PAINT AND VARNISH CALIBRATION

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ry Systems 92 (2008) 131–137www.elsevier.com/locate/chemolab

Chemometrics and Intelligent Laborato

Calibration of paint and varnish properties: Potentialities using X-raySpectroscopy and Partial Least Squares

Fabíola Manhas Verbi Pereira ⁎, Maria Izabel Maretti Silveira Bueno

Departamento de Química Analítica, Instituto de Química, Universidade Estadual de Campinas (UNICAMP), P.O. Box 6154, 13084-971 Campinas, SP, Brazil

Received 4 December 2007; received in revised form 21 January 2008; accepted 14 February 2008Available online 20 February 2008

Abstract

This work presents a method for multivariate calibration of the properties of varnishes and paints, combining X-ray Spectroscopy (XRS) withPartial Least Squares (PLS) data treatment. The potential calibrated property for varnishes was density (calibrated values raging from 0.88 to0.92 g cm−3 and absolute errors from 0.0014 to 0.055 g cm−3). For paints, the potential calibrated property was Stormer viscosity (calibratedvalues ranging from 72 to 89 KU and absolute errors from 0.4 to 8 KU). The information here presented was also promoted by the influence ofCompton and Rayleigh effects observed in X-ray spectra. X-ray fluorescence spectra were obtained using an ED- XRF system equipped with a Rhtarget X-ray tube. The importance of the X-ray scattering region (from 18.00 to 24.00 keV), where Compton and Rayleigh effects occur, wereevidenced by the best PLS models and lower error values. With the PLS method and X-ray spectra it was also possible to calibrate the content %(m/m) of two organic elements, C and H. It is important to highlight that the proposed analytical method permitted to save time in the qualitycontrol of the studied samples, reducing the generation of residues.© 2008 Elsevier B.V. All rights reserved.

Keywords: X-ray Spectroscopy; Compton and Rayleigh effects; Partial Least Squares; Quality control; Paint; Varnish

1. Introduction

Several analytical techniques contribute to the analysis ofpaint products. Within this context, works based on chromato-graphy [1] for determining organic composition (polymers,resins and additives) of paints can be mentioned, even thermalanalysis, as for example, DSC (Differential Scanning Calori-metry) for degradation and/or volatility monitoring [2]. NuclearMagnetic Resonance Spectroscopy (NMR) has been used tostudy the oxidative and hydrolysis processes associated withpaints [3]. Fourier Transform Infrared Spectroscopy (FTIR) hasbeen extensively applied to the process of organic compositionstudies [4]; among other analytical techniques [5]. In this study,X-ray Spectroscopy (XRS) potentialities for quality control ofvarnishes and paints are presented.

XRS is traditionally used to identify and quantify inorganicspecies [6]. Due to the non-destructive property of XRS, its

⁎ Corresponding author.E-mail address: [email protected] (F.M.V. Pereira).

0169-7439/$ - see front matter © 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.chemolab.2008.02.003

most common use in paints is dedicated to the investigation ofarcheological and art objects [6–9]. Other significant attributesassociated with this technique are: the availability of portableequipments for in loco analyses [10] and the facilities forselecting sample areas with different sizes of collimator (inconventional equipments from 1 to 10 mm) [7,11].

For quality control of paints, this technique is mainly em-ployed in raw material analyses, such as additives, mineralcompounds and inorganic pigments [12]. Investigations about thequality control of end-products (varnishes and paints, forexample) have not yet been reported in previousXRS applicationsreviews [6].

Some preliminary results obtained by our research groupreported the use of XRS for qualitative evaluation of paintssubmitted to performance tests [13]. For this purpose threedifferent paints (latex, corrective fluid and varnish) were appliedover wood substrates and then treated at room temperature withinfrared radiation and solar light. X-ray fluorescence spectrawere obtained in brand new and irradiated samples using aRh target X-ray tube ED-XRF system and PCA (PrincipalComponent Analysis) was conducted using the spectral region

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http://dx.doi.org/10.1016/j.chemolab.2008.02.003

Table 1Reference values (RV) for properties of different commercial samples ofvarnishes

Samples Resin base RV

Non-volatilecontent (%)

Density(g cm−3)

Carboncontent% (m/m)

Hydrogencontent% (m/m)

1 – 42.2 0.91 70.30 9.882 Alkyd 42.9 0.88 76.45 10.173 Alkyd 43.8 0.91 70.52 10.134 Alkyd 33.8 0.88 72.65 10.585 Alkyd 51.9 0.92 75.18 10.926 Phenolic 42.3 0.89 76.36 11.477 Alkyd 53.6 0.95 69.14 10.108 Urethane oil 53.3 0.92 73.30 10.689 Alkyd 43.7 0.89 75.15 11.0210 Alkyd 47.3 0.88 73.45 10.7011 Alkyd 43.9 0.88 71.13 10.1512 Alkyd 48.2 0.93 67.50 9.7713 Alkyd 41.8 0.89 76.28 11.4214 Alkyd 44.4 0.91 74.76 10.6315 Phenolic 49.5 0.89 80.51 10.8016 Alkyd 51.5 0.92 73.82 10.65

132 F.M.V. Pereira, M.I.M.S. Bueno / Chemometrics and Intelligent Laboratory Systems 92 (2008) 131–137

from 18 to 24 keV, where Compton and Rayleigh scatteringsmanifested [14–19]. The mentioned work shows that by usingthe scattering spectral region it was possible to discriminate thesamples in different stages of degradation.

The X-ray fluorescence is not the only process verified inspectra from samples excited by X-ray source. A fraction ofX-ray photons from the source (in our case, Rh target X-ray tube)is scattered by atoms of the samples, mainly by the light ones.The information detected on spectrum is related to (i) X-rayfluorescence radiation from heavy atoms of the samples(pigments and additives, for example), (ii) continuous scatteredradiation (background in the X-ray spectra) and (iii) Comptonand Rayleigh effects. The Compton (incoherent scatter) andRayleigh (coherent scatter) effects contribute deeply in quanti-tative XRF analysis; then, fundamental parameter methods canbe used. Examples that can be mentioned are in environmental,biological and geological analysis. The scattering region fromX-ray spectra is mainly associated with light elements (C, H, O,among others) from the matrix, and the characteristics line forthese elements are not visualized on ED-XRF spectra, this regioncan present powerful properties for qualitative or even quanti-tative determination of light elements [6,7]. In this work, ascommented in the reference 13, this region can be used to detectchanges inmatrices composed by light elements (as example, theresins from the paint samples).

The studies for this work were developed with Partial LeastSquares (PLS) [20–23] for multivariate calibration propositionsof paint and varnish properties, with possible industrialapplication aimed for quality control. The reference values ofthese properties were obtained from ASTM reference methods(American Standard Test and Methods).

2. Experimental

2.1. Apparatus

XRS experiments were performed using bench-top EnergyDispersive X-ray Fluorescence (ED-XRF) equipment, ShimadzuEDX 700 (Kyoto, Japan). This equipment is provided with a Rhtarget X-ray tube that can be operated at a maximum voltage of50 kV. The measurements were performed in air, with a beamcollimation of 10mm, 25% of detector dead time, with the currentautomatically adjusted during spectrum acquisition. The Shi-madzu EDX 700 has the following characteristics: (1) X-raygenerator – Tube voltage from 5 to 50 kV, tube current from1 to 1000 μA and exposure area from 1 to 10 mm diameter;(2) detector Si(Li), detection area of 10 mm2 and resolution ofb155 eV (Mn Kα, 1500 cps) (3) the angle of X-ray photonsincident beamwas 45°, and (4) the angle between its Rh target X-ray tube and detector was 90°.

2.2. Samples

A total of 26 samples (16 varnishes and 10 paints) from 7different manufacturers purchased in a local market were eval-uated in this work. Among the 16 varnish samples, 12 sampleswere composed mainly of alkyd resin base, 2 of phenolic resin,

1 with no specific information about the resin base on the labelproduct and 1 of urethane oil resin. The 10 samples of paintswere all white colored and composed of alkyd resin.

2.3. Analytical measurements

For the spectra acquisition, approximately 2 g of liquid sampleswere put into Teflon cells and then covered with a Mylar™ film(3 μm thickness). The measurement time was 100 s. In all casesthe spectra were recorded from 0 to 40 keV, with a resolution of0.02 keV, resulting in 2047 points for each spectrum. For the datamatrix (independent variables) construction, each sample wasdivided in 3 replicates and each one was analyzed three times.This procedure was adopted in order to avoid heterogeneity inthe samples. At the end 9 spectra per sample were obtained andaveraged. The average variance and average standard deviation(n=9, for each sample) were calculated for all spectral data.

2.4. PLS application

The data set was divided into two separate matrices: one forthe varnish samples and another for the paint samples.

For the construction of the validation and calibration data set,12 and 4 commercial varnish samples were used, respectively.The matrix of the varnish samples consisted of 16 lines (samples)resulting in the average spectra (procedure explained in Section2.3 Analytical measurements) and 2047 columns (variables).

In the case of paints, validation and calibration data sets wereconstructed using 8 and 2 samples, respectively. The sameprocedure for the varnish samples was fitted for the constructionof the data matrix, generating 10 lines and 2047 columns.

For data treatment and chemometrics applications, MatLab6.5 (The MathWorks, Natick, USA) and PLS Toolbox 2.02(Eigenvector Research, Wenatchee, USA) were used.

Table 2Reference values (RV) for properties of different commercial samples of paints

Samples Resinbase

RV

Non-volatilecontent(%)

Density(g cm−3)

Fordviscosity(s−1)

Stormerviscosity(KU) a


Hydrogencontent%(m/m)

1 Alkyd 58.7 1.93 150.0 80 52.37 6.392 Alkyd 51.2 1.09 237.0 89 44.06 7.653 Alkyd 45.0 1.09 210.0 87 36.05 7.254 Alkyd 59.4 1.09 201.0 86 54.11 7.535 Alkyd 39.6 1.02 204.0 84 38.34 7.456 Alkyd 43.6 1.05 249.0 85 40.99 8.247 Alkyd 36.9 1.01 240.0 84 35.22 7.968 Alkyd 41.1 1.03 185.0 78 34.62 8.609 Alkyd 60.9 1.08 141.0 72 58.53 8.2710 Alkyd 38.7 1.03 260.0 84 36.13 9.85

a KU = Krebs units.

133F.M.V. Pereira, M.I.M.S. Bueno / Chemometrics and Intelligent Laboratory Systems 92 (2008) 131–137

2.5. Reference methods

The reference values for the PLS models were obtained fromthe following tests. The properties of 16 varnish samples wereevaluated by two different tests: non-volatile content (%) fol-lowingASTM-D 1644 and density usingASTM-D 1475. Besidesthe above-mentioned ASTM tests, two additional tests wereapplied to the paints; Ford viscosity cup usingASTM-D 1200 andStormer viscosity followingASTM-D 562. Additionally, C andHcontent % (m/m) determination for both groups (varnishes andpaints) of samples was performed using an Elemental analyser(CHNS/O), model PE-2400 SERIES II (Perkin-Elmer, Waltham,USA). For the most part, the mentioned ASTM tests requiredmany different equipments and long performance time, as well asgenerating residues.

Fig. 1. X-ray spectra for sample 2 of varnish (alkyd resin

3. Results and discussion

3.1. Selection of varnish and paint samples for calibrationmodel

The obtained reference value properties are resumed in Table 1for varnishes and Table 2 for paints. The data set was divided intotwo separate matrices: one for varnish samples and another forpaint samples. This procedure was adopted due to the fact that thechemical composition of these samples has some special particu-larities, as for instance and to a great degree, resin base and insome samples the presence of pigments (mainly, for paints).

PCA was used to help the selection of samples in both datasets (calibration and validation). For this analysis, the pre-pro-cessing data was mean centered for variables. From the sampleclusters visualized in the score plots it is helpful to perform asample selection with less subjectivity and those that are morerepresentative for both data sets (calibration and validation).

Then, for composition of the calibration set the varnish sam-ples chosen were 1, 2, 4–9, 12–14 and 16. For the validation set,the samples were 3, 10, 11 and 15.

For composition of the calibration set paint samples, thesamples selected were 1–5 and, 8–10. For the validation set, thesamples were 6 and 7.

3.2. Data evaluation from XRS for varnishes samples

The data variability can be estimated by the mean of someparameters; therefore the average variance and average standarddeviation for each sample's spectral data were calculated,including their replicates (n=9). These ranges of values werefrom 0.0002 to 0.001 for average variance, and the averagestandard deviation was from 0.01 to 0.02. The calculated values

base) (a) and sample 1 of paint (alkyd resin base) (b).

Table 3Parameters required for PLS models of varnish samples

Properties Non-volatilecontent (%)

Density(g cm−3

)


Hydrogencontent% (m/m)

Spectral region 0.00–40.00 keV

LV 2 5 7 9RMSECV 5.5 0.021 1.95 0.56RMSEV 3.6 0.023 3.71 0.54% explained variance (X) 98 100 100 100

(Y) 98 100 100 100



(Y) 99 100 100 100



(Y) 95 99 98 100


corresponded to the maximum and minimum values, includingall samples.

Fig. 1a depicts an example of XRS spectra generated fromvarnish samples. The spectrum shows the presence of Kαcharacteristic lines of emission for Mn, Fe, Zn and Zr and Kβfor Fe and Zr. In the cases of Zn and Zr, they originated fromdrying agents, such as, these elements' octoates, which alsodemonstrate other end-product functions: gloss intensification,increasing of adherence film and flexibility [12]. The most

Table 4Parameters required for PLS models of paint samples

Properties Non-volatilecontent (%)

Density(g cm−3)

Fordviscos


LV 1 1 1RMSECV 6.6 0.35 50.5RMSEV 2.2 0.14 92.4% explained variance (X) 99 99 99

(Y) 98 93 87



(Y) 98 91 97



(Y) 97 92 87

relevant information from this spectrum is the high intensitylines for X-ray scattering from X-ray source (Rh target X-raytube), assigned with a dotted black line in Fig. 1a. In thesesamples it is justified by the high organic content.

3.3. Data evaluation for paint samples from XRS

For the spectral data of paints, the calculated average vari-ance was from 0.0001 to 0.002 and the average standarddeviation ranged from 0.01 to 0.04 (n=9).

The spectrum of Fig. 1b shows the presence of lines Kα forTi, Zn and Zr; and Kβ for Ti and Zr. The presence of Ti, as TiO2,acts as paint pigment attributed to the white color. The otherelements (Zn and Zr) have the same function mentioned for thevarnishes; these are related to compounds acting as dryingagents. The important fact is that X-ray scattering is also veryexpressive for paint samples.

3.4. Evaluations for the construction of PLS models

The construction of PLS models was studied by pre-processing the variables with mean centered data. However,no significative difference was verified on the calibrated valueswith or without pre-processing variables for all models. Becauseof that, the samples and the spectral data were not pre-processed.

In this study, three regions of XRS spectra (Tables 3 and 4)were evaluated. In the first one, the whole spectrum range from0.00 to 40.00 keV (2047 variables) was considered. In thesecond, only the Rh scattering region was used, from 18.00 to24.00 keV (301 variables). This region is related to theCompton and Rayleigh effects. The third and last approach wasapplied from 0.00 to 17.98 keV (900 variables), which excludesthe Rh X-ray scattering region.

ity (s−1)Stormerviscosity (KU)

Carbon content% (m/m)

Hydrogencontent % (m/m)

2 1 47 8.40 0.922 2.98 0.48100 99 100100 96 100

2 3 17 5.12 0.942 1.48 0.66100 100 100100 99 99

2 1 17 8.40 0.943 3.99 2.37100 99 99100 95 86

Fig. 2. Reference and predicted values for calibration and validation sets ofdensity property of varnish samples using X-ray scattering region with theirrelative errors (%) – see triangle symbols.

Fig. 3. Reference and predicted values for calibration and validation sets ofStormer viscosity of paint samples using X-ray scattering region with theirrelative errors (%) – see triangle symbols.


The PLS method requires a preliminary evaluation of someparameters for the construction of models. In this proposition afull cross validation (leave-one-out) method was used. Consider-ing this method, the selection of the number of adequate latentvariables (LV)was related to the low values of PRESS (PredictiveResidual Error Sum of Squares) and other relevant parameters,such as RMSECV (RootMean Square Error for Cross Validation)and RMSEV (Root Mean Square Error for Validation), these areresumed in Tables 3 and 4 for varnishes and paints, respectively.

3.5. Generation of PLS models for varnishes

For generating the PLS models, 2 properties (from ASTM)for varnishes were studied as shown in Table 3. Among thestudied properties, the most potential calibrated property wasdensity. This fact was related to the values of absolute errors forthe whole spectral range using 5 latent variables (LV), whichwere from 0.000073 to 0.041 g cm−3 (calibration set) andfrom 0.013 to 0.043 g cm−3 (validation set). With only X-rayscattering the region, the absolute errors were from 0.0014 to0.055 g cm−3 (calibration set) and, from 0.010 to 0.037 g cm−3

(validation set). For this model, the number of LVs was 3. Usingthe range of the XRS spectra from 0.00 to 17.98 keV, 3 LV werenecessary, these values were from 0.0016 to 0.15 g cm−3

(calibration set) and, 0.062 to 0.13 g cm−3 (validation set).This study verified the potentiality of X-ray scattering region

considering the lower absolute errors of the model. This infor-mation was based on the minimal difference between the modelsof the range from 18.00 to 24.00 keV and on the whole spectralrange (0.00 to 40.00 keV). In addition, the model studied,excluding the X-ray scattering region (0.00 to 17.98 keV),showed the highest absolute errors.

The figure of merit – accuracy – was related to the RMSEVvalues [24], where the best model was the one that presented thelowest value. Consequently, the model with the X-ray scatteringregion showed favorable results, where the RMSEV value was0.019 g cm−3 (Table 3) when compared to the others regionsthis same parameter was 0.023 g cm−3 with the whole spectral

range and 0.11 g cm−3 with the range of the XRS spectra from0.00 to 17.98 keV.

The interval of relative errors for calibration and for valida-tion sets can be verified in Fig. 2. The difference was from −4 to6%, indicated by the triangles.

3.6. Generation of PLS models for paints

Four paint properties determined by reference methods (fromASTM) were evaluated, as seen in Table 4. Stormer viscosity ofpaints was the most evident property to be calibrated with thisdata set and for all models, 2 LV were required. The first modelusing whole spectral range showed absolute errors from 2 to8 KU for the calibration set and from 2 to 3 KU for thevalidation set. With the X-ray scattering region, the errors hadthe following values for the calibration from 0.4 to 8 KU and forthe validation set these were from 1 to 3 KU. In the last region(0.00–17.98 keV), the errors characterized for calibration werefrom 1 to 12 KU and validation from 3 to 4 KU. These lastresults refocus the minimal importance of the range from 0.00 to17.98 keV, because the influence of the light matrix elementswas excluded.

The values for accuracy were the same for the models withX-ray scattering region and the whole spectral range (Table 4),with the RMSEV value equal to 2 KU. And, for the model withthe range from 0.00 to 17.98 keV this value was 3 KU. Theseresults can be related to the greatest influence of the X-rayscattering effects on the model fitting.

The interval difference of relative errors for calibration andvalidation models was from −11 to 7%, shown by the trianglesymbols in Fig. 3.

3.7. Calibration of C and H content % (m/m) in varnish andpaint samples

With the PLS method and X-ray spectra it was possible tocalibrate two content % (m/m) organic elements, C and H.This result was relevant due to the fact that the measurements


had been performed in conventional ED-XRF equipment.Moreover, the main objective was to demonstrate the potentialof this X-ray range for detection of light elements matrix, suchas C and H in the samples' composition [25]. The results of themodels were also divided in two parts, for varnishes and forpaints.

The key point for multivariate calibration of C and H usingX-ray spectra information was the possibility of obtainingaccuracy for organic determination by using RMSEV values.

For the calibration of C content % (m/m) in varnishes usingthe whole spectral range, the calibration set showed absoluteerrors from 0.13 to 0.90% (m/m) and for validation from 1.70 to5.60% (m/m). With the information of X-ray scattering region,the calibration set indicated absolute errors from 0.19 to 3.67%(m/m) and, for validation absolute errors from 1.04 to 3.43%(m/m). The region from 0.00 to 17.98 keV presented absoluteerrors from 0.75 to 27.60% (m/m) and for validation from 0.87to 20.68% (m/m).

The PLS calibration of H content % (m/m) using wholeX-ray spectral range had the following absolute errors: for thecalibration set it was from 0.0017 to 0.041% (m/m) and for thevalidation set, from 0.074 to 0.94% (m/m). With the X-rayscattering region, the calibration set showed errors from 0.063to 1.07% (m/m) and with the validation set from 0.15 to 0.64%(m/m). The model excluding the Compton and Rayleigh effects(0.00–17.98 keV) showed absolute errors from 0.22 to 1.21%(m/m) for the calibration samples, and from 0.66 to 1.80%(m/m) for the validation samples.

For both (C and H), the best model was related to Rhscattering region in general. Comparison of the RMSEV values(Table 3) for the three regions studied showed the lower values2.60% (m/m) and 0.52% (m/m) for C and H, respectively.

The C content % (m/m) results for calibration of paintsamples, considering the whole spectral range, indicatedabsolute errors of calibration in the set from 0.53 to 15.07%(m/m) and, in the validation set from 0.084 to 4.22% (m/m). TheX-ray scattering region showed absolute errors for thecalibration set from 0.87 to 8.04% (m/m) and, for the validationset from 0.85 to 1.90% (m/m). The absolute errors for the regionfrom 0.00 to 17.98 keV were very elevated for the calibrationset from 0.93 to 16.38% (m/m) and, for the validation set, theywere from 0.65 to 5.61% (m/m).

For the H content % (m/m) models, the results for the wholespectral range, considering the calibration set, were from 0.017to 0.87% (m/m) and, for the validation set, from 0.47 to 0.48%(m/m). The X-ray scattering region again showed the lowervalues of absolute errors, for the calibration set they were from0.047 to 1.25% (m/m) and, for the validation set, from 0.21 to0.91% (m/m). For the region without the X-ray scattering, theabsolute errors were from 0.51 to 6.54% (m/m) for calibrationand, from 1.49 to 3.01% (m/m) for validation.

The paint results were somewhat different in comparison tothe varnishes, where the lower RMSEV value for C was 1.48%(m/m), considering the Rh scattering region and for H thisvalue was 0.48% (m/m), with the model using whole spectralrange. In the last case, the value for H excluding Rh scatteringregion was 2.37% (m/m), thus, the importance of the inclu-

sion of Compton and Rayleigh effects for PLS models wasevidenced.

4. Conclusions

The PLS models for some properties of varnishes and paintsdemonstrated favorable potentialities, mainly for the industrialsector. The values for properties such as, density for varnishsamples and Stormer viscosity for paints are only inceptivepossibilities for the use of the XRS and PLS method.

The absolute errors for other properties were relativelyhigher, considering the three X-ray spectra range. The groupproviding more variations to the range of property values can beexplained as being heterogeneousness, but the results showedinitial potentialities for multivariate calibration of these proper-ties by applying XRS combined to PLS for quality control ofpaints and related products.

The importance of the X-ray scattering region (from 18.00 to24.00 keV), where Compton and Rayleigh effects occur, wereevidenced by the best models of calibration and lower absoluteerrors values. And, with these variables it was possible to verifythe potential of this X-ray range for detection of light elementsmatrix, such as C and H in the samples' composition, whencombined with multivariate calibration.

Finally, it is important to highlight the analytical step reductionpossibility for quality control of the studied samples, decreasingthe generation of residues.

Acknowledgement

The authors are grateful to FAPESP (Fundação de Amparo àPesquisa do Estado de São Paulo) for the fellowship to FMVP(process 04/02094-3).

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PAINT AND VARNISH CALIBRATION

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