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Detection and quantication of milk adulteration using time domainnuclear magnetic resonance (TD-NMR)

Poliana M. Santos a,⁎, Edenir R. Pereira-Filho b, Luiz A. Colnago c

a Department of Chemistry and Biology, Federal Technological University of Paraná (UTFPR), Rua Deputado Heitor de Alencar Furtado, 5000, 81280-340 Curitiba, PR, Brazilb Department of Chemistry, Federal University of São Carlos (UFSCar), Rodovia SP 310, km 235, P. O. Box 676, 13565-905 São Carlos, SP, Brazilc Embrapa Instrumentation, Rua XV de Novembro, 1452, P. O. Box 741, 13560-970 São Carlos, SP, Brazil

a b s t r a c ta r t i c l e i n f o

 Article history:Received 18 May 2015

Received in revised form 14 July 2015

Accepted 17 July 2015

Available online 26 July 2015

Keywords:

Milk adulteration1H TD-NMR 

Multivariate analysis

This study explores the possibilities for application of   1H Time Domain Nuclear Magnetic Resonance (1HTD-NMR) as a rapid method for assessment of milk quality. Whey, urea, hydrogen peroxide, synthetic urine

and synthetic milkwere added to the milk samples at concentrations of 5, 15, 25, 35 and 50% v/v. Discrete expo-nentialanalysis of the1H TD-NMR relaxationdecay revealedthat the milksamples contained a single water com-

ponent as well as that the T2 relaxation times differed signicantly with respect to the level of adulteration.Regression models obtained with the full   1H TD-NMR (multivariate approach) and T2  value (univariate

approach) demonstrate a strong correlation to estimate the level of adulteration in milk samples, with standarderrors of prediction of 2.34 and 3.79% v/v, respectively. SIMCA and kNN classication models were developed to

classify control from adulterated milk samples and adulterated milk samples based on the level of adulteration.The results obtained with both models showed a similar and quite satisfactorily predictability, with sensitivityand specicity ranging from 0.66 to 1.00. This study clearly demonstrates that  1H TD-NMR could be applied as

an alternative rapid method for detecting and quantifying milk adulteration.

© 2015 Elsevier B.V. All rights reserved.

1. Introduction

The application of   1H time domain nuclear magnetic resonance(1H TD-NMR) spectroscopy in food science has been widely investigat-ed in the last few years [1]. Several factors have contributed to the

rapidly increasing use of this technique in the analysis and qualitycontrol of foods, including: no sample preparation required, simple andfast measurement procedures, instrumental stability, noninvasiveness,and the potential for through-package analysis. Additionally, the low

cost of commercial benchtop NMR system, with permanent magnettechnology, has contributed to the growing popularity of TD-NMR.

In   1H TD-NMR spectroscopy, the proton relaxation (transversalrelaxation, T2) is monitored providing information about the mobility

of the nuclei. In the foodstuffs, 1H TD-NMR provide unique informationabout the nuclei described in terms of bound water, free water and byan exchange between these two states. One of the most reported appli-cations of  1H TD-NMR in food science has focused in the meat analysis.

This technique was suggested as an alternative method for determina-tion of water-holding capacity (WHC) [2–4] and the physical changesduring the conversion of muscle into meat [5], cooking [6] and freezing

storage [7].  1H TD-NMR has been also demonstrated to be a powerfultechnique for quality control of milk [8], cheese [9], honey [10], potato

[11], oil [12], intact fruits [13,14], and sauces [15], as it provides impor-

tant information about sensory attributes, food texture and ripeningstatus, in a completely non-invasive way.

Recently, the feasibility to apply 1H TD-NMR to check the authentic-ity of food was investigated [16,17]. Ribeiro et al. [16] have used  1H TD-

NMR to detect honey adulteration. Pure blossom honey samples werecollected by beekeepers and adulterated by adding of high fructosesyrup at a ratio of 0, 10, 25, 50, 75 and 100% (w/w). The results showedthat the relaxation times were signicantly affected by adulterant con-

centration in pure honey, suggesting that  1H TD-NMR could be used todiscriminate pure blossom honey from honey adulterated with highfructose corn syrup. In a different study, Zhang et al. [17] have applied1H TD-NMR to discriminate edible vegetable oil adulteration mixed

with used frying oil. Commercial corn, peanut, rapeseed and soybeanoil were purchased from a local supermarket and adulterated with fry-ing oil collected from local restaurants. The T2  distribution curvesshowed the presence of three peaks (three types of hydrogen protons),

and revealed that the major differences between the unused and usedoil appeared in the third peak. Using linear correlation against thepeak area and the percentage of adulteration, proved that  1H TD-NMR 

could be used to detect adulteration of vegetable oils mixed with usedfrying oil.

The goal of this study was to examine the feasibility of the applica-tion of   1H TD-NMR combined with multivariate analysis to identify

and quantify milk adulteration by the addition of tap water, whey,

Microchemical Journal 124 (2016) 15–19

⁎   Corresponding author. Tel.: +55 41 3279 4575.

E-mail address: polianasantos@utfpr.edu.br (P.M. Santos).

http://dx.doi.org/10.1016/j.microc.2015.07.013

0026-265X/© 2015 Elsevier B.V. All rights reserved.

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synthetic milk, synthetic urine, urea and hydrogen peroxide. Accordingto the database of food fraud developed by members of the USPharmacopeia Convention's Food Ingredients Intentional Adulterants,milk is one of the most common targets for adulteration and the

major issue for the dairy industry [18]. The establishment of a generalrapid method can have practical use in the dairy industry forinspectionof milk authenticity.

2. Materials and methods

 2.1. Samples

Bovine milk samples were furnished by Embrapa Southeast Live-stock (Brazilian Agricultural ResearchCorporation)located at São Carlos(São Paulo, Brazil). Milk samples were spiked with known levels of 

whey, urea, hydrogen peroxide, synthetic urine   [19] and syntheticmilk [20], following the same dilution process (5 to 50% v/v). A total of 78 milk samples were analyzed.

 2.2. TD-NMR measurements

1H TD-NMR experiments were evaluated using SLK-IF-1399 NMR spectrometer (Spinlock Magnetic Resonance Solution, Cordoba, AG)equipped with a Halbach permanent magnet of 0.23 T (9 MHz for  1H)

and 10 cm bore.   1H transverse relaxation time, T2, measurementswere performed using CPMG (Carr–Pursell–Meiboon–Gill) pulsesequence with a   π /2 pulse width of 16   μ s, time between echoes of 400 μ s and a recycle delay of 15 s.

 2.3. Data analysis

The   1H TD-NMR relaxation curves were maximum normalized

(normalized against the rst echo amplitude) and analyzed by discreteexponential   tting in the Origin software version 8.1 (OriginLab,Northampton, MA, USA). T2 values were obtained by mono-exponentialfunction according to the equation:

M t ð Þ¼XN n¼1

M 0;n exp   −t 

T  2;n

þ e t ð Þ

where M(t) is the residual magnetization at time t, M0,n is the concentra-tion or magnitude parameter of the  nth exponential,T2,n is the corre-

sponding transverse relaxation time constant, and e(t) is the residualerror.

Multivariate analysis based on principal component analysis (PCA),soft independent modeling of class analogy (SIMCA), k nearest neigh-

bors (kNN), and partial least squares regression (PLSR) were appliedin the full   1H TD-NMR relaxation curves for exploratory analysis(PCA), classication (SIMCA and kNN), and proposition of regressionmodels (PLSR). These analyses were developed in Pirouette® software

version 4.5 (Infometrix Inc., Woodville, WA, USA). Prior to the

multivariate analysis, the 1H TD-NMR relaxation curves were maximumnormalized and mean centered.

PCA was applied to obtain a global view of the main variation in the1H TD-NMR relaxation data. In PCA, the original data matrix   X   isdecomposed into a score matrix T , loading matrix P  and the residualsare collected in a matrix E:

X  ¼  TP T  þ E:

The scores contain information about the samples, and the loadingsinformation about the variables.

Partial least squares regression (PLSR) was used to predict the level

of adulteration ( Y , dependent variables) from the instrumental data(X , independent variables). The milk sample was randomly divided

into a calibration and validation sets for the PLSR modeling. Regression

model was also evaluated using linear regression by correlating the T2values against to the level of adulteration. Linear regression wasperformed according to thefollowing equation y = mx + b. The perfor-mance of multivariate and univariate models was evaluated in terms of 

the gures of merit accuracy, precision and linearity.SIMCA and kNN classication models were evaluated using the full

1H TD-NMR relaxation decays. For each chemometric classication

technique two strategies were made: the  rst model was developed

to classify the control milk samples from adulterated milk samples(authentication model); the second one was evaluated to discriminatethe milk samples based on the level of adulteration. Both models were

evaluated using sensitivity and specicity parameters. To build theclassication models, the  1H TD-NMR decays were divided into twothirds for the training set (calibration) and one third for the test set(validation). Prior to the analysis,   1H TD-NMR decays were mean

centered.

3. Results and discussion

 3.1. T  2 relaxation measurements

Fig. 1   shows the maximum normalized   1H TD-NMR relaxationcurves of the control and milk samples adulterated with water (from5 to 50% v/v). The effect of adulteration level in the T2 relaxation time

is visually apparent. Samples with high level of adulteration, those be-tween 35 and 50% v/v, relaxed more slowly than the control samples.

In order to verify the number of components in the milk samples,

Laplace inversion was performed in the 1H TD-NMR decays. The results

showedthat the T2 relaxation was characterized by one1H component.

This result agrees with previously studies found in the literature, whichreported that the proton transverse relaxation in skin milk is mainlymono-exponential due to the fast diffusive exchange of water between

the different compartments [21–23].Table 1 describes the T2 found by mono-exponential  tting. The

average and standard deviation of T2 ranged from 0.178 ± 0.002 s (for

control milk samples) to 0.358 ± 0.011 s (for milk adulterated withwater in 50% v/v). This behavior was obtained for all adulterants, except

for hydrogen peroxide. Hydrogen peroxide rapidly decomposes to oxy-gengas, which is paramagnetic, resulting in the decrease of T2 time. Re-

sults showed that samples adulteratedwith5% and 15% v/v of hydrogenperoxide relaxedfaster than control milk samples (0.178 ± 0.002 s).Onthe other hand, samples with 35% and 50%  v/v of hydrogen peroxideshowed similar T2 values as the milk samples adulterated at the same

level using other adulterants. This indicates that the dilution process isdriving the relaxation time.

Fig. 1. Maximum normalized  1H TD-NMR relaxation decays of control and milk samples

adulterated with water (5–

50% v/v).

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This result clearly showed that 1H TD-NMR is a sensitive method formonitoring the milk quality with signicant variation of T2   valuesamong control and adulterated milk samples.

 3.2. PCA analysis

To illustrate the different behavior of the control and adulteratedmilk samples, PCA was performed on the maximum normalized   1HTD-NMR relaxation decays (Fig. 1). Due to the nonlinear variation of 

the T2 values in the samples adulterated with hydrogen peroxide, the1H TD-NMR of this samples was not used in this analysis.

PC1, which accounted for 73.2% of the variation in the data, clearlyshowed differentiation in the samples according to the dilution process.

Control milk samples (T2 ~ 0.178 s) were grouped at one end of PC1,whereas milk samples adulterated in 50% (T2 ~ 0.362 s) were groupedat the other end (Fig. 2). No signicant differences were observedwith relation to the type of adulterant. This agreed with the T 2 values

shown in Table 1, assimilarT2 values were obtained for all milk samplesdiluted with targeted adulterant in the same level.

In summary, the PCA score plot indicated that the clustering of themilk samples is presumably based on the variation of T2 values listed

in Table 1.

 3.2. Regression models

Although the results obtained with PCA analysis suggested that only

the T2 variable is responsible for the main variationin the data, a multi-variate regression model was developed using the full   1H TD-NMR 

(PLSR model) and the performance was compared with the univariatemodel. A summary of prediction performance of the models is shownin Table 2. The best PLSR model was obtained with 2 latent variables(LV), which accounts more than 90% of the total variance of the data.

The accuracy of the models was estimated mainly by RMSEP (rootmeansquare of prediction) [24,25]. PLSR model showed better accuracythan the univariate model with RMSEP values of 2.35% (v/v), indicating

that the method provided results that agree with those in the referencevalues (Table 2). Since these parameters are not recognized by of cialregulations, a non-paired t  tests was applied to compare the referencevalues with those predicted. No signicant differences were observed

at 95% condence level for all level of adulteration (estimated t  valuesbelow the critical t value), which conrms the accuracyof the proposedmethod.

The precision of the models was assessed for levels of repeatability

and intermediary precision through the relative standard deviations(RSD) [24,25]. The generated models properly showed similarprecision,with RSD  b  1.95% for repeatability and 3.33% for intermediary precision(Table 2). These values are within the limits dened by the Brazilian

guidelines [26], which establishes a maximum acceptable RSD of 4.9%for repeatability and 10% for intermediate precision.

The linearity of the regression models was estimated by  tting thereference values  versus  predicted ones.  Fig. 3 shows the regression

graphics obtained with PLSR (Fig. 3A) and univariate (Fig. 3B) models.The slope, intercept and correlation coef cient for the models are alsoshown in Table 2. PLSR model showed better correlation coef cient(N0.99) and slope (closer to 1), while univariate model showed better

intercept (closer to 0).Overall, regression models developed using the full   1H TD-NMR 

relaxation show better predictability than the model obtained byusing the T2   values (univariate model). The lower predictionperformance of the univariate model could be attributed to the errorgenerated during the application of the discrete exponential  tting to

obtain the respective relaxation time.A variety of methods has been used to quantify milk adulteration.

PLSR models developed using different NIR and MIR spectrometers

showed RMSEP values ranging from 0.83 to 4.74% [27]. Similarly, PLSR models obtained with digital image showed a RMSEP of 5.85%  [28].

 3.3. SIMCA and kNN models

Based on the results obtained with the regression models, classica-tion models were developed using the full   1H TD-NMR relaxation

curves. The performance of test set of SIMCA and kNN classicationmodels are presented in Table 3. In general, themodelsshowed a similarand quite satisfactorily predictability. The best SIMCA and kNN modelsdeveloped to discriminate control from adulterated samples (authenti-

cation models) were obtained using 3–4 principal components and 3number of neighbors, respectively. The values of sensitivity, the abilityof a method to detect truly positive samples as positive, ranged from

 Table 1

Relaxation time constants (T2) obtained by discrete mono-exponential tting of the Carr-Purcell-Meiboom-Gill (CPMG) decays. The values are expressed as mean ± standard deviation.

Adulterant T2 (s)

5% 15% 25% 35% 50%

Water 0.188 ± 0.002 0.213 ± 0.003 0.241 ± 0.003 0.284 ± 0.014 0.358 ± 0.011

Whey 0.188 ± 0.002 0.209 ± 0.004 0.233 ± 0.002 0.279 ± 0.014 0.335 ± 0.011

Synthetic milk 0.188 ± 0.002 0.212 ± 0.002 0.239 ± 0.002 0.281 ± 0.013 0.360 ± 0.012

Synthetic urine 0.188 ± 0.004 0.215 ± 0.003 0.243 ± 0.004 0.295 ± 0.013 0.369 ± 0.011

Hydrogen peroxide 0.135 ± 0.006 0.161 ± 0.006 0.205 ± 0.005 0.283 ± 0.014 0.403 ± 0.017

Fig.2. PCAscoreplotof themaximumnormalized 1H TD-NMR relaxationdecays of control

and adulterated milk samples.

 Table 2

Regression models performance to quantify milk adulteration.

Figures of merit Parameter PLSR Univariate

Accuracy RMSEP 2.34 3.79

Precision Repeatability 1.95b 1.93b

Intermediate precision 3.33b 3.00b

Linearity Slope 0.97a 0.95

Intercept 0.64a 0.25

Correlation coef cient 0.99a 0.94

a Values for the line tted to the calibration samples.b

Results for milk samples adulterated at 25%  v/v.

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0.66 (control samples) to 1.00 (adulterated samples). This means thatsome false positives or unadulterated sample have been classied as if 

they were adulterated. False positive results are a lesser concern, as allpositive samples may be conrmed by the reference methods. Specic-

ity values, the ability of a method to detect truly negative samples asnegative ranged from 1.00 (control samples) to 0.66 (adulteratedsamples). The greatest number of misclassications in these modelsmay be due to the close similarity between the control and samples

with lower concentration of adulterant, as 5% (v/v). Even though, noneof the adulterated samples were predicted as control using the modeldeveloped (false negative). False negative results should be morerigorously controlled due to the economic and safety issue.

SIMCA and kNN models based on the level of adulteration were alsoobtained using 3–4 principal components and 3 neighbors, respectively.These models demonstrated proper sensitivity and specicity, withvalues higher than 0.66 and 0.80, respectively. Even though the

detection of control samples had a lower success rate than in the caseof adulterated milk samples, the model still gave similar or betterperformance compared to literature data on milk classication frominfrared spectroscopy [27] and digital image [28].

4. Conclusion

This is the   rst study that has demonstrated the feasibility of 

TD-NMR to detect andquantifymilk adulterationin a fast, nondestructive,andnoninvasivemanner. Thiswould mean thatthe possibility of using 1H

TD-NMR to measure milk adulteration without any sample preparationand through milk package.

Regression models obtained with multivariate analysis (PLSR method) showed better predictability when compared with univariate

model, with low RMSEP (b 2.35% v/v) and high correlation coef cient(N0.99). SIMCA and kNN classication models discriminated control

milk samples from several potential adulterants (whey, urea, hydrogenperoxide, synthetic urine and synthetic milk) at level of 

adulteration N

 5%  v/v .Overall,  1H TD-NMR combined with chemometrics analysis may be

used for online automatization of milk analysis with a higher samplingfrequency, in portable equipments.

 Acknowledgments

The authors would like to acknowledge Brazilian agencies FAPESP(2009/01345-6) and CNPq (572859/2008-7) for their nancial support

of this research.

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Fig. 3. Reference values versus estimated values by (a) PLSR and (b) univariate regression models.

 Table 3

Sensitivity and specicity of SIMCA and kNN models. Results obtained with the training

set.

Authentication model Level of adulteration model

Control Adulterated Control 5% 15% 25% 35% 50%

SIMCA

Sensitivity 0.66 1.00 0.66 1.00 0.88 1.00 1.00 1.00

Specicity 1.00 0.66 1.00 1.00 1.00 0.97 1.00 1.00

kNN 

Sensitivity 0.66 1.00 0.66 1.00 1.00 1.00 1.00 0.83

Specicity 1.00 0.66 1.00 0.97 1.00 1.00 0.80 1.00

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