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Partial least squares regression calculation for quantitative analysis of metals submerged in water measured using laser-induced breakdown spectroscopy

Partial least squares regression calculation for quantitative analysis of metals submerged in water measured using laser-induced breakdown spectroscopy
Partial least squares regression calculation for quantitative analysis of metals submerged in water measured using laser-induced breakdown spectroscopy
Effects of different parameters regarding partial least squares (PLS) regression analysis are investigated for quantitative analysis of water-submerged brass samples. The concentrations of Cu and Zn in various brass alloys were quantified using PLS and the performances after different signal processing steps (normalisation, smoothing and background subtraction) and database segmentation by excitation temperature are compared. In addition, the effects of averaging numbers on the results are examined. From the results, normalisation was found to be the most effective among three established signal processing methods. The effects of both peak and background fluctuations seen in the signals are reduced by normalisation. It was found that temperature segmentation of database in an appropriate range, which should be high enough for reliable peak detection, can further improve the accuracy of PLS calculations. The proposed method is applicable in real time, and can potentially be used for automated fast and accurate measurements of solids at oceanic pressures.
0003-6935
5872-5883
Takahashi, Takahashi
db1c63d1-c951-4e99-91f7-6ca94248dd88
Thornton, Blair
8293beb5-c083-47e3-b5f0-d9c3cee14be9
Sato, Takumi
839ad99c-91f6-4f88-9d5d-d39022eb990b
Ohki, Toshihiko
e8962f22-9e79-46b8-8b97-8a99c3dc47d4
Ohki, Koichi
9ab2be8f-8868-431e-b15f-92ada71fa9eb
Sakka, Tetsuo
0b65b678-002f-4a00-933b-ffc0a974f46a
Takahashi, Takahashi
db1c63d1-c951-4e99-91f7-6ca94248dd88
Thornton, Blair
8293beb5-c083-47e3-b5f0-d9c3cee14be9
Sato, Takumi
839ad99c-91f6-4f88-9d5d-d39022eb990b
Ohki, Toshihiko
e8962f22-9e79-46b8-8b97-8a99c3dc47d4
Ohki, Koichi
9ab2be8f-8868-431e-b15f-92ada71fa9eb
Sakka, Tetsuo
0b65b678-002f-4a00-933b-ffc0a974f46a

Takahashi, Takahashi, Thornton, Blair, Sato, Takumi, Ohki, Toshihiko, Ohki, Koichi and Sakka, Tetsuo (2018) Partial least squares regression calculation for quantitative analysis of metals submerged in water measured using laser-induced breakdown spectroscopy. Applied Optics, 57 (20), 5872-5883. (doi:10.1364/AO.57.005872).

Record type: Letter

Abstract

Effects of different parameters regarding partial least squares (PLS) regression analysis are investigated for quantitative analysis of water-submerged brass samples. The concentrations of Cu and Zn in various brass alloys were quantified using PLS and the performances after different signal processing steps (normalisation, smoothing and background subtraction) and database segmentation by excitation temperature are compared. In addition, the effects of averaging numbers on the results are examined. From the results, normalisation was found to be the most effective among three established signal processing methods. The effects of both peak and background fluctuations seen in the signals are reduced by normalisation. It was found that temperature segmentation of database in an appropriate range, which should be high enough for reliable peak detection, can further improve the accuracy of PLS calculations. The proposed method is applicable in real time, and can potentially be used for automated fast and accurate measurements of solids at oceanic pressures.

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More information

Accepted/In Press date: 10 June 2018
e-pub ahead of print date: 11 June 2018

Identifiers

Local EPrints ID: 421778
URI: http://eprints.soton.ac.uk/id/eprint/421778
ISSN: 0003-6935
PURE UUID: 1dcadfaa-b33b-407c-b113-1de86854fb12

Catalogue record

Date deposited: 27 Jun 2018 16:30
Last modified: 09 Aug 2018 16:30

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