34 results about "Partial least squares analysis" patented technology

The present invention is a method for detecting an abnormal event for process units of a Fluidized Catalytic Cracking Unit. The method compares the operation of the process units to a statistical and engineering models. The statistical models are developed by principle components analysis of the normal operation for these units. In addition, the engineering models are based on partial least squares analysis and correlation analysis between variables. If the difference between the operation of a process unit and the normal model result indicates an abnormal condition, then the cause of the abnormal condition is determined and corrected.

A system for the identification and quantification of a biological sample suspended in a liquid includes a fluorescence excitation module with at least one excitation light source; a sample interface module optically coupled to the fluorescence excitation module for positioning a biological sample to receive excitation light from the at least one excitation light source; a fluorescence emission module optically coupled to the sample interface module and comprising at least one detection device for detecting fluorescence excitation-emission matrices of the biological sample; and a computer module operatively coupled to the fluorescence emission module. The computer module performs multivariate analysis on the fluorescence excitation-emission matrices of the biological sample to identify and quantify the biological sample. The multivariate analysis may comprise extended partial least squared analysis for identification and quantification of the biological sample. A method for the identification and quantification of a biological sample suspended in a liquid is also provided.

A non-invasive method for analyzing the blood-brain barrier includes obtaining a Raman spectrum of a selected portion of the eye and monitoring the Raman spectrum to ascertain a change to the dynamics of the blood brain barrier. Also, non-invasive methods for determining the brain or blood level of an analyte of interest, such as glucose, drugs, alcohol, poisons, and the like, comprises: generating an excitation laser beam (e.g., at a wavelength of 600 to 900 nanometers); focusing the excitation laser beam into the anterior chamber of an eye of the subject so that aqueous humor, vitreous humor, or one or more conjunctiva vessels in the eye is illuminated; detecting (preferably confocally detecting) a Raman spectrum from the illuminated portion of the eye; and then determining the blood level or brain level (intracranial or cerebral spinal fluid level) of an analyte of interest for the subject from the Raman spectrum. In certain embodiments, the detecting step may be followed by the step of subtracting a confounding fluorescence spectrum from the Raman spectrum to produce a difference spectrum; and determining the blood level and / or brain level of the analyte of interest for the subject from that difference spectrum, preferably using linear or nonlinear multivariate analysis such as partial least squares analysis. Apparatus for carrying out the foregoing methods are also disclosed.

A non-invasive method for analyzing the blood-brain barrier includes obtaining a Raman spectrum of a selected portion of the eye and monitoring the Raman spectrum to ascertain a change to the dynamics of the blood brain barrier.Also, non-invasive methods for determining the brain or blood level of an analyte of interest, such as glucose, drugs, alcohol, poisons, and the like, comprises: generating an excitation laser beam at a selected wavelength (e.g., at a wavelength of about 400 to 900 nanometers); focusing the excitation laser beam into the anterior chamber of an eye of the subject so that aqueous humor, vitreous humor, or one or more conjunctiva vessels in the eye is illuminated; detecting (preferably confocally detecting) a Raman spectrum from the illuminated portion of the eye; and then determining the blood level or brain level (intracranial or cerebral spinal fluid level) of an analyte of interest for the subject from the Raman spectrum. In certain embodiments, the detecting step may be followed by the step of subtracting a confounding fluorescence spectrum from the Raman spectrum to produce a difference spectrum; and determining the blood level and / or brain level of the analyte of interest for the subject from that difference spectrum, preferably using linear or nonlinear multivariate analysis such as partial least squares analysis. Apparatus for carrying out the foregoing methods are also disclosed.

An apparatus and method for detecting phosphorus in soil and vegetation are developed. In one embodiment, a portable Raman-based sensor is provided to obtain significant phosphorus absorption band in soils and to determine phosphorus concentrations. The portable sensor can have the capability to measure phosphorus concentrations in wet and dry soil samples as well as fresh and dry vegetations. In one embodiment, the portable sensor of the invention uses a 600 mW laser light source at 785 nm with a full width at half maximum of about 0.2 nm and a spectrometer that covers 340 and 3640 cm−1. Software, written in Visual C++, and partial least squares analysis were used to produce calibration and predictions models.

An apparatus and method for detection of a feature etch completion within an etching reactor. The method includes determining a correlation matrix by recording first measured data regarding a first etch process over successive time intervals to form a first recorded data matrix, assembling a first endpoint signal matrix using target endpoint data for a specific etch process, performing a partial least squares analysis on the recorded data matrix and the first endpoint signal matrix to refine the recorded data matrix, and computing a correlation matrix based upon the refined recorded data matrix and the first endpoint signal matrix. The method further includes performing a second etch process to form a second recorded data matrix. The correlation matrix and the second recorded data matrix are analyzed to determine whether an endpoint of the second etch process has been achieved.

The invention provides a characteristic spectrum area selection method for a near infrared spectrum. The characteristic spectrum area selection method comprises the following steps of: applying a Monte Carlo probability selection combined ant colony optimization algorithm to a characteristic spectrum area selection problem of the near infrared spectrum; setting a dynamic section range and initializing algorithm parameters to obtain each spectrum section of an object to be taken as an equivalent searching point; establishing a partial least square analyzing model by taking the quality or characteristics of the object to be detected as a standard reference; predicating a root-mean-square error by the model to repeatedly carry out weighting calculation to update pheromone vectors according to the predicated root-mean-square error; carrying out iterative computation and searching to obtain the optimal characteristic spectrum area of the near infrared spectrum; and carrying out multiple circulating calculation and automatically judging to obtain the optimal characteristic spectrum area of the near infrared spectrum. The characteristic spectrum area selection method disclosed by the invention combines the wholeness of Monte Carlo probability selection and ant colony optimization algorithm positive feedback so as to effectively avoid the disadvantages of experience selection in a modeling process and data redundancy of all selections, rapidly obtain a global optimum characteristic spectrum area, and improve the precision and the stability of modeling.

The invention provides a targeted metabo lomics analysis method for determining metabolites of a living body. The targeted metabo lomics analysis method can simultaneously determine 700 main metabolites of a living body based on series connection of ultra-high performance liquid chromatography and triple quadrupole and comprises the following steps: firstly, 700 main metabolites in a living body are screened; standard products of compounds are optimized to obtain optimal parent ions, daughter ions and mass spectrography conditions of the metabolites; the metabolites in samples are extracted by methanol, acetonitrile and water; liquid chromatograph conditions are optimized; a hydrophilic interaction is adopted to separate the metabolites; the separated metabolites enters a mass spectrum; the 700 main metabolites are determined at the same time by use of a real-time scheduled multi-reaction monitoring mode with quick positive and negative polarity switching function; the metabolite differences between all the samples are analyzed via partial least square analysis; changes of the metabolic pathway are found by pathway enrichment analysis. The targeted metabo lomics analysis method remarkably increases the number of detected compounds, and greatly shortens the analysis time.

The invention provides a characteristic wavelength selecting method for a near infrared spectrum in an ant colony optimization algorithm. The method comprises the following steps: creating a partial least squares analysis model by utilizing all wavelength points of the near infrared spectrum as initial selection equivalent variables of the ant colony optimization algorithm and utilizing the quality or characteristics of a test object as reference standard, re-weighing and calculating to update a pheromone vector according to the predicated mean square error of the model, searching and obtaining the optimal near infrared spectrum wavelength combination through using iterative computations, performing circular computation for a plurality of times, and automatically judging so as to obtain the optimal characteristic wavelength of the near infrared spectrum. The characteristic wavelength selecting method provided by the invention adopts the global search and positive feedback mechanisms of the ant colony optimization algorithm so as to effectively avoid the defects of subjective wavelength selection in the modeling process, so that the model has strong robustness and applicability.

The invention discloses a Jinhua ham grading and identifying method based on an electronic nose technology. The method comprises the following steps of: putting standard samples into a sealed container, and sealing the standard samples in a thermostat at the temperature of 30 degrees for 30 minutes, wherein special grade, first grade and second grade Jinhua hams which are identified by a sensing method according to national recommended standard of GB / T19088-2008 original producing area products of Jinhua hams are taken as standard samples; pumping headspace gas into an electronic nose through a sampling channel, adsorbing a certain amount of volatile substances by a sensor so as to change the conductivity, acquiring signals by a data acquisition system, and storing the signals in a computer; and performing principal component analysis, linear discriminant analysis and partial least square analysis on the acquired data by using Unscrambler software, an WinMuster software and the like, establishing discriminant models of the hams in various grades, and predicting the samples to be tested by using the models. The method provided by the invention is convenient to operate and accurate and objective in result, and the traditional sensory grading can be replaced..

An apparatus and method for detection of a feature etch completion within an etching reactor. The method includes determining a correlation matrix by recording first measured data regarding a first etch process over successive time intervals to form a first recorded data matrix, assembling a first endpoint signal matrix using target endpoint data for a specific etch process, performing a partial least squares analysis on the recorded data matrix and the first endpoint signal matrix to refine the recorded data matrix, and computing a correlation matrix based upon the refined recorded data matrix and the first endpoint signal matrix. The method further includes performing a second etch process to form a second recorded data matrix. The correlation matrix and the second recorded data matrix are analyzed to determine whether an endpoint of the second etch process has been achieved.

The invention provides a GC / MS metabonomics analysis method based on osteoblast cell tissue. The method includes the steps of collecting normal osteoblast and osteoblast samples interfered with hydrogen peroxide, conducting gas chromatography separation after processing is conducted, conducting detection analysis through mass spectra, extracting original data of instruments after GC / MS detection analysis is conducted to obtain data which are low in signal to noise ratio and capable of being used for statistic analysis, analyzing the data through principal component analysis and partial least squares analysis to obtain difference metabolin between two sets, and primarily authenticating the metabolin. By means of the method, the variation situations of metabolite of the normal osteoblast and hydrogen peroxide molding cells can be totally, comprehensively and effectively embodied, and the favorable technical support is provided for early diagnosis and prognosis of osteoporosis.

The invention belongs to the field of spectral analysis. Automatic and fast selection of multiple characteristic spectral lines related to specific element content in massive spectral data can be realized without artificial determination processes; selection efficiency can be greatly improved; the number of characteristic spectral lines participating in modeling can be effectively reduced; calculating processes can be simplified; the steadiness and accuracy of a quantitative prediction model can be enhanced; and a verification method is also provided. An automatic characteristic spectral lineselection method used for laser induced breakdown spectroscopy analysis includes the following steps: step one, performing first full spectrum correction on spectral raw data X; step two, performing second full spectrum correction on the spectral raw data X' corrected by the step one; step three, rejecting unimportant spectral line variables; step four, a step of iteration, namely the step of performing partial least squares analysis on the residual variables selected by the step three; and step five, a step of determination, namely the step of determining that cyclic residual spectral lines are the final selection results. The method is mainly applied to spectral analysis occasions.

The invention relates to an identification method for three polygonatum sibiricum with a legal origin. Collected medicinal materials are extracted by utilizing an ultrasonic method; representation isperformed on chemical compositions by utilizing serial connection of ultra-high performance liquid chromatography and electrostatic field orbitrap high resolution mass spectrum (UPLC-Orbitrap MS) technology after an extract solution is purified by a PS / AL solid phase extraction column; and data are analyzed and processed by utilizing multivariate statistical analysis, wherein the multivariate statistical analysis includes principal component analysis (PCA), partial least squares discrimination analysis (PLS-DA), orthogonal corrected partial least square analysis (OPLS-DA), and cluster analysis(HCA). We found that the chemical components of the medicinal materials of three polygonatum sibiricum with the legal origin are significantly different; the constructed analytical method can be usedto distinguish the three polygonatum sibiricum with the legal origin; and research results provide a basis for in-depth study of the medicinal materials of polygonatum sibiricum and scientific formulation with a quality standard.

A method for constructing a process performance prediction model for a material processing system, the method including the steps of: recording tool data for a plurality of observations during a process in a process tool, the tool data comprises a plurality of tool data parameters; recording process performance data for the plurality of observations during the process in the process tool, the process performance data comprises one or more process performance parameters; performing a partial least squares analysis using the tool data and the process performance data; and computing correlation data from the partial least squares analysis.

The invention discloses a method for predicting the thickness of hot-rolled strip steel on the basis of an improved partial robust M-regression algorithm, and relates to a method for predicting the thickness of hot-rolled strip steel. By the aid of the method, the problem of incapability of acquiring an accurate analytical model or extremely high time consumption of a modeling procedure of an existing method for predicting the thickness of hot-rolled strip steel is solved. The method includes monitoring operational data of seven finishing mills to acquire observational variables (x, y), defining an input data matrix X and an output data matrix Y and computing initial values omega of robust weighting factors; weighting the observational variables (x, y) to acquire predicted data, performing partial least-square analysis on the predicted data to acquire a partial least-square model of the predicted data and continuously computing to obtain a partial least-square regression model and regression coefficients B; judging whether estimation errors of a k regression coefficient B and a (k-1) regression coefficient B are smaller than a set threshold value or not, acquiring a certain regression coefficient B and determining a partial least-square regression model which is a prediction result for the thickness of the hot-rolled strip steel. The method can be widely applied to predicting the thickness of the hot-rolled strip steel.

The invention provide a magnetic resonance imaging decoupling hydrogen spectrum method for identifying true and adulterated honey and relates to nuclear magnetic resonance spectroscopy. The method comprises the following steps: adding an identified honey sample into a buffer solution, centrifuging the prepared sample, removing supernatant liquid, and transferring the sample into a magnetic resonance resonance tube; importing a precompiled decoupling NMR spectrum pulse sequence into a nuclear magnetic resonance spectrometer; setting pulse sequence parameters and sampling parameters, and collecting data; after the sampling is finished, carrying out relevant data processing so as to obtain a decoupling spectrogram of all samples, and preprocessing corresponding experimental data, so as to obtain subsection integral data of the decoupling spectrogram; and importing the decoupling the subsection integral data into statistical analysis software, carrying out principal component analysis and orthogonal partial least square analysis so as to judge the sizes of inter-group and in-group differences of two groups, analyzing a compound which causes the inter-group differences, and judging an adulteration manner of the adulterated honey. The pulse sequence is homonuclear wide-band decoupling pure chemical displacement spectrum method, and a 1HNMR spectrum with relatively high resolution for eliminating coupled splitting can be obtained.

The invention discloses a radix scutellariae identification method. According to the identification method, radix scutellariae is used for preparing a solution of a test product, solutions in which abaicalin reference substance, a wogonoside reference substance, a baicalein reference substance and a wogonin reference substance are dissolved are used as reference substance solutions, and through high performance liquid chromatograph measurement, liquid chromatograms of the solution of the test product and the reference substance solutions are obtained respectively; the obtained liquid chromatograms are guided into a traditional Chinese medicine chromatograph fingerprint similarity evaluation system for analysis, through analysis, a radix scutellariae fingerprint is obtained, and hierarchical cluster analysis or principal component analysis or partial least square analysis is adopted for processing data of the radix scutellariae fingerprint, so that classification identification is conducted on the radix scutellariae or processed products of the radix scutellariae. According to the radix scutellariae identification method, on the basis of building the radix scutellariae fingerprint,the radix scutellariae index component content is measured, and by combining different chemical mode identification methods, the origin places, processing and appearance characters of the radix scutellariae can be effectively judged and distinguished.

The invention discloses a method for quantitatively analyzing crude oil by Raman spectroscopy based on a partial least square method. The method comprises the following steps: S1, obtaining a Raman spectrogram of a to-be-detected substance, and preprocessing the obtained Raman spectrogram data by adopting a Raman spectroscopy analysis technology; S2, performing feature extraction on the preprocessed data; S3, selecting data after feature extraction based on a partial least squares algorithm to establish a mathematical model; S4, completing qualitative analysis judgment on whether the to-be-detected substance contains crude oil or not; and S5, completing quantitative calculation of the crude oil content in the to-be-detected substance through the established mathematical model. According tothe method, the steps of preprocessing Raman spectrum data of a to-be-detected substance and extracting features are introduced, meanwhile, a corresponding mathematical algorithm is adopted to improve the processing precision of the data, then a mathematical model is established based on a partial least square analysis method, qualitative analysis and quantitative calculation of the crude oil inthe drilling fluid mixture are completed according to the mathematical model, and the final result shows relatively ideal calculation precision.

The invention provides a method for identifying organic milk and conventional milk based on metabonomics, and relates to the technical field of milk identification and detection. The method comprises the following steps: (1) carrying out sample pretreatment on organic milk and conventional milk under the same condition; (2) carrying out data acquisition on the pretreated sample through high performance liquid chromatography-tandem high-resolution mass spectrometry to obtain a metabolite detection multimodal map; (3) performing peak extraction and correction on the chromatographic peak to obtain the relative peak area and retention time of all metabolites; (4) carrying out principal component analysis and orthogonal partial least square analysis, screening differential markers, and obtaining an identification model to distinguish organic milk and conventional milk; and (5) comparing the difference marker with an online database for identification. The method disclosed by the invention can be used for rapidly and comprehensively detecting and identifying the milk products, the detection result is accurate, and the operation method is simple.

An apparatus and method for detection of a feature etch completion within an etching reactor. The method includes determining a correlation matrix by recording first measured data regarding a first etch process over successive time intervals to form a first recorded data matrix (110), assembling a first endpoint signal matrix using target endpoint data for a specific etch process, performing a partial least squares analysis (130) on the recorded data matrix and the first endpoint signal matrix to refine the recorded data matrix, and computing a correlation matrix (180) based upon the refined recorded data matrix and the first endpoint signal matrix. The method further includes performing a second etch process to form a second recorded data matrix. The correlation matrix and the second recorded data matrix are analyzed to determine whether an endpoint of the second etch process has been achieved.

The invention belongs to the field of spectral analysis, and provides a method for rapidly selecting multiple characteristic spectral lines related with specific element content from a lot of spectrum data, which can greatly increase the selection efficiency, effectively reduces the characteristic spectral line quantity which participates in modeling, simplifies a calculating process, and increases the stability and accuracy of a quantitative prediction model. The rapid selection method comprises the following steps: 1) performing spectrum pretreatment on spectral original data; 2) dividing the all spectral variable spectral lines X into several groups of Xi at intervals; 3) performing integral partial least squares on all variables X' corrected by the step 2); 4) performing partial least squares on the current all variables X'; and 5) comparing the importance of the current variables with threshold one by one; and 6) if quantitative analysis requirement is satisfied, stopping an iteration process, otherwise returning to the step 4). The method is mainly used for spectral analysis occasion.

The invention discloses a cortex phellodendron chinese identification method. According to the identification method, cortex phellodendron chinese is used for preparing a solution of a test product, solutions in which a berberine hydrochloride reference substance, a phellodendrine chloride reference substance and a magnoflorine reference substance are dissolved are used as reference substance solutions, and through high performance liquid chromatograph measurement, liquid chromatograms of the solution of the test product and the reference substance solutions are obtained respectively; the obtained liquid chromatograms are guided into a traditional Chinese medicine chromatograph fingerprint similarity evaluation system for analysis, through analysis, a cortex phellodendron chinese fingerprint is obtained, and hierarchical cluster analysis or principal component analysis or partial least square analysis is adopted for processing data of the cortex phellodendron chinese fingerprint, so that classification identification is conducted on the cortex phellodendron chinese or processed products of the cortex phellodendron chinese. According to the cortex phellodendron chinese identification method, on the basis of building the cortex phellodendron chinese fingerprint, the cortex phellodendron chinese index component content is measured, and by combining different chemical mode identification methods, the cortex phellodendron chinese, the processed products of the cortex phellodendron chinese and fake cortex phellodendri amurensis can be effectively identified and distinguished.

The scheme discloses the technical field of Maotai-flavor Baijiu identification, and a method for identifying a Maotai-flavor Baijiu of a specific brand by combining a nuclear magnetic resonance hydrogen spectrum (<1>H NMR) technology with a partial least square analysis method based on non-volatile substances. The method comprises:respectively establishing a data set of a Maotai-flavor Baijiu of a specific brand and a data set of Maotai-flavor Baijius of other brands, constructing an identification model, carrying out reliability verification on the identification model, and finally identifying an unknown Baijiu sample to be detected. The method is used for discriminating Maotai-flavor Baijiu of a specific brand and other brands, whether the detected Maotai-flavor Baijiu is the Maotai-flavor Baijiu of the specific brand or not is highly truly judged, risks are avoided to the maximum extent, and economic losses of enterprises with the Baijiu of the specific brand are reduced.

The invention relates to a method for identifying tea leaf growth area differences through pure chemical shift NMR spectrums. A PSYCHE pure chemical shift spectrum process and a multivariate statistical analysis process are combined and used for composition analysis of low-concentration natural food tea leaves. An obtained pure chemical shift spectrum is subjected to principal component analysis and an orthogonal partial least square analysis, growth area identification for tea leaves is achieved, and differential substances in the tea leaves of different growth areas are found out. The PSYCHEpure chemical shift spectrum process adopted by the method can remove broad-band homonuclear 1H-1H coupling, and only preserve chemical shift information so that a high-quality pure chemical shift hydrogen spectrum is obtained by applying the method to a low-concentration sample and the spectrum resolution is increased. A PSYCHE pulse sequence is simple, operation is easy, and conventional spectrometer conditions can meet experiment requirements. Compared with present tea leaf growth area identification methods, the NMR method provided is repeatable, non-invasive, high in spectrum resolution,and the like.

The invention discloses a method for identifying local wild astragalus membranaceus. The method comprises a step of analyzing characteristic elements in an astragalus membranaceus main root slice by adopting inductively coupled plasma mass spectrometry (ICPMS) to obtain characteristic element fingerprints of astragalus membranaceus and characterizing the repeatability and accuracy of an analysis method by using an astragalus membranaceus standard substance to ensure that obtained data is real and reliable, a step of establishing a multivariate statistical discrimination model of the wild astragalus membranaceus in Shanxi province and other astragalus membranaceus by using partial least squares analysis, a step of analyzing an unknown type of the astragalus mongholicus sample by using the established model, and a step of performing judgment, wherein the accuracy of model prediction is high, and the accuracy of model prediction of the astragalus mongholicus in Shanxi province can reach 100%. The method is simple and rapid to operate, a result is accurate and reliable, the method is suitable for identification of the wild astragalus mongholicus in Shanxi province in astragalus membranaceus with different growth years in Inner Mongolia, Jilin, Gansu and Shanxi production places, and the method can provide a reference for characteristic inorganic element analysis and source researchof the astragalus membranaceus.

The invention provides a characteristic wavelength selecting method for a near infrared spectrum in an ant colony optimization algorithm. The method comprises the following steps: creating a partial least squares analysis model by utilizing all wavelength points of the near infrared spectrum as initial selection equivalent variables of the ant colony optimization algorithm and utilizing the quality or characteristics of a test object as reference standard, re-weighing and calculating to update a pheromone vector according to the predicated mean square error of the model, searching and obtaining the optimal near infrared spectrum wavelength combination through using iterative computations, performing circular computation for a plurality of times, and automatically judging so as to obtain the optimal characteristic wavelength of the near infrared spectrum. The characteristic wavelength selecting method provided by the invention adopts the global search and positive feedback mechanisms of the ant colony optimization algorithm so as to effectively avoid the defects of subjective wavelength selection in the modeling process, so that the model has strong robustness and applicability.

The invention discloses a stage division method based on dynamic feature extraction, which is used for solving the problem that dynamic feature changes in the capturing process of an existing method are not sensitive enough. The stage division method comprises the following steps of: firstly, expanding original data along batches, performing partial least square analysis on each time slice matrix to obtain score matrixes of process variables and quality variables, and clustering the joint score matrixes by adopting an AP algorithm to realize first-step division; and extracting dynamic features representing process dynamics by adopting an encoder-decoder model, carrying out second-step division on the dynamic features by adopting the AP algorithm, comprehensively analyzing two-step division results, and finally dividing the production process into different stable stages and transition stages. After stage division is completed, an attention LSTM quality prediction model can be further established for each divided stage, and expansion application is carried out.

The invention discloses a method for identify kiwi fruits from different producing areas by means of combination of an electronic tongue and a partial least squares analysis method. The method includes: collecting kiwi fruit samples from Hunan, Shaanxi and Sichuan; peeling the kiwi fruit samples and then carrying out freeze drying; respectively adding water into the kiwi fruit samples subjected tofreeze drying for manufacturing a plurality of samples in different regions; sealing and placing the samples under the condition of 20 DEG C to 25 DEG C, so that solution generated by the sample is collected by using the electronic tongue; determining electronic tongue sensor response values of methane, ethanol, alkane, oxynitride, aromatic components and organic sulfide in the solution, and respectively marking the parameters as Y(Hunan), Y(Shaanxi) and Y(Sichuan). According to a discrimination model, an electronic tongue system is utilized to extract and detect fruit compounds, sources of fruit production places are rapidly discriminated, characteristic differences of the compounds of fruits from different territory sources are analyzed by combining a partial least square analysis method, and the fruit production places are discriminated and classified by utilizing the characteristic differences.