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Spectrometry, Fluorescence

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Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy.

Scientific reports
Pollen monitoring have become data-intensive in recent years as real-time detectors are deployed to classify airborne pollen grains. Machine learning models with a focus on deep learning, have an essential role in the pollen classification task. With...

Front-face excitation-emission matrix fluorescence spectroscopy combined with interpretable deep learning for the rapid identification of the storage year of Ningxia wolfberry.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Ningxia wolfberry stored for many years may be disguised as fresh wolfberry by unscrupulous traders and sold for huge profits. In this work, the front-face excitation-emission matrix (FF-EEM) fluorescence spectroscopy coupled with interpretable deep ...

Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence.

Sensors (Basel, Switzerland)
This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectan...

Rapid detection of cholecystitis by serum fluorescence spectroscopy combined with machine learning.

Journal of biophotonics
While cholecystitis is a critical public health problem, the conventional diagnostic methods for its detection are time consuming, expensive and insufficiently sensitive. This study examined the possibility of using serum fluorescence spectroscopy an...

Dissolved organic matter evolution and straw decomposition rate characterization under different water and fertilizer conditions based on three-dimensional fluorescence spectrum and deep learning.

Journal of environmental management
Straw returning is a sustainable way to utilize agricultural solid waste resources. However, incomplete decomposition of straw will cause harm to crop growth and soil quality. Currently, there is a lack of technology to timely monitor the rate of str...

Non-invasive Characterization of Glycosuria and Identification of Biomarkers in Diabetic Urine Using Fluorescence Spectroscopy and Machine Learning Algorithm.

Journal of fluorescence
The current study presents a steadfast, simple, and efficient approach for the non-invasive determination of glycosuria of diabetes mellitus using fluorescence spectroscopy. A Xenon arc lamp emitting light in the range of 200-950 nm was used as an ex...

Deep Learning-Assisted Intelligent Artificial Vision Platform Based on Dual-Luminescence Eu(III)-Functionalized HOF for the Diagnosis of Breast and Ovarian Cancer.

Analytical chemistry
Developing an advanced analytical method to detect spermine (Spm) and -acetylneuraminic acid (NANA), the biomarkers of breast and ovarian cancers, respectively, is critical for the early diagnosis of the two cancers, which is very meaningful for wome...

Deep learning reduces data requirements and allows real-time measurements in imaging FCS.

Biophysical journal
Imaging fluorescence correlation spectroscopy (FCS) is a powerful tool to extract information on molecular mobilities, actions, and interactions in live cells, tissues, and organisms. Nevertheless, several limitations restrict its applicability. Firs...

Multiple marine algae identification based on three-dimensional fluorescence spectroscopy and multi-label convolutional neural network.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Accurate identification of algal populations plays a pivotal role in monitoring seawater quality. Fluorescence-based techniques are effective tools for quickly identifying different algae. However, multiple coexisting algae and their similar photosyn...

Neural network informed photon filtering reduces fluorescence correlation spectroscopy artifacts.

Biophysical journal
Fluorescence correlation spectroscopy (FCS) techniques are well-established tools to investigate molecular dynamics in confocal and super-resolution microscopy. In practice, users often need to handle a variety of sample- or hardware-related artifact...