What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Oxygen
- Statistics
His primary areas of study are Near-infrared spectroscopy, Analytical chemistry, Principal component analysis, Artificial intelligence and Mean squared error.
The various areas that Quansheng Chen examines in his Near-infrared spectroscopy study include Nonlinear regression and Calibration.
His Analytical chemistry study combines topics in areas such as Acetamiprid, Random coil, Nanoparticle, Colloidal gold and Nanosensor.
His Principal component analysis research is multidisciplinary, incorporating elements of Hyperspectral imaging and Sensor fusion.
He focuses mostly in the field of Artificial intelligence, narrowing it down to matters related to Pattern recognition and, in some cases, Green tea.
As part of one scientific family, Quansheng Chen deals mainly with the area of Mean squared error, narrowing it down to issues related to the Correlation coefficient, and often Partial least squares regression, Calibration and Multivariate statistics.
His most cited work include:
- Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM). (192 citations)
- Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms. (191 citations)
- Determination of total volatile basic nitrogen (TVB-N) content and Warner–Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy (171 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Analytical chemistry, Artificial intelligence, Near-infrared spectroscopy, Pattern recognition and Mean squared error.
When carried out as part of a general Analytical chemistry research project, his work on Raman spectroscopy, Photon upconversion and Linear range is frequently linked to work in Content, therefore connecting diverse disciplines of study.
His Near-infrared spectroscopy study combines topics from a wide range of disciplines, such as Calibration and Partial least squares regression.
His Pattern recognition research incorporates elements of Artificial neural network, Sensor array, Electronic nose and Green tea.
The Mean squared error study combines topics in areas such as Correlation coefficient, Coefficient of determination, Biological system, Regression analysis and Calibration.
His Principal component analysis research focuses on Pattern recognition and how it connects with Identification.
He most often published in these fields:
- Analytical chemistry (26.94%)
- Artificial intelligence (23.25%)
- Near-infrared spectroscopy (20.66%)
What were the highlights of his more recent work (between 2019-2021)?
- Detection limit (18.45%)
- Fluorescence (15.87%)
- Chromatography (15.87%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Detection limit, Fluorescence, Chromatography, Aptamer and Raman spectroscopy.
He has included themes like Photochemistry and Photon upconversion in his Fluorescence study.
His Raman spectroscopy study is concerned with the field of Analytical chemistry as a whole.
In the subject of general Analytical chemistry, his work in Partial least squares regression, Chemometrics and Linear range is often linked to Mercury, thereby combining diverse domains of study.
His Partial least squares regression research is multidisciplinary, incorporating perspectives in Mean squared error, Visible near infrared, Linear discriminant analysis and Correlation coefficient.
The concepts of his Mean squared error study are interwoven with issues in Near-infrared spectroscopy and Feature, Artificial intelligence.
Between 2019 and 2021, his most popular works were:
- Mesoporous silica supported orderly-spaced gold nanoparticles SERS-based sensor for pesticides detection in food. (44 citations)
- Signal-enhanced SERS-sensors of CAR-PLS and GA-PLS coupled AgNPs for ochratoxin A and aflatoxin B1 detection. (40 citations)
- Designing an aptamer based magnetic and upconversion nanoparticles conjugated fluorescence sensor for screening Escherichia coli in food (33 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Oxygen
Detection limit, Analytical chemistry, Correlation coefficient, Fluorescence and Aptamer are his primary areas of study.
His primary area of study in Analytical chemistry is in the field of Raman spectroscopy.
His Correlation coefficient study integrates concerns from other disciplines, such as Mean squared error, Residual, Artificial intelligence, Aroma and Pattern recognition.
His study looks at the relationship between Pattern recognition and fields such as Sensor fusion, as well as how they intersect with chemical problems.
His study in the fields of Förster resonance energy transfer under the domain of Fluorescence overlaps with other disciplines such as Conjugate.
The study incorporates disciplines such as Biological system, Partial least squares regression and Near-infrared spectroscopy in addition to Transmittance.
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