12 results about "Standard normal variate" patented technology

The invention discloses a method for detecting the water absorption rate of silicone rubber by combining near infrared spectroscopy with CPO-SVM, and belongs to the technical field of detection of high- The invention aims to solve the problems of destructive sampling, complex operation, sensitivity interference, strong subjectivity of indirect evaluation and the like in the traditional detection method. The method comprises the following steps: firstly, obtaining a composite insulator silicone rubber sample, and collecting near infrared spectrum data of the composite insulator silicone rubber sample; then, carrying out standard normal variable transformation pretreatment on the spectroscopic data to eliminate interference caused by physical property difference on the surface of the sample; then, accurately extracting a key wavelength from the high-dimensional spectral data by using a competitive self-adaptive reweighting algorithm; and finally, establishing a support vector machine model optimized by a crown porcupine optimization algorithm, and carrying out quantitative analysis on the water absorption rate of the silicone rubber. According to the method, rapid, accurate and nondestructive detection of the water absorption rate of the silicone rubber is realized, real-time and reliable data support is provided for operation and maintenance of a high-voltage insulating material, safe and stable operation of a power grid is effectively guaranteed, and the method has a wide application prospect.

The invention discloses a multi-index rapid detection method for Tangyou food, and belongs to the technical field of food quality detection. The method comprises the following steps: collecting spectral data and temperature data of a to-be-detected sample through a near infrared spectrum technology, sequentially carrying out standard normal variable transformation and smooth filtering pretreatment on the spectral data, and screening characteristic wavelengths related to the pickling depth, the oil content and the water activity by adopting a competitive self-adaptive reweighting sampling algorithm; and inputting the characteristic spectrum data into a joint partial least squares regression model to carry out multi-index collaborative prediction, carrying out temperature compensation correction on a predicted value according to the temperature data, and finally comparing the corrected predicted value with a quality standard threshold value and outputting a detection result. According to the method, the model complexity is reduced through characteristic wavelength screening, the prediction precision and efficiency are improved through joint modeling, the detection stability is ensured through temperature compensation, and rapid nondestructive detection of the quality index of the Tangyang food is realized.

The invention discloses a rapid detection method for fatty acid composition in walnut oil, and relates to the field of edible vegetable oil quality analysis, and the rapid detection method comprises the following steps: preparing a sample set of walnut oil samples, and determining a reference true value of fatty acid in the walnut oil samples by adopting a gas chromatographic method; carrying out spectrum scanning on the walnut oil sample by using a Fourier transform near-infrared spectrometer to obtain an original spectrum, and carrying out standard normal variable transform and detrending and first-order derivative series pretreatment on the original spectrum to obtain a pretreated spectrum matrix; and associating the preprocessed spectrum matrix with the reference truth value, and screening a characteristic wavelength variable subset and optimizing a main factor number for each target fatty acid by adopting a partial least squares regression algorithm. According to the method, the quality judgment report is output by comparing the product quality standard, so that the problems that the traditional gas chromatography is complicated in pretreatment, long in time consumption and incapable of meeting the real-time quality control requirement of a production line are effectively solved.

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.

The invention discloses a method for measuring the content of sulfate radicals in a reaction solution in real time by using online Raman spectrums.The method relates to the technical field of analytical chemistry and industrial process monitoring, and comprises the steps that firstly, a standard sample set is prepared by simulating a chemical background matrix of a reaction solution to be measured, and Raman spectrums of the standard sample set are collected; then, specific interval interception and standard normal variable transformation-first-order derivative combination preprocessing are carried out on the spectrum, so that feature information is enhanced, and interference is eliminated; then, based on the preprocessed spectrum and the known concentration, establishing a steady quantitative correction model by adopting a partial least square method; finally, in the monitoring stage, the real-time spectrum is subjected to consistency preprocessing and then is input into the model, and the sulfate radical concentration can be output in real time. The method solves the problem that real-time, accurate, high-interference-resistance and low-maintenance online monitoring cannot be realized in a complex industrial environment by a traditional method, and has the remarkable advantages of high selectivity, no need of reagents, quick response, adaptability to complex matrixes and the like.

The invention relates to the technical field of soil content monitoring, in particular to a soil selenium content hyperspectral inversion method, which comprises the following steps: firstly, obtaining visible-near infrared hyperspectral reflectivity data and a selenium content measured value of a soil sample; original spectral data is subjected to standard normal variable transformation combined with second-order differential preprocessing, so that effective spectral signals are enhanced; then, screening out a key characteristic wave band subset from the preprocessed spectral data by adopting a competitive self-adaptive reweighted sampling algorithm; and by taking the feature subset as an input variable, constructing a support vector regression initial model, and introducing a myxomycete algorithm to globally optimize key parameters of the model so as to obtain an optimal inversion model. The method effectively solves the problems of weak spectral response and limited inversion precision of the soil selenium element, and realizes rapid and high-precision quantitative prediction and spatial mapping of the soil selenium content.

The application relates to the technical field of water content detection, in particular to a non-contact detection method and system for straw water content based on near-infrared hyperspectrum, and the following scheme is provided: hyperspectrum data of straw in a preset wave band range is acquired through a hyperspectrum camera, and environment parameters and stacking density information are synchronously collected through a temperature and humidity sensor, an illumination sensor and a laser ranging sensor. After the hyperspectrum data is subjected to wave band cutting, wavelet denoising and standard normal variable correction, the hyperspectrum data is input into a detection network together with normalized environment parameters and straw basic information. The detection network realizes water content prediction through a feature extraction subnetwork, an attention fusion subnetwork and a multi-scale regression output module, and when the representative score is high, the hyperspectrum data and the environment parameters are weightedly corrected based on perturbation spectral difference, so that the matching degree and volume representativeness of the multi-source data are improved.

The application discloses a controlled-release fertilizer field detection system and method, and relates to the technical field of agricultural detection. The system comprises a spectrum acquisition module, an environmental parameter acquisition module, an edge preprocessing module and a cloud analysis module. The spectrum acquisition module collects near-infrared spectrum of controlled-release fertilizer particles to form original signals, and the environmental parameter acquisition module collects environmental parameter signals such as soil temperature, humidity and conductivity. The edge preprocessing module sequentially completes dark current deduction, standard normal variable transformation and first derivative transformation to generate preprocessed spectrum signals. The cloud analysis module takes environmental parameters as auxiliary variables, calculates a nutrient cumulative release rate through a partial least squares regression model, and generates a state signal containing a dynamic trend and a warning mark. The application realizes real-time monitoring of in-situ nutrient release of controlled-release fertilizer in the field, and solves the existing monitoring problem.

The invention discloses a near-infrared hyperspectral imaging method for real-time detection of the number of microbial colonies of salmon fillets, and the method comprises the following steps: S1, preparing salmon samples exposed in air at different times, and carrying out the detection of the number of microbial colonies; s2, carrying out spectral imaging measurement, and collecting hyperspectral imaging HSI data; s3, performing region-of-interest identification and spectral data extraction based on the data acquired in the S2; s4, dividing samples into a training set, a prediction set and an independent external test set; s5, establishing an APC spectrum quantitative analysis model of the salmon fillets and a spectrum identification model for APC standard exceeding judgment of the salmon fillets; s6, carrying out standard normal variable transformation SNV and Norris derivative filtering NDF preprocessing on the spectral data; s7, optimizing a wavelength combination through a two-stage wavelength selection method of moving a window and gradually eliminating multiple wavelengths; and S8, verifying the precision of the model by using an independent test set. Compared with the prior art, the near-infrared hyperspectral imaging method for real-time detection of the microbial colony count of the salmon fillets has the advantages that a few-wavelength combination model is provided, and a solution is provided for developing a small-sized special NIR-HSI detector for salmon fillet detection through the near-infrared hyperspectral imaging method for real-time detection of the microbial colony count of the salmon fillets.

This invention discloses a rapid stability determination method for modified asphalt based on near-infrared spectral characteristic parameters, belonging to the field of optical detection technology. The method includes processing the effective reflectance spectrum using a standard normal variable and performing Savitzky-Golay differentiation; integrating the effective reflectance spectrum within an oxidation-sensitive window, a segregation-sensitive window, and a reference window to obtain the three-window integral, and constructing an oxidation index and a segregation index; centering the segregation index and removing components linearly correlated with the oxidation index to obtain a segregation orthogonal index; using the oxidation index for applicable domain gate determination, and comparing the absolute value of the segregation orthogonal index with a stability threshold when the gate is passed, outputting a stable or unstable conclusion. This invention suppresses the aliasing effect of oxidation or aging signals on segregation determination through the segregation orthogonal index, improving the accuracy and consistency of determination; and reduces the risk of misjudgment caused by threshold drift by pre-intercepting and verifying samples exceeding the calibrated oxidation level through applicable domain gate determination.

The invention relates to the technical field of intelligent identification, and provides an edible starch species rapid identification method and system. The method comprises the following steps: placing a powdery starch sample to be detected in a culture dish, striking off the surface, placing the culture dish on a turntable which rotates at a constant speed, and dynamically collecting original spectral data of the sample in a diffuse reflection mode by using a portable near-infrared spectrometer which is fixed on a bracket and has a vertically downward probe in a dark box environment; carrying out standard normal variable transformation and Savitzky-Golay first-order derivative filtering processing on the original spectral data in sequence; and inputting the preprocessed spectral data into a pre-trained starch species classification model, and outputting a species identification result. By constructing a standardized dynamic spectrum acquisition process, an optimized spectrum preprocessing scheme and an integrated classification model, rapid, lossless and high-accuracy field identification of various common edible starches can be realized.

The invention relates to the technical field of water content detection, in particular to a straw water content non-contact detection method and system based on near-infrared hyperspectrum, and provides the following scheme: obtaining straw hyperspectral data in a preset wave band range through a hyperspectral camera; and environment parameters and stacking density information are synchronously acquired by utilizing a temperature and humidity sensor, an illumination sensor and a laser distance measuring sensor. And after carrying out wave band cutting, wavelet denoising and standard normal variable correction on the hyperspectral data, inputting the hyperspectral data, the normalized environmental parameters and the straw basic information into a detection network together. The detection network realizes water content prediction through a feature extraction sub-network, an attention fusion sub-network and a multi-scale regression output module, and performs weighted correction on hyperspectral data and environmental parameters based on disturbance spectrum difference when a representative score is relatively high so as to improve the matching degree and volume representativeness of multi-source data.