Quantitative analysis of chromium in potatoes by laser-induced breakdown spectroscopy coupled with linear multivariate calibration

Tianbing Chen; Lin Huang; Mingyin Yao; Huiqin Hu; Caihong Wang; Muhua Liu

doi:10.1364/AO.54.007807

Applied Optics
Vol. 54,
Issue 25,
pp. 7807-7812
(2015)
•https://doi.org/10.1364/AO.54.007807

Quantitative analysis of chromium in potatoes by laser-induced breakdown spectroscopy coupled with linear multivariate calibration

Tianbing Chen, Lin Huang, Mingyin Yao, Huiqin Hu, Caihong Wang, and Muhua Liu

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Tianbing Chen, Lin Huang, Mingyin Yao, Huiqin Hu, Caihong Wang, and Muhua Liu, "Quantitative analysis of chromium in potatoes by laser-induced breakdown spectroscopy coupled with linear multivariate calibration," Appl. Opt. 54, 7807-7812 (2015)

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Abstract

Laser-induced breakdown spectroscopy (LIBS) coupled with the linear multivariate regression method was utilized to analyze chromium (Cr) quantitatively in potatoes. The plasma was generated using a Nd:YAG laser, and the spectra were acquired by an Andor spectrometer integrated with an ICCD detector. The models between intensity of LIBS characteristic line(s) and concentration of Cr were constructed to predict quantitatively the content of target. The unary, binary, ternary, and quaternary variables were chosen for verifying the accuracy of linear regression calibration curves. The intensity of characteristic lines Cr (CrI: 425.43, 427.48, 428.97 nm) and Ca (CaI: 422.67, 428.30, 430.25, 430.77, 431.86 nm) were used as input data for the multivariate calculations. According to the results of linear regression, the model of quaternary linear regression was established better in comparing with the other three models. A good agreement was observed between the actual content provided by atomic absorption spectrometry and the predicted value obtained by the quaternary linear regression model. And the relative error was below 5.5% for validation samples S1 and S2. The result showed that the multivariate approach can obtain better predicted accuracy than the univariate ones. The result also suggested that the LIBS technique coupled with the linear multivariate calibration method could be a great tool to predict heavy metals in farm products in a rapid manner even though samples have similar elemental compositions.

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$λ / nm$	$A_{k i} / 10^{8} s^{- 1}$	$E_{i} - E_{k} / eV$	Terms	$g_{i} - g_{k}$
CaI 422.67	2.18	0–2.9325	$3 p^{6} 4 s^{2} - 3 p^{6} 4 s 4 p$	1–3
CrI 425.43	$3.15 \times 10^{- 1}$	0–2.9134	$3 d^{5} ({}^{6}S) 4 s - 3 d^{5} ({}^{6}S) 4 p$	7–9
CrI 427.48	$3.07 \times 10^{- 1}$	0–2.8995	$3 d^{5} ({}^{6}S) 4 s - 3 d^{5} ({}^{6}S) 4 p$	7–7
CaI 428.30	$4.34 \times 10^{- 1}$	1.8858–4.7797	$3 p^{6} 4 s 4 p - 3 p^{6} 4 s^{2}$	3–5
CrI 428.97	$3.16 \times 10^{- 1}$	0–2.8894	$3 d^{5} ({}^{6}S) 4 s - 3 d^{5} ({}^{6}S) 4 p$	7–5
CaI 430.25	1.36	1.8858–4.7631	$3 p^{6} 4 s 4 p - 3 p^{6} 4 s^{2}$	5–5
CaI 430.77	1.99	1.8858–4.7631	$3 p^{6} 4 s 4 p - 3 p^{6} 4 s^{2}$	3–1
CaI 431.86	$7.4 \times 10^{- 1}$	1.8989–4.7690	$3 p^{6} 4 s 4 p - 3 p^{6} 4 s^{2}$	5–3

Calibrate Method	Input Variables	Correlation Coefficient ( $R^{2}$ )	Predicted Value ( $μg / g$ )	Relative Error (%)
Unary	$I_{Cr}$	0.007	S1:138.208	7.674
Unary	$I_{Cr}$	0.007	S2:140.308	7.771
Binary	$I_{Cr}$ , $I_{\sum Cr}$	0.887	S1:143.649	11.913
Binary	$I_{Cr}$ , $I_{\sum Cr}$	0.887	S2:132.129	13.147
Ternary	$I_{Cr}$ , $I_{\sum Cr}$ , $I_{Ca}$	0.890	S1:142.413	10.950
Ternary	$I_{Cr}$ , $I_{\sum Cr}$ , $I_{Ca}$	0.890	S2:132.687	12.780
Quaternary	$I_{Cr}$ , $I_{\sum Cr}$ , $I_{Ca}$ , $I_{Σ C a}$	0.987	S1:133.659	4.130
Quaternary	$I_{Cr}$ , $I_{\sum Cr}$ , $I_{Ca}$ , $I_{Σ C a}$	0.987	S2:159.965	5.151

Variables	Coefficients	$t$ Value	$t$ -test Sig.
Intercept	205.3757	10.4382	0.0019
$I_{Cr}$	0.3358	11.3662	0.0015
$I_{\sum Cr}$	−0.1069	−11.3911	0.0015
$I_{Ca}$	0.3471	4.9468	0.0158
$I_{\sum Ca}$	−0.0229	−5.1722	0.0140

$λ / nm$	$A_{k i} / 10^{8} s^{- 1}$	$E_{i} - E_{k} / eV$	Terms	$g_{i} - g_{k}$
CaI 422.67	2.18	0–2.9325	$3 p^{6} 4 s^{2} - 3 p^{6} 4 s 4 p$	1–3
CrI 425.43	$3.15 \times 10^{- 1}$	0–2.9134	$3 d^{5} ({}^{6}S) 4 s - 3 d^{5} ({}^{6}S) 4 p$	7–9
CrI 427.48	$3.07 \times 10^{- 1}$	0–2.8995	$3 d^{5} ({}^{6}S) 4 s - 3 d^{5} ({}^{6}S) 4 p$	7–7
CaI 428.30	$4.34 \times 10^{- 1}$	1.8858–4.7797	$3 p^{6} 4 s 4 p - 3 p^{6} 4 s^{2}$	3–5
CrI 428.97	$3.16 \times 10^{- 1}$	0–2.8894	$3 d^{5} ({}^{6}S) 4 s - 3 d^{5} ({}^{6}S) 4 p$	7–5
CaI 430.25	1.36	1.8858–4.7631	$3 p^{6} 4 s 4 p - 3 p^{6} 4 s^{2}$	5–5
CaI 430.77	1.99	1.8858–4.7631	$3 p^{6} 4 s 4 p - 3 p^{6} 4 s^{2}$	3–1
CaI 431.86	$7.4 \times 10^{- 1}$	1.8989–4.7690	$3 p^{6} 4 s 4 p - 3 p^{6} 4 s^{2}$	5–3

Calibrate Method	Input Variables	Correlation Coefficient ( $R^{2}$ )	Predicted Value ( $μg / g$ )	Relative Error (%)
Unary	$I_{Cr}$	0.007	S1:138.208	7.674
Unary	$I_{Cr}$	0.007	S2:140.308	7.771
Binary	$I_{Cr}$ , $I_{\sum Cr}$	0.887	S1:143.649	11.913
Binary	$I_{Cr}$ , $I_{\sum Cr}$	0.887	S2:132.129	13.147
Ternary	$I_{Cr}$ , $I_{\sum Cr}$ , $I_{Ca}$	0.890	S1:142.413	10.950
Ternary	$I_{Cr}$ , $I_{\sum Cr}$ , $I_{Ca}$	0.890	S2:132.687	12.780
Quaternary	$I_{Cr}$ , $I_{\sum Cr}$ , $I_{Ca}$ , $I_{Σ C a}$	0.987	S1:133.659	4.130
Quaternary	$I_{Cr}$ , $I_{\sum Cr}$ , $I_{Ca}$ , $I_{Σ C a}$	0.987	S2:159.965	5.151

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Applied Optics