93 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 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 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.

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 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.

Disclosed are a THz-TDS system (S), a sample preparation and chemometric-based analysis method that enables the determination of quality control, adulteration and authenticity of all organic substances and materials, especially samples with high water content.

This invention proposes a method for rapid determination of the content of Phellodendron amurense charcoal slices processed at different temperatures using near-infrared spectroscopy. Near-infrared spectroscopy is used to collect spectral information of Phellodendron amurense charcoal slices processed at different temperatures. Background interference is eliminated through preprocessing methods such as baseline correction, standard normal variable transformation, multivariate scattering correction, and first derivative. Combined with competitive adaptive reweighted sampling, CARS combined with iterative retention of information variables, and iterative combination optimization, a partial least squares regression quantitative prediction model is established. This model achieves accurate prediction of the content of five key components: 5-O-FQA, 3-O-FQA, berberine, berberine, and berberine, with prediction correlation coefficients all exceeding 0.90. This invention combines spectral information with chemometric methods to establish a digital content prediction model based on near-infrared spectroscopy for online monitoring of the Phellodendron amurense charcoal slice processing process. It has the advantages of objectivity, speed, non-destructive nature, and reliability, effectively solving the technical problem of lack of objectivity in existing technologies based on human judgment.

A computer-implemented method is provided. The method includes obtaining a chemometric model for one or more levels of parameters associated with composition training data. The composition training data includes data representative of one or more components of a vesicle. Raman spectra data representative of at least one Raman spectrum associated with one or more components of a sample of a vesicle is received. The Raman spectra data representative of the at least one Raman spectrum associated with the one or more components of the sample of the vesicle is transferred into the chemometric model. A level of one or more parameters of the one or more components of the sample of the vesicle is determined based on the chemometric model.

The invention provides a tobacco leaf raw material spectrogram data management method and system and a storage medium. The tobacco leaf raw material spectrogram data management method comprises the steps of S1, obtaining initial spectrogram data of tobacco leaf raw materials; s2, classifying and managing the initial spectrogram data according to preset spectrogram data types to obtain target spectrogram data; and S3, performing data analysis processing on the target spectrogram data to generate an analysis result corresponding to the tobacco leaf raw material. The invention aims to provide core technical support for design and implementation of a tobacco scientific research big data management system. According to the method, spectrogram sources and formats are investigated, spectrogram data are classified, deep analysis mining is performed according to the classified spectrogram data in combination with multiple chemometrics algorithms, a foundation is laid for centralized storage, efficient retrieval, unified management and safe sharing of the spectrogram data and other scientific research data, the tobacco scientific research efficiency can be improved, and the tobacco scientific research efficiency can be improved. And powerful support is provided for industry scientific and technological innovation and decision making.

The invention provides a system for predicting the content of methanol and ethanol in automotive gasoline, and relates to the technical field of chemical analysis and detection. The method comprises the following steps: firstly, carrying out first chemical treatment on first gasoline to improve the significance of aromatic hydrocarbon, olefin and other components, determining the proportion of an association body according to the components except alcohols in the first gasoline, and further determining a first spectral peak position offset; secondly, carrying out second chemical treatment on the first gasoline, respectively obtaining a characteristic peak combination of each alcohol component so as to remarkably improve the significance, and determining a plurality of first key wavelengths based on each characteristic peak combination; and finally, determining a plurality of corresponding first absorbance values based on the plurality of target key wavelengths so as to finally determine the content of each target component in the first gasoline. According to the method, chemometrics, near infrared spectroscopy and artificial intelligence means are combined, the gasoline component recognition precision is greatly improved, and nonlinear effects such as hydrogen bond association in high-concentration alcohols are effectively corrected.

This invention discloses a method for wheat grain quality detection and functional gene analysis based on hyperspectral imaging technology, belonging to the field of agricultural testing technology. The method includes: extracting hyperspectral indices; measuring wheat grain quality indicators as manual measurements; assigning weight coefficients to each quality indicator to digitize the wheat quality phenotype; extracting characteristic wavelengths to establish predictive models for each indicator and wheat quality values; performing GWAS on hyperspectral indices and chemical indicators as phenotypic traits with genotypes to screen SNP loci, conduct co-location analysis, obtain candidate genes, and perform functional verification. This invention, through hyperspectral technology combined with chemometric modeling, is suitable for real-time monitoring during wheat quality breeding and processing; by combining spectral phenotype with GWAS gene mining, it overcomes the limitations of traditional quality detection that only serves phenotypic screening, forming a closed loop of "non-destructive detection - gene localization," providing targeted genes for wheat quality improvement.

The invention relates to the technical field of metal surface treatment and intelligent monitoring, and discloses an intelligent monitoring management system in a NiP binary alloy preparation process. The method comprises the following steps: acquiring ultraviolet-visible-near-infrared full-spectrum scanning data of a plating solution sample, constructing a three-dimensional absorption spectrum matrix, generating a separated spectral feature vector by utilizing principal component analysis, carrying out simultaneous quantitative analysis on multiple components based on a Lambert-Beer law and a chemometrics principle, and predicting a byproduct accumulation rate in combination with a recurrent neural network. Real-time monitoring and intelligent management of by-products in the NiP chemical plating process are achieved, and the technical problem that traditional single-wavelength detection is insufficient in selectivity is solved.

The present application belongs to the technical field of cross of spectrum analysis and artificial intelligence, specifically to a Raman spectrum quantitative modeling closed-loop optimization method and system based on a large language model, wherein the method comprises: constructing a historical task memory bank containing a spectrum state vector; extracting spectrum quality, baseline, spectral peak, sample distribution and other features of a new task to construct a state vector; respectively retrieving successful and failed memories based on a weighted comprehensive distance; generating a candidate modeling process using a large language model; performing verification and discriminating failure modes based on rules; generating improved actions constrained by spectroscopy and chemometrics rules; iteratively optimizing until passing the deployment checklist; and finally packaging as a spectrum workstation deployment package. The present application converts expert experience into reusable structured knowledge, realizes automatic closed loop from data to deployment, reduces the modeling threshold, and improves the model reliability and interpretability.

The present application belongs to the field of health wine quality evaluation, and particularly relates to a quality evaluation method and application of health wine with liver damage reduction function, which is based on GC-MS and LC-MS technology to establish a content detection method of various chemical components in health wine, combines chemometrics to distinguish health wines of different quality grades and screen differential markers of health wines of different quality grades, more comprehensively and objectively explores chemical components of health wines of different quality grades, and provides a reference for health wine quality control.

The invention belongs to the technical field of intelligent control, and discloses a multi-element material control system and method based on quantum sensing. The method comprises the following steps: acquiring an original quantum sensing signal through a quantum sensor mounted at a preset position, and preprocessing the original quantum sensing signal to obtain preprocessed data; performing inversion on the preprocessed data based on a chemometrics model to obtain a multivariate component prediction vector; constructing a digital twinborn body, updating the digital twinborn body according to the multivariate component prediction vector, and obtaining an updated global material twinborn state; calculating based on the multivariate component prediction vector and a preset target component formula to obtain component deviation, and performing composite optimization control in combination with the updated global material twinning state to obtain a composite control quantity; according to the method, the multi-element material component information can be obtained in real time and with high precision, the control strategy can be dynamically adjusted based on the component deviation, the limitation of traditional mechanical control is thoroughly eliminated, and the multi-element material component control precision and the product percent of pass are remarkably improved.