129 results about "Partial least squares regression" patented technology

The invention discloses an automatic control method and system for secondary granulation of a high-voltage zinc oxide resistor disc, relates to the technical field of intelligent manufacturing of power equipment, and solves the problems of out-of-control particle morphology caused by dynamic coupling parameter identification lag and control instability caused by multi-physical field parameter coupling in an existing method. According to the invention, a dynamic physical property parameter matrix is generated in real time based on multi-band dielectric relaxation spectrum analysis and terahertz wave tomography; predicting a fluidized phase change threshold value and an energy gathering area through multi-physics field coupling modeling; a time sequence attention deep reinforcement learning algorithm is adopted to generate a multi-field cooperative adjustment instruction; positioning a parameter conflict source and triggering decoupling compensation by combining a high-frequency vibration and acoustic emission combined monitoring module; performing closed-loop correction on the control network weight based on the laser spectrum data and a partial least squares regression model; the real-time performance of fluidization parameter identification, the stability of multi-field coupling control and the recovery efficiency of abnormal working conditions are remarkably improved, and meanwhile the batch consistency of the electrical performance of the resistor discs is guaranteed.

The invention relates to the technical field of mechanical performance testing of power transformer winding materials, in particular to a method for evaluating the mechanical strength of a transformer winding under a reclosing working condition, and the method comprises the steps: obtaining and preprocessing historical operation data, finite element simulation data and laboratory simulation test data; deploying a micro foil strain gauge, an MEMS acceleration sensor and a flexible piezoresistive sensor based on the preprocessed data, and synchronously collecting radial strain, axial acceleration and cushion block pressure data; after noise filtering, time alignment and feature extraction, multi-directional deformation cooperative influence is analyzed through methods of Pearson's correlation coefficients, partial least square regression and the like; and in combination with a historical damage sample training evaluation model, outputting a mechanical strength degradation level in real time. According to the method, the problems of synchronous acquisition and correlation analysis of multi-direction deformation data under reclosing impact are solved, and accurate evaluation of overall mechanical strength change caused by accumulated deformation is realized.

Disclosed is a method and device for predicting soil organic carbon based on spectrum-guided ensemble learning. The method includes: obtaining a soil sample and a real organic carbon content and an original soil spectrum thereof, pre-processing the original soil spectrum to obtain a soil spectrum sample, constructing, based on the soil spectrum sample, a sample set, grouping the sample set into a first training set and a validation set, and using the real organic carbon content as a label; training, based on the first training set and the corresponding labels, a partial least squares regression model, a Cubist model and a random forest model to obtain carbon content predicted value sets of the three models; constructing, based on the carbon content predicted value sets of the three models and soil spectrum principal component data, a second training set, and training a second random forest model with the second training set and corresponding labels to obtain a spectrum-guided ensemble model. The method combines the advantages of different predictive models and can accurately predict the soil carbon content.

The invention relates to the technical field of production monitoring control, and discloses a method and a system for monitoring production of eggs capable of reducing blood sugar based on fermented feed. The method comprises the following steps: detecting the hypoglycemic active components of the fermented feed through a multispectral sensor array to obtain spectral characteristic data; establishing a laying hen blood glucose metabolism correlation model by using a partial least squares regression algorithm; dynamically predicting the egg hypoglycemic index based on the incidence matrix and performing function grading; regulating and controlling formula parameters of the fermented feed in real time by adopting a particle swarm optimization algorithm; and carrying out production process quality tracing monitoring and abnormity early warning according to the optimized formula instruction. According to the method, the monitoring precision, the prediction accuracy and the quality stability of the production of the blood-sugar-reducing eggs are improved.

The invention discloses a chicken manure crude protein content detection model construction method, a chicken manure crude protein content detection system and a detection method, relates to the technical field of bioinformatics, solves the problems of high cost, serious pollution and low efficiency of an existing chicken manure crude protein content detection technology, and improves the accuracy of chicken manure resource application. The obtained sample set comprises visible-near infrared reflection spectrum and crude protein content of the chicken manure; carrying out pretreatment on the visible-near infrared reflection spectrum by adopting a plurality of pretreatment methods; screening out an optimal preprocessing method and a corresponding preprocessing spectrum by using a partial least square regression model; obtaining a characteristic wave band by using a plurality of characteristic selection methods; and based on the crude protein content and the characteristic wave band, constructing and screening an optimal chicken manure crude protein content detection model. The method provided by the invention is suitable for detecting the content of crude proteins in chicken manure by using manure resources.

The invention discloses a soil water content prediction method, a soil water content prediction device, soil water content prediction equipment and a computer readable storage medium based on a spectral analysis technology. The method comprises the following steps: acquiring target spectral data of a target soil area and standard spectral data of the target soil area after drying; performing optimal coupling spectrum calculation based on the target spectrum data and the standard spectrum data to obtain optimal coupling spectrum data; carrying out wavelength screening processing on the optimal coupling spectrum data by adopting a wavelength optimization algorithm to obtain model input data; wherein the wavelength optimization algorithm is a hybrid algorithm of a competitive adaptive reweighted sampling algorithm and a continuous projection algorithm; reasoning model input data based on the fusion prediction model to obtain a soil water content estimation value; wherein the fusion prediction model is a prediction model obtained by training and fusing an initial partial least square regression model and an initial convolutional neural network model based on spectrum training data.

The invention discloses a distillate oil property prediction method based on deep learning feature extraction and partial least squares regression. The method comprises the following steps: firstly, carrying out classification training on a near infrared spectrum through a convolution-attention double-branch fusion network, and extracting high-dimensional spectral features with local and global information; then, historical samples are retrieved from a database based on prediction categories, a plurality of most similar samples are selected by adopting cosine similarity measurement to construct a correction set, and the spectral features and property labels are subjected to standardization processing; and finally, carrying out partial least squares regression modeling on the correction set, extracting latent variables to maximize covariance between spectral features and physicochemical properties, and inputting feature vectors of an oil sample to be detected into the trained PLS model to obtain a corresponding property prediction result. According to the method, the modeling requirement and the category specificity characteristics under the small sample condition are considered while the prediction precision is guaranteed, and the method is suitable for rapid property detection and intelligent analysis in the refining process.

The invention relates to the technical field of laser cladding repair, and provides a positioning and clamping system and method for repairing the surface of a large working roll of a steel mill based on laser cladding, a three-field coupling prediction model of a temperature field, a stress field and a displacement field is established, and a partial least squares regression algorithm is combined with a Kalman filter to realize multi-step prospective prediction. According to the predicted thermal deformation, self-adaptive compensation control is implemented through a three-layer cascade compensation mechanism composed of a hydraulic drive, a piezoelectric ceramic driver and a laser head adjusting mechanism. The system adopts a time domain, frequency domain and time-frequency domain joint analysis method to extract vibration characteristics, vibration types are classified and identified through a support vector machine, and an active-passive hybrid suppression strategy is adopted for different vibration sources. High-precision positioning and stable clamping of the large working roller in the laser cladding repairing process are achieved, and the thickness uniformity and the surface quality of a cladding layer are remarkably improved.

The invention discloses a method for detecting the nitrogen content of organ tissues of different varieties of oilseed rapes based on visible near-infrared spectroscopy, which comprises the following steps: firstly, collecting oilseed rape leaf, shell, stalk and root system samples under the conditions of multiple varieties and multiple nitrogen fertilizer levels, and acquiring spectral data within the range of 430-2500nm by using a visible near-infrared spectroscopy; and a Kjeldahl method is synchronously adopted to measure the real nitrogen content as a reference value. Preprocessing the spectral data, including de-noising, standard normal variable transformation, multivariate scatter correction and derivative processing, so as to weaken the influence of scattering and baseline drift; characteristic wavelengths related to the nitrogen content are screened through stepwise regression, variable projection importance, competitive self-adaptive reweighted sampling, a continuous projection algorithm and other methods, and a random forest model, a support vector machine model, a partial least squares discriminant analysis model, a partial least squares regression model, a support vector regression model, an XGBoost model and other models are combined. And respectively constructing a classification identification model and a regression prediction model.

The invention discloses a plant extraction method and system based on image processing, and relates to the technical field of image processing. The method comprises the following steps: acquiring and standardizing a plant raw material image; extracting feature vectors of the images by using a neural network convolutional layer algorithm, comparing the feature vectors with a plant raw material standard feature database, and screening out qualified images of the plant raw materials; qualified images are processed through multispectral image analysis and an image segmentation algorithm, and an effective component spatial distribution diagram is obtained; constructing a partial least squares regression linear model, extracting spectral feature vectors of the effective components, inputting the spectral feature vectors into the model to obtain concentration values, and calculating quantitative data of the effective components; establishing an extraction process rule base, selecting a process parameter combination according to a spatial distribution diagram and quantitative data, and extracting to obtain a primary extracting solution and plant residues; and calculating an effective component residual error rate by matching and mapping to a qualified image coordinate, and if the effective component residual error rate is greater than a preset threshold, performing secondary extraction to obtain a secondary extracting solution. And quantitative basis is provided for extracting process parameters through the spatial distribution diagram of the effective components.

The invention discloses a wheat yield remote sensing prediction method combining phenological parameters, which comprises the following steps: S1, performing field observation in a key growth period of winter wheat, synchronously collecting canopy hyperspectral reflectivity data, leaf area index (LAI) and SPAD value of each observation sample point, and recording wheat grain yield of the corresponding sample point; s2, performing noise reduction preprocessing on the acquired canopy hyperspectral data, and extracting sensitive spectral parameters by combining principal component analysis (PCA) and correlation analysis methods; s3, taking the sensitive spectrum parameters, LAI and SPAD values as independent variables, taking the wheat grain yield as a dependent variable, and adopting partial least squares regression (PLSR) to construct a multivariable yield prediction model; and S4, performing wheat yield prediction on an independent test sample or regional scale remote sensing data by using the trained model. The method overcomes the defect that only yield sensitive spectrum parameters are used for predicting the effect, and accurate estimation of the model is achieved.

The invention belongs to the technical field of physical and chemical index prediction of jerusalem artichoke, and particularly relates to a method for rapidly predicting main physical and chemical indexes in jerusalem artichoke based on a near infrared spectrum technology. The method mainly comprises the steps of pretreatment of a jerusalem artichoke sample, acquisition of physicochemical indexes and spectral data, pretreatment of the spectral data, screening of characteristic wave bands, establishment of a quantitative prediction model, evaluation and verification of model performance and the like. According to the invention, a partial least squares regression (PLSR) model of inulin, protein, total polyphenol and ash content in jerusalem artichoke is established, and an external verification result of the model shows that detection results obtained by a near infrared spectrum and a standard chemical method have no significant difference. Compared with a traditional chemical detection method, the near-infrared technology improves the analysis efficiency and reduces the detection cost on the basis of meeting basic detection requirements, and has an application prospect in rapid prediction of the physical and chemical indexes of the jerusalem artichoke in actual production.

The invention provides a full-dynamic non-invasive blood glucose detection method based on a full-spectrum peak segment optimization algorithm. The method comprises the following steps: step S1, performing blood glucose testing on a nail bed part of a human little finger by matching an infrared dual-wavelength semiconductor laser with a spectrograph, and recording blood glucose Raman spectrum data obtained in each blood glucose testing; step S2, carrying out pretreatment on the obtained blood glucose Raman spectrum data; s3, dividing the preprocessed blood glucose Raman spectrum data into a training set and a prediction set, analyzing through a principal component analysis method and a partial least square regression analysis method, and establishing a model; s4, inputting the preprocessed blood glucose Raman spectrum data as an independent variable into the model in the step S3, analyzing the blood glucose concentration of the human body, and giving a reference value; according to the full-dynamic non-invasive blood glucose detection method based on the full-spectrum peak segment optimization algorithm, the accuracy of quantitative analysis of a high-fluorescence background sample through a Raman spectrum is achieved, complex treatment on the sample is not needed, and the accuracy of human body non-invasive blood glucose detection is improved.

The invention discloses establishment and application of a white kidney bean HPLC (High Performance Liquid Chromatography) fingerprint spectrum. The HPLC fingerprint spectrum of 114 batches of white kidney bean methanol extracts from different geographical sources is established, 9 common peaks are identified, and the spectrum-effect relationship between the 9 characteristic peaks of the HPLC fingerprint spectrum and the alpha-amylase inhibition rate is established through grey correlation analysis and partial least squares regression analysis. The invention finds that the myricetin component is highly correlated with the activity index of the alpha-amylase inhibitor and is possibly a key component of the hypoglycemic activity of the white kidney beans, and provides methodological reference for evaluating the hypoglycemic activity and quality control of the white kidney beans and also provides reference for development and utilization of the white kidney beans.

The invention relates to a method for improving portable near infrared spectrum data analysis precision, which comprises the following steps: carrying out spectrum scanning on different grape samples to obtain a plurality of original near infrared spectrum data; preprocessing the original near infrared spectrum data to obtain corresponding preprocessed data, and performing dimension transformation on the preprocessed data to obtain a two-dimensional image; processing the two-dimensional image to obtain a reconstructed image, and training the initial convolutional neural network model based on the reconstructed image to obtain a target convolutional neural network model; and taking the output result of the feature extraction layer of the target convolutional neural network model as the input of a partial least square regression model, and constructing a fusion model so as to detect the soluble solid content of the to-be-detected grape through the fusion model. According to the invention, the hardware limitation of the portable near-infrared spectrometer is overcome, and the precision and efficiency of the portable near-infrared spectrum data analysis precision are improved.

The invention relates to the technical field of agricultural remote sensing and precise irrigation, in particular to a method and equipment for inverting leaf water potential of grapes before dawn based on multispectral remote sensing, and the method comprises the steps: measuring leaf water potential of representative grape plants in each planting test plot before dawn in different growth periods, and collecting canopy multispectral images in the noon period; preprocessing the remote sensing original image data; calculating a vegetation index; on the basis of the model fitting data set and the training feature set, constructing basic prediction models in different growth periods by adopting a unary linear regression algorithm, a multiple linear regression algorithm and a partial least square regression algorithm respectively; determining a PLSR inversion model according to the decision coefficient and the root-mean-square error; and carrying out pre-dawn leaf water potential prediction through the PLSR inversion model. Through the method, high-precision inversion of the water potential of the leaves before dawn based on the noon canopy multispectral data can be realized, and reliable technical support is provided for accurate management of vineyard moisture and intelligent irrigation.

The invention relates to the technical field of quality detection, and provides a method for detecting the quality of an aminobutyric acid extract in peanut sprouts, which comprises the following steps: performing specific recognition on aminobutyric acid by a molecular imprinting sensor, and calculating the impurity contribution by combining a preset peanut sprout interference database and partial least square regression; the interference of complex matrix impurities in the peanut bud extract can be specifically eliminated, and the problem that the specificity is insufficient when a traditional method is used for detecting a complex sample is solved; the molecularly imprinted sensor and the quartz crystal microbalance are connected in series through the microfluidic channel, and resonance frequency detection is triggered by using capacitance variation, so that a coherent process is formed from identification to quality analysis of sample detection, and the detection efficiency is improved; the corrected frequency offset, the phase angle and the temperature are input into the recurrent neural network, accurate output of the concentration of the aminobutyric acid is achieved by integrating multi-dimensional detection information, compared with a single parameter detection method, the real content of the aminobutyric acid in an extract can be reflected more comprehensively, and a reliable basis is provided for quality judgment.

The invention relates to a method for detecting the INDF content in TMR of lactating cattle based on a near infrared spectrum. The method comprises the following steps: constructing a near infrared spectrum model capable of being used for detecting the INDF content; wherein a sample set for constructing the near infrared spectrum model comprises near infrared spectrum data corresponding to a lactating cattle TMR sample and an INDF actual value; the method comprises the following steps: extracting a wavelength point with a non-zero coefficient in near infrared spectrum data, and taking the wavelength point as a characteristic variable; constructing a near infrared spectrum model based on the characteristic variables and the corresponding INDF actual values by adopting a partial least squares regression algorithm; model parameters are optimized through a cross validation method, an optimal model parameter combination is determined by combining a cross validation root mean square error RMSECV and an external validation root mean square error RMSEP, and a near infrared spectrum model capable of being used for INDF content detection is obtained; and inputting near infrared spectrum data of a to-be-detected lactating cattle TMR sample into the near infrared spectrum model to obtain an INDF content detection result.

The invention discloses a pork safety traceability quality nondestructive testing method and system for livestock breeding. The method comprises the following steps: constructing a stable detection environment by utilizing a constant temperature and humidity device, a dynamic environment monitoring unit and an optical shielding material; a Fourier transform infrared spectrometer is adopted to collect spectral data, and a non-contact texture detection device is combined to measure physical parameters; denoising, standardization processing and feature fusion are carried out on the collected data, and key variables are extracted through principal component analysis; grouping the samples by using a K-means algorithm, and verifying sample classification by using linear discriminant analysis; quantitative prediction and classification of pork quality are realized based on partial least squares regression and a support vector machine model; the model is applied to an actual scene, real-time processing and visual display are achieved through online data collection, and a data sample library is continuously expanded. The method has the advantages of high detection speed, high precision, wide application range and the like, and can be widely applied to the fields of meat quality detection and food safety.

The invention relates to the technical field of leather wastewater treatment, and provides an ultraviolet-visible spectrum-based leather wastewater ozone dosage and COD (Chemical Oxygen Demand) prediction method. The method comprises the following steps: firstly, determining the types of organic matters in a tannery wastewater sample, and determining the distribution characteristics of the organic matters in different fluorescent regions through three-dimensional fluorescence spectrum analysis, so as to discriminate the types of organic pollutants; secondly, screening key wavelengths, and determining characteristic absorption wavelengths representing different organic matters through an ultraviolet-visible light spectrum; then carrying out an ozone oxidation experiment, and synchronously measuring a COD value and an ultraviolet absorption spectrum under different ozone addition amounts; based on experimental data, a multiple linear regression and exponential regression prediction model of ozone dosage and a COD prediction model based on partial least squares regression are established. According to the invention, accurate control of ozone dosage and real-time monitoring of COD are realized, the treatment efficiency is improved, the operation cost is reduced, the adaptability to water quality fluctuation is enhanced, and the method has a wide engineering application prospect.

The invention relates to the technical field of wheat flour nondestructive testing, in particular to a rapid nondestructive testing method for inferior (mildewed and germinated) wheat flour based on a hyperspectral imaging technology and machine learning, which comprises the following steps: acquiring an original spectral image of a wheat sample by adopting a hyperspectral imaging system, and processing the original spectral image by adopting SpecView software; the original spectral image is preprocessed in multiple preprocessing modes; screening the most suitable preprocessing method by adopting partial least squares regression, and constructing a regression model; feature selection algorithms including a non-information variable rejection algorithm, a sequence projection algorithm and a competitive self-adaptive reweighted sampling algorithm are adopted to extract feature wavelengths in the original spectral image, and the feature wavelengths serve as input of a follow-up quantitative analysis experiment; and sensing the preprocessed original spectral image, and constructing a hyperspectral detection model by combining five regression algorithms, namely partial least square regression, random forest regression, multi-layer perceptron regression, support vector regression and XGBoost regression gradient, with a feature selection algorithm.

The present application provides a kind of biogas slurry chemical oxygen demand online monitoring method and system, the method comprises: obtaining the current characteristic data of biogas slurry, the characteristic data includes multiple in dissolved oxygen, turbidity, oxidation-reduction potential and conductivity;The current characteristic data of the biogas slurry is input into least square regression model, and the current chemical oxygen demand predicted value of the biogas slurry output by the least square regression model is obtained;The least square regression model is obtained by training by taking the characteristic data sample of the biogas slurry as characteristic variable, and taking the measured value of the chemical oxygen demand corresponding to the biogas slurry as target variable.The present application realizes the rapid, low-cost detection of biogas slurry chemical oxygen demand by real-time monitoring of multiple characteristic data, constructing multi-parameter fusion partial least square regression prediction model.

The invention relates to a structural network-genetic map biological network model for predicting ischemic stroke and a construction method thereof, and the method comprises the steps: extracting and calculating seven multi-scale morphological features and pairwise Pearson correlation coefficients among the features from T1 weighted imaging data and diffusion tensor imaging data; constructing a 308 * 308 morphological similarity network matrix and a brain network module for identifying ischemic stroke neural dysfunction; 1782 sampling points are extracted from the Airy human brain map, and each sampling point comprises expression data of 10185 genes; the method comprises the following steps: mapping space coordinates of AHBA sampling points to a cortex package of a Desikan-Killiany map, carrying out normalization processing to output 308 * 10185 brain region gene-by-gene expression matrixes, and constructing a structural network-gene map biological network model for predicting ischemic stroke by adopting a partial least square regression method and a bootstrap method. Compared with the prior art, the model determines the specific molecular mechanism related to the phenotypic structure change of ischemic stroke injury, and the stroke occurrence probability is predicted according to the specific molecular mechanism.

The invention belongs to the technical field of insulator detection, and discloses an on-line non-contact detection method for the density of sand attached to the surface of an insulator, and the method comprises the following steps: S1, obtaining a hyperspectral line of an insulating sheet with known sand attached density; s2, establishing a parameter detection model of the sediment density, and solving by adopting a partial least square regression method; and S3, carrying out online non-contact detection on the sand-attached density of the insulator. According to the online non-contact detection method for the density of the sand attached to the surface of the insulator, the problems that an existing detection method needs power-off operation, the detection process is complex, deviation is prone to being generated, and the density of the local sand attached cannot be accurately reflected are solved; on-line non-contact detection is carried out on the surface sand density of the insulator in the power transmission line under the condition that power is not cut off, operation is easy and convenient, the influence of the sand attaching state on the insulation performance of the insulator can be accurately evaluated, and a reliable test basis is provided for insulation design and maintenance of the insulator.

The application provides a night image quality evaluation method. First, different scales of night images are obtained through downsampling, and then the original image and the image sequence after downsampling are simultaneously sent into a feature extraction unit; in the feature extraction stage, two types of quality-related features, namely, bottom visual features and high-level semantic features, are extracted. The bottom visual features include texture features and contrast features of the night image; for the high-level semantic features, first, a deep convolution network is used to extract primary high-level semantic features of the night image, and then mean and variance two feature functions are used to aggregate the primary high-level semantic features to obtain secondary high-level semantic features as the final high-level semantic features of the night image. Then, the extracted bottom features and high-level features are aggregated. Finally, a partial least squares regression method is used to regress the aggregated features, so as to obtain an objective score of the night image quality.

The application discloses an evaporation dish evaporation amount homogenization sequence reconstruction method based on multi-source data fusion, specifically, daily evaporation dish evaporation amount data of each station in a target area, meteorological data and station metadata are screened, automatic secondary quality control is performed on the screened data, and a high-quality basic data set is obtained; a random forest regression and recursive feature elimination cross-validation method is used for selecting features, and finally a partial least squares regression model is used for splicing and interpolation on the daily evaporation dish evaporation amount data, so that the evaporation dish evaporation amount reconstruction sequence is generated; the evaporation dish evaporation amount reconstruction sequence is subjected to homogenization test, breakpoints are identified and corrected, and the evaporation dish evaporation amount homogenization reconstruction sequence is obtained, so that the problem of discontinuous evaporation amount data sequence caused by non-climatic factors such as mixed use of large and small evaporation dishes is solved, and the limitations of traditional conversion coefficient method and multivariate regression equation, such as large difference between stations, insufficient consideration of meteorological conditions and the like, are overcome.

The invention discloses a method and system for constructing a drinking water taste quantitative evaluation model, and belongs to the technical field of drinking water quality evaluation, and the method comprises the steps: S1, collecting a plurality of drinking water samples; s2, performing standardized sensory evaluation to obtain a comprehensive taste score of each drinking water sample; s3, performing water quality analysis to obtain water quality detection data; s4, based on the taste comprehensive score and the water quality detection data, screening out a preset number of key water quality indexes from multiple water quality indexes by adopting a variable importance projection method; and S5, taking the key water quality index as an independent variable, taking the taste comprehensive score as a dependent variable, and adopting a partial least square regression method to construct a drinking water taste quantitative evaluation model. The method solves the problem that the existing drinking water taste evaluation can only depend on subjective evaluation of a large number of testers and is poor in uncertainty and timeliness. The method is objective in quantification, accurate in evaluation, high in guidance and convenient to apply, and can realize high-frequency daily taste evaluation.

This invention relates to the field of food component detection technology, specifically a method for detecting the content of components in sesame paste based on spectral analysis. The method includes: collecting near-infrared transmission spectral data of a target sesame paste sample at different temperature gradients to construct a temperature-compensated spectral feature matrix; inputting this matrix into a multi-task partial least squares regression model to simultaneously predict the content of protein, fat, and carbohydrates; comparing the predicted values ​​with a spectral library of sesame paste standard substances, and fine-tuning the model's latent variable space to converge the fitting error; recalculating the corrected predicted values ​​using the fine-tuned model, and fusing them with the theoretical values ​​of the formula to generate a comprehensive component content estimation vector; finally, generating a detection result record containing the specific values ​​of each target component. This method can improve the accuracy and efficiency of sesame paste component detection and is suitable for sesame paste food quality testing.

The invention discloses an insulating oil dielectric loss factor detection method and system, and relates to the field of oil-immersed power equipment detection.The method comprises the steps that terahertz testing is conducted on insulating oil samples of different aging degrees, and corresponding terahertz time-domain spectrums are obtained; determining a terahertz absorption spectrum; according to the terahertz absorption spectrums of the insulating oil samples with different aging degrees and the corresponding dielectric loss factor values, determining terahertz absorption spectrum characteristics based on a partial least squares regression model; establishing a terahertz dielectric loss value quantitative evaluation model based on a random forest algorithm according to the terahertz absorption spectrum characteristics of the insulating oil samples with different aging degrees and the corresponding dielectric loss factor values; and according to the terahertz absorption spectrum characteristics of the to-be-detected insulating oil, determining a corresponding dielectric loss factor value based on the trained terahertz dielectric loss value quantitative evaluation model. According to the invention, the dielectric loss factor of the insulating oil can be quickly obtained without damage, and the insulating oil can be quickly evaluated without damage.

The application belongs to the technical field of food safety early warning, and particularly relates to a method for predicting heavy metal content in table grapes, comprising the following steps: using a partial least squares regression method to analyze the correlation between soil physicochemical indexes and grape heavy metal content, and retaining indexes with a VIP value greater than 1 as key variables; taking the key variables as input variables and grape detection results as output to construct a random forest regression model; training the random forest regression model through repeated execution of a sampling and training process to obtain a prediction model of heavy metals in grapes; and predicting the heavy metal content in table grapes according to the generated prediction model of heavy metals in grapes.