AIMC Topic: Principal Component Analysis

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Preventing mislabeling of organic white button mushrooms (Agaricus bisporus) combining NMR-based foodomics, statistical, and machine learning approach.

Food research international (Ottawa, Ont.)
Organic foods are among the most susceptible to fraud and mislabeling since the differentiation between organic and conventionally grown food relies on a paper-trail-based system. This study aimed to develop a differentiation model that combines nucl...

An improved cancer diagnosis algorithm for protein mass spectrometry based on PCA and a one-dimensional neural network combining ResNet and SENet.

The Analyst
Cancer is one of the most serious health problems worldwide. Because cancer has no specific symptoms in its early stages, it is often not diagnosed until it is in advanced stages, reducing the likelihood of successful treatment. Therefore, early diag...

Accurate and efficient prediction of atmospheric PM, PM, PM, and O concentrations using a customized software package based on a machine-learning algorithm.

Chemosphere
Particulate matter (PM) and ozone (O) pollution have been attracting increasing attention recently due to their severe harm to human health. PM and O are secondary pollutants, and there remain significant challenges in accurately and efficiently pred...

Machine Learning Approaches for the Fusion of Near-Infrared, Mid-Infrared, and Raman Data to Identify Cartilage Degradation in Human Osteochondral Plugs.

Applied spectroscopy
Vibrational spectroscopy methods such as mid-infrared (MIR), near-infrared (NIR), and Raman spectroscopies have been shown to have great potential for in vivo biomedical applications, such as arthroscopic evaluation of joint injuries and degeneration...

Few-shot learning for inference in medical imaging with subspace feature representations.

PloS one
Unlike in the field of visual scene recognition, where tremendous advances have taken place due to the availability of very large datasets to train deep neural networks, inference from medical images is often hampered by the fact that only small amou...

Machine learning assisted dual-modal SERS detection for circulating tumor cells.

Biosensors & bioelectronics
Detecting circulating tumor cells (CTCs) from blood has become a promising approach for cancer diagnosis. Surface-enhanced Raman Spectroscopy (SERS) has rapidly developed as a significant detection technology for CTCs, offering high sensitivity and s...

A new approach to assess post-mortem interval: A machine learning-assisted label-free ATR-FTIR analysis of human vitreous humor.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
A crucial issue in forensics is determining the post-mortem interval (PMI), the time between death and the finding of a body. Despite various methods already employed for its estimation, only approximate values are currently achievable. Vitreous humo...

Enhancing endometrial cancer detection: Blood serum intrinsic fluorescence data processing and machine learning application.

Talanta
Endometrial cancer (EC) is the most prevalent cancer within the female reproductive system in developed countries. Despite its high incidence, there is currently no established laboratory screening test for EC, making early detection challenging. Thi...

Chemical composition alterations in rat brain hypothalamus induced by irisin administration using spectroscopic and machine learning techniques.

Analytical biochemistry
This study employed Fourier transform infrared (FTIR) spectroscopy to determine the chemical composition of brain tissues and the changes induced by irisin at doses of 50 mg and 100 mg. Brain tissues were collected from control rats and those adminis...