Parallelized N2O Frontal Chromatography for the Fast Determination of Copper Surface Areas

Stefan Olejnik; Christian Baltes; Martin Muhler; Ferdi Schüth

doi:10.1021/cc700144y

Parallelized N2O Frontal Chromatography for the Fast Determination of Copper Surface Areas

Journal of Combinatorial Chemistry ◽

10.1021/cc700144y ◽

2008 ◽

Vol 10 (3) ◽

pp. 387-390 ◽

Cited By ~ 2

Author(s):

Stefan Olejnik ◽

Christian Baltes ◽

Martin Muhler ◽

Ferdi Schüth

Keyword(s):

Copper Surface ◽

Fast Determination ◽

Surface Areas ◽

Determination Of Copper ◽

Frontal Chromatography

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Reaction of nitrous oxide with copper surfaces. Application to the determination of free-copper surface areas

Transactions of the Faraday Society ◽

10.1039/tf9696502465 ◽

1969 ◽

Vol 65 ◽

pp. 2465 ◽

Cited By ~ 118

Author(s):

J. J. F. Scholten ◽

J. A. Konvalinka

Keyword(s):

Nitrous Oxide ◽

Copper Surface ◽

Copper Surfaces ◽

Surface Areas

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Fast Determination of Copper Content in Tobacco (Nicotina tabacum L.) Leaves Using Laser-Induced Breakdown Spectroscopy with Univariate and Multivariate Analysis

Transactions of the ASABE ◽

10.13031/trans.12393 ◽

2018 ◽

Vol 61 (3) ◽

pp. 821-829 ◽

Cited By ~ 3

Author(s):

Jiyu Peng ◽

Lanhan Ye ◽

Tingting Shen ◽

Fei Liu ◽

Kunlin Song ◽

...

Keyword(s):

Response Surface Methodology ◽

Multivariate Analysis ◽

Copper Content ◽

Laser Induced Breakdown Spectroscopy ◽

Fast Determination ◽

Tobacco Leaves ◽

Breakdown Spectroscopy ◽

Laser Induced Breakdown ◽

Determination Of Copper

Abstract. Fast and effective measures to determine heavy metals play an important role in ensuring food quality and safety. In this experiment, laser-induced breakdown spectroscopy (LIBS) was used to detect copper content (Cu) in tobacco ( L.) leaves. The experimental parameters for detection, including laser energy, delay time, and camera gate width, were optimized by response surface methodology (RSM). Univariate analysis and multivariate analysis, including partial least squares regression (PLSR) and extreme learning machine (ELM), were used to establish calibration models. In addition, different preprocessing methods were used to eliminate the signal variations and further improve the calibration performance, including baseline correction, background normalization, area normalization, and standard normal variate (SNV) normalization. The results showed that LIBS combined with both univariate and multivariate methods could be used to detect copper content in tobacco leaves. SNV and area normalization were efficient in dealing with signal variations and improving the calibration performance. The ELM model with SNV normalized variables in the spectral region of 324.02 to 325.98 nm achieved the best performance (R2 = 0.9552 and RMSE = 4.8416 mg kg-1 in the testing set). The results provide the first proof-of-principle data for fast determination of copper content in tobacco leaves. Keywords: Copper content, Laser-induced breakdown spectroscopy, Multivariate calibration, Response surface methodology, Tobacco leaves, Univariate calibration.

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