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Spectroscopy, Near-Infrared

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NIR spectroscopy combined with 1D-convolutional neural network for breast cancerization analysis and diagnosis.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Near-infrared (NIR) spectroscopy with deep penetration can characterize the composition of biological tissue based on the vibration of the X-H group in a rapid and high-specificity way. Deep learning is proven helpful for rapid and automatic identifi...

Application of Deep-Learning Algorithm Driven Intelligent Raman Spectroscopy Methodology to Quality Control in the Manufacturing Process of Guanxinning Tablets.

Molecules (Basel, Switzerland)
Coupled with the convolutional neural network (CNN), an intelligent Raman spectroscopy methodology for rapid quantitative analysis of four pharmacodynamic substances and soluble solid in the manufacture process of Guanxinning tablets was established....

Prediction of the proximate analysis parameters of refuse-derived fuel based on deep learning approach.

Environmental science and pollution research international
Determination of proximate characteristics can be achieved using conventional analyses methods that require a certain amount of time. In cement factories, refuse-derived fuel (RDF) is continuously fed to a kiln by a conveyor belt, so even if an inapp...

A Machine Learning Perspective on fNIRS Signal Quality Control Approaches.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Despite a rise in the use of functional Near Infra-Red Spectroscopy (fNIRS) to study neural systems, fNIRS signal processing is not standardized and is highly affected by empirical and manual procedures. At the beginning of any signal processing proc...

Near infrared spectroscopy quantification based on Bi-LSTM and transfer learning for new scenarios.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
This study proposed a deep transfer learning methodology based on an improved Bi-directional Long Short-Term Memory (Bi-LSTM) network for the first time to address the near infrared spectroscopy (NIR) model transfer issue between samples. We tested i...

Linear or non-linear multivariate calibration models? That is the question.

Analytica chimica acta
Concepts from data science, machine learning, deep learning and artificial neural networks are spreading in many disciplines. The general idea is to exploit the power of statistical tools to interpret complex and, in many cases, non-linear data. Spec...

Detection of Water pH Using Visible Near-Infrared Spectroscopy and One-Dimensional Convolutional Neural Network.

Sensors (Basel, Switzerland)
pH is an important parameter for water quality detection. This study proposed a novel calibration regression strategy based on a one-dimensional convolutional neural network (1D-CNN) for water pH detection using visible near-infrared (Vis-NIR) spectr...

Relation between Cortical Activation and Effort during Robot-Mediated Walking in Healthy People: A Functional Near-Infrared Spectroscopy Neuroimaging Study (fNIRS).

Sensors (Basel, Switzerland)
Force and effort are important components of a motor task that can impact rehabilitation effectiveness. However, few studies have evaluated the impact of these factors on cortical activation during gait. The purpose of the study was to investigate th...

Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers.

Sensors (Basel, Switzerland)
Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diag...

A powerful tool for near-infrared spectroscopy: Synergy adaptive moving window algorithm based on the immune support vector machine.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Traditional trial-and-error methods are time-consuming and inefficient, especially very unfriendly to inexperienced analysts, and are sometimes still used to select preprocessing methods or wavelength variables in near-infrared spectroscopy (NIR). To...