10 results about "Multivariate statistics" patented technology

This invention provides a combination of metabolic biomarkers for T-staging assessment of colorectal cancer, comprising four groups of metabolite combinations specifically distinguishing different T stages of colorectal cancer, each group containing 10 endogenous small molecule metabolites. This invention also provides detection methods and applications for the above-mentioned metabolic biomarker combinations. Furthermore, this invention provides a method for T-staging colorectal cancer for non-diagnostic and therapeutic purposes, as well as a combination of metabolic biomarkers related to the evolution of T-staging and tumor invasion depth in colorectal cancer. The advantages of this invention are: by combining iEESI-MS detection with multivariate statistics and machine learning, specific metabolic biomarkers related to T-staging of colorectal cancer are screened, a T-staging discrimination model is constructed, and objective, accurate, and rapid assessment of T-staging of colorectal cancer is achieved, providing molecular support for preoperative individualized treatment.

The present invention relates to the field of oil and gas exploration and development, and discloses a coal seam thickness prediction method, device and equipment based on well-seismic combination, which can obtain the real acoustic wave curve corresponding to the target layer segment and the constructed coal seam comprehensive identification factor curve. The real acoustic wave curve is subjected to wavelet transformation to extract the low-frequency component curve; the coal seam comprehensive identification factor curve is subjected to multivariate statistics to extract the high-frequency component curve. The low-frequency component curve and the high-frequency component curve are modulated to obtain a pseudo-acoustic wave curve. Based on the pseudo-acoustic wave curve and the seismic wave impedance inversion method, the thickness distribution of the coal seam to be measured is determined. The present invention combines the coal seam comprehensive identification factor curve and the seismic wave impedance inversion method to predict the coal seam thickness, effectively solving the problem of the difficulty in predicting the coal seam thickness in the complex structural area of ​​the oil field with no wells or few wells, improving the efficiency and accuracy of coal seam thickness prediction in the complex structural area of ​​the oil field, and diversifying the coal seam thickness prediction method in the complex structural area of ​​the oil field.

The invention discloses a strong wine authenticity identification method fusing multivariate statistics and metabolic feature extraction, relates to the technical field of strong wine authenticity identification, and overcomes the limitation that a traditional method only depends on peak area or mass-to-charge ratio for distinguishing by incorporating non-dominant structure difference in a metabolic map into an analysis model. A traditional method is difficult to distinguish metabolic differences caused by different processes or environmental changes, but according to the method, through multi-dimensional non-targeted metabolic characteristic analysis, the influence of the processing processes on the metabolic structures of the wine products can be accurately captured, and the distinguishing precision of the wine products of the same variety, different processing batches, the same raw material and different processes is remarkably improved. According to the method disclosed by the invention, the comprehensive isomerism index FCY is constructed by combining the structural isomerism variance and the map complexity factor, so that the difficulty in judgment caused by complex structures and hidden changes of wine samples is effectively solved.

The invention discloses a pyrazine-flavor-based multi-mode intelligent identification method for a Maotai-flavor liquor process. The method comprises the following steps: pretreatment: pretreating Maotai-flavor liquor samples of different brewing processes; collecting sensory data of electronic noses and electronic tongues of Maotai-flavor baijiu of different brewing processes; establishing a liquid phase detection method for the characteristic components of the alkylpyrazine in the Maotai-flavor liquor; verifying a component analysis method through precision, stability and repeatability experiments; component quantitative analysis based on liquid chromatography fluorescence detection; performing clustering and difference evaluation on Maotai-flavor liquor samples of different brewing processes by adopting multivariate statistical analysis; establishing a Maotai-flavor liquor process identification model: utilizing VIPgt in multivariate statistics; establishing different machine learning multi-classification models for different brewing processes of Maotai-flavor liquor according to the common characteristics of 1, and performing multi-model comparison; and based on the optimal performance classification model, establishing an SHAP model of the Maotai-flavor liquor brewing process identification model, and globally explaining the identification model by adopting an SHAP algorithm.

The invention provides a tea processing strategy determination method based on chemical and sensory characteristics and a related device, and relates to the technical field of tea processing. Obtaining a tea sample; carrying out sensory evaluation on the tea samples, wherein the sensory evaluation comprises subjective sensory scoring and objective electronic sensory analysis; carrying out chemical component analysis on the tea leaf sample, wherein the chemical component analysis comprises detection of non-volatile compounds and volatile compounds; on the basis of sensory evaluation and chemical analysis results, correlation chemical data and sensory attributes are analyzed through multivariate statistics to generate analysis results; optimized tea leaf processing parameters including time, temperature or intensity parameters of a processing technology are determined according to an analysis result, and the processing parameters are scientifically optimized by integrating sensory evaluation and chemical analysis and applying multivariate statistics to analyze associated data, so that the method has the advantages.

This invention discloses a power quality anomaly tracing method based on multivariate statistical decoupling, comprising: acquiring historical power quality data and relevant data at the time of analysis, and processing the data; identifying overall anomalies based on Hotelling T² control charts; if an overall anomaly is identified, performing anomaly statistical decoupling analysis based on Mason decomposition, and obtaining the order conditional statistics, unconditional statistics, and complete conditional statistics of each node, and determining whether each statistic reaches a significance level; when both the unconditional and complete conditional statistics of a node are significant, the node is the root cause; when the unconditional statistics of a node are not significant but the complete conditional statistics are significant, the node is a relational anomaly; when the unconditional statistics of a node are significant but the complete conditional statistics are not significant, the node is a cascading phenomenon; this invention improves the location efficiency, analysis accuracy, and decision-making pertinence of power quality operation and maintenance in distribution networks.

The invention relates to the technical field of ecological environment monitoring, in particular to a glacier permafrost region multi-underlying surface carbon flux dynamic monitoring and environmental factor collaborative analysis method, which realizes long-term continuous monitoring of underlying surface carbon flux of bare rock permafrost, alpine meadow and the like through coupling of a vortex related system, a microclimate sensor network and an ecological model. And combining multivariate statistics and a machine learning algorithm to analyze a driving mechanism of the environmental factors to the carbon flux. By constructing a distributed monitoring network, a multi-source data fusion model and a climatic change scene simulation module, the technical problems of lack of carbon flux monitoring data and unclear environment response mechanism in the cold and cold mountainous area are solved. The method has the advantages of high monitoring precision, wide space-time coverage, high prediction reliability and the like.

The present application belongs to the field of engineering technology and environmental protection, and discloses a sustainable tunnel construction water inflow prediction method based on machine learning technology, which comprises the following steps: data collection and preprocessing; using multivariate statistics and machine learning methods to identify the main control factors of water inflow; using Python programming language, according to the key influencing factors of water inflow, based on random forest, XGBoost and deep neural network model algorithm, respectively establishing efficient water inflow prediction model, and according to the evaluation parameters of the output performance of the initial water inflow prediction model, evaluating and comparing the performance of different efficient water inflow prediction models. The efficient water inflow prediction model with the closest output performance of the training set and test set of each efficient water inflow prediction model is determined as the best water inflow prediction model; and a sustainable tunnel construction water inflow intelligent prediction system is constructed. The present application optimizes the traditional water inflow prediction method by combining various machine learning technologies.

Diagnostic method (100) of determining the presence of a label-free antibody-antigen complex of interest in a biological sample: Providing (101) a trained model based on a training data set processed by multivariate statistics and a machine learning algorithm, wherein the training data set comprises: immunoassay training data of the labeled antibody-antigen complex obtained from at least one immunoassay technique; spectral training data comprising one or more training vibrational spectra of the labeled and / or label-free antibody-antigen complex obtained from at least one vibrational spectroscopy technique; Recording (102) at least one sample vibrational spectrum of a biological sample using the at least one vibrational spectroscopy technique; and Applying (103) the trained model onto the at least one sample vibrational spectrum and determine whether or not the label-free antibody-antigen complex is present in the biological sample.

The invention provides a columnar sediment-based heavy metal pollution source identification and ecological risk inversion method. The method comprises the following five steps of: 1, collecting and preprocessing columnar sediment; 2, determining the physical and chemical properties of the columnar sediment; thirdly, heavy metal sources are analyzed based on multivariate statistics; 4, heavy metal pollution level and ecological risk assessment based on multiple indexes; and 5, a comprehensive identification module of a heavy metal pollution risk layer. According to the method, unified standards are established to perform layered identification on pollution intensity and ecological risk levels of different depth layers in the sediment, and a complete method system from data acquisition, pollution index calculation, pollution source identification, risk level output to section horizon division is formed. The method has a good application prospect in the technical field of environmental monitoring and environmental evaluation.