204 results about "Multiple linear regression analysis" patented technology

A color space transformation matrix calculating system is provided that optimizes a color space transformation matrix. The matrix is obtained as a product of a first and a second matrix and transforms colors in a first color space to colors in a second color space. The system comprises first and second optimizers that calculate elements of the first matrix and second matrix by multiple linear regression analysis. The input colors which correspond to color patches and hue corrected colors obtained by using the first matrix and the input colors are set as explanatory variables. First and second goal colors respectively relating to hue and saturation in a second color space, and which correspond to the color patches, are set as criterion variables. The elements of matrices are set as partial regression coefficients.

The invention provides an electronic system multi-goal reliability allowance design method based on EDA technology. The method comprises the following steps: building the Pspice simulation model of electronic system; building key element reliability regression model according to the environment condition of the electronic system; confirming the constraint condition according to the reliability index of the system or the actual stress of the element; analyzing the sensitivity of the goal function to find the sensitive element which influences the goal function; building the regression equation between the goal function and the parameter allowance through multiple linear regression analysis to find all allowance combinations which meet the characteristic index; and building the multi-goal allowance optimization function based on mass loss. The invention controls the allowance of the sensitive element, reduces the fluctuation of the element stress caused by parameter and noise error; and the service life of each element in the system is improved without changing the system parameter and structure, thereby improving the reliability of the electronic system and prolonging the service life.

The invention discloses an establishing method for an energy consumption model of a base station. The establishing method comprises the following steps of: a, presetting an independent variable and obtaining historical energy consumption data and base station service and configuration data corresponding to the variable; b, fitting a straight line for the obtained data by using multivariate linear regression analysis, returning regression analysis parameters that describe the fitted straight line and establishing the energy consumption model; c, carrying out model validation; and d, carrying out model modification according to a model validation result until the model validation result indicates that the obtained energy consumption model achieves the expected result. The invention further discloses an energy consumption predicating method of the base station, a corresponding establishing device for the energy consumption model of the base station, and an energy consumption predicating device of the base station. Compared with the conventional energy consumption management system, the energy consumption model obtained by the establishing method provided by the invention has an accurate and effective energy consumption predicating function.

This invention provides a method for assessing arterial stiffness noninvasively using photoplethysmography. The method of the invention for assessing arterial stiffness using photoplethysmography comprises: a user information input step, characteristic point extraction step, and arterial stiffness assessment step. In particular, the characteristic point extraction step includes the correction of the characteristic points, and the arterial stiffness assessment step includes the result of performing multiple linear regression analysis using the baPWV (brachial-ankle pulse wave velocity) value. In addition, according to this invention, arterial stiffness assessment, which was previously an expensive procedure which the user could only obtain at a specialized institution, can be carried out at low cost in the course of daily life, e.g. at home or at work, and can thus be applied in the u-healthcare and home health management service environments.

The invention discloses a magnetic resonance imaging (MRI) based brain disease individual prediction method and a magnetic resonance imaging (MRI) based brain disease individual prediction system. The method comprises the following steps: 1: obtaining the MRI of the brain of a patient with mental diseases; 2: carrying out denoising and dimension reduction treatment on the MRI of the brain of the patient; 3: carrying out feature selection by utilizing a ReliefF algorithm; 4: adaptively obtaining a spatial brain area by using a spatial cluster analysis method; 5: removing redundant features by utilizing a correlation-based feature selection algorithm, thus obtaining an optimal feature subset; 6: carrying out multiple linear regression analysis based on the optimal feature subset to recognize potential biomarkers. The method has the beneficial effects that the embodiment of the invention integrates various machine learning methods and can rapidly and conveniently achieve quantitative and individual accurate prediction of the interest features of mental diseases, such as clinical indexes, based on various image data in different mode types, thus being beneficial to understanding the brain structures, function abnormity and potential pathogenesis of the diseases.

The invention relates to a mobile video quality assessment method based on hierarchy analysis and multiple linear regressions. The method comprises the following steps: firstly, determining each layer of end-to-end performance indexes of user QoE (Quality of Experience) influencing mobile video services; dividing various end-to-end performance indexes of the QoE into a target layer, a criterion layer and an index layer by utilizing a hierarchy analysis method according to the attributes and the types of the performance indexes; respectively constructing hierarchical influence models in manners that the index layer is aligned to the criterion layer which is aligned to the target layer from bottom to top by utilizing the multiple linear regression analysis; constantly adjusting regression coefficients for influencing the models and various performance indexes of the models; and finally, establishing the influence model in the manner that the index layer is aligned to the target layer, i.e., establishing a total regression model influencing the quality of a mobile video according to various end-to-end performance indexes of QoE so as to assess the quality of the mobile video by utilizing the hierarchical total assessment model. According to the method, the subjective and objective performance indexes of the mobile video are combined with the QoE of the user, so that the method provided by the invention is a comprehensive and effective user experience quality assessment method.

The invention discloses a store site selection system and a store site selection method. The method comprises the following steps: determining a trading area range according to the business type of a new store, determining existing stores and residential quarters in the trading area and searching attraction influence factor information of the stores; quantizing the environment in the store by using factor analysis; performing multiple linear regression analysis on the amount of sales of a plurality of stores by using a multiple linear regression formula according to the obtained environment in the store and other collected influence factors, determining the influence degree of each influence factor on attraction in the trading area and taking the influence degree as an adjusting index; calculating the attraction of the new store by using a multi-factor attraction model according to the influence factor and the corresponding adjusting index thereof obtained by regression analysis. According to the system and the method, retail enterprises can very conveniently determine the store customer sources and the distribution characteristics according to the store scales, the environments, the service and the like.

The invention discloses a hot-rolled H profile steel mechanical property forecasting method based on multivariate linear regression analysis. The method comprises the following steps of (A) establishing a hot-rolled H profile steel mechanical property forecasting model; (B) collecting chemical components, technological parameters and rolled mechanical property data of steel on a production field of the hot-rolled H profile steel and using the data as inputting parameters of the mechanical property forecasting model; (C) calculating the mechanical property forecasting model on the basis of multivariate linear regression analysis; (D) calculating and predicting mechanical properties of the rolled H steel on the basis of the mechanical property forecasting model according to the chemical components and the preset technological parameters of the hot-rolled H profile steel to obtain a predicted value of the mechanical property of the H-shaped steel; (E) comparing the predicted value of the mechanical property of the H-shaped steel to a target value, and judging whether hot-rolled technological parameters are optimized or not so as to obtain the mechanical property target value meeting a production performance standard; and (F) repeatedly carrying out the steps according to a next batch of production steel.

The invention belongs to the technical field of land crop planting and discloses a land crop planting suitability evaluation method. The method comprises steps: data in a database are displayed according to a user query condition and a statistical chart is then generated according to a chart type specified by a user, wherein selectable chart types comprise a broken line chart, a column chart and a pie chart; a vector layer and an image layer are built newly; roaming, zoom in, zoom out, full chart and management on layer display attribute data are realized; data are modified; a multiple linear regression analysis method and a stepwise regression analysis method are adopted; data gridding and gridding of an evaluation area are carried out; according to a specified cultivated variety, based on biological characteristics, the evaluation standard is determined, a scientific method is applied, the data are analyzed and estimated, and a professional suggestion is given; and a calculation result through processing and analysis is outputted. The method of the invention has a high-efficiency massive data management ability, and an important role is played in guiding adjustment and layout of an agricultural production structure.

The invention discloses a pair of movement energy consumption monitoring shoes based on a flexible force sensor and a monitoring method of the movement energy consumption monitoring shoes. The method includes that movement parameter information of a user such as step frequency and step speed is obtained by collecting the sole pressure distribution information, and then the movement parameter information is combined with the human property parameters such as gender, stature and the length of lower limbs; a movement energy consumption model is built by using the multiple linear regression analysis method, and the real-time monitoring on movement energy consumption is realized. The pair of movement energy consumption monitoring shoes comprises a pair of sports shoes with flexible force sensitive insoles and a wristwatch; the flexible force sensitive insoles inside the shoes collect the sole pressure distribution information of the user in real time and transmit the information to the wristwatch through a ZigBee in a wireless way; the wristwatch substitutes the parameters such as the sole pressure distribution information, age, stature and the length of lower limbs into the movement energy consumption module, so that the movement energy consumption information is obtained, and is then stored and displayed. The pair of movement energy consumption monitoring shoes is movement energy consumption monitoring equipment, which is low in cost, easy to wear, accurate in energy consumption calculation and has real-time feedback function.

A color space transformation matrix calculating system is provided that optimizes a color space transformation matrix. The matrix is obtained as a product of a first and a second matrix and transforms colors in a first color space to colors in a second color space. The system comprises first and second optimizers that calculate elements of the first matrix and second matrix by multiple linear regression analysis. The input colors which correspond to color patches and hue corrected colors obtained by using the first matrix and the input colors are set as explanatory variables. First and second goal colors respectively relating to hue and saturation in a second color space, and which correspond to the color patches, are set as criterion variables. The elements of matrices are set as partial regression coefficients.

The invention discloses a method for analyzing the ice and climate relationship on the basis of the grey MLR (Multiple Linear Regression), which comprises the following steps of: a step 1 of acquiring microclimate data comprising the environment humidity, the environment wind speed and the environment temperature and wire tensile force by a power transmission line online monitoring system and converting the microclimate data into an actual ice thickness value by a theories calculation model according to the wire tensile force; a step 2 of selecting effective data and applying a grey GM (1, 1) model construction to forecast the microclimate data; and a step 3 of establishing an ice thickness model by applying a MLR analysis method according to the selected effective data, forecasting the ice thickness on the basis of the microclimate GM (1,1) model construction and establishing an ice thickness forecasting model of the grey multiple regression analysis. The invention solves the problems that in the prior art, an equation system shows morbidness due to accumulation and subtraction of the data, the parameter estimation shows instability and the like. Detailed information such as the weight variation before and after a monitored power transmission line is iced, the inclination angle of an insulator string, the environment temperature and humidity, the wind speed and the like can be provided; and the ice thickness of the power transmission line is obtained by calculation according to field acquisition information.

The invention discloses a method for rapidly identifying the years of production of lycium barbarum by gustation finger-prints. The method comprises the following steps: selecting samples of lycium barbarum produced in different years, adding cooled boiled water at temperature of 60-80 DEG C into the lycium barbarum samples at a set solid-to-liquid ratio of 1:15 to 1:25, extracting for 5-15 minutes, filtering to obtain leach liquor of gustation matters, contacting an electronic tongue sensor array with the sample leach liquor so as to obtain sensor responding signals, extracting characteristic data from the gustation finger-prints, establishing correlation between the gustation finger-prints and the years of production by virtue of multiple linear regression analysis, and establishing a module for predicating the years of production, thus realizing the purpose of rapidly identifying the years of production of the lycium barbarum by the gustation finger-prints. With the adoption of the method, the detection difficulty caused by the fact that external characters of lycium barbarum are similar, and the detection on internal bioactive constituents is complicated and wastes time is solved, the lycium barbarum produced in different years can be conveniently, rapidly and objectively evaluated, and therefore, the basis is provided for accurately, rapidly and reliably detecting medicinal values of lycium barbarum.

The invention discloses a method for estimating the number of people in a public place based on two cameras. According to the method, at first a multivariate linear model which is used for estimating is set up, a training sample set of the multivariate linear model is acquired in a scene, and then model parameters are acquired through the analysis of multiple linear regression, so that a complete multiple linear regression equation is acquired. Only corresponding characters are extracted from a surveillance video image to be substituted into the set multiple linear regression equation so that people flow density estimation can be carried out in real time. According to the method for estimating the number of people in the public place, the estimation of the number of people can be carried out according to a multivariate linear relation of prospect pixels, face area pixels, prospect outer / inner edge pixels and people flow density, the method for estimating the number of people in the public place based on the two cameras is suitable for a low-density crowd, and estimation accuracy under the condition of a high-density crowd is improved.

The invention discloses an objective assessment method based on voice enhancement algorithm subjective assessment. The method is a new objective combination method proposed by utilizing multi-linear regression analysis based on five voice enhancement objective assessment methods as follows: PESQ, LLR, CEP, fwSNRseg and fwSNRsegVar. An objective score for assessing the quality of a voice signal, an objective score for assessing the quality of background noise and an objective score for assessing overall quality are obtained by the combinations of different weighting coefficients and five objective methods. The subjective and objective correlation coefficients of the method are superior to a plurality of other traditional objective algorithms, the standard deviation of error is lower, and the assessment of performance of the voice enhancement algorithm is easier, therefore, the invention has great practical value.

The invention discloses a method for predicating surface roughness of a titanium alloy material based on milling. The method includes the following steps that whether a material is a titanium alloy material or not is judged, if the material is the titanium alloy material, the second step is conduced, and if the material is not the titanium alloy material, the predicating method is not applicable; a plurality of workpieces made of the same material are milled with different types of cutters and under different milling parameters, and afterwards the surface roughness value Ra of each workpiece and the average abrasion capacity VB of the rear cutter face of each cutter are measured; multiple linear regression analysis is conducted on measurement results of the workpieces milled through the same type of cutters, and undetermined coefficients C, k1, k2, k3, k4 and k5 in a surface roughness predicating model (please see the model in the specifications) are determined, wherein v is the milling speed, fz is the feed engagement, ap is the milling depth, ac is the milling width, VB is the average abrasion loss of the rear cutter faces of the cutters, and therefore surface roughness predicating models corresponding to different types of cutters can be obtained; and therefore the surface roughness for milling of the titanium alloy can be predicated.

The invention provides a method and a device for determining history matching adjustment parameters in numerical reservoir simulation. The method comprises the following steps of determining a function relation of oil production and water yield of an oil well and a water injection rate of a connected well layer according to reservoir data in the numerical reservoir simulation; carrying out multiple linear regression analysis on the function relation of the oil production and the water yield of the target oil well and the water injection rate of the connected well layer, and determining a multiple linear regression formula of the oil production of the target oil well and the water injection rate of the connected well layer; and determining the to-be-adjusted history matching parameters of the target oil well according to regression coefficients of the determined multiple linear regression formula of the oil production of the target oil well and the water injection rate of the connected well layer. By adopting a method of multivariate statistics, relation is established between the water absorbing capacity of each layer of a water injection well and the yield of a production well, which are obtained through analog computation, and through analyzing the influence of each water absorbing layer on the yield of the oil well, the layer position is judged and adjusted, the parameters of the corresponding small layer are adjusted and modified, and the history matching is guided, so that the purpose of detailed history matching is achieved.

The invention relates to a vehicle curve safety vehicle speed predication and pre-warning system based on multiple regression analysis. The vehicle curve safety vehicle speed predication and pre-warning system based on the multiple regression analysis comprises a test data collection module, a test data pre-processing module, a test data analysis module, a safety vehicle speed model database, a curve information module, a vehicle type judging module and a curve safety vehicle speed pre-warning module. According to the vehicle curve safety vehicle speed predication and pre-warning system basedon the multiple regression analysis, desired test data are collected, multiple linear regression analysis is carried out on processed test data to obtain a critical transverse adhesion factor model and a transverse load transfer rate model, and the critical transverse adhesion factor model and the transverse load transfer rate model are converted by using a formula to obtain a safety vehicle speedmodel. Collected curve pavement data and vehicle types are input into the safety vehicle speed model database. A curve safety predicated vehicle speed is obtained after calculation and displayed on an LED display screen at the entrance of a curve to warn a driver of a curve safety passing vehicle speed. According to the vehicle curve safety vehicle speed predication and pre-warning system based on the multiple regression analysis, the influences of multiple factors on the curve safety vehicle speed are comprehensively considered, and the curve safety vehicle speed can be accurately predicatedto provide a safety pre-warning vehicle speed for the driver so that the vehicle stably and safely runs through the curve.

The invention discloses a Pacific Ocean European flying squid resource complimentary quantity prediction method and application. The Pacific Ocean European flying squid resource complimentary quantity prediction method comprises steps of counting SST data of a sea area during a range of 125 degree-140 degree E, 25 degree to 40 degree N from 1997-2009, counting chl a concentration data of the sea area during a range of 125 degree-140 degree E, 25 degree-40 degree N from 1997 to 2009, counting CPUE of the Pacific Ocean European flying squid population from 1998 to 2010, counting an area of a sea area during a range of 125 degree-140 degree E, 25 degree-40 degree N from October to December, wherein the sea surface temperature in the sea area is from 15 DEG to 20 DEG and then calculating PS; obtaining a point of a time sequence of the CPUE, wherein the point has a highest correlation degree with the SST, chl a concentration data and the time sequence of the PS value; performing multi-linear regression analysis; using BP nerve network model prediction; and obtaining the CPUE prediction value of the Pacific Ocean European flying squid and calculating prediction accuracy.

The invention discloses a mental stress evaluation method based on cardiogenic respiratory signals. The method comprises the following steps that 1, electrocardiosignals are collected, the cardiogenic respiratory signals are extracted by calculating the QRS wave area in an electrocardiogram waveform, and Fourier transform is conducted on the cardiogenic respiratory signals to acquire the respiratory fundamental frequency; 2, electrocardiogram R wave peaks are extracted to acquire RR interphase signals, instantaneous heart rate signals are calculated according to the RR interphase signals, and Fourier transform is conducted on the instantaneous heart rate signals to obtain an instantaneous heart rate power spectrum; 3, nervous system characteristic parameters including low frequency power (LFP), high frequency power (HFP) and LFP / HFP are calculated by combining the respiratory fundamental frequency solved in the step 1 and the heart rate power spectrum solved in the step 2 on the basis of a floating frequency band method, multi-variable linear return analysis is conducted on the nervous system characteristic parameters and a mental stress value, a mental stress evaluation model based on the nervous system characteristic parameters is output, and the mental stress value is calculated. The method has the advantages of the feasibility, the clinical practicability and the like.

The invention discloses a simplified calculating method of a double-fed motor three-phase short circuit current maximum. The method comprises the steps of firstly giving a double-feed motor three-phase short circuit current calculating formula which calculates a Crowbar resistor; then utilizing a sensitivity analyzing method to analyze the influencing degrees of various physical quantities on the sizes of the short circuit currents in the formula, and extracting a group of parameters which have greater influence degrees on the short circuit currents; finally utilizing the extracted parameters and multiple linear regression to analyze and deduce the simplified calculating formula of the double-feed motor three-phase short circuit current maximum. A simple method is provided for calculation of the double-fed motor three-phase short circuit current maximum, the calculating accuracy and practicability are achieved at the same time, and the simplified calculating method of the double-fed motor three-phase short circuit current maximum has great significance on economic security design of electrical equipment of a wind power plant.

The invention provides a global operating condition evaluating and quality tracking method for the hydrocracking process. The method comprises the steps of based on difference forms of quality standard requirements of each typical production procedure in the hydrocracking process, calculating a process capability index of each index in a quality evaluating index model of each production procedure; based on the process capability indexes, calculating a QA score of each index in the quality evaluating index model of each production procedure; based on the QA score of each index, building a weight coefficient of each index in a hydrocracking process global operating condition quality evaluating index model for the QA score contribution degree of an upper layer index by using multi-element linear regression analysis method; and based on the QA scores and the corresponding weight coefficients, positioning global operating performance degradation links and parameters of the hydrocracking process. The method provided by the invention can realize an operating condition evaluating function and a product quality tracking function, and realize efficient, economic and green production of refining and chemical enterprises.

The present invention discloses a passage passenger traffic density estimation method, which comprises: establishing a multivariable linear model for estimation, acquiring a training sample set of the multivariable linear model in the class of scenes, and adopting multivariable linear regression analysis to calculate model parameters to obtain a complete multivariable linear regression equation; and extracting corresponding features from a monitoring video image to substitute into the established multivariable linear regression equation so as to real-timely carry out passenger traffic density estimation. According to the passage passenger traffic density estimation method, passenger traffic density estimation is performed based on multivariable linear relation among front view pixels, human face region pixels, front view outer / inner edge pixels, and passenger traffic density, such that the method is suitable for low density population, and estimation accuracy in the case of high density population is increased.

The invention provides a personalized head related transfer function generation system and method based on sectional type multiple linear regression. When a function is generated, time-delay removing, redundancy information simplification and data grouping processing are carried out on head-related impulse response data in a sample head-related transfer function information base. Then the grouped data and anthropometric parameter data in the sample database are subjected to multivariate linear regression analyzing to establish a personalized model. In personalization calculation, by inputting the anthropometric parameter information of a personalized object, the head-related pulse response data corresponding to various directions are obtained through personalization model operation; and fine adjustment of anthropometric parameters is carried out to finally generate the personalized head-related transfer function suitable to a determined object. The personalized head related transfer function generation system and method of the invention directly process head-related impulse responses in the head-related transfer function sample base without frequency domain analysis of the data, and has the characteristics of high operation efficiency, high data compression rate and an obvious personalization effect.

The invention discloses a method for adopting a quantitative structure-activity relationship model to predicting soil or sediment adsorption coefficients of an organic compound. On the basis that a molecular structure of the organic compound is known, the soil or sediment adsorption coefficients of the organic compound can be quickly and efficiently predicted only by calculating a molecular descriptor with the molecular structure and applying a built QSAR (quantitative structure-activity relationship) model. The method is simple, quick, low in cost and capable of saving manpower, material resources and financial resources needed for experiment testing. Modeling is performed according to guidelines on building and using the QSAR model of the Organization for Economic Cooperation and Development, and a simple and transparent multiple linear regression analysis method is applied, so that easiness in understanding and applying is realized; the method has clear application domain, good fitting capacity, robustness and predicting capability; by the method, the soil or sediment adsorption coefficients of the organic compound in the application domain can be effectively predicted, and necessary basic data are provided for ecological risk evaluation and management of the compound; the method has important significance.

The invention discloses a correlated color temperature and color rendering index analysis method of a light source. Different types of light sources in which correlated color temperature and color rendering index are known and the range covers the types, correlated color temperatures and color rendering indexes of a light source to be measured serve as reference light sources; irradiance spectra of the reference light sources are measured, standard format processing is carried out on spectral data, and a spectral matrix of the reference light sources is obtained; a gradual multivariate linear regression analysis model is established between the spectral matrix of the reference light sources and corresponding correlated color temperatures and color rendering indexes; an irradiance spectrum of the light source to be measured is measured, standard format processing is carried out on spectral data, and a spectral matrix of the light source to be measured is obtained; and an analysis-model dependent variable corresponding to spectra data in the spectral matrix of the light source to be measured is substituted into the analysis model, and analysis values of the correlated color temperature and color rendering index of the light source to be measured are obtained. The method needs not to measure a standard test color and a standard illumination body, and is simple in process, high in automatic degree and high in test efficiency.

The invention relates to an atmospheric corrosion prediction technology, in particular to a method for predicting the atmospheric corrosion rate of metal material, which is mainly applicable to the atmospheric corrosion prediction research on regional carbon. The metal material serves as test material, test points are selected in an area which needs to be predicted, a natural exposure test is taken according to ISO / DIS8565-1987 'General Rules on Outdoor Atmospheric Corrosion Test of Metal and Alloy', and simultaneously, meteorological factors of all the test points are monitored, and the test cycle is 1 to 3 years; after the test, the rust of the test material is removed, after a sample is dried and weighed, the corrosion weight loss is calculated, and simultaneously, the meteorological data of all the test points is calculated; a statistical method of multiple linear regression analysis is adopted for regression analysis, a regression equation is established, and the atmospheric corrosion rate of the area which needs to be predicted is predicted with the regression equation. The method for predicting the atmospheric corrosion rate of metal material has reoccurrence, and can predict the atmospheric corrosion of various steel in various area environments.

The invention discloses a method for rapidly calculating the compressibility of the rock based on logging data, rock mechanics triaxial test, X-ray diffraction full-rock quantitative analysis and transformation volume measured by microseism, so that the rapid calculation of the compressibility of the shale reservoir under the stratum condition can be realized, and the high-efficiency development of the shale gas reservoir can be promoted. The method is mainly based on the influence of mineral brittleness and natural cracks, the better the brittleness of the minerals is, the larger the influence factor of the natural crack is, the better the compressibility of the rock is, the compressibility of the mineral is optimized by utilizing the number of cracks of the rock mechanics triaxial test,then the natural crack characteristics of the reservoir are combined, the compressibility characteristics of the rock can be well described, the volume is improved by combining the microseism, the weight coefficients of the mineral compressibility and the natural crack influence factors are analyzed and calculated through multivariate linear regression analysis, so that a novel method for calculating the compressibility evaluation of the rock is established. According to the method for rapidly calculating the compressibility of the rock, the reasonability of rock compressibility evaluation isimproved, and efficient development of shale gas reservoirs is promoted.

The invention discloses a method for rapidly detecting mutton adulteration. The method comprises the following steps: leaching 10-25g of an adulterated mutton sample in 100ml of a potassium chloride solution for 30 minutes, filtering to obtain leach liquor of taste sense substances, contacting an electronic tongue sensor array with the leach liquor of the sample, and generating a sensor response signal; setting detection conditions of the sensor, including the sampling time of 120 seconds and the cleaning time of 10 seconds; extracting a steady-state value responded by the sensor at 80-120th second to serve as characteristic parameters, and obtaining content of doped chicken by utilizing multiple linear regression analysis and partial least squares regression respectively. The detection conditions of adulteration through the electronic tongue are optimized, and the adulterated mutton mixed with chicken is conveniently, objectively, rapidly, qualitatively and quantitatively determined.

The invention discloses a method for establishing an engine lubricating oil quality prediction model. The method comprises steps of selecting the initial oxidation temperature as a lubricating oil quality reference index; selecting N motor vehicles running under conventional urban working conditions as test objects, carrying out long-time running condition and lubricating oil performance trackingmonitoring, carrying out regular sampling, and at least recording the type of used lubricating oil, the total running mileage, the total running time and the initial oxidation temperature of an oil sample; selecting an exponential function to fit the independent variable and the dependent variable; performing linear transformation on the exponential function; introducing intermediate variables, carrying out multiple linear regression analysis on the intermediate variable, the running mileage and the running time, and completing establishment of the engine lubricating oil quality prediction model; by means of the engine lubricating oil quality prediction model established through the method, the relation between the lubricating oil initial oxidation temperature and the vehicle running mileage and running time can be established, the reasonable oil change period is predicted, lubricating oil waste is reduced, and resources are saved.