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Least-Squares Analysis

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Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal.

IEEE transactions on bio-medical engineering
OBJECTIVE: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of...

Geographical origin identification of Khao Dawk Mali 105 rice using combination of FT-NIR spectroscopy and machine learning algorithms.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The mislabelled Khao Dawk Mali 105 rice coming from other geographical region outside the Thung Kula Rong Hai region is extremely profitable and difficult to detect; to prevent retail fraud (that adversely affects both the food industry and consumers...

Predicting drug solubility in organic solvents mixtures: A machine-learning approach supported by high-throughput experimentation.

International journal of pharmaceutics
A novel approach based on supervised machine-learning is proposed to predict the solubility of drugs and drug-like molecules in mixtures of organic solvents. Similar to quantitative structure-property relationship (QSPR) models, different solvent typ...

Modeling risk assessment of soil heavy metal pollution using partial least squares and fuzzy logic: A case study of a gully type coal-based solid waste dumpsite.

Environmental pollution (Barking, Essex : 1987)
Continuous release and migration of heavy metals from coal-based solid waste (CSW) dumpsites often results in significant encroachment on ecological lands and pollution of natural environments. As a result, there is an urgent need for long-term and r...

Modeling and Optimization of an Enhanced Soft Sensor for the Fermentation Process of .

Sensors (Basel, Switzerland)
This paper proposes a novel soft sensor modeling approach, MIC-TCA-INGO-LSSVM, to address the decline in performance of soft sensor models during the fermentation process of , caused by changes in working conditions. Initially, the transfer component...

Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques.

Journal of food science
Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible-near-infrared (Vis-NIR) and NIR) were utilized to acquire separate HSI data, which were t...

Bias-reduced neural networks for parameter estimation in quantitative MRI.

Magnetic resonance in medicine
PURPOSE: To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound.

Comparing visible and near infrared 'point' spectroscopy and hyperspectral imaging techniques to visualize the variability of apple firmness.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
In this work, visible and near-infrared 'point' (Vis-NIR) spectroscopy and hyperspectral imaging (Vis-NIR-HSI) techniques were applied on three different apple cultivars to compare their firmness prediction performances based on a large intra-variabi...

Machine learning-assisted chromium speciation using a single-well ratiometric fluorescent nanoprobe.

Chemosphere
Chromium is widely recognized as a significant pollutant discharged into the environment by various industrial activities. The toxicity of this element is dependent on its oxidation state, making speciation analysis crucial for monitoring the quality...

Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm.

PloS one
Spectral collinearity and limited spectral datasets are the problems influencing Chemical Oxygen Demand (COD) modeling. To address the first problem and obtain optimal modeling range, the spectra are preprocessed using six methods including Standard ...