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Discriminant Analysis

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High-throughput, rapid, and non-destructive detection of common foodborne pathogens via hyperspectral imaging coupled with deep neural networks and support vector machines.

Food research international (Ottawa, Ont.)
Foodborne pathogens such as Bacillus cereus, Staphylococcus aureus, and Escherichia coli are major causes of gastrointestinal diseases worldwide and frequently contaminate dairy products. Compared to nucleic acid detection and MALDI-TOF MS, hyperspec...

Sexual dimorphism of the humerus bones in a French sample: comparison of several statistical models including machine learning models.

International journal of legal medicine
Sex estimation is an important part of skeletal analysis and forensic identification. Traditionally pelvic traits are utilized for accurate sex estimation. However, the long bones, especially humerus, have been proved to be as effective for determine...

Automated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.

PloS one
Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art painting...

Advancing DNA Structural Analysis: A SERS Approach Free from Citrate Interference Combined with Machine Learning.

The journal of physical chemistry letters
Surface-enhanced Raman spectroscopy (SERS) has become an indispensable tool for biomolecular analysis, yet the detection of DNA signals remains hindered by spectral interference from citrate ions, which overlap with key DNA features. This study intro...

Label-Free Exosomal SERS Detection Assisted by Machine Learning for Accurately Discriminating Cell Cycle Stages and Revealing the Molecular Mechanisms during the Mitotic Process.

Analytical chemistry
Cell cycle analysis is crucial for disease diagnosis and treatment, especially for investigating cell heterogeneity and regulating cell behaviors. Exosomes are highly appealing as noninvasive biomarkers for monitoring real-time changes in the cell cy...

Development of a method for detecting and classifying hydrocarbon-contaminated soils via laser-induced breakdown spectroscopy and machine learning algorithms.

Environmental science and pollution research international
In recent years, there has been a significant increase in oil exploration and exploitation activities, resulting in spills that pose a severe threat to the environment and public health. The present work aims to develop a method to detect and classif...

A holistic strategy for the in-depth discrimination and authentication of 16 citrus herbs and associated commercial products based on machine learning techniques and non-targeted metabolomics.

Journal of chromatography. A
Citrus-derived raw medicinal materials are frequently used for health care, flavoring, and therapeutic purposes. However, Due to similarities in origin or appearance, citrus herbs are often misused in the market, necessitating effective differentiati...

Machine learning and metabolomics identify biomarkers associated with the disease extent of ulcerative colitis.

Journal of Crohn's & colitis
BACKGROUND AND AIMS: Ulcerative colitis (UC) is a metabolism-related chronic intestinal inflammatory disease. Disease extent is a key parameter of UC. Using serum metabolic profiling to identify noninvasive biomarkers of disease extent may inform the...

Convolutional Neural Networks Assisted Peak Classification in Targeted LC-HRMS/MS for Equine Doping Control Screening Analyses.

Analytical chemistry
Doping control screening analyses usually involve visual inspection of extracted ion chromatograms (EIC) by a trained analytical chemist, followed by further investigations if needed. This task is both highly repetitive and time-consuming, given the ...

Enhancing detection of SSVEPs using discriminant compacted network.

Journal of neural engineering
. Steady-state visual evoked potential-based brain-computer interfaces (SSVEP-BCIs) have gained significant attention due to their simplicity, high signal to noise ratio and high information transfer rates (ITRs). Currently, accurate detection is a c...