Laser-induced breakdown spectroscopy and digital image data fusion for determination of the Al, Ca, Fe, Mg, and P in mineral fertilizer: overcome matrix effects in solid direct analysis

Diego Victor Babos; Diego Victor Babos; Jéssica Franciele Kaminski Ramos; Jéssica Franciele Kaminski Ramos; Gabriel Carlos Francisco; Vinícius de Melo Benites; Débora Marcondes Bastos Pereira Milori

doi:10.1364/JOSAB.482619

Journal of the Optical Society of America B
Vol. 40,
Issue 3,
pp. 654-660
(2023)
•https://doi.org/10.1364/JOSAB.482619

Laser-induced breakdown spectroscopy and digital image data fusion for determination of the Al, Ca, Fe, Mg, and P in mineral fertilizer: overcome matrix effects in solid direct analysis

Diego Victor Babos, Jéssica Franciele Kaminski Ramos, Gabriel Carlos Francisco, Vinícius de Melo Benites, and Débora Marcondes Bastos Pereira Milori

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Diego Victor Babos, Jéssica Franciele Kaminski Ramos, Gabriel Carlos Francisco, Vinícius de Melo Benites, and Débora Marcondes Bastos Pereira Milori, "Laser-induced breakdown spectroscopy and digital image data fusion for determination of the Al, Ca, Fe, Mg, and P in mineral fertilizer: overcome matrix effects in solid direct analysis," J. Opt. Soc. Am. B 40, 654-660 (2023)

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Abstract

Laser-induced breakdown spectroscopy (LIBS) and digital images were evaluated in the modeling for the prediction of Al, Ca, Fe, Mg, and P contents in mineral fertilizer samples. For modeling, univariate [matrix-matching calibration (MMC)] and multivariate [multiple linear regression (MLR) using only LIBS data, and data fusion (LIBS $+$ digital image)] calibration strategies were evaluated. The predictive capacity of the models was increased in the following order: ${\rm MMC} \lt {\rm MLR}$ (LIBS) $\lt$ data fusion. Compared with the MMC and MLR (LIBS data only), the root mean square error (data fusion) values were 17% to 80% lower, demonstrating the improvement in accuracy.

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Fertilizer	Code	Country	Origin	Al (%)	Ca (%)	Fe (%)	Mg (%)	P (%)
Bayovar	F1	Peru	Sedimentary rock	0.24	34.28	0.29	0.89	12.66
Khouribga	F2	Morocco	Sedimentary rock	0.38	31.70	0.27	0.69	12.04
Onk-Djebel	F3	Algeria	Sedimentary rock	0.24	34.28	0.27	0.97	12.57
Khouribga^a	F4	Morocco	Sedimentary rock	0.38	33.88	0.29	0.79	12.62
Bonito	F5	Brazil	Sedimentary rock	4.74	15.11	2.73	0.29	7.67
Pratápolis	F6	Brazil	Sedimentary rock	1.59	14.61	5.20	0.44	6.86
Arraias	F7	Brazil	Sedimentary rock	2.33	17.04	1.54	0.18	8.03
Registro	F8	Brazil	Igneous rock	1.77	21.80	7.18	0.10	10.89
Catalão	F9	Brazil	Igneous rock	0.59	30.02	5.12	0.49	14.07
Triple superphosphate	F10	Brazil	Soluble phosphate	0.20	13.97	0.70	0.61	20.24
Biodigester	F11	Brazil	Secondary P^b	n.d.^c	23.49	0.19	11.46	3.70
Coefficient of variation (%)				116	35	117	215	40

				MLR LIBS $+$ Digital Image (Data Fusion)^b	RMSE Improvement of the Data Fusion Approach
Analyte	Parameter	MMC LIBS	MLR LIBS	MLR LIBS $+$ Digital Image (Data Fusion)^b	MMC LIBS	MLR LIBS
Al	RMSE (%)	0.6	0.5	0.4	–17%	20%
Al	$R^{2}$	0.8489	0.8496	0.8768	–17%	20%
Ca	RMSE (%)	7	4	3	–57%	–25%
Ca	$R^{2}$	0.5420	0.7446	0.8398	–57%	–25%
Fe	RMSE (%)	0.4	0.2	0.1	–75%	–50%
Fe	$R^{2}$	0.9643	0.9787	0.9858	–75%	–50%
Mg	RMSE (%)	0.5	0.2	0.1	–80%	–50%
Mg	$R^{2}$	0.9762	0.9908	0.9955	–80%	–50%
P	RMSE (%)	2	1	0.7	–65%	–30%
P	$R^{2}$	0.7873	0.8420	0.9416	–65%	–30%

	Al (%)			Ca (%)			Fe (%)			Mg (%)			P (%)
Sample	Reference	LIBS	Trueness	Reference	LIBS	Trueness	Reference	LIBS	Trueness	Reference	LIBS	Trueness	Reference	LIBS	Trueness
F1	0.24	$0.27 \pm 0.11$	114	34.28	$32.8 \pm 0.9$	94	0.29	$0.54 \pm 0.03$	185	0.89	$0.97 \pm 0.14$	109	12.66	$12.1 \pm 0.3$	95
F2	0.38	$0.5 \pm 0.1$	131	31.70	$34.1 \pm 1.2$	108	0.27	$0.2 \pm 0.1$	74	0.69	$0.5 \pm 0.1$	73	12.04	$12.8 \pm 0.3$	106
F3	0.24	$0.03 \pm 0.13$	13	34.28	$35.0 \pm 0.5$	102	0.27	$0.2 \pm 0.1$	74	0.97	$0.9 \pm 0.1$	93	12.57	$11.8 \pm 0.5$	94
F4	0.38	$0.4 \pm 0.1$	105	33.88	$33.8 \pm 0.2$	100	0.29	$0.15 \pm 0.05$	52	0.79	$0.9 \pm 0.1$	114	12.62	$12.2 \pm 0.3$	97
F5	4.74	$3.7 \pm 0.1$	78	15.11	$15.7 \pm 1.4$	104	2.73	$2.7 \pm 0.3$	99	0.29	$0.2 \pm 0.2$	69	7.67	$8.3 \pm 0.8$	108
F6	1.59	$1.6 \pm 0.1$	101	14.61	$19.0 \pm 0.8$	130	5.20	$5.2 \pm 0.2$	100	0.44	$0.5 \pm 0.2$	114	6.86	$7.1 \pm 0.5$	104
F7	2.33	$3.0 \pm 0.1$	129	17.04	$18.4 \pm 2.3$	108	1.54	$1.7 \pm 0.1$	110	0.18	$0.3 \pm 0.1$	167	8.03	$7.4 \pm 0.5$	92
F8	1.77	$2.4 \pm 0.1$	135	21.80	$21.8 \pm 1.2$	100	7.18	$7.0 \pm 0.9$	98	0.10	$0.09 \pm 0.06$	90	10.89	$10.3 \pm 2.1$	95
F9	0.59	$0.4 \pm 0.1$	68	30.02	$23.1 \pm 1.0$	77	5.12	$5.2 \pm 0.2$	102	0.49	$0.3 \pm 0.2$	61	14.07	$13.8 \pm 0.9$	98
F10	0.20	$0.1 \pm 0.1$	50	13.97	$17.2 \pm 0.1$	123	0.70	$0.7 \pm 0.1$	100	0.61	$0.6 \pm 0.1$	98	20.24	$20.5 \pm 1.5$	101
F11	n.d.^a	−	−	23.49	$19.2 \pm 0.6$	82	0.19	$0.2 \pm 0.1$	105	11.46	$11.4 \pm 0.6$	99	3.70	$5.0 \pm 0.5$	135

Fertilizer	Code	Country	Origin	Al (%)	Ca (%)	Fe (%)	Mg (%)	P (%)
Bayovar	F1	Peru	Sedimentary rock	0.24	34.28	0.29	0.89	12.66
Khouribga	F2	Morocco	Sedimentary rock	0.38	31.70	0.27	0.69	12.04
Onk-Djebel	F3	Algeria	Sedimentary rock	0.24	34.28	0.27	0.97	12.57
Khouribga^a	F4	Morocco	Sedimentary rock	0.38	33.88	0.29	0.79	12.62
Bonito	F5	Brazil	Sedimentary rock	4.74	15.11	2.73	0.29	7.67
Pratápolis	F6	Brazil	Sedimentary rock	1.59	14.61	5.20	0.44	6.86
Arraias	F7	Brazil	Sedimentary rock	2.33	17.04	1.54	0.18	8.03
Registro	F8	Brazil	Igneous rock	1.77	21.80	7.18	0.10	10.89
Catalão	F9	Brazil	Igneous rock	0.59	30.02	5.12	0.49	14.07
Triple superphosphate	F10	Brazil	Soluble phosphate	0.20	13.97	0.70	0.61	20.24
Biodigester	F11	Brazil	Secondary P^b	n.d.^c	23.49	0.19	11.46	3.70
Coefficient of variation (%)				116	35	117	215	40

				MLR LIBS $+$ Digital Image (Data Fusion)^b	RMSE Improvement of the Data Fusion Approach
Analyte	Parameter	MMC LIBS	MLR LIBS	MLR LIBS $+$ Digital Image (Data Fusion)^b	MMC LIBS	MLR LIBS
Al	RMSE (%)	0.6	0.5	0.4	–17%	20%
Al	$R^{2}$	0.8489	0.8496	0.8768	–17%	20%
Ca	RMSE (%)	7	4	3	–57%	–25%
Ca	$R^{2}$	0.5420	0.7446	0.8398	–57%	–25%
Fe	RMSE (%)	0.4	0.2	0.1	–75%	–50%
Fe	$R^{2}$	0.9643	0.9787	0.9858	–75%	–50%
Mg	RMSE (%)	0.5	0.2	0.1	–80%	–50%
Mg	$R^{2}$	0.9762	0.9908	0.9955	–80%	–50%
P	RMSE (%)	2	1	0.7	–65%	–30%
P	$R^{2}$	0.7873	0.8420	0.9416	–65%	–30%

Abstract

Data availability

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Journal of the Optical Society of America B