166 results about "Chemometrics" patented technology

The invention provides a degradable plastic production method which comprises the following steps: acquiring an optimal raw material proportioning scheme, and controlling an intelligent batching system to perform automatic batching; temperature and humidity parameters of raw material storage and conveying links are obtained, and raw material pretreatment process parameters are dynamically adjusted in combination with time sequence data analysis; performing quality detection on the preprocessed raw materials, identifying defects and impurities of the raw materials by adopting machine vision and a deep learning algorithm, and removing unqualified raw materials; various raw materials are accurately weighed through an industrial robot and mixed according to the optimized formula proportion; a thermal infrared imager is adopted to monitor temperature field distribution of a plastic extrusion forming mold in real time, and finite element simulation analysis is combined to optimize mold structure design and a heating and refrigerating system; the extrusion molding process is monitored on line, material component changes are analyzed through a chemometrics model, and if degradation products exceed the standard, extrusion process parameters are adjusted or a mold is replaced.

The invention discloses a high-precision photoacoustic spectrum monitoring method for gas in transformer oil, and relates to the technical field of transformer oil gas monitoring, and the method comprises the following steps: constructing a photoacoustic spectrum detection system, sampling the gas in the transformer oil, and obtaining photoacoustic spectrum data of the gas, the photoacoustic spectrum detection system comprises a near-infrared tunable fiber laser, a photoacoustic cell, a lock-in amplifier and a data acquisition system; preprocessing the acquired photoacoustic spectrum data, and extracting features related to fault diagnosis from the preprocessed photoacoustic spectrum data; and the photoacoustic spectroscopic data of the mixed gas is decoupled by using a chemometrics method. According to the method, the gas photoacoustic spectroscopic data in the transformer oil are collected and analyzed in real time, abnormal conditions in the transformer are found as soon as possible, the mixed gas spectroscopic data are decoupled and separated in combination with chemometrics and a machine learning algorithm, and various gas components are accurately recognized and quantitatively analyzed.

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 an infrared spectrum chemometrics analysis method and system, and relates to the technical field of infrared spectrum analysis. Comprising the following steps: acquiring a plurality of infrared spectrum images with known spectral characteristic parameters, and preprocessing the infrared spectrum images to obtain a training sample image set; based on the training sample image set, establishing a neural network prediction model, taking the infrared spectral image as input and the spectral characteristic parameters as labels, and training to obtain a spectral characteristic parameter extraction model; acquiring an infrared spectrum image of an object to be detected, preprocessing the infrared spectrum image, inputting the preprocessed infrared spectrum image into the trained model, outputting spectrum characteristic parameters, and acquiring environmental parameters; correcting the spectral characteristic parameters based on the environmental parameters, and further correcting the spectral characteristic parameters in combination with the physical characteristic parameters of the to-be-detected article to obtain an accurate value; and completing the stoichiometric analysis of the property and content of the to-be-detected article based on the accurate spectral characteristic parameters. The true values of the spectral characteristic parameters are accurately simulated, and the accuracy of an analysis result is remarkably improved.

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 discloses a method for intelligently controlling RNA (Ribonucleic Acid) enzyme reaction by applying Raman, and particularly relates to the fields of molecular biology and biological reaction engineering. Comprising the following specific preparation steps: S1, establishing a reaction model; S2, finding a characteristic peak which is most suitable for tracking; S3, tracking the change of the characteristic peak along with time; S4, comparing a result with a model which is independently calculated by chemometrics; according to the present invention, the enzyme reaction conversion rate can be monitored by using Raman, the reaction process can be subjected to in-situ, real-time, online and non-destructive composition analysis monitoring, and the previous HPLC off-line test is not required; the content of four nucleotides and the reaction conversion rate can be quickly reflected in real time; meanwhile, the reaction can be controlled to a certain degree, the device is communicated with an ultrafiltration device, the enzyme utilization rate is increased, the total RNA reaction amount is increased, the conversion rate is increased, the production cost is saved, and efficient intelligent control is achieved.

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 provides a rapid detection method and device for adulteration of cod surimi based on a hyperspectral imaging technology, and the method comprises the steps: preparing a to-be-detected cod surimi sample containing different component adulteration concentration proportions, collecting hyperspectral image information of the to-be-detected cod surimi sample, and carrying out black and white correction processing; extracting an ROI (Region of Interest) of the hyperspectral image after black and white correction; dividing all the obtained spectral data into a training set and a prediction set, and constructing a BP-ANN classification model; preprocessing the spectral data set, constructing a PLS regression model, and determining an optimal preprocessing mode; two variable screening algorithms, namely a self-adaptive reweighting algorithm CARS and an interval combinatorial optimization algorithm ICO, are adopted to simplify the model, the robustness of the model is improved, and the optimal model is selected. By adopting the hyperspectral imaging technology and combining the traditional chemometrics and deep learning, the method has the advantages of no damage, no pollution, automation, rapidness and high efficiency in the detection of the adulteration of the cod surimi.

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 belongs to the technical field of medicine detection, and particularly relates to a rapid detection method for antioxidant activity of dendrobium officinale medicinal materials and application of the method, and the method comprises the following steps: (1) collecting a plurality of batches of dendrobium officinale medicinal materials and crushing; (2) obtaining ABTS free radical scavenging rate, iron ion reducing capacity and DPPH free radical scavenging rate in the dendrobium officinale sample through a chemometrics method; (3) collecting the near-infrared original spectrum of the dendrobium officinale medicinal material; (4) after abnormal spectrums are removed, processing original near infrared spectrums by a standard normal variable transformation spectrum preprocessing method, and dividing a correction set and a verification set by adopting a KS algorithm to establish a partial least square regression model; the near infrared spectrum technology is adopted to detect the antioxidant activity of the dendrobium officinale medicinal material, the method has the advantages of being easy to operate, rapid, free of damage and high in precision, production cost can be saved, economic benefits can be improved, and the purpose of efficient quality control can be achieved on the traditional Chinese medicine production source.

The invention provides a multivariate curve resolution real-time monitoring method for an energetic compound synthesis process, and belongs to the technical field of spectral analysis, and the method comprises the following steps: obtaining infrared spectrum data during an energetic compound synthesis reaction, and carrying out empirical mode decomposition de-noising treatment on the infrared spectrum data; then, baseline correction is carried out on the denoised data, and the optimal chemical grade is determined through a KMeans clustering algorithm and differential analysis; performing interactive self-modeling hybrid analysis on the processed data by utilizing the determined optimal chemical grade to obtain a spectrum matrix and a concentration matrix; the two matrixes are converted into spectrograms and chemical component concentration change diagrams of different substances respectively, analysis results are displayed in real time through a graphical interface, and meanwhile the results are continuously monitored and updated. Real-time data processing and displaying are achieved through the spectral analysis technology and the chemometrics method, key indexes and change information in the reaction process can be rapidly obtained, and the response capacity to the reaction dynamic state is improved.

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 an intelligent modeling method for near infrared spectrum quantitative analysis, and belongs to the technical field of chemometrics modeling. The method comprises the following steps: acquiring a training set of a near infrared spectrum of a tested sample, and performing sample preprocessing and spectrum preprocessing on the training set to obtain a preprocessed data set; dividing the preprocessed data set into a plurality of groups of corresponding training subsets and verification subsets, for each group of training subsets in each group of preprocessed data set, based on an extreme learning machine, establishing a plurality of extreme learning machine submodels, predicting the corresponding verification subsets, obtaining an alternative model library, integrating a plurality of optimal submodels, and obtaining a candidate model library; and obtaining a final model. The near infrared spectrum quantitative analysis intelligent modeling method is high in adaptability, easy to use and high in prediction accuracy, the generalization ability and stability of the model are effectively improved, and dependence on professional knowledge is reduced.

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.

According to the method for tracing the cyperus esculentus producing area by adopting an infrared combined chemometrics method, an optimal classification and identification model and optimal model parameters for tracing the cyperus esculentus producing area are obtained, the cyperus esculentus producing area can be accurately and rapidly identified, complicated sample treatment is not needed, the analysis efficiency is high, the producing area identification is accurate, and the method is suitable for large-scale popularization and application. The method is suitable for large-scale popularization and application.

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 disclosure features methods that include obtaining a vibrational spectrum of a solution in a biological manufacturing system, analyzing the vibrational spectrum using a first chemometrics model to determine a value of a first quality attribute associated with the solution, analyzing the vibrational spectrum using a second chemometrics model to determine a value of a second quality attribute associated with the solution, and adjusting at least one parameter of a purification unit of the biological manufacturing system based on at least one of the values of the first and second quality attributes.

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 invention relates to the field of drug analysis technology, and specifically to a Da Shanzhao Pill HPLC detection method, Da Shanzhao Pill fingerprint and application; the detection method includes Da Shanzhao Pill fingerprint and main index component quantitative method. A systematic investigation was conducted on different extraction methods, extraction time, extraction solvent, and extraction volume of samples to screen out the optimal extraction conditions; and the measurement data was systematically analyzed using chemometric tools. The results of the method are reliable and highly accurate, and can reflect the overall quality of commercially available Da Shanzhao Pills; the method can effectively monitor the quality status of Da Shanzhao Pills, and provide a scientific evaluation strategy for the control of the quality consistency and effectiveness of the preparation.

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.