Direct Determination of Contaminants and Major and Minor Nutrients

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Article

Direct Determination of Contaminants, Major and Minor Nutrients in Solid Fertilizers using Laser-Induced Breakdown Spectroscopy (LIBS) Daniel Fernandes Andrade, and Edenir Rodrigues Pereira Filho J. Agric. Food Chem., Just Accepted Manuscript • Publication Date (Web): 27 Sep 2016 Downloaded from http://pubs.acs.org on September 28, 2016

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Journal of Agricultural and Food Chemistry

40x19mm (300 x 300 DPI)

ACS Paragon Plus Environment


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Direct Determination of Contaminants, Major and Minor Nutrients in

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Solid Fertilizers using Laser-Induced Breakdown Spectroscopy

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(LIBS)

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Daniel F. Andrade, Edenir Rodrigues Pereira-Filho*

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Grupo de Análise Instrumental Aplicada (GAIA), Departamento de Química

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(DQ), Centro de Ciências Exatas e de Tecnologia (CCET), Universidade

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Federal de São Carlos (UFSCar), São Carlos, São Paulo State, P.O. Box 676,

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Zip code 13565-905, Brazil

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* corresponding author: Edenir Rodrigues Pereira-Filho

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phone: +55 16 3351-8092

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fax: +55 16 3351-8350

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e-mail address: [email protected]

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1 ACS Paragon Plus Environment

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Abstract: Contaminants (Cd, Cr and Pb) as well as minor (B, Cu, Mn, Na,

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and Zn) and major (Ca and Mg) elements were directly determined in solid

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fertilizer samples using laser-induced breakdown spectroscopy (LIBS). Factorial

25

designs were used to define the most appropriate LIBS parameters and pellet

26

pressure on solid fertilizers. Emission lines for all the analytes were collected

27

and employed 12 signal normalization modes. The best results were obtained

28

using a laser energy of 75 mJ, a spot size of 50 µm, a pressure of 10 t/inch and

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a delay of 2.0 µs. Good correlation was obtained between the calibration

30

models prediction using the proposed LIBS method and the reference values

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obtained with ICP-OES. The limits of detection (LOD) for the proposed method

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varying from 2 mg/kg (for Cd) to 1% (for Zn).

33 34

Keywords: laser-induced breakdown spectroscopy (LIBS), fertilizers,

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chemometrics, solid sample analysis,

36 37 38 39 40 41 42 43 44



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Introduction

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Population growth and the increasing demand for food in recent years

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has brought challenges for agriculture in order to improve food quality with high

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productivity. Consequently, this increased demand has contributed to the

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intensification of soil fertilization, providing essential nutrients for the growth and

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development of crops. The use of fertilizers is common in agricultural

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applications for supplying the nutritional deficiencies of plants. Several fertilizer

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classes are available in the market like, natural, mineral, organic (e.g. animal

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manure) or organic mineral and residues from mines.1 Fertilizers can be

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composed of many different material sources and matrix types, being a source

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of contaminants. They may contain harmful substances such as potentially toxic

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elements like As, Cd, Cr and Pb among others. In this case, the development of

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reliable and inexpensive analytical procedures with a high analytical throughput

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is in urgent demand in several countries.2

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Considering that the determination of the fertilizer composition is of great

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importance, several techniques have been used for this purpose, such as

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inductively coupled plasma optical emission spectrometry (ICP-OES),3,4

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inductively coupled plasma mass spectrometry (ICP-MS),5,6 flame atomic

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absorption spectrometry (FAAS),7-9. However, with these techniques the

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procedures usually require conversion of solid samples into homogenous

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solutions prior to analysis, and it is necessary that samples are suitably

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prepared presenting both low dissolved solid content and acidity. The standard

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method specified by the United States Environmental Protection Agency (EPA)

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for the determination of elements in fertilizers dictates the use of wet digestion

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for sample dissolution.10 Laser-induced breakdown spectroscopy (LIBS) has


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become an attractive and popular technique for the direct analysis of solid

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samples in different fields. Using a single laser pulse, it is possible to detect the

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spectral characteristics of atomic and molecular species for several elements in

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the sample.11

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Over the last few years, LIBS technique has been applied in several

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fields,12-17 including fertilizer analysis.18-23 One of the major advantages of this

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technique is its capability of obtaining fast measurements of a range of

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elements simultaneously with minimal or no sample preparation. Nowadays,

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quantitative analysis methods using LIBS mainly include a calibration curve or

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multivariate methods,24 both of which require a comparative or reference

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technique. Furthermore, once the calibration method is established, the LIBS

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technique can be used to obtain compositional information of the sample in a

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short period of time. On the other hand, as various instrumental setups are

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presented in the literature, the reproducibility of results are compromised

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because the data is affected by several parameters like laser energy and

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wavelength and delay time25. In addition, problems related to sample surface

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and laser interaction with the sample can also reduce the reproducibility of the

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results.26,27 The studies published in the literature that analyzed fertilizers using

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LIBS18-23 present different instrumental configurations and reproducibility is

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clearly jeopardized. This type of situation is expected when an analytical

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technique is becoming established.

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The main goal of this study was the development of a LIBS analytical

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method for multi-element analysis of solid fertilizer samples. A commercial LIBS

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system was used and emission lines for B, Ca, Cd, Cr, Cu, Mg, Mn, Na, Pb and

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Zn were collected. Chemometric strategies were employed in order to optimize



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the equipment conditions. Emission lines selection free of interference is a

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challenge in LIBS and in this study a strategy combining multivariate and

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univariate calibration was tested.

98 99 100

Materials and Methods Instrumentation

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Fertilizer samples were digested with a Speedwave 4 microwave system

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(Berghof, Eningen, Germany). The digests were analyzed by iCAP 6000 ICP-

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OES (Thermo Fisher, Madison, WI) in the radial viewing mode, and Table 1

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shows the instrumental parameters as well as wavelengths chosen for each

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analyte of interest.

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Direct solid sample analysis using LIBS was carried out with a model

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J200 (Applied Spectra, Fremont, CA) Q-switched Nd:YAG laser at 1064 nm with

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a single laser pulse with a duration of 8 ns at a frequency of 10 Hz. The

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maximum laser energy was 100 mJ, and the signal acquisition time was fixed at

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1.05 ms. Operational parameters of this instrument were controlled by Axiom

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2.5 software with an automated XYZ stage and a 1280×1024 CMOS

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(complementary metal-oxide semiconductor) color camera imaging system. The

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plasma emission was collected and focused into an optical fiber bundle coupled

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to a 6-channel CCD spectrometer covering wavelengths from 186 to 1042 nm

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(12288 variables).

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Sample, reagents and solutions

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A certified reference material (NIST SRM 695–Trace Elements in Multi-

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Nutrient Fertilizer) was analyzed in order to verify the accuracy of the


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measurements. Nine mineral fertilizer samples were furnished by LANAGRO

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(National Laboratory of Agriculture, Brazil) and employed for method evaluation.

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The laboratory samples were dried to constant weight and sieved in order to

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obtain particle size smaller than 42 µm.

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Aqueous multi-element standard solutions were prepared from stock

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standard solutions of 1000 mg/L (Fluka Analytical, Buchs SG, Switzerland) by

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subsequent dilutions in 2% v/v HNO3. All solutions were prepared with

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deionized water (resistivity > 18.2 MΩ cm) produced using a Milli-Q Plus Total

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Water System (Millipore Corp., Bedford, MA). Nitric acid (Merck, Darmstadt,

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Germany) was previously purified by sub-boiling distillation using Distillacid

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BSB-939-IR (Berghof, Eningen, Germany).

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Analytes reference concentrations

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In this part of experimental setup, AOAC Official Method 2006.328

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procedure applicable for the determination of As, Cd, Co, Cr, Pb, Mo, Ni and Se

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in all classes of fertilizers was used as a reference method. In the

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recommended microwave-assisted acid digestions for further ICP-OES

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determination, 1.0 g of sample was accurately weighed in TFM closed vessels

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(Berghof, Eningen, Germany), 10 mL of 65% w/w HNO3 was added and the

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sample was kept overnight at room temperature (approximately 14 h). The

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microwave heating program was established according to the following

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procedure: ramp temperature from ambient to 200 ºC in 15 min, hold at 200 ºC

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for 20 min and cooled down to room temperature. After sample mineralization,

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the extracts were transferred to 50 mL volumetric flasks, and the volume was

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made up with high purity deionized water.



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LIBS Analysis

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The fertilizer samples were weighed (approx. 500 mg) and pressed under

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10 t/inch to form pellets (12 mm diam.). Preliminary studies were carried out

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with pellet samples S2, S5 and S9 in order to investigate the effects of LIBS

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system parameters on the emission signal intensities of Ca(II) 393.37, Cu(I)

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515.32, Mg(I) 518.36, Mn(II) 257.61, Na(I) 589.59 and Zn(I) 481.05 nm lines.

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The variables chosen for preliminary experiments were laser energy (50 and 75

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mJ), delay time (0.5 and 1.0 µs), spot size (50 and 100 µm) and pellet pressure

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(5 and 10 t/inch). These variables were studied using a fractional factorial

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design assessed (24-1), and 8 experiments were performed (experiments 1 to 8).

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The variable levels were coded between -1 (lower level) and 1 (higher level).

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The first experiment is characterize by an energy of 50 mJ, a delay time of 0.5

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µs, a spot size of 50 µm and a pressure of 5 t/inch (all variables were coded as

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-1).

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After the identification of the most important variables, a second set of

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experiments was performed in order to obtain more information about the

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system assessed and identify adequate working conditions to cover all the

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analytes simultaneously. These experiments were configured in a central

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composite design,29,30 and the variables evaluated were laser energy (61-89

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mJ) and delay time (0-2 µs). These two variables were studied at 5 levels

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(experiments 9 to 19), and the variables spot size and pellet pressure were

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fixed.

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For each pellet, approximately 800 spectra (both sides of the pelletized

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samples) were obtained at different parts of the samples. These spectra were

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obtained in 18 lines, and in each one approximately 50 laser pulses were



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obtained. Each set of 6 lines was considered a replicate (n = 3). The following

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additional laser settings were used: a scan length of 9 mm, a laser repetition

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rate of 5.0 Hz, and a speed of 1.0 mm/s.

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Data evaluation

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For data processing and evaluation, the following computer programs were

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used:

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- Aurora (Applied Spectra) and National Institute of Standards and Technology

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(NIST)31 for emission lines identification of the analytes under determination;

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- Microsoft Excel for data matrices organization and calculating univariate

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calibration models;

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- Pirouette version 4.5 (Infometrix, Bothell, WA) and Matlab 2009a (The

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Matworks, Natick, MA) to calculate the PLS multivariate calibration models. In

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Matlab two homemade routines were used for data preliminary inspection: (1)

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libs_treat. The goal of this routine is to detect outlier spectra. In this case, for

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each sample (rows in the data matrix) are calculated: standard deviation, area,

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maximum and Euclidean norm. If an outlier is detected (for example, standard

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deviation equal to 0) this sample is manually removed and later 12

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normalization modes are calculated.32 (2) libs_par. After normalization using the

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previous routine, libs_par enables the calculation of signal-to-background ratio

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(SBR), and both signal area and height for a specified emission line. In order to

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use this routine it is necessary to establish the emission line intervals that

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contains the background and the signal.

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Results and Discussion

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ICP-OES determinations

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The certified reference material NIST SRM 695 was employed for quality

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control of the analytical procedure. After comparison between the certified and

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obtained (n = 3) concentrations, the calculated recovery values ranged from 77

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(for Zn) to 124% (for Cu).. In addition, an unpaired t test was calculated, and the

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obtained t value was compared with the tabulated one. In several cases, no

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significant difference between the reference and obtained concentration at 95%

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of confidence level was observed for the majority of the analytes.

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LIBS equipment conditions

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The properties of laser-induced plasmas are mainly affected by the laser

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operating conditions (i.e., laser wavelength, pulse energy and duration), as well

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as emission measurement parameters (i.e., delay time, gate width, amplification

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detector gain and emission line selection).26,33 In addition, the interaction of the

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laser with the sample is extremely dependent on the matrix, analyte

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homogeneity and sample surface.26 In this context, LIBS equipment parameters

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need to be optimized in order to obtain both high signal-to-noise and signal-to-

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background ratios (SNR and SBR) for all the emission lines monitored.

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First, the LIBS instrumental parameters (laser energy, delay time, and

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spot size) along with the pellet pressure were preliminary evaluated using

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fractional factorial design (experiments 1 to 8). The goal of this design was to

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establish an order of variables relevance. For the experiments performed in the

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variables evaluation, the desirability function was calculated to cover the SBR,

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signal area and signal height for Ca, Cu, Mg, Mn, Na and Zn. In this case, each



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response was coded from 0 (undesired response; the lowest SBR, area and

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height) to 1 (desired response; the highest SBR, area and height). Therefore,

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the individual desirability was combined into one single response (D, the global

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desirability),34,35 and effects were calculated for samples S2, S5 and S9 . The

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effects obtained were those related to individual variables (first order effects: 1,

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2, 3 and 4) and interactions between them (second order effects: 12, 13 and

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14). Other effects (for example, 23, 24, 34, 123, 124, 134, 234 and 1234) were

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not calculated because they are mixed with those already calculated. Variables

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1 (laser energy) and 2 (delay time) showed significant effects for Ca(II) 393.37,

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Cu(I) 515.32, Mg(I) 518.36, Mn(II) 257.61, Na(I) 589.59 and Zn(I) 481.05 nm

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emission measurements (effects higher than 0.1), and the second order

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interaction between these two variables (12) was the most significant (0.17).

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The effects of spot size (variable 3) and pellet pressure (variable 4) were of

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small magnitude, and for further experiments, they were fixed at 50 µm and 10

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t/inch, respectively.

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Using this approach, the laser energy and delay time that presented

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remarkable effects in LIBS analysis were re-evaluated for samples S2, S5 and

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S9 through central composite design (experiments 9 to 19) in order to obtain

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adequate conditions for the analytes of interest. For these experiments, three

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models were calculated using the global desirability (one for each sample), and

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the following 6 coefficients were obtained: constant (b0), linear coefficients (b1

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and b2), quadratic coefficients (b11 and b22) and interaction coefficient (b12).

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The significance of these coefficients was calculated using analysis of variance

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(ANOVA) table at the 95% confidence level.29,30


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Figure 1 shows the contour plot obtained after model calculation for

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sample S9. For samples S2 and S5, it was not possible to adjust a regression

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model with low lack of fit. Black squares in Figure 1 represent the fractional

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factorial design previously performed, and the best results were those using

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both a high energy and long delay time. The central point for the central

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composite design (white triangles) was the previous conditions characterized by

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an energy of 75 mJ and a delay time of 1.0 µs. From the contour plot depicted

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in Figure 1, it is observed that high desirability is obtained with an energy

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between 60 to 90 mJ and a delay time higher than 1.7 µs. In this case,

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adequate conditions were chosen using an energy of 75 mJ (intermediate value

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between 60 and 90) and a delay time of 2.0 µs (star in Figure 1). These

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conditions were tested for sample S9, and high SBRs, signal areas and signal

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heights were shown for Ca(II) 393.37, Cu(I) 515.32, Mg(I) 518.36, Mn(II)

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257.61, Na(I) 589.59 and Zn(I) 481.05 nm. These optimized parameters were

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applied in the further LIBS analyses of all samples.

261 262

Univariate Calibration

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Spectral information obtained using LIBS has a high complexity due to

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the abundance of emission lines presented by some elements. In addition,

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signal fluctuation and sample heterogeneity can jeopardize the precision of the

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measurements. In this case, previous data processing is sometimes mandatory

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in order to minimize these problems.

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Before calibration models, the raw data were submitted to the following

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12 normalization signal modes:32 signal average; signal average normalized by

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individual norm (vector length), area (sum of all signals) and maximum (the



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highest signal); signal sum; signal sum normalized by individual norm, area and

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maximum; and signal average and sum after normalization by C emission lines

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(C(I) 193.09 and 247.86 nm). As a poor prediction ability was observed (higher

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prediction errors) using the most intense emission lines, an alternative was tried

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using multivariate calibration. Partial least squares (PLS) method was employed

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in order to construct 12 calibration models for each analyte using the entire

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signal profile to identify the most important emission lines, i.e., those that

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present the highest regression coefficients (186 to 1042 nm, 12288 variables)

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and the lowest errors evaluated through. This chemometric tool calculates

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principal component analysis (PCA) for spectral information (matrix X) and

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dependent variables (analytes concentration, matrix Y). The scores values from

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both matrices are combined and regression coefficients calculated. The highest

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coefficients indicates whose emission lines present a high linear correlation with

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the concentration. These emission lines were then selected, the area and height

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for each analyte were calculated using libs_par function , and univariate models

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were established. Figure 2 shows a pictorial description of this procedure.

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After regression vectors inspection, the emission lines selected were as

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follows: B(I) 249.68, Ca(II) 527.02, Cd(I) 467.81, Cr(I) 425.43, Cu(I) 510.55,

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Cu(I) 515.32, Cu(I) 521.81, Mg(I) 517.27, Mg(I) 518.36, Mn(I) 475.40, Mn(I)

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478.34, Mn(I) 482.35, Na(I) 588.99, Na(I) 589.59, Pb(I) 368.35, Zn(I) 334.50,

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Zn(I) 472.21 and Zn(I) 481.05) nm.

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Figure 3 shows the selected emission lines for samples to calculate the

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univariate calibration models. With the selected emission lines, univariate

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calibration models with a normalized data set were calculated, and Table 2

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shows some figures of merit obtained for the best results.


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The first row of Table 2 shows the parameters obtained for boron. The

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first column shows the univariate linear model obtained with the emission line B

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I 249.68 nm, and as can be observed, slope is significant with a 95%

299

confidence level. The samples concentration range (second column) is typically

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high and the same behavior was observed for most of samples, with a wide

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range of concentrations from mg/kg for harmful elements (Cd and Cr) to %

302

concentration for the other analytes. The third column shows the linear equation

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when the reference and predicted values are compared. As can be noted, the

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intercept is not significant (2409±3099), and the slope value can be 1

305

(0.85±0.28). Columns from 4 to 7 show the SEC, emission line selected, limit of

306

detection and limit of quantitation, respectively. Column 8 shows the relative

307

standard deviation (RSD) for the signal height (information described in column

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9) obtained for the selected line (n = 3), and it can be noted that the RSD was

309

from 1 to 6% for B. The RSD values presented in Table 2 are ranging from the

310

lowest to the highest for all samples with concentrations higher than SEC. The

311

results obtained with the proposed procedure (LIBS) were satisfactory and

312

presented low RSDs with values ranging from 1% (case of B, Ca, Cd and Na) to

313

15% (case of Mn and Pb). The normalization mode selected for B was signal

314

averaged after normalization by the spectrum area. The same parameter

315

analysis was performed for the other analytes, and the best results for

316

calibration models (low SEC) were obtained with the signal sum for Cr; signal

317

average for Ca; and signal average normalized by the area (Mn, Na and Zn), by

318

the norm for Cd, by C 247.86 (Mg and Pb) and by the maximum for Cu.

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Concentrations of the elements determined after sample mineralization

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and prediction concentrations using LIBS are depicted at Table 3. Arsenic, Cd



321

and Cr are expressed in mg/kg and the remainder in % (w/w). Regarding As,

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determinations using LIBS were assessed and no signals were detected. It

323

must be pointed out that Pb concentrations were found in alarming % level,

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ranging from 0.05% (sample S8) to 1% (sample S3). Figure 4 shows the

325

comparison between the proposed method (LIBS) and reference values for Cu

326

(A), Mg (B), Mn (C), Na (D) and Zn (E). For these elements were obtained a

327

good prediction using more than one analytical line. The SEC limit depicted at

328

Figure 4 (red dotted line) are for those emission lines with the lowest error

329

values (Cu(I) 515.32, Mg(I) 518.36, Mn(I) 475.40, Na(I) 588.99 and Zn(I)

330

334.50). Real and predicted concentrations showed strong correlation (linear

331

models) for most of the evaluated analytical lines. Good concordance between

332

reference and predicted concentrations was obtained for Cu(I) 515.32 (r =

333

0.9892), Mg(I) 518.36 (r = 0.9942), and Mn(I) 475.40 (r = 0.9916), with error

334

values (SEC) of 1, 0.7 and 0.3%, respectively.

335

Manganese is an excellent example of the success of this strategy to

336

identify the most important variables using multivariate calibration (PLS).

337

Standard error of calibration (SEC) using the most intense emission line (Mn(II)

338

257.61) was 1.5%. On the other hand, using the emission line Mn(I) 475.40 the

339

error was 0.3% (around 5 times lower than when 257.61 was used). An F test

340

was performed and these SEC values are significantly different at 95% of

341

confidence level. Another important aspect observed for Mn, is the fact that 3

342

emission lines presented similar concentrations (Figure 4C). This aspect

343

complements the accuracy evaluation of the proposed method. The same

344

behavior was observed for Cu, Mg, Na and Zn (more details in Figure 4). A

345

paired t test was calculated comparing the reference and the obtained values


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using different emission lines and no difference was observed at 95%

347

confidence level. Dotted red line presented in this figure represents the SEC

348

values for the selected emission line (Table 2). As can be observed, the

349

samples with concentration values below SEC presented the worst prediction.

350

In Table 4 several and different instrumental setup have been used for

351

fertilizers analysis by LIBS: CCD and ICCD detector operating with Nd:YAG

352

laser at 1064 nm and a large range of spot size (from 100 to 750 µm)18,22, and

353

one study employed a Nd:YAG laser at 532 nm20 and another both lasers to

354

perform double pulse analyses23. A fair comparison is hard to perform and wide

355

range of LOD values from 0.02 to 89 mg/kg for Ca and from 13.3 mg/kg to 0.5%

356

for P were observed. Calcium was determined by two studies14,23 and

357

phosphorus by five studies14,20-23, and a single element that was analyzed by all

358

studies was not found. These are the main drawbacks of LIBS analyses: the

359

lack of reproducibility. On the other hand, the present study shows for the first

360

time the use of a commercial equipment for fertilizer analysis enabling a fair

361

evaluation of the results.

362

For mineral fertilizers, the Brazilian legislation allows the following

363

concentrations for Cd and Pb: 450 and 10000 mg/kg, respectively. As can be

364

observed, the proposed method has adequate LOD values for both analytes

365

(Table 2). In the case of Cr the limit depends on the sum of concentrations for

366

micronutrients. In the specific case of the samples analyzed in this study the

367

allowed Cr concentration varies from 725 (the lowest mineral content) to 17500

368

mg/kg (the highest mineral content). Both values are higher than the LOD (32

369

mg/kg) presented in Table 2. Finally, the proposed method can be employed for

370

fertilizer inspection or quality control with minimal sample preparation and



371

adequate LOD and precision. In addition, the analytical frequency obtained is

372

around 30 samples per hour with no chemical residues generation.

373

Supporting information

374

The Supporting Information is available free of charge on the ACS Publications

375

website at DOI:

376

- Matlab functions for twelve different types of data normalization and

377

preliminary inspections (libs_treat), and SBR, area and height calculation

378

(libs_par).

379

- Fragments of LIBS spectra (190 nm to 640 nm) from pellets of samples S3,

380

S5, S8 and S9 and identification of the main elements evaluated.

381

- Fractional factorial design 24-1 and central composite design for LIBS

382

operational conditions evaluation.

383

- Concentrations determined (mean ± SD, n = 3) and recoveries (%) calculated

384

for NIST SRM 695 - Trace Elements in Multi-Nutrient Fertilizer by ICP-OES.

385 386

Funding

387

This study was supported by the São Paulo Research Foundation (FAPESP,

388

grants 2015/14488-0, 2012/01769-3 and 2012/50827-6) and the Conselho

389

Nacional de Desenvolvimento Científico e Tecnológico (CNPq, 401074/2014-5

390

and 305637/2015-0).

391 392

Notes

393

The authors declare no competing financial interest.

394


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Acknowledgments

396

The authors are also grateful to Embrapa Pecuária Sudeste and Coordenadoria

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de Aperfeiçoamento de Pessoal de Nível Superior (Capes) for the fellowship of

398

D.A.F. The authors are grateful to Analítica, Thermo Scientific for the loan of

399

instruments (ICP-OES and microwave oven).

400 401

References

402

1. Isherwood, K. F. Mineral fertilizer use and the environment by international

403

fertilizer industry association, Revised Edition, Paris, 2000.

404

2. Jiao, W.; Chen, W.; Chang, A. C.; Page, A. L. Environmental risks of trace

405

elements associated with long-term phosphate fertilizers applications: a review.

406

Environ. Pollut. 2012, 168, 44–53.

407

3. Souza, S. O.; Costa, S. S. L.; Santos, D. M.; Pinto, J. S.; Garcia, C. A. B.;

408

Alves, J. P. H.; Araujo, R. G. O. Simultaneous determination of macronutrients,

409

micronutrients and trace elements in mineral fertilizers by inductively coupled

410

plasma optical emission spectrometry. Spectrochim. Acta, Part B. 2014, 96, 1–

411

7.

412

4. Bizarro, V. G.; Meurer, E. J.; Tasch, F. R. P. Cadmium contents of phosphate

413

fertilizer marketed in Brazil. Cienc. Rural. 2008, 38, 247–250.

414

5. Rui, Y. K.; Shen, J.B. Application of ICP-MS to detecting ten kinds of heavy

415

metals in KCl fertilizer. Spectrosc. Spectral Anal. 2008, 28, 2428–2430.

416

6. Machado, R. C.; Virgilio, A.; Amaral, C. D. B.; Schiavo, D.; Nóbrega, J. A.;

417

Nogueira, A. R. A. Evaluation of inductively coupled plasma tandem mass

418

spectrometry for determination of As in agricultural inputs with high REE

419

contents. J. Braz. Chem. Soc. 2016, DOI 10.5935/0103-5053.20160098. 18 ACS Paragon Plus Environment


Page 20 of 34

420

7. Milinović, J.; Lukić, V.; Mandić, N. S.; Stojanović, D. Concentrations of heavy

421

metals in NPK fertilizers imported in Serbia. Pestic. Phytomed. 2008, 23, 195–

422

200.

423

8. Teixeira, L. S.; Vieira, H. P.; Windmöller, C. C.; Nascentes, C. C. Fast

424

determination of trace elements in organic fertilizers using a cup-horn reactor

425

for ultrasound-assisted extraction and fast sequential flame atomic absorption

426

spectrometry. Talanta. 2014, 119, 232–239.

427

9. Machado, R. C.; Pereira-Filho, E. R.; Nogueira, A. R. A. Strategy of sample

428

preparation for arsenic determination in mineral fertilizers. J. Braz. Chem. Soc.

429

2016, 27, 1273–1278.

430

10. EPA Method 3051A, Microwave assisted acid digestion of sediments,

431

sludges, soils and oils (2007). United states environmental protection agency

432

(EPA); available at: https://www.epa.gov/hw-sw846/sw-846-test-method-3051a-

433

microwave-assisted-acid-digestion-sediments-sludges-soils-and-oils

434

June 15, 2016).

435

11. Hahn, D. W.; Omenetto, N. Laser-induced breakdown spectroscopy (LIBS),

436

Part II: review of instrumental and methodological approaches to material

437

analysis and applications to different fields. Appl. Spectrosc. 2012, 66, 347–41.

438

12. Pořízka, P.; Prochazka, D.; Pilát, Z.; Krajcarová, L.; Kaiser, J.; Malina, R.;

439

Novotný, J.; Zemánek, P.; Ježek, J.; Šerý, M.; Bernatová, S.; Krzyžánek, V.;

440

Dobranská, K.; Novotný, K.; Trtílek, M.; Samek, O. Application of laser-induced

441

breakdown spectroscopy to the analysis of algal biomass for industrial

442

biotechnology. Spectrochim. Acta, Part B. 2012, 74–75, 169–176.


(acessed

Page 21 of 34


443

13. Rehse, S. J.; Salimnia, H.; Miziolek, A. W. Laser-induced breakdown

444

spectroscopy (LIBS): an overview of recent progress and future potential for

445

biomedical applications. J. Med. Eng. Technol. 2012, 36, 77–89.

446

14. Diaz, D.; Hahn, D. W.; Molinat, A. Evaluation of laser-induced breakdown

447

spectroscopy (LIBS) as a measurement technique for evaluation of total

448

elemental concentration in soils. Appl. Spectrosc. 2012, 66, 99–106.

449

15. McMillan, N. J.; Rees, S.; Kochelek, K.; McManus, C. Geological

450

applications of laser-induced breakdown spectroscopy. Geostand. Geoanal.

451

Res. 2014, 38, 329–343.

452

16. Godoi, Q.; Santos Junior, D.; Nunes, L. C.; Leme, F. O.; Rufini, I. A.; Agnelli,

453

J. A. M.; Trevizan, L. C.; Krug, F. J. Preliminary studies of laser-induced

454

breakdown spectrometry for the determination of Ba, Cd, Cr and Pb in toys.

455

Spectrochim. Acta, Part B. 2009, 64, 573–581.

456

17. Aquino, F. W. B.; Paranhos, C. M.; Pereira-Filho, E. R. Method for the

457

production

458

(PC)/ABS standards for direct Sb determination in plastics from e-waste using

459

laser-induced breakdown spectroscopy. J. Anal. At. Spectrom. 2016, 31, 1228.

460

18. Nunes, L. C.; Carvalho, G. G. A.; Santos Júnior, D.; Krug, F. J.

461

Determination of Cd, Cr and Pb in phosphate fertilizers by laser-induced

462

breakdown spectroscopy. Spectrochim. Acta, Part B. 2014, 97, 42–48.

463

19. Groisman, Y.; Gaft, M. Online analysis of potassium fertilizers by laser-

464

induced breakdown spectroscopy. Spectrochim. Acta, Part B. 2010, 65, 744–

465

749.

of

acrylonitrile–butadiene–styrene

(ABS)


and

polycarbonate


Page 22 of 34

466

20. Yao, S. C.; Lu, J. D.; Li, J. Y.; Chen, K.; Li, J.; Dong, M. R.; Multi-elemental

467

analysis of fertilizer using laser-induced breakdown spectroscopy coupled with

468

partial least squares regression. J. Anal. At. Spectrom. 2010, 25, 1733–1738.

469

21. Farooq, W. A.; Al-Mutairi, F. N.; Khater, A. E. M.; Al-Dwayyan, A. S.;

470

AlSalhi, M. S.; Atif, M. Elemental analysis of fertilizer using laser induced

471

breakdown spectroscopy. Opt. Spectrosc. 2012, 112, 874–880.

472

22. Marangoni, B. S.; Silva, K. S. G.; Nicolodelli, G.; Senesi, G. S.; Cabral, J. S.;

473

Villas-Boas, P. R.; Silva, C. S.; Teixeira, P. C.; Nogueira, A. R. A.; Benites, V.

474

M.; Milori, D. M. B. P. Phosphorus quantification in fertilizers using laser

475

induced breakdown spectroscopy (LIBS): a methodology of analysis to correct

476

physical matrix effects. Anal. Methods. 2016, 8, 78.

477

23. Nicolodelli, G.; Senesi, G. S.; Perazzoli, I. L. O.; Marangoni, B. S.; Benites,

478

V. D.; Milori, D. M. B. P. Double pulse laser induced breakdown spectroscopy:

479

A potential tool for the analysis of contaminants and macro/micronutrients in

480

organic mineral fertilizers. Sci. Total Environ. 2016, 565, 1116–1123.

481

24. Tian-Long, Z.; Shan, W.; Hong-Sheng, T.; Kang, W.; Yi-Xiang, D.; Hua, L.

482

Progress of chemometrics in laser-induced breakdown spectroscopy analysis.

483

Chin. J. Anal. Chem. 2015, 43, 939–948.

484

25. Castle, B. C.; Talabardon, K.; Smith, B. W.; Winefordner, J. D. Variables

485

influencing

486

measurements. Appl. Spectrosc. 1998, 52, 649–657.

487

26. Fortes, F. J.; Moros, J.; Lucena, P; Cabalín, L. M.; Laserna, J. J. Laser-

488

induced breakdown spectroscopy. Anal. Chem. 2013, 85, 640−669.

489

27. Pasquini, C.; Cortez, J.; Silva, L. M. C.; Gonzaga, F. B. Laser induced

490

breakdown spectroscopy. J. Braz. Chem. Soc. 2007, 18, 463–512.

the

precision

of

laser-induced

breakdown


spectroscopy

Page 23 of 34


491

28. Official methods of analysis of the AOAC. Method 2006.03, 18th ed.; AOAC

492

international: Gaithersburg, Maryland, 2010; 03, 42.

493

29. Barros Neto, B.; Scarminio, I. S.; Bruns, R. E. Andando na superfície de

494

resposta. In Como fazer experimentos: pesquisa e desenvolvimento na ciência

495

e na indústria, 2th ed.; Editora UNICAMP: Campinas, Brazil, 2003; 280–285.

496

30. Myers, D.; Montgomery, D. C.; Anderson-Cook, C. M. Response surface

497

methodology: process and product optimization using designed experiments.

498

John Wiley and Sons: Hoboken, New Jersey, 2009.

499

31. National institute of standards and technology (NIST). NIST atomic spectra

500

database

501

http://physics.nist.gov/PhysRefData/ASD/lines_form.html

502

2016).

503

32. Castro, J. P.; E. R. Pereira-Filho. Twelve different types of data

504

normalization for the proposition of classification, univariate and multivariate

505

regression models for the direct analyses of alloys by laser-induced breakdown

506

spectroscopy. Accepted for publication, J. Anal. At. Spectrom. 2016, DOI

507

10.1039/C6JA00224B.

508

33. Cremers, D. A.; Radziemski, L. J. Basics of the LIBS plasma. In Handbook

509

of laser-induced breakdown spectroscopy. John Wiley and Sons, 2006.

510

34. Costa, N. R.; Lourenço, J.; Pereira, Z. L. Desirability function approach: a

511

review and performace evaluation in adverse conditions. Chemom. Intell. Lab.

512

Syst. 2011, 107, 234–244.

513

35. Derringer, G.; Suich, R. Simultaneous optimization of several response

514

variables. J. Qual. Technol. 1980, 12, 214–219.

lines

form;

515


available (acessed

at: July

12,


Figure captions

Figure 1. Contour plot obtained for the model after LIBS parameter optimization for sample S9 and comparison of LIBS instrumental parameter optimization between the experiments of fractional factorial (squares) and central composite (triangles) designs. The star represents the selected working conditions.

Figure 2. Pictorial description of the strategy used for the evaluation of LIBS spectra and calibration model development.

Figure 3. Signal profiles for the 17 emission lines selected for B (A), Pb (B), Cr (C), Cd (D), Zn (E and H), Mn (F, G and I), Cu (J, K and N), Mg (L and M), Ca (O) and Na (P) to calculate the univariate regression models.

Figure 4. Results comparison between the reference (ICP-OES) and proposed (LIBS) methods for Cu (A), Mg (B), Mn (C), Na (D) and Zn (E). The red dotted line represents the SEC value for the selected emission line (Table 2).


Page 24 of 34

Page 25 of 34


Table 1. Instrumental Parameters for ICP-OES Determinations Instrument parameter Integration time (s) Sample introduction flow rate (mL/min) Pump stabilization time (s)

Operational conditions 15 s for low and 5 s for high wavelengths 2.1 5

Radio frequency (RF) applied power (kW)

1.15

Auxiliary gas flow rate (L/min)

0.50

Nebulizer gas flow rate (L/min)

0.70

Argon gas flow rate (L/min) Viewing mode

12 Radial As (189.04*, 193.76*), B (249.77*), Ca (317.93**), Cd (226.50**, 228.80*), Cr (283.56**,

Elements and wavelengths (nm)

357.87*), Cu (324.75*), Mg (285.21*), Mn (294.92**), Na (588.99*, 589.59*, 818.33*), Pb (216.99*) and Zn (481.05*)

* atomic lines ** ionic lines

24



Page 26 of 34

Table 2. Parameters for the Univariate Calibration Models Calculated R, and univariate linear model

Analytes concentration

R, and linear model (reference

range (%)

vs. predicted)

0.02–2

[B] = 0.85 x + 2409

0.9505,

± 0.28

± 3.0.10

0.9474, ±1155

0.8911,

0.9858, 0.03–11

0.3

B(I) 249.68

0.1

0.3

1–6

Height

Average normalized by area

1

Ca(I) 527.03

0.3

1.0

1–6

Area

Average

41*

Cd(I) 467.81

2*

7*

1–10

Area

Average normalized by norm

665*

Cr(I) 425.43

32*

108*

6–7

Height

Sum

1

Cu(I) 515.32

0.6

2.0

6–11

Height

0.7

Mg(I) 518.36

0.07

0.2

4–13

Area

Average normalized by C 247.86

0.3

Mn(I) 475.40

0.05

0.2

3–15

Area


0.3

Na(I) 588.99

0.02

0.1

1–12

Area


0.2

Pb(I) 368.35

0.2

0.6

2–15

Area

Average normalized by C 247.86

1.5

Zn(I) 334.50

1

3

3–11

Area


± 59

[Cr] = 0.82 x + 531 ± 434

[Cu] = 0.94 x + 0.45 ± 0.58

Average normalized by maximum (height)

0.9942, 0.4–17

[Mg] = x − 0.42 ± 0.66

± 6.4

0.9936,

0.9916,

-7

-5

[Mn] = 1.2.10−8 x − 5.0.10- 3

0.3–7

[Mn] = x − 0.10 ± 0.37

±1.0.10

0.9176,

0.9537, 0.1–2

[Na] = 1.0.10- 6-5 x + 0.01

[Na] = 0.60 x + 0.50 ± 0.17

± 0.02

0.8992,

± 0.17

0.7864,

[Pb] = 1.0.10- 4-4 x − 1.2.10 -4

0.05–1

[Pb] = x − 7.2

± 3379

± 0.26

0.9787,

0.9302,

-9

-5

[Zn] = 3.0.10-9 x + 1.0.10- 4 ±1.0.10

[Cd] = 0.78 x + 24

± 0.13

± 0.004

0.9929,

±1.0.10

Normalization mode

0.9892,

[Mg] = 1.0.10- 4-3 x − 2.0

± 2.0.10

Signal type

±1.2

± 0.32

[Cu] = 6.0.10− 7-7 x + 0.001

± 2 .2 .10

RSD (%)

0.9219, 18*–3951*

± 6.0.10

±1.0.10

[Ca] = 0.47 x + 2.8

± 0.64

± 0.015

[Cr] = 134 x + 5.0.104 4

±1.0 .10

LOQ (%)

0.7504, 5*–159*

0.8711, ± 42

LOD (%)

± 3099

± 0.24

[Cd] = 3.0.10- 4-4 x + 0.002 ± 2.0.10

(nm)

0.8732, 2–7

[Ca] = 0.1 x + 360 ± 0.02

Emission line

0.9347,

[B] = 1.0 . 10− 8−7 x + 1.0.10- 4-4 ± 3.0.10

SEC (%)

±1.0.10

1–13

[Zn] = 0.90 x + 1.2 ± 0.32

± 2.7

*Values are in mg/kg

25


Page 27 of 34


Table 3. Concentrations Determined (% w/w ± SD, n = 3) of As, B, Ca, Cd, Cr, Cu, Mg, Mn, Na, Pb and Zn in Fertilizers Samples by Reference (ICP-OES) and Proposed Method (LIBS). S1 Element

As** B Ca Cd** Cr** Cu Mg Mn Na Pb Zn

ICPOES

S2

LIBS

S3

ICPOES

LIBS

S4

ICPOES

LIBS

S5

ICPOES

LIBS

S6

ICPOES

LIBS

ICPOES

S7

LIBS

ICPOES

S8

LIBS

17 ± 4

-

29 ± 5

-

28 ± 2

-

25 ± 2

-

41 ± 7

-

25 ± 4

-

23 ± 3

-

2.0 ± 0.5

1.95 ± 0.02

1.59 ± 0.03

1.10 ± 0.01

0.085* ± 0.003

0.43 ± 0.04

0.051* ± 0.001

0.377 ± 0.002

0.082* ± 0.004

0.13 ± 0.01

1.6 ± 0.1

1.9 ± 0.1

0.16* ± 0.01

0.54 ± 0.05

2.0 ± 0.6 39* ± 2 142* ± 20

3.9 ± 0.2 37* ± 3 275* ± 36

6.1 ± 0.2 68 ± 7

5.1 ± 0.3 46 ± 2

3.87 ± 0.04 129 ± 5

6.1 ± 0.2 106 ± 7

6.1 ± 0.3 35* ± 1

1415 ± 184

3951 ± 354

3670 ± 257

335* ± 36

6.7 ± 1.0 137 ± 16 89* ± 15

6.2 ± 0.3 163 ± 9

61* ± 6

2.2 ± 0.1 22* ± 1 176* ± 7

3.5 ± 0.2 63 ± 2

1704 ± 91

6.4 ± 0.3 161 ± 6 1214 ± 139

2.84 ± 0.05 112 ± 8

1009 ± 79

7.3 ± 0.1 159 ± 18 259* ± 19

7.5 ± 0.4 0.4* ± 0.1 5.1 ± 0.4 1.6 ± 0.2 0.28 ± 0.03 9.0 ± 0.4

7.5 ± 0.5 1.0 ± 0.1 5.0 ± 0.4 1.25 ± 0.03 0.36 ± 0.04 6.0 ± 0.4

1.26 ± 0.05 1.60 ± 0.04 1.70 ± 0.02 1.5 ± 0.1 0.46 ± 0.02 7.0 ± 0.1

2.7 ± 0.1 1.8 ± 0.2 1.2 ± 0.1 1.40 ± 0.02 0.36 ± 0.02 7.2 ± 1

0.17* ± 0.01 3.95 ± 0.03 0.35 ± 0.01 0.20* ± 0.01 1.0 ± 0.03 13.5 ± 0.4

1.1 ± 0.1 3.0 ± 0.2 0.34 ± 0.03 0.55 ± 0.01 1.1 ± 0.1 13 ± 1

0.029* ± 0.004 3.30 ± 0.04 0.60 ± 0.01 0.11* ± 0.01 0.43 ± 0.01 8.2 ± 0.2

0.05 ± 0.04 2.6 ± 0.3 0.60 ± 0.04 0.56 ± 0.01 0.35 ± 0.03 9.0 ± 1

0.68* ± 0.01 1.6 ± 0.1 0.94 ± 0.03 1.0 ± 0.1 0.521 ± 0.004 3.2 ± 0.1

0.45 ± 0.01 1.3 ± 0.1 0.7 ± 0.1 1.23 ± 0.02 0.163 ± 0.004 6±1

0.98 ± 0.02 1.02 ± 0.05 2.0 ± 0.1 1.3 ± 0.1 0.20* ± 0.01 8.0 ± 0.5

1.0 ± 0.1 0.87 ± 0.04 2.3 ± 0.1 1.25 ± 0.02 0.10 ± 0.04 8.0 ± 0.3

0.17* ± 0.02 2.3 ± 0.2 0.36 ± 0.01 0.20* ± 0.01 0.6 ± 0.1 12.3 ± 0.3

1.009 ± 0.004 1.5 ± 0.1 0.29 ± 0.04 0.6 ± 0.1 0.6 ± 0.1 13.0 ± 0.3

* Values lower than SEC ** Values are expressed in mg/kg “-“ not found in LIBS determinations

26


355* ± 107

ICPOES

S9

LIBS

ICPOES

LIBS

11.3 ± 0.1 1.30 ± 0.05

-

18 ± 4

-

1.6 ± 0.1

0.022* ± 0.003

0.034 ± 0.005

4.3 ± 0.2 4.7* ± 0.3 18* ± 5

4.2 ± 0.1 12* ± 1 310* ± 8

3.1 ± 0.2 33* ± 3

5.3 ± 0.3 103 ± 1

25* ± 2

190* ± 26

0.074* ± 0.003 17 ± 1

0.12 ± 0.01 17.9 ± 1.2 0.6 ± 0.1 1.4 ± 0.1 0.25 ± 0.02 1.1 ± 0.3

10.9 ± 0.3 4.5 ± 0.2 7.0 ± 0.4 0.11* ± 0.01 0.20 ± 0.01 7.0 ± 0.3

11 ± 1

0.38 ± 0.02 1.0 ± 0.1 0.048 ± 0.002 0.789* ± 0.005

4.0 ± 0.2 7.2 ± 0.3 0.55 ± 0.01 0.31 ± 0.04 7.3 ± 0.3


Page 28 of 34

Table 4. Comparison Among Selected References and the Present Study Regarding Fertilizer Analysis by LIBS. Matrix Soil, commercial fertilizers, mixtures of soil and fertilizers Phosphate fertilizers

Potassium fertilizers

Phosphate and potassium fertilizers

Analytes P, Fe, Mg, Ca and Na

Remarks Soil was sieved (1 mm mesh)

Cd, Cr and Pb

Samples were homogenized by cryogenic and planetary ball milling

K, Na and Mg

N, P, K, Ca and Mg

No special sample preparation

Synthetic samples were prepared by mixing powdered material from real fertilizer samples and raw materials

Diammonium phosphate (DAP) fertilizers Organic and inorganic fertilizers

P, Mn, Mg, Fe, Ti, Mo, Ni, Co, Al, Cr, Pb and U P

Organic mineral fertilizers

Al, Ca, Cr, Cu, Fe, K, Mg, Mn, Na, Zn and P

Double pulse was used

B, Ca, Cd, Cr, Cu, Mg, Mn, Na, Pb and Zn

Samples were ground and sieved (42 µm)

Mineral fertilizers

No special sample preparation

Samples were ground and sieved (100 mesh sieve)

LIBS setup Nd:YAG laser (1064 nm) operating at 53 mJ, 10 ns pulse, 0.1-5.3 µs of delay and ICCD camera Nd:YAG laser (1064 nm) operating with 30 pulses of 50 J/cm2, 750 µm of spot size, 2.0 µs of delay and ICCD (intensified charged couple device) camera

LOD (mg/kg) P (46-251), Fe (10-50), Mg (4-7), Ca (89) and Na (22)

14 Cd (1), Cr (2) and Pb (15) 18

Nd:YAG laser (1064 nm) operating with 1000 pulses of 20-45 mJ, 0.75 µs of delay and CCD spectrometer Nd:YAG laser (532 nm), 8 ns pulse operating with 100 pulses of 70 mJ, 1.7 µs of delay and CCD spectrometer

Not mentioned

Nd:YAG laser (1064 nm) operating at 50 mJ, 6 ns pulse, 3.0 µs of delay and ICCD camera Nd:YAG laser (1064 nm) operating with 100 pulses of 60 mJ, 2.5 µs of delay, 100 µm of spot size and CCD spectrometer Two Nd:YAG lasers operating at 1064 (75 mJ and width of 6s) and 532 nm (180 mJ and width of 4s), 1.0 µs of delay and ICCD camera with 1024 × 1024 pixels.

Relative abundance: P (27*), Mn (18*), Mg (14*), Fe (8*), Ti (7*), Mo (8*), Ni (5*), Co (2*), Al (0.6*), Cr (0.4*), Pb (1*) and U (0.3*) P (0.5*)

Nd:YAG laser (1064 nm), 8 ns pulse operating at 850 pulses of 75 mJ, 50 µm of spot size, 2.0 µs of delay and multi-channel CCD spectrometer

*Values are in % **SP = Single pulse ***DP = Double pulse

27


Reference

19 P (13.3) and K (11.4) 20

21

22 Ca (SP**: 0.04-0.68 and DP***: 0.02-0.34), Cr (SP: 78 and DP: 28-54), K (SP: 196 and DP: 128), Fe (SP: 4900 and DP: 310-2103), Mn (SP: 46 and DP: 9.8), Mg (SP: 38-84 and DP: 5-15), Na (SP: 101 and DP: 38), P (SP: 2386-5430 and DP: 922-1430) and Zn (SP: 960 and DP: 323) B (0.1*), Ca (0.3*), Cd (2.1), Cr (32), Cu (0.6*), Mg (0.07*), Mn (0.05*), Na (0.02*), Pb (0.2*) and Zn (1.0*)

23

This study

Page 29 of 34


Figure 1 2.5 Fractional factorial design Central composite design Optimum conditions

2.0

0.60

D = 0.48 + 0.13v1

0.52

Delay time (µs)

0.44

1.5

0.36 0.28

1.0

0.5

0.0 45

50

55

60

65

70

75

80

85

Energy (mJ)


90

95

100


Page 30 of 34

Figure 2 50000

12 normalization modes

Raw data

Signal Intensity

40000

30000

Mean center

12 PLS models

20000

10000

Regression vector 0 200

400

600

800

1000

Inspection and normalization selection

Wavelength (nm)

0.010

Linear calibration

0.006

0.004

Regression vector

Signal Intensity

0.008

0.002

Selected wavelength 0.000 0

1

2

3

4

5

6

7

200

8

400

600

Wavelength (nm)

Mn reference concentration (%)


800

1000

Page 31 of 34


Figure 3 300

6000

A

2400

800

B

B(I) 249.68

C

D

Cr(I) 425.43

Cd(I) 467.81

Pb(I) 368.35 600

4500

1800

1500

100

249.4

249.6

249.8

368.0

250.0

368.2

E

200

368.4

425.2

F

600

425.4

467.6

425.6

10000

20000

G

Mn(I) 475.40

468.0

468.4

Wavelength (nm)

Wavelength (nm)

16000

Zn(I) 472.21

1200

0

368.6

Wavelength (nm)

Wavelength (nm) 5600

400

0

0

0

Singnal intensity

3000

Signal intensity

Signal intensity

Signal intensity

200

Mn(I) 478.34

H Zn(I) 481.05

15000 8000 4200

7500

1400

1600

800

0 471.8

472.2

Wavelength (nm)

472.4

474.9

3000

2000

5000

2500

1000

0

0 472.0

Signal intensity

2800

2400

Signal intensity

Signal intensity

Signal intensity

10000

475.2

475.5

477.9

475.8

0 478.2

478.5

Wavelength (nm)

Wavelength (nm)

30


480.8

481.0

Wavelength (nm)

481.2


24000

I

5500

Mn(I) 482.35

2600

J

15000

K

Cu(I) 515.32

Cu(I) 510.55

4400

12000

Page 32 of 34

L

Mg(I) 517.27

1950

3300

2200

1000

1100

0

0 482.0

482.2

482.4

482.6

1300

650

0

510.2

Wavelength (nm)

510.4

510.6

3400

N

515.1

515.4

515.7

517.0

Wavelength (nm)

4500

Mg(I) 518.36

5000

0

514.8

510.8

Wavelength (nm)

22000

M

Signal intensity

2000

Signal intensity

Signal intensity

Signal intensity

10000

3000

O

517.2

517.4

Wavelength (nm) 60000

Ca(I) 527.03

P

Cu(I) 521.81

Na(I) 588.99 2550

16500

45000

Na(I) 589.59

5500

1500

518.0

518.3

Wavelength (nm)

518.6

1700

850

521.5

521.8

522.1

0 526.7

Wavelength (nm)

30000

15000

0

0

0

Signal intensity

11000

Signal intensity

Signal intensity

Signal intensity

3000

526.9

527.1

Wavelength (nm)

31


588.4

588.9

589.4

Wavelength (nm)

589.9

Page 33 of 34


Figure 4 12

A

20

Reference (ICP OES) Cu(I) 510.55 (Height) Cu(I) 515.32 (Height) Cu(I) 521.81 (Height)

10 8

8

B

C

Reference (ICP OES) Mg(I) 517.27 (Area) Mg(I) 518.36 (Area)

16

Reference (ICP OES) Mn(I) 475.40 (Area) Mn(I) 478.34 (Area) Mn(I) 482.35 (Area)

7

Concetration (%)

Concentration (%)

2

4

3

2

5 4 3 2

SEC

1

1

1

SEC

SEC 0

0

S2

S3

S4

S5

S6

S7

S8

S9

0 S1

S2

S3

Sample

S4

S5

S6

S7

S8

S9

S1

S2

S3

S4

Sample 18

2.0

Reference (ICP OES) Na(I) 588.99 (Area) Na(I) 589.59 (Area)

D

1.2

0.8

0.4

SEC

S5

Sample

E

Reference (ICP OES) Zn(I) 334.50 (Area) Zn(I) 472.21 (Area) Zn(I) 481.05 (Area)

15

1.6

Concentration (%)

S1

Concentration (%)

Concentration (%)

6

3

12

9

6

3

SEC 0.0

0 S1

S2

S3

S4

S5

S6

S7

S8

S9

S1

S2

S3

Sample

S4

S5

Sample

32


S6

S7

S8

S9

S6

S7

S8

S9


TOC

33


Page 34 of 34

Direct Determination of Contaminants and Major and Minor Nutrients

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