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 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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM).
Quansheng Chen;Jiewen Zhao;C.H. Fang;Dongmei Wang.
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy (2007)
Determination of total polyphenols content in green tea using FT-NIR spectroscopy and different PLS algorithms.
Quansheng Chen;Jiewen Zhao;Muhua Liu;Jianrong Cai.
Journal of Pharmaceutical and Biomedical Analysis (2008)
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
Jianrong Cai;Quansheng Chen;Xinmin Wan;Jiewen Zhao.
Food Chemistry (2011)
Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques.
Lin Huang;Jiewen Zhao;Quansheng Chen;Yanhua Zhang.
Food Chemistry (2014)
Recent advances in emerging imaging techniques for non-destructive detection of food quality and safety
Quansheng Chen;Chaojie Zhang;Jiewen Zhao;Qin Ouyang.
Trends in Analytical Chemistry (2013)
Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration.
Quansheng Chen;Jiewen Zhao;Haidong Zhang;Xinyu Wang.
Analytica Chimica Acta (2006)
Nondestructive detection of total volatile basic nitrogen (TVB-N) content in pork meat by integrating hyperspectral imaging and colorimetric sensor combined with a nonlinear data fusion
Huanhuan Li;Quansheng Chen;Jiewen Zhao;Mengzi Wu.
Lwt - Food Science and Technology (2015)
Study on discrimination of Roast green tea (Camellia sinensis L.) according to geographical origin by FT-NIR spectroscopy and supervised pattern recognition.
Quansheng Chen;Jiewen Zhao;Hao Lin.
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy (2009)
Identification of the green tea grade level using electronic tongue and pattern recognition
Quansheng Chen;Jiewen Zhao;Saritporn Vittayapadung.
Food Research International (2008)
Simultaneous determination of total polyphenols and caffeine contents of green tea by near-infrared reflectance spectroscopy
Quansheng Chen;Jiewen Zhao;Xingyi Huang;Haidong Zhang.
Microchemical Journal (2006)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: