4 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 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.

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.