Journal Analytical Methods

The basicity analysis of sintered ore using laserinduced breakdown spectroscopy (LIBS) combined with random forest regression (RFR)

Author: Yang, G. ; et. al.

Journal: Analytical Methods (2017)

Applications Chemometrics

DOI: 10.1039/c7ay01389b

Authors
Ding, Y. ; Han, X. ; Liu, K. ; Tian, D. ; Wang, C. H. ; Yao, L.

Keywords

SUPPORT VECTOR MACHINES; ELEMENTAL ANALYSIS; IRON-ORES; CLASSIFICATION; SPECTROMETRY; SAMPLES; PLASMA; INDUSTRY; ALLOYS; MODEL

Abstract

The basicity of sintered ore, which is related to the melting point of the sinter, is vital to ore mining and blastfurnace smelting. Laser-induced breakdownspectroscopy (LIBS) with random forest regression (RFR) has been applied for measuring the basicity of sintered ore, which can be defined by the concentrations of oxides: CaO, SiO2, Al2O3 and MgO. In this work, thirty sintered ore samples are used, of which twenty samples are used for the calibration set to construct the random forest regression (RFR) calibration model for the above-mentioned oxides and ten samples are used for the test set. The characteristic lines of the main components in the sintered ore are identified using the National Institute of Standards and Technology (NIST) database. Two model parameters (the number of decision trees -ntree and the number of random variables - m(try)) of the RFR were optimized by out-of-bag (OOB) error estimation for improving the predictive accuracy of the RFR model. The RFR model was applied to sample measurements and the results were compared with partial least squares regression (PLSR) models. The RFR model has shown better predictive capabilities than the PLSR model. In order to verify the stability of the RFR model, fifty measurements were made and the relative standard deviation (RSD) of the data is between 0.27% and 0.59%. Therefore, LIBS combined with RFR could be a promising method for realtime online, rapid analysis in mining and mineral processing industries.

Area:

Industry ; Mining

Material:

Ore

Elements:

Emmision lines:

Different sample preparation methods for the analysis of suspension fertilizers combining LIBS and liquid-to-solid matrix conversion: determination of essential and toxic elements

Author: Andrade, D. F. ; et. al.

Journal: Analytical Methods (2017)

Applications Chemometrics

DOI: 10.1039/c7ay01049d

Authors
Pereira, E. R. ; Speranca, M. A.

Keywords

INDUCED BREAKDOWN SPECTROSCOPY; OPTICAL-EMISSION-SPECTROMETRY; TRACE-ELEMENTS; MICRONUTRIENTS; MEMBRANE; CADMIUM; WATER

Abstract

A novel calibration method was developed for the determination of macronutrients in suspension fertilizer samples by laser-induced breakdown spectroscopy(LIBS). The fertilizer samples were mixed with 10% m/v polyvinyl alcohol (PVA) and dried for approximately 2 h at 50 degrees C, resulting in a solid polymer film with the immobilized liquid sample. This study describes the use of LIBS and 12 normalization strategies to determine Cu, K, Mg, Mn and Zn as well as As, B, Ca, Cd, Cr, Fe, Na, P and Pb in suspension fertilizers by inductively coupled plasma optical emission spectrometry (ICP OES) and inductively coupled plasma mass spectrometry (ICP-MS) analyses. LIBS equipment parameters were chosen following optimization with a Doehlert design. The results clearly demonstrate a good agreement between the LIBS predicted concentrations and the reference concentrations determined by ICP OES after microwave-assisted acid digestion (AOAC 2006.03 Official Method). Correlations of R-2 = 0.9958, 0.9489, 0.9992, 0.9968 and 0.9809 for Cu, K, Mn, Mg and Zn, respectively, were obtained. Significant levels of potentially toxic elements such as 10 mg L-1 As, 4.0 mg L-1 Cd, 8.0 mg L-1 Cr and 20 mg L-1 Pb were detected after ICP-MS analysis. The results with low standard errors of prediction for LIBS (from 0.02 to 0.06%) indicate that this proposed suspension sample preparation procedure exhibits significant potential for the development and implementation of methods for determining essential nutrients in suspension fertilizers and other samples for which mineralization is difficult.

Area:

Agriculture ; Nutrition

Material:

Fertilizer

Elements:

Mg

Lines [nm]:

Mg II 279.55 nm Mg II 279.08 nm Mg II 279.80 nm Mg II 280.27 nm Mg I 285.21 nm Mg I 383.23 nm Mg I 383.83 nm Mg I 516.73 nm Mg I 517.27 nm Mg I 518.36 nm

K

Lines [nm]:

K I 766.49 nm K I 404.41 nm K I 404.72 nm K I 578.24 nm K I 580.17 nm K I 581.21 nm K I 583.19 nm K I 693.88 nm K I 691.11 nm K I 769.90 nm

Mn

Lines [nm]:

Mn II 257.61 nm Mn II 259.37 nm Mn II 260.57 nm Mn II 293.93 nm Mn II 294.92 nm Mn I 354.78 nm Mn I 356.95 nm Mn I 403.08 nm Mn I 403.31 nm Mn I 403.45 nm

Cu

Lines [nm]:

Cu I 324.75 nm Cu II 213.60 nm Cu II 219.57 nm Cu I 222.57 nm Cu I 224.43 nm Cu II 224.70 nm Cu II 236.99 nm Cu I 327.40 nm Cu I 521.82 nm Cu I 515.32 nm

Zn

Lines [nm]:

Zn I 213.86 nm Zn II 250.20 nm Zn II 255.80 nm Zn I 328.23 nm Zn I 330.26 nm Zn I 334.50 nm Zn I 468.01 nm Zn I 472.22 nm Zn I 481.05 nm Zn I 636.23 nm

Emmision lines:
Cu II 213.60 nm
Zn I 213.86 nm
Cu II 219.57 nm
Cu I 222.57 nm
Cu I 224.43 nm
Cu II 224.70 nm
Cu II 236.99 nm
Zn II 250.20 nm
Zn II 255.80 nm
Mn II 257.61 nm
Mn II 259.37 nm
Mn II 260.57 nm
Mg II 279.08 nm
Mg II 279.55 nm
Mg II 279.80 nm
Mg II 280.27 nm
Mg I 285.21 nm
Mn II 293.93 nm
Mn II 294.92 nm
Cu I 324.75 nm
Cu I 327.40 nm
Zn I 328.23 nm
Zn I 330.26 nm
Zn I 334.50 nm
Mn I 354.78 nm
Mn I 356.95 nm
Mg I 383.23 nm
Mg I 383.83 nm
Mn I 403.08 nm
Mn I 403.31 nm
Mn I 403.45 nm
K I 404.41 nm
K I 404.72 nm
Zn I 468.01 nm
Zn I 472.22 nm
Zn I 481.05 nm
Cu I 515.32 nm
Mg I 516.73 nm
Mg I 517.27 nm
Mg I 518.36 nm
Cu I 521.82 nm
K I 578.24 nm
K I 580.17 nm
K I 581.21 nm
K I 583.19 nm
Zn I 636.23 nm
K I 691.11 nm
K I 693.88 nm
K I 766.49 nm
K I 769.90 nm

Sorting zamak alloys via chemometric analysis of their LIBS spectra

Author: Aberkane, S. M. ; et. al.

Journal: Analytical Methods (2017)

Applications Chemometrics

DOI: 10.1039/c7ay01138e

Authors
Abdelhamid, M. ; Abdelli-Messaci, S. ; Harith, M. A. ; Mokdad, F. ; Yahiaoui, K.

Keywords

INDUCED BREAKDOWN SPECTROSCOPY; QUANTITATIVE-ANALYSIS; STEEL SAMPLES; CLASSIFICATION; PLASMA; RECOGNITION; TEMPERATURE; EXPLOSIVES; MODELS; SOILS

Abstract

Laser induced breakdown spectroscopy with chemometric methods has been employed for sorting zamak alloy (zinc based alloy) samples. Plasma plumes were created on the sample surface using 50 mJ Nd:YAG laser pulses at its fundamental wavelength (lambda = 1064 nm). Six types of zinc alloy samples with different elemental compositions have been used to perform the LIBS measurements under optimized experimental conditions. On the obtained LIBS data three different chemometric classification models were applied, namely K Nearest Neighbor (KNN), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). The effect of the input database choice and its normalization on the classification model efficiency has been investigated and found to play a crucial role. It was found that KNN, SVM and ANNs permit an appropriate classification of different zinc alloys using LIBS spectra and the best results were obtained from the largest input database. The effect of data normalization has been discussed for each model. The obtained results demonstrate the feasibility of using chemometric methods associated with the LIBS technique as an industrial tool for in situ zamak sorting.

Area:

Industry

Material:

Zinc alloy

Elements:

Zn

Lines [nm]:

Zn I 328.23 nm Zn I 330.26 nm Zn I 330.29 nm Zn I 334.50 nm Zn I 334.56 nm Zn I 334.59 nm Zn I 468.01 nm Zn I 472.22 nm Zn I 481.05 nm Zn I 636.23 nm

Al

Lines [nm]:

Al I 308.22 nm Al I 309.27 nm Al I 309.28 nm Al I 394.40 nm Al I 396.15 nm

Ca

Lines [nm]:

Ca II 393.37 nm Ca II 396.85 nm

Cu

Lines [nm]:

Cu I 324.75 nm Cu I 327.40 nm

Mg

Lines [nm]:

Mg II 279.55 nm

Emmision lines:
Mg II 279.55 nm
Al I 308.22 nm
Al I 309.27 nm
Al I 309.28 nm
Cu I 324.75 nm
Cu I 327.40 nm
Zn I 328.23 nm
Zn I 330.26 nm
Zn I 330.29 nm
Zn I 334.50 nm
Zn I 334.56 nm
Zn I 334.59 nm
Ca II 393.37 nm
Al I 394.40 nm
Al I 396.15 nm
Ca II 396.85 nm
Zn I 468.01 nm
Zn I 472.22 nm
Zn I 481.05 nm
Zn I 636.23 nm

Fast and direct detection of metal accumulation in marine sediments using laser-inducedbreakdown spectroscopy (LIBS): a case study from the Bay of Cienfuegos, Cuba

Author: Pena-Icart, M. ; et. al.

Journal: Analytical Methods (2017)

Applications Chemometrics

DOI: 10.1039/c7ay00578d

Authors
Alonso-Hernandez, C. ; Bolanos-Alvarez, Y. ; de Aquino, F. W. B. ; Pereira, E. R. ; Pomares-Alfonso, M. S.

Keywords

MULTIVARIATE-ANALYSIS; TRACE-METALS; CLASSIFICATION; POLLUTION; CHEMOMETRICS; COMBINATION; DIGESTION; SAMPLES

Abstract

Laser-induced breakdown spectroscopy (LIBS) is an emerging, multi-elemental and green technique, particularly suitable for the direct analysis of solid samples. In this study, thirty-two superficial sediments collected from Cienfuegos Bay in 2011-2013 were analyzed by LIBS without requiring sample digestion. Al, Ca, Fe, K, Mg, Mn, Na and Ti emission lines were detected in all samples. The full LIBS spectra of the samples were subjected to principal component analysis (PCA) and hierarchical cluster analysis (HCA). Most samples showed a similar emission signal pattern, whereas two samples collected in 2012 and another two in 2013 showed anomalously higher emission signals for Na, Mg and Ca, Mg, respectively. In three of the sediments, the impact of external factors on the ecosystem is the presumable cause of the observed anomalies. The combination of LIBS with multivariate data analysis demonstrated a high capacity for the fast detection of anomalous metal compositions in marine sediments.

Area:

Geology

Material:

Marine sediment

Elements:

Ca

Lines [nm]:

Ca I 616.22 nm Ca I 616.91 nm Ca II 393.37 nm Ca II 396.85 nm

Na

Lines [nm]:

Na I 589.00 nm Na I 589.59 nm

Mg

Lines [nm]:

Mg II 279.55 nm Mg I 280.98 nm

Al

Lines [nm]:

Al I 394.40 nm Al I 396.15 nm

Emmision lines:
Mg II 279.55 nm
Mg I 280.98 nm
Ca II 393.37 nm
Al I 394.40 nm
Al I 396.15 nm
Ca II 396.85 nm
Na I 589.00 nm
Na I 589.59 nm
Ca I 616.22 nm
Ca I 616.91 nm

Laser induced breakdown spectroscopy for quantification of sodium and potassium in minced beef: a potential technique for detecting beef kidney adulteration

Author: Dixit, Y. ; et. al.

Journal: Analytical Methods (2017)

Applications Chemometrics

DOI: 10.1039/c7ay00757d

Authors
Cama-Moncunill, R. ; Cama-Moncunill, X. ; Casado-Gavalda, M. P. ; Cullen, P. J. ; Markiewicz-Keszycka, M. ; Sullivan, C.

Keywords

NIR SPECTROSCOPY; DATA NORMALIZATION; MOTION CONDITIONS; INFANT FORMULA; DIETARY-SODIUM; BLOOD-PRESSURE; MEAT; LIBS; CLASSIFICATION; PREDICTION

Abstract

Beef is a rich source of important minerals, with potassium (K) being the most abundant mineral quantitatively except in cured meats where sodium (Na) from the added salt predominates. This study evaluates the capability of LIBS for quantification of the Na and K contents of minced beef as a potential method of detecting beef kidney adulteration. Additionally, the study aims to demonstrate the ability of LIBS to provide spatial mineral information of minced beef. A LIBS system was employed to collect spectral information of adulterated minced beef samples. Atomic absorption spectroscopy (AAS) was used to obtain reference values for Na and K. The chemometric technique of partial least squares regression (PLSR) was used to build the prediction models. Spatial mineral maps of minced beef samples were generated based on the predicted percentages of Na and K. The models for Na and K yielded calibration coefficients of determination (R-c(2)) of 0.97 and 0.91 respectively. Similarly, a good calibration model was obtained for adulteration yielding a R-c(2) of 0.97. Good prediction accuracy was observed for all models. Spatial mapping provided two major advantages: (a) representative measurements of samples and (b) spatial distribution of multi-elements. The results observed illustrate the ability of LIBS combined with chemometrics as a potential monitoring tool for mineral quantification as well as adulteration detection for the meat processing industry.

Area:

Health

Material:

Beef

Elements:

Ca

Lines [nm]:

Ca II 393.37 nm Ca II 396.85 nm Ca I 422.67 nm Ca I 430.25 nm Ca I 443.50 nm Ca I 445.48 nm Ca I 612.22 nm Ca I 616.22 nm Ca I 643.91 nm Ca I 646.26 nm

Na

Lines [nm]:

Na II 445.47 nm Na I 568.26 nm Na I 568.82 nm Na I 589.00 nm Na I 589.59 nm Na I 818.33 nm Na I 819.48 nm

Fe

Lines [nm]:

Fe I 445.50 nm Fe II 512.88 nm Fe I 516.46 nm Fe I 568.25 nm Fe I 643.88 nm

Mg

Lines [nm]:

Mg II 279.55 nm Mg II 280.27 nm Mg I 285.21 nm Mg I 518.36 nm

N

Lines [nm]:

N I 742.36 nm N I 744.26 nm N I 746.83 nm

...

Emmision lines:
Mg II 279.55 nm
Mg II 280.27 nm
Mg I 285.21 nm
Zn I 330.26 nm
Ca II 393.37 nm
Ca II 396.85 nm
Ca I 422.67 nm
Ca I 430.25 nm
Ca I 443.50 nm
Na II 445.47 nm
Ca I 445.48 nm
K II 445.50 nm
Fe I 445.50 nm
Fe II 512.88 nm
Fe I 516.46 nm
Rb II 516.46 nm
Mg I 518.36 nm
Fe I 568.25 nm
Na I 568.26 nm
Na I 568.82 nm
Na I 589.00 nm
Na I 589.59 nm
Ca I 612.22 nm
Ca I 616.22 nm
Fe I 643.88 nm
Ca I 643.91 nm
Ca I 646.26 nm
H I 656.28 nm
N I 742.36 nm
N I 744.26 nm
N I 746.83 nm
K I 766.49 nm
K I 769.90 nm
O I 777.19 nm
O I 777.42 nm
Rb I 780.03 nm
Na I 818.33 nm
Na I 819.48 nm

Journal: Analytical Methods

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