Table VIII. Gas Chromatographic Data Tabulated on Basis of Serial Number

Automotive fuels. Determination of iodine value in fatty acid methyl esters (FAME). Calculation method from gas chromatographic data

10.3403/30245756u ◽

2015 ◽

Keyword(s):

Fatty Acid ◽

Iodine Value ◽

Calculation Method ◽

Fatty Acid Methyl Esters ◽

Methyl Esters ◽

Chromatographic Data ◽

Acid Methyl ◽

Automotive Fuels

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The Comparison of Deep Learning Driven Optical Character Recognition for Hard Disk Head Slider Serial Number

2020 International Conference on Power, Energy and Innovations (ICPEI) ◽

10.1109/icpei49860.2020.9431431 ◽

2020 ◽

Author(s):

Palakorn Imsamer ◽

Vorachat Boonyaphon ◽

Somporn Tiacharoen

Keyword(s):

Deep Learning ◽

Character Recognition ◽

Optical Character Recognition ◽

Hard Disk ◽

Head Slider ◽

Optical Character ◽

Serial Number

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Quality evaluation of fingerprints of herbal medicine with chromatographic data

Analytica Chimica Acta ◽

10.1016/j.aca.2004.03.041 ◽

2004 ◽

Vol 514 (1) ◽

pp. 69-77 ◽

Cited By ~ 58

Author(s):

Bo-Yan Li ◽

Yun Hu ◽

Yi-Zeng Liang ◽

Pei-Shan Xie ◽

Yi-Ping Du

Keyword(s):

Herbal Medicine ◽

Quality Evaluation ◽

Chromatographic Data

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Automated chiral method screening – Evaluation of generated chromatographic data sets to further optimize screening efficiency

Journal of Chromatography A ◽

10.1016/j.chroma.2021.462094 ◽

2021 ◽

Vol 1644 ◽

pp. 462094

Author(s):

Ernst Freund ◽

Daniel Meyer ◽

Nadine Schneider ◽

Marie-Anne Lozac'h ◽

Harald Schröder ◽

...

Keyword(s):

Data Sets ◽

Chromatographic Data ◽

Screening Efficiency

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Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework

Journal of Intelligent Manufacturing ◽

10.1007/s10845-020-01730-7 ◽

2021 ◽

Author(s):

Feiyang Li ◽

Nian Cai ◽

Xueliang Deng ◽

Jiahao Li ◽

Jianfa Lin ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Ceramic Membranes ◽

Serial Number ◽

End To End

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Chromatographic Data

Journal of Chromatography A ◽

10.1016/s0021-9673(01)85421-2 ◽

1974 ◽

Vol 93 (2) ◽

pp. D43-D46

Keyword(s):

Chromatographic Data

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Prediction of Activity Coefficients in Low-Molecular-Weight Paraffins from Gas Chromatographic Data

Analytical Chemistry ◽

10.1021/ac981255m ◽

1999 ◽

Vol 71 (16) ◽

pp. 3503-3512 ◽

Cited By ~ 5

Author(s):

András Dallos ◽

Ervin sz. Kováts

Keyword(s):

Molecular Weight ◽

Activity Coefficients ◽

Low Molecular Weight ◽

Chromatographic Data

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Artificial neural network model for photosynthetic pigments identification using multi wavelength chromatographic data

10.1063/1.4945074 ◽

2016 ◽

Author(s):

K. R. Prilianti ◽

S. Hariyanto ◽

F. D. D. Natali ◽

Indriatmoko ◽

M. A. S. Adhiwibawa ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Model ◽

Photosynthetic Pigments ◽

Neural Network Model ◽

Artificial Neural Network Model ◽

Chromatographic Data ◽

Multi Wavelength ◽

Artificial Neural

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Chromatographic Data (1972)

Journal of Chromatography A ◽

10.1016/s0021-9673(01)86176-8 ◽

1972 ◽

Vol 74 (2) ◽

pp. D143-D152

Keyword(s):

Chromatographic Data

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Multivariate analysis of variance of designed chromatographic data. A case study involving fermentation of rooibos tea

Journal of Chromatography A ◽

10.1016/j.chroma.2017.02.007 ◽

2017 ◽

Vol 1489 ◽

pp. 115-125 ◽

Cited By ~ 6

Author(s):

Federico Marini ◽

Dalene de Beer ◽

Nico A. Walters ◽

André de Villiers ◽

Elizabeth Joubert ◽

...

Keyword(s):

Multivariate Analysis ◽

Analysis Of Variance ◽

Multivariate Analysis Of Variance ◽

Chromatographic Data ◽

Rooibos Tea

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