with gradient aging time. Subsequently, the uniformity index of the compounds in Chinese liquor is calculated by chemometrics, and then the esterification hydrolysis reversible reaction is screened out and the esterification reaction concentration quotient is obtained. After combining the three data sets, through a sorting algorithm and a machine learning model, the key characteristic values closely related to the identification of the aging time of Chinese liquor are screened out, and the identification accuracy of the aging time of Chinese liquor is verified, realizing the accurate detection of the aging time of Chinese liquor.

The application discloses a quality analysis method for extracting effective components of Gastrodia elata by using HDES, and comprises the following steps: (1) synthesizing HDES by using L-menthol and DL-lactic acid; (2) adding the HDES in step (1) into Gastrodia elata medicinal material powder, stirring to extract effective components, centrifuging and diluting the obtained mixture after the extraction is completed, obtaining a test sample solution, and performing chromatographic analysis to determine the content of the effective components; (3) detecting the quality indexes of the test sample solution in step (2); and (4) analyzing the quality of Gastrodia elata by using a chemometrics analysis method. The extraction method is green, simple, fast and high in extraction efficiency, and is favorable for the extraction of effective components in medicine and food homologous medicinal materials.

A medication assurance system for verification of both the medication and the patient is disclosed. A portable spectrometer is used to obtain a light spectrum of the medication. A subject identification or biometric device is used to identify the patient. A controller coupled to the portable spectrometer and the subject identification device identifies the medication by performing a chemometric analysis of the light spectrum. Based on the medication identified and the patient identified, the controller can determine if the medication is to be taken by the patient.

The invention discloses a method, equipment, medium and product for predicting the year of Maotai-flavor liquor, and relates to the technical field of chromatography and spectrum, chemometrics and artificial intelligence analys.The method comprises the steps that a chromatographic separation technology is utilized, chromatographic data and spectrum data of a Maotai-flavor liquor sample to be detected are collected, the chromatographic data are preprocessed, and the year of the Maotai-flavor liquor sample to be detected is obtained; determining the preprocessed data; decomposing and processing the three-dimensional fluorescence spectrum data based on a high-order cyclic factor algorithm, and determining a decomposition result; taking the processed data as four trained Bayesian network models, and outputting four year prediction results of the Maotai-flavor liquor; and taking the median of the four year prediction results of the Maotai-flavor liquor as the final year prediction result of the Maotai-flavor liquor sample to be detected. According to the method, the year prediction accuracy and prediction efficiency of the Maotai-flavor liquor can be improved.

The present application relates to the field of on-line analysis of material chemical composition, and discloses an on-line monitoring process for the composition of a lead frame electroplating solution, comprising: calculating the concentration of the electroplating solution by conventional monitoring of the spectrum; calculating a spectrum entropy index based on the degree of deviation of the real-time spectrum from a pre-established statistical baseline model, and comparing the spectrum entropy index with a pre-set failure trigger threshold; starting a dynamic calibration step only when the spectrum entropy index exceeds the pre-set failure trigger threshold, the dynamic calibration step generating a model error vector by comparing the physically real differential spectrum with the model virtual differential spectrum through injection of a standard pulse, and correcting the chemometrics model using the model error vector, so that the present application realizes on-demand calibration, ensures real-time accuracy of the model, and reduces the consumption of the standard.

The invention relates to a white tea quality discrimination method and related equipment. The method comprises the following steps: acquiring ultraviolet-visible diffuse reflection spectrum data of a white tea sample to be detected; wherein the wavelength range of the spectral data covers an ultraviolet region to a near-infrared short wave region; performing first pretreatment on the spectral data, and performing production place authenticity judgment on the white tea sample to be detected by adopting a chemometrics method based on the spectral data subjected to the first pretreatment and the first characteristic wavelength set to obtain a production place authenticity judgment result; and when the authenticity discrimination result of the production area is a pure material in the target production area, carrying out second pretreatment on the spectral data, and carrying out grade discrimination on the white tea sample to be detected by adopting a chemometrics method based on the spectral data subjected to the second pretreatment and the second characteristic wavelength set to obtain a grade discrimination result. According to the method, the discrimination cost of the white tea quality is reduced, and the discrimination accuracy is improved.

The invention provides a calibration curve establishment method based on multi-weight parallel fitting in liquid chromatogram data management software, relates to the cross technical field of a chemometrics algorithm and chromatographic data analysis, and aims to establish and implement a full-automatic technical process of parallel fitting-automatic evaluation-intelligent decision making. According to the invention, the establishment of the correction curve is converted from a process depending on user experience and manual trial and error into a technical optimization process which is automatically completed by the system and based on objective quantitative indexes. According to the method, the data processing efficiency can be improved, the subjectivity and complexity of human intervention can be reduced, and better fitting results can be obtained under different given data sets through comprehensive comparison and automatic optimization of multiple models, so that the accuracy, the reliability and the automation level of liquid chromatogram quantitative analysis are remarkably improved.

According to the present invention, the gas chromatography-ion mobility spectrometry (GC-IMS) technology is adopted to analyze the types and the differences of the volatile components of the herba schizonepetae and the processed product so as to provide reference for the characterization and the accurate identification of the volatile components of the herba schizonepetae and the processed product, and the GC-IMS is adopted to analyze and identify the volatile components in the herba schizonepetae, the fried herba schizonepetae and the herba schizonepetae charcoal, the difference of volatile components of the herba schizonepetae, the fried herba schizonepetae and the herba schizonepetae charcoal is analyzed in combination with chemometrics, the GC-IMS technology and the chemometrics analysis method are researched and utilized, the different volatile organic compounds of the herba schizonepetae, the fried herba schizonepetae and the herba schizonepetae charcoal are screened out, and an idea is

The present invention discloses a pattern recognition analysis method for flavor and fragrance systems based on GC-IMS, which includes: detecting various samples of the flavor and fragrance systems to obtain GC-IMS data of various types of samples; using a supervised pattern recognition method to perform feature pattern recognition of sensory omics data on the GC-IMS data of various types of samples, and establishing a classification model for the complex flavor and fragrance systems; and determining the sample category to which the unknown sample belongs according to the classification model. The pattern recognition analysis method for flavor and fragrance systems based on GC-IMS provided by the present invention performs feature pattern recognition based on GC-IMS sensory omics data in a supervised manner, conducts omics data analysis on the aroma compound information in combination with chemometrics, performs pattern recognition on volatile compounds and analyzes differential compounds, and determines the changes in the aroma components corresponding to each stage of tobacco flavor and fragrance and the influence law thereof on sensory stability.

The invention relates to an intelligent grading and quantitative nondestructive testing method for cornu cervi pantotrichum decoction pieces, which is used for training an artificial intelligence recognition model by means of machine learning and the like to realize quick and high-throughput quality grading of cornu cervi pantotrichum. Meanwhile, through bioinformatics and chemometrics analysis means, spectral characteristics of the cornu cervi pantotrichum decoction pieces at all levels are extracted, association of the characteristics, physicochemical properties and spectral characteristics of the cornu cervi pantotrichum decoction pieces is established, a scientific basis is provided for quantitative prediction of the quality of the cornu cervi pantotrichum decoction pieces through infrared spectroscopy, and then lossless intelligent grading and quantitative grading of the cornu cervi pantotrichum decoction pieces are achieved. According to the nondestructive testing method established through multi-data fusion, the problem that traditional quality grading of the cornu cervi pantotrichum decoction pieces depends on subjective experience is solved, and the quality of the cornu cervi pantotrichum decoction pieces can be rapidly and accurately evaluated.

The invention belongs to the technical field of polymer material analysis and detection, and particularly relates to a regenerated polyester identification method based on UV-Vis combined with chemometrics and a machine learning algorithm. The method comprises the following steps: respectively extracting original and regenerated polyester fiber samples by adopting an ultrasonic-assisted solvent, and carrying out UV-Vis determination on the extracted components to respectively obtain spectral characteristic data of the original and regenerated polyester fibers; sequentially carrying out data preprocessing and chemometrics analysis on the obtained spectral characteristic data to obtain a key data preprocessing mode for effectively distinguishing the native polyester fiber from the regenerated polyester fiber; a machine learning algorithm is adopted to establish an identification model according to spectral characteristic data obtained in a key data preprocessing mode, and the identification model is adopted to realize rapid and accurate identification of polyester fibers. A new solution is provided for identification of polyester fibers, the problem that raw polyester counterfeit regenerated polyester is solved, and healthy development of the polyester fiber material industry is promoted.

The invention discloses a full-automatic Raman spectrum baseline correction method and system, and the method comprises the steps: introducing a deep learning algorithm on the basis of chemometrics, recognizing peak and background regions in a spectrogram through a weight output model based on deep learning, and outputting an initial weight vector; and then adaptively optimizing a smoothness parameter based on a multi-index evaluation strategy, finally performing dynamic weighted iteration by adopting a more robust dynamic iteration reweighted penalty least square method algorithm to fit a background curve, and outputting a final corrected baseline. The method can solve the problems that in an existing Raman spectrum baseline correction method, parameters need to be manually adjusted, balance between peak intensity information reserving and noise interference removing is difficult to obtain, the processing speed is low and the like, baseline correction of the Raman spectrum is achieved on the premise that the reliable peak position and intensity of the Raman spectrum peak are obtained, and the method is suitable for large-scale popularization and application. The method has a supporting effect on qualitative and quantitative analysis of Raman spectrum, is high in operation speed, and can be used for processing large-scale data.

The application discloses a kind of material evaluation methods, pharmaceutical compositions and application of sophora fruit products.It is specific, disclose a kind of material evaluation methods of sophora fruit products, it includes the following steps: S1, using liquid chromatography, the fingerprint of the sophora fruit product of different processing time is established, and common characteristic peak is extracted;S2, the hemostatic effect of sophora fruit product of different processing time is determined;S3, according to the characteristic peak data of the sophora fruit product of different processing time and its hemostatic effect data, statistical analysis is carried out by chemometrics method, and the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect of the hemostatic effect

The invention provides a variable selection method based on variable importance vector peak value combination, and belongs to the field of chemometrics and spectral analysis, and the method comprises the steps: building a current partial least square model based on a preprocessed data set through K-Fold cross validation, and calculating a variable importance vector of the current partial least square model; calculating a peak value position of the variable importance vector based on a peak searching algorithm, and screening out a plurality of spectral intervals according to the peak value position; generating a plurality of different interval combinations based on the plurality of spectral intervals, establishing a partial least square model by adopting K-Fold cross validation based on each interval combination, and calculating a cross validation value RMSECV of the partial least square model corresponding to each interval combination; taking an interval combination corresponding to the partial least square model with the minimum cross validation value RMSECV as a result interval, and performing variable selection on the spectral data according to the result interval; the invention further provides a variable selection system based on variable importance vector peak value combination. The problem that a threshold method ignores a small peak value is avoided, and small peak value information is effectively reserved.

The application belongs to the technical field of food safety, and particularly relates to a volatile organic compound detection device based on olfactory visualization technology and a detection method thereof. The device comprises a fixed frame structure, a control module is arranged above the fixed frame structure, a detection platform is arranged below the fixed frame structure, a pipette and an image acquisition device and a sensor control device are respectively arranged above both sides of the detection platform, and the control module is connected with the pipette, the image acquisition device and the sensor control device. The control module is used for controlling the pipette, the image acquisition device and the sensor control device to operate in the detection platform area. The application can prepare a color-sensitive sensor printed with a metal porphyrin, obtain volatile gas of an organic compound, and cause a coordination reaction with the volatile gas. Image data before and after the reaction of the volatile gas with the organic compound is detected and analyzed through the image acquisition device, and corresponding chemometrics methods are combined, so that rapid quantitative analysis of the organic gas can be realized.

The application discloses a detection method for carbonation depth of cement-based materials, comprising the following steps: placing mud test pieces into a rapid carbonation test box to perform rapid carbonation to different carbonation ages; testing dielectric constants of the cement test pieces of different carbonation ages from top to bottom along the carbonation depth direction, taking the depth value as the carbonation depth I based on the dielectric constant when the dielectric constant changes greatly; testing the carbonation depth II of the cement test pieces of the carbonation age by adopting a phenolphthalein method; fitting two groups of data by adopting a chemometrics method according to the obtained carbonation depth I and the carbonation depth II, and establishing a fitting equation of the carbonation depth I and the carbonation depth II; and then realizing rapid and efficient detection of the carbonation depth of the cement test pieces. The test method is simple and convenient without damaging the test pieces, the detection result is relatively accurate, and a new thought is provided for rapid detection of the carbonation depth of cement-based materials.

The present invention provides a method for detecting the HPLC fingerprint of Atractylodes macrocephala. The present invention provides an Atractylodes macrocephala medicinal material, which uses the above-mentioned detection method to detect the content of four components: atractylodes lactone I, atractylodes lactone II, atractylodes lactone III, and atractylodes ketone. Each gram of Atractylodes macrocephala medicinal material contains 0.21-0.32 mg of atractylodes lactone I, 0.13-0.25 mg of atractylodes lactone II, 0.1-0.19 mg of atractylodes lactone III, and 5.21-14.08 mg of atractylodes ketone. The present invention also provides an HPLC fingerprint of Atractylodes macrocephala and a method for determining the quality of Atractylodes macrocephala medicinal material. The present invention also provides a method for screening quality-marking components of Atractylodes macrocephala medicinal material. The present invention adopts the HPLC-DAD multi-wavelength switching method to establish the HPLC fingerprint of Atractylodes macrocephala, conducts chemometric research at the same time, and determines the contents of atractylodes lactone I, atractylodes lactone II, atractylodes lactone III and atractylodesone. On the one hand, it can comprehensively evaluate the quality of Atractylodes macrocephala, provide a reference for the quality evaluation and control of Atractylodes macrocephala medicinal materials and decoction pieces, and also lay the foundation for the research on its pharmacological substance basis and related compound research; on the other hand, it can provide a reference for the improvement of the quality standard of Atractylodes macrocephala.

This invention relates to a method for compressing GC-HRMS fingerprint data, specifically for metabolomics analysis of plant, food essential oils, or biological samples. The method includes three steps: extraction of EICs, column isometry, and row isometry, which converts the data into an equal-row, equal-column matrix comparable to the original data, and then combines this with classical chemometric methods. This method overcomes the drawbacks of existing GC-HRMS fingerprints, such as their massive size and complexity, and is also applicable to LC-HRMS data. While the data compression method may sacrifice m / z accuracy, the peak intensities corresponding to each m / z range can be placed in their appropriate dictionary positions, ensuring accurate m / z readings in subsequent mass spectrometry. After row isometry, the retention time remains unchanged compared to the original data. Compared to existing ROI techniques, this data compression method offers significantly faster processing speeds.

This invention relates to a method for rapid cereal food ingredient detection based on spectral imaging technology. The method involves obtaining a cereal food model and matching it to an ingredient list in a product library to generate a predicted spectrum as a detection reference; scanning the sample using hyperspectral imaging technology to obtain detection spectral data; extracting characteristic wavelengths through chemometrics and performing quantitative analysis to generate chemical composition detection results; comparing the detection results with the ingredient list to generate initial deviation data; comparing the detected spectrum with the predicted spectrum to generate spectral difference data, and correcting for component deviations; finally, generating corrected component deviation data and outputting a food composition detection report and food safety recommendations. This solution can rapidly and accurately detect cereal food ingredients, improving cereal food safety.

The invention belongs to the technical field of food detection, and particularly relates to a construction method and application of a salmon germplasm identification and origin traceability discrimination model. According to the application, 54 salmon samples from Norway Atlantic salmon, Chile Atlantic salmon and Chinese Qinghai rainbow trout are taken as research objects, acid hydrolysis extraction-gas chromatography is applied to collect fatty acid data, and chemometrics are combined to take an Atlantic salmon discrimination rate and a salmon origin traceability discrimination rate as indexes; a salmon germplasm identification and origin traceability discrimination model is established, and a theoretical basis is provided for rapidly discriminating the authenticity of the Atlantic salmon and the origin of the salmon by using fatty acid fingerprint spectrum analysis.