Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms

Claudia Gonzalez Viejo; Sigfredo Fuentes; Damir Torrico; Kate Howell; Frank R Dunshea

doi:10.1002/jsfa.8506

Assessment of beer quality based on foamability and chemical composition using computer vision algorithms, near infrared spectroscopy and machine learning algorithms

Journal of the Science of Food and Agriculture ◽

10.1002/jsfa.8506 ◽

2017 ◽

Vol 98 (2) ◽

pp. 618-627 ◽

Cited By ~ 26

Author(s):

Claudia Gonzalez Viejo ◽

Sigfredo Fuentes ◽

Damir Torrico ◽

Kate Howell ◽

Frank R Dunshea

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Infrared Spectroscopy ◽

Chemical Composition ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Learning Algorithms ◽

Machine Learning Algorithms

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Rapid discrimination of coal geographical origin via near-infrared spectroscopy combined with machine learning algorithms

Infrared Physics & Technology ◽

10.1016/j.infrared.2019.103180 ◽

2020 ◽

Vol 105 ◽

pp. 103180 ◽

Cited By ~ 1

Author(s):

Xinhui Yu ◽

Weidong Guo ◽

Nan Wu ◽

Liang Zou ◽

Meng Lei

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Geographical Origin ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Rapid Discrimination

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A comparative study between a new method and other machine learning algorithms for soil organic carbon and total nitrogen prediction using near infrared spectroscopy

Chemometrics and Intelligent Laboratory Systems ◽

10.1016/j.chemolab.2019.103873 ◽

2019 ◽

Vol 195 ◽

pp. 103873 ◽

Cited By ~ 7

Author(s):

Rabie Reda ◽

Taoufiq Saffaj ◽

Bouzida Ilham ◽

Ouadi Saidi ◽

Kadmiri Issam ◽

...

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Comparative Study ◽

Total Nitrogen ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Learning Algorithms ◽

Machine Learning Algorithms

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Machine learning algorithms for estimating powder blend composition using near infrared spectroscopy

2018 2nd International Symposium on Small-scale Intelligent Manufacturing Systems (SIMS) ◽

10.1109/sims.2018.8355297 ◽

2018 ◽

Cited By ~ 1

Author(s):

Niall O'Mahony ◽

Trevor Murphy ◽

Krishna Panduru ◽

Daniel Riordan ◽

Joseph Walsh

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Powder Blend ◽

Blend Composition

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Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data

Journal of Psychiatric Research ◽

10.1016/j.jpsychires.2022.01.026 ◽

2022 ◽

Author(s):

Cyrus SH. Ho ◽

Y.L. Chan ◽

Trevor WK. Tan ◽

Gabrielle WN. Tay ◽

T.B. Tang

Keyword(s):

Machine Learning ◽

Major Depressive Disorder ◽

Infrared Spectroscopy ◽

Depressive Disorder ◽

Diagnostic Accuracy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Machine Learning Algorithms ◽

Spectroscopy Data ◽

Major Depressive

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Improving the accuracy of soil organic carbon content prediction based on visible and near-infrared spectroscopy and machine learning

Environmental Earth Sciences ◽

10.1007/s12665-021-09582-x ◽

2021 ◽

Vol 80 (8) ◽

Author(s):

Mingxing Xu ◽

Xianyao Chu ◽

Yesi Fu ◽

Changjiang Wang ◽

Shaohua Wu

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Carbon Content ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Organic Carbon Content ◽

Soil Organic Carbon Content

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In Situ Measurement of Soil Chemical Composition by Near-Infrared Spectroscopy: A Tool Toward Sustainable Vineyard Management

Communications in Soil Science and Plant Analysis ◽

10.1080/00103624.2013.768263 ◽

2013 ◽

Vol 44 (10) ◽

pp. 1610-1619 ◽

Cited By ~ 18

Author(s):

D. Cozzolino ◽

W. U. Cynkar ◽

R. G. Dambergs ◽

N. Shah ◽

P. Smith

Keyword(s):

Infrared Spectroscopy ◽

Chemical Composition ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

In Situ Measurement ◽

Vineyard Management

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Predicting soil phosphorus and studying the effect of texture on the prediction accuracy using machine learning combined with near-infrared spectroscopy

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2020.118736 ◽

2020 ◽

Vol 242 ◽

pp. 118736

Author(s):

Rabie Reda ◽

Taoufiq Saffaj ◽

Salah Eddine Itqiq ◽

Ilham Bouzida ◽

Ouadi Saidi ◽

...

Keyword(s):

Machine Learning ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Prediction Accuracy ◽

Near Infrared ◽

Soil Phosphorus

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Non-destructive detection of foreign contaminants in toast bread with near infrared spectroscopy and computer vision techniques

Journal of Food Measurement & Characterization ◽

10.1007/s11694-020-00627-6 ◽

2020 ◽

Author(s):

Jifan Yin ◽

Saima Hameed ◽

Lijuan Xie ◽

Yibin Ying

Keyword(s):

Computer Vision ◽

Infrared Spectroscopy ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Non Destructive

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Computer Vision Technologies and Machine Learning Algorithms for Construction Safety Management: A Critical Review

ICCREM 2019 ◽

10.1061/9780784482308.008 ◽

2019 ◽

Author(s):

Yongyue Liu ◽

Yaowu Wang ◽

Xiaodong Li

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Critical Review ◽

Safety Management ◽

Learning Algorithms ◽

Construction Safety ◽

Machine Learning Algorithms ◽

Construction Safety Management

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Prediction of the chemical composition and nutritive value of lucerne (Medicago sativa L.) by Near Infrared Spectroscopy

Italian Journal of Animal Science ◽

10.4081/ijas.2005.2s.141 ◽

2005 ◽

Vol 4 (sup2) ◽

pp. 141-143

Author(s):

S. Colombini ◽

M. Confalonieri ◽

G. Borreani ◽

E. Tabacco ◽

P.G. Peiretti ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Chemical Composition ◽

Medicago Sativa ◽

Near Infrared Spectroscopy ◽

Nutritive Value ◽

Near Infrared ◽

Medicago Sativa L

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