www.elsevier.com/locate/microc

Microchemical Journal 78 (2004) 27–33

A novel strategy to verification of adulteration in alcoholic beverages

based on Schlieren effect measurements and chemometric techniques

Renata S. da Costaa, Sergio R.B. Santosa, Luciano F. Almeidab, Elaine C.L. Nascimentoa,Marcio J.C. Pontesa, Ricardo A.C. Limab, Simone S. Simoesa, Mario Cesar U. Araujoa,*

aUniversidade Federal da Paraıba, CCEN, Departamento de Quımica, P.O. Box 5093, 58051-970 Joao Pessoa, PB, BrazilbUniversidade Federal de Pernambuco, CCEN, Departamento de Quımica Fundamental, Brazil

Accepted 11 March 2004

Available online 10 May 2004

Abstract

A novel strategy to evaluation of adulteration in alcoholic beverages based on the measurement of the Schlieren effect using an automated

FIA system with photometric detection is proposed. The assay is based on the Schlieren effect produced when beverage samples are injected

in a single-line FIA system that uses water as carrier stream and a light-emitting diode–phototransistor photometer controlled by

microcomputer as detector. The flow system presents limited mixing conditions which make possible to create gradients of refractive index

(Schlieren effect) in the injected sample zone. These gradients are reproducible, characteristic of each alcoholic beverage and undergo

specific modifications when adulterations with water or ethanol are imposed. Schlieren effect data of brandies, cachac�as, rums, whiskies and

vodkas were treated by SIMCA to elaborate class models applied in the evaluation of alcoholic beverages adulteration. Samples of the

original matrix of each sort of beverages were adulterated in laboratory by adding water, methanol and ethanol in levels of 5% and 10% (v/v).

These samples were used as test set to validate SIMCA class models. The verification of authenticity using Schlieren effect measurements

presented good results making possible to identify 100% of the beverages samples adulterated in laboratory and 93% of the actual adulterated

alcoholic beverages with confidence levels of 95%. As principal advantage, the automated system does not use reagents to carry out the

analysis.

D 2004 Elsevier B.V. All rights reserved.

Keywords: Schliren; Beverages; FIA; Adulteration

1. Introduction

The presence of falsified products in the Brazilian market

generates economical damages about US$12 billion a year,

according to data presented in the last Seminar of the

National Confederation of the Industry, Brazil. In 2001,

the performance of forgers and smugglers impeded the

Federal Govern to collect about R$2.5 billion in imposts [1].

The adulteration is usually accomplished by addition of

alcohol, water, dyes and aromas to beverages of minor

commercial value. As these beverages are usually produced

with inadequate conditions of hygiene, they become of

high risk for the human health. Another risk for the

consumer of adulterated alcoholic beverages is the inges-

tion of products not elaborated with raw materials con-

0026-265X/$ - see front matter D 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.microc.2004.03.006

* Corresponding author. Tel./fax: +55-83-216-7438.

E-mail address: laqa@quimica.ufpb.br (M.C.U. Araujo).

trolled by control organs, mainly those containing high

methanol levels. The maximum limits of methanol in

beverages are established by the Brazilian legislation in

0.25 ml/100 ml of absolute alcohol for liquors and other

distilled beverages and 0.35 g/l for wines. These limits are

based on the acceptable daily ingestion and establish the

parameters that aid in the methanol detection added delib-

erate or accidentally in beverages. The toxicant doses of

methanol vary of an individual to another. Some authors

consider that the consumption of 20 ml causes blindness

and 60 ml constitutes a lethal dose [2].

Generally, analysis of volatile compounds is used to

characterise different types of beverages or varieties of a

same type, as well as analysis of minerals is used to

determine the origin area. Independently of the analytical

technique used for the characterisation of the alcoholic

beverage, chemometric techniques as the Principal Compo-

nents Analysis (PCA), Soft Independent Modelling of Class

R.S. da Costa et al. / Microchemical Journal 78 (2004) 27–3328

Analogy (SIMCA), Principal Component Regression

(PCR), Partial Least Square Analysis (PLS), Hierarchical

Cluster Analysis (HCA), Discriminant Analysis (DA) and

Canonical Analysis (CA) have been applied to elaborate

models that represent in a trustworthy way the intrinsic

characteristic of each beverage analysed.

Huerta et al. [3] used pH and acidity measurements,

titrable acidity and content of alcohol, ashes, reducers sugars

and volatile compounds, as parameters to classify several

types of wine. The data analysed by DA provided unsatis-

factory results.

Raptis et al. [4] used the period of maturation, the first

years of use of the barrel and its number of replenishments

to elaborate neural network and fuzzy logic models for

classification of aged wines distillates with good.

Schreyer and Mikkelsen [5] applied square wave vol-

tammetry with platinum electrodes in the analysis of several

kinds of beverages, including wines, coffees, milks, etc.,

and the resulting voltamograms were analysed by PCA,

PCR and PLS. A relative mean error of 3.5% was obtained.

Arvanitoyannis et al. [6] reviewed the principal methods

of analyses used for the characterisation and verification of

authenticity of alcoholic beverages. It was verified that the

high performance liquid chromatography (HPLC), the atom-

ic absorption spectroscopy (AAS), the gas chromatography

(GC) and the mass spectroscopy (MS) are the most used

analytical techniques.

Gonzales-San Jose et al. [7] applied methods of multi-

variate calibration on data of AAS and of several methods of

analysis of phenolic components, in order to classify rose

wines of different regions of Spain and observed that

ethanol and calcium were the most important parameters

for the characterisation of these drinks.

Recently, Palma and Barroso [8] applied Middle Infrared

Spectroscopy with Fourier transform for the characterisation

and classification of wines, brandies and other distilled

beverages using PCA and CA. Different degrees of ageing

and types of beverages were characterised by applying this

methodology with correlation coefficients superior to 0.986.

Nagato et al. [9] analysed 608 samples of alcoholic

beverages suspected to be falsified and determined that

391 drinks were falsified and 2 of them presented methanol

in an amount superior to that established by the Brazilian

Legislation. The adulteration percentile observed was as

follows: national whisky 81%, imported whisky 59%,

vodkas 65%, brandies 75%, ginger brandy 80%, bitter

88%, cachac�a 43%.

Chromatography is applied by fiscal organs in Brazil for

verification of authenticity but it is an expensive instrumen-

tation and a laborious technique besides presenting low

analytic frequency. Thereby, analytical methods based on

the flow injection analysis systems are important alterna-

tives to minimise the inherent difficulties of the chromato-

graphic methods.

In flow injection analysis (FIA) systems, the sample is

injected in a carrier stream continually pumped towards the

detection unit. While it is transported, the sample zone

disperses due to the contact with the carrier stream and

generates, consequently, a concentration gradient that pro-

duces a transient analytical signal in the detector. In some

cases, gradients of refraction index associate to the concen-

tration gradients formed by the sample zone so that the noise

increases and the analytical sensibility reduces (Schlieren

effect) [10,11]. Schlieren effect in FIA system was firstly

described by Krug et al. [12] when a turbidimetric procedure

for sulphate determination was proposed.

When the flow conditions and the optical configuration

of the detection system are appropriate, the gradients of

refraction index of colourless samples generate reproducible

Schlieren signals whose intensity depends on the refraction

indexes of the carrier stream and sample. The refraction

index of a colourless substance depends on the wavelength

in which it is measured, on the intensities of the ultraviolet

absorptions, on the temperature and, in the case of solutions,

on the solute concentration [13]. The exploitation of the

Schlieren effect with the aim at determining refraction

indexes of colourless solutions of organic and inorganic

compounds was proposed by Betteridge et al. [14], which

developed a flow refractometer based on LED and photo-

transistor and described the origin of the phenomenon and

the importance of the control the flow cell geometry and the

hydrodynamic conditions.

Zagatto et al. [11] studied the relation between the nature

of the injected substances and the profile of the Schlieren

signals and observed that the signals are highly dependent of

the chemical species. Easily soluble samples or samples

injected into a system with improved mixing conditions

present small intensity Schlieren signals.

Santos et al. [15] developed an automated flow injection

system with LED–phototransistor detection for the deter-

mination of the alcoholic grade of distilled beverages based

on the measure of the Schlieren effect. As advantages, no

reagents are used, only distilled water as carrier stream, and

it was possible to analyse 120 samples per hour.

In this work is proposed a novel strategy to determinate

adulteration of alcoholic beverages based on measurements

of Schlieren effect produced when beverage samples are

transported by a flow stream of deionised water in a FIA

system with limited mixing conditions. To carry out the

analysis, a flow injection automated system (FIAS) similar

to that described by Santos et al. [15] is used. As chemo-

metric technique, SIMCA is applied to data treatment so that

class models are created to describe with fidelity the

different sorts of the analysed alcoholic beverages.

2. Material and methods

2.1. Samples and reagents

To carry out the analysis in the FIAS, deionised water

was always used as carrier stream.

R.S. da Costa et al. / Microchemical Journal 78 (2004) 27–33 29

Pure vodkas, rums, whiskies, cachac�as and brandies

acquired in Brazilian markets were used as calibration sets

to construct chemometric models.

Two sets of adulterated beverages were used. One set

formed by laboratory adulterated beverages characterised by

addition of deionised water and ethanol (99.8% w/w) and

methanol (99.8% w/w) (Merck) in the proportions of 5% v/v

and 10% v/v to the pure beverages and the other set formed

by actual adulterated beverages (beverages acquired in

markets as a pure product but that presented unconformities)

furnished by a reference laboratory.

All measures were carried out at 26 jC.

2.2. The FIAS

Fig. 1 shows the schematic diagram of the FIAS. A

proportional injector controlled by a microcomputer is

responsible for the sample injection in a water carrier

stream. A sample loop of 30 Al and a flow rate of 0.60 ml

min�1 were used. After injection, the sample is transported

to a RGB LED–phototransistor detector (D) where a

transient signal is generated and recorded by the microcom-

puter. The RGB LED emits radiation on the following

wavelengths: 435, 560 and 625 nm. To the measurements

carried out in this work, 625 nm was used. The detector

flow cell presents 1.3 cm of optical path and 0.25 cm of

internal diameter with a ‘‘Z’’ geometry which allows min-

imising the retention of bubbles [15].

2.3. Software of the FIAS

The FIAS control is carried out by a friendly program

elaborated in Labview 5.1, a graphic programming lan-

guage. The software controls the proportional injector, the

photodetector and the operations of data acquisition and

treatment.

Fig. 1. Schematic diagram of the FIAS. PI—proportional injector; W—

water carrier stream; S—sample; SM—step motor; PC—personal computer;

D—detector; WST—waste; Ph—phototransistor; LED—RGB LED; FC—

flow cell.

2.4. Procedure

In the software window, the analyst establishes the

spectral region of work (435, 560 or 625 nm), the magnitude

of the gain to the signals amplification and the filtering

frequencies. At the beginning of the analysis, a blank signal

is obtained in the 3 s after injection and is automatically

recorded. Afterwards, the sample is injected in the carrier

stream towards to detector and its analytical signal is

acquired. During the analysis, the software indicates the

elapsed time, the profile of the analytical signal (signal as a

function of time), the peak signal and the recorded signal in

milivolts. The data are then recorded in file to further

chemometric treatment.

3. SIMCA

The classification of the beverages was carried out by

SIMCA, a chemometric technique where objects (pure and

adulterated beverages in this case) are classified based on its

analogy with objects belonging to a class (pure beverages)

defined by principal components (PCs). To model a class by

SIMCA, PCA is applied in a training set prepared with

known samples and confidence levels (class boundaries) are

determined to the PCs furnished by the PCA model in order

to define the space covered by the objects of the class. To

determine the class boundaries, the distribution of Euclidean

distance (s0) between the objects of a class and the origin in

the space of the residual matrix of the PCA model are used

so that a critical distance (scrit) is determined based on an F-

test at a certain level of significance, as follows:

scrit ¼ ðFs20Þ1=2

To verify if a new object belongs to a class, it is projected

into the PC space and its distance towards the class model

(sk) is compared to scrit. If sk<scrit, the object is considered

part of the class, otherwise, it is considered an outlier.

4. Results and discussion

Pure alcoholic beverages were analysed in order to create

a database consisting of a set of analytical signals as a

function of time that characterises the analytical profile of

each sample. This database was used to elaborate chemo-

metric models applied on the identification of each drink.

To carry out an analysis, only 30 Al of sample is

injected on the FIAS and 100 s was necessary to record

the analytical profiles, providing a sampling rate of 36

samples per hour. Vodkas, rums, brandies, cachac�as and

whiskies were analysed in the FIAS. One variety of each

one of these drinks was injected in the system and its

signals were recorded for subsequent analysis. The char-

acteristic signals of each beverage are presented in Fig. 2

Fig. 2. Profiles of the analytical signals of the alcoholic beverages generated by Schlieren effect. Each profile is an average plot of four measurements. AU—

arbitrary units.

R.S. da Costa et al. / Microchemical Journal 78 (2004) 27–3330

and each profile represents the average plot of four

measurements. These profiles are characterised by an

inverted peak followed by two peaks coupled to the

normal FIA signal. In spite of the similarity of recorded

signals of the beverages, these signals are sufficiently

different in their peak intensity and gradients. Also,

independently of the beverage variety, the signals pre-

sented a good reproducibility. Therefore, the analytical

profiles recorded for each alcoholic beverage and pro-

duced by Schlieren effect associated with the sample

zone, besides being reproducible, are sufficiently different

to characterise each kind of beverage. Thus, these profiles

were used to construct chemometric models to classify the

alcoholic beverages.

PCA models were elaborated using 10 samples from

different lots of each variety of drink as a calibration set in

order to analyse the possibility of chemometric character-

isation of these beverages. Data was smoothed applying

Savitzky–Golay algorithm and models were constructed

using cross validation. These models are valid only to

analyse alcoholic beverages of these varieties. To analyse

other marks of alcoholic beverages, new models must be

elaborated. Fig. 3 shows the score plots obtained by appli-

cation of PCA.

The alcoholic beverages were clearly classified in five

classes, represented by the vodkas, whiskies, cachac�as,rums and brandies. Some interesting aspects can be

observed by the PCA classification. PC1 makes possible

a clear separation between beverages with minor and

major alcoholic grade. Vodkas, whiskies and cachac�aswhich presented 39% (v/v) of alcohol were classified with

positive score values while brandies and rums which

presented 38% (v/v) were classified with negative ones.

The alcoholic grade was not the principal responsible for

the beverages clustering. For example, whiskies and

cachac�as that presented the same alcoholic grade (39%

v/v) were classified with positive and negative score

values, respectively, on PC2 (Fig. 3). This behaviour

confirms the importance of the Schlieren effect in the

selective characterisation of each alcoholic beverage.

Thereby, considering the possibility of beverages distinc-

tion by Schlieren effect measurements, chemometric models

were elaborated for each class. Each class model obtained

by PCA defined the SIMCA models used to verify the

authenticity of the alcoholic beverages. Both pure and

adulterated beverages were used as test sets to validate the

SIMCA class models.

Fig. 4 shows the profiles of the analytical signals

recorded for the adulterated whiskies to illustrate the effect

of the solvent used in the adulteration.

The principal modification on the whiskies profile is

the displacement of its peak magnitudes to positive

values when the alcohol (ethanol or methanol) content

is increased. On the other hand, increasing the water

content, peaks are displaced to more negative values.

However, the different types of adulteration impose

specific changes in the whole profile and not only in

the peak magnitude. This behaviour makes possible to

Fig. 3. 3D score plots of five different kinds of alcoholic beverages. B—brandies, C—cachac�as, R—rum, V—vodkas, W—whiskies.

R.S. da Costa et al. / Microchemical Journal 78 (2004) 27–33 31

discriminate each kind of adulteration by PCA using the

Schlieren effect measurements as presented in the score

graph of Fig. 5.

The use of PCA makes possible to identify each kind

of adulteration imposed on the drinks. As it was ob-

served to the classification of the different kinds of

alcoholic beverages, adulterated samples were classified

in different groups based on its alcohol (ethanol or

UA

Fig. 4. Profiles of the analytical signals of pure and adulterated whiskies. Larger

respectively.

methanol) content. The higher the alcohol contents on

the sample, the higher the score value of this sample on

PC1. Besides, a distinction between alcoholic beverages

adulterated with methanol or ethanol is possible based on

the score values defined by PC2. The score values

calculated to drinks adulterated by methanol are lower

than the ones presented by beverages adulterated by

ethanol.

and smaller peaks were obtained by adulteration with ethanol and water,

Fig. 5. 3D score plots obtained by PCA to verification of whiskies adulteration. SW—whiskies used on the calibration set; W1–2—actual adulterated whiskies;

WE, WM and WW—whiskies adulterated on the laboratory using ethanol, methanol and water, respectively; 5%/10%—grade of the adulteration (v/v).

R.S. da Costa et al. / Microchemical Journal 78 (2004) 27–3332

Two samples of actual adulterated whiskies (W1 and W2

in Fig. 5) acquired on Brazilian market as a pure brandy and

furnished by a reference laboratory after analysis were

analysed by the FIAS. As presented in Fig. 5, W1 and

W2 were classified with positive values of scores on PC1

and this behaviour indicates a possible adulteration by water

(considering that adulteration was carried out using pure

varieties of the same kind of whisky used to elaborate the

chemometric models and that only the solvents cited in this

paper were used on the adulteration).

The same procedure cited above to the whiskies

analysis by the FIAS was used to analyse vodkas, rums,

cachac�as and brandies and the results of the analysis are

summarised in Table 1. In the case of cachac�as, only

laboratory adulteration measurements were carried out

because no actual adulterated samples were furnished.

When the SIMCA models were applied to the classifica-

tion, a very clear distinction between pure and falsified

alcoholic beverages was obtained. Ninety-three percent of

the analysed beverages were correctly identified at a

confidence level of 95%.

Table 1

SIMCA results from the analysis of actual adulterated beverages

Samples Brandy Rum Vodka Whisky

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Reference resultsa A O A O A O A A A O A A O A

FIAS A O A O A O A A A O O A O A

a Results based on gas– liquid chromatography with flame ionization

detector, furnished by an official reference laboratory. A—adulterated; O—

original.

5. Conclusion

An automated FIA system presenting photometric

detector based on LED–phototransistor was developed

in order to make possible to apply a novel strategy of

verification of adulteration in alcoholic beverages based

on the Schlieren effect measurement and chemometric

techniques. To analyse an alcoholic beverage, 30 Al of

the sample is injected in a 0.60 ml min�1 water carrier

stream and the Schlieren signal generated on the detector

is recorded by a microcomputer. These signals are treated

by chemometric techniques in order to verify the authen-

ticity of the alcoholic beverage. To perform a verification

of authenticity by SIMCA, pure beverages are analysed

and used as a calibration set to construct PCA models

that are applied on the identification of a test set formed

by beverages adulterated in laboratory and actual adulter-

ated beverages found illegally in the market. Brandies,

cachac�as, rums, vodkas and whiskies were analysed with

the proposed system and 100% of the laboratory adulter-

ation and 93% of the actual adulterated beverages. The

system permits to analyse 36 samples per hour and no

reagent is used to perform the assay.

Acknowledgements

The authors are grateful to Miriam S.F. Caruso and

Letıcia A.F. Nagato for supplying actual adulterated samples

of alcoholic beverages and to CNPq and CAPES for the

scholarships.

R.S. da Costa et al. / Microchemical Journal 78 (2004) 27–33 33

References

[1] R.J. Lombardi, O Estado de Sao Paulo, (2002 October) 21.

[2] F. Blinder, E. Voges, P. Lauge, Food Addit. Cont. 3 (1988) 343.

[3] M.D. Huerta, M.R. Salinas, T. Masoud, G.L. Alonso, J. Food

Compos. Anal. 11 (1998) 363.

[4] C.G. Raptis, C.I. Siettos, C.T. Kiranoudis, G.V. Bafas, J. Food Eng.

46 (2000) 267.

[5] S.K. Schreyer, S.R. Mikkelsen, Sens. Actuators 71 (2000) 147.

[6] I.S. Arvanitoyannis, M.N. Katsota, E.P. Psarra, E.H. Soufleros,

S. Kallithraka, Trends Food Sci. Technol. 10 (1999) 321.

[7] S. Perez-Magarino, M. Ortega-Heras, M.L. Gonzalez-San Jose, Anal.

Chim. Acta 458 (2002) 187.

[8] M. Palma, C.G. Barroso, Talanta 58 (2002) 265.

[9] L.A.F. Nagato, M.C. Duran, M.S.F. Caruso, R.C.F. Barsotti,

E.S.G. Badolato, Cienc. Tecnol. Aliment. 21 (2001) 39.

[10] J.A. Nobrega, F.R.P. Rocha, Quım. Nova 19 (1996) 636.

[11] E.A.G. Zagatto, M.A.Z. Arruda, A.O. Jacintho, I.L. Mattos, Anal.

Chim. Acta 234 (1990) 153.

[12] F.J. Krug, E.A.G. Zagatto, B.F. Reis, Anal. Chim. Acta 145 (1983)

179.

[13] A. Hanning, J. Roeraade, Anal. Chem. 69 (1997) 1496.

[14] D. Betteridge, E.L. Dagles, B. Fields, N.F. Graves, Analyst 103

(1978) 897.

[15] S.R.B. Santos, M.C.U. Araujo, R.A. Barbosa, Analyst 127 (2002)

324.