Analytica Chimica Acta 513 (2004) 263–268

Spectroscopic interferences in Fourier transform infrared wine analysis

José Luıs Moreira∗, Lúcia Santos

LEPÆ, Departamento de Engenharia Qu´ımica, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal

Received 10 July 2003; received in revised form 26 August 2003; accepted 18 September 2003

Available online 13 November 2003

Abstract

Fourier transform infrared (FTIR) spectrometry has brought many advantages to wine analysis, such as fast analysis and good precisionand accuracy for a great number of parameters. This technology has to be cautiously applied, therefore the need for analytical validation.Recovery results of several current wine control parameters using a FTIR wine analyser were determined. Good results were obtained forethanol (addition of ethanol), total acid (addition of tartaric acid), total sugars in sweet wines (addition of glucose) and sulfate (addition ofsulfuric acid). On the contrary, worse results were obtained for total acid (addition of acetic and sulfuric acids), volatile acid (addition ofacetic acid) and total sugars in dry wines (addition of glucose). These findings can be explained by spectroscopic interferences that were alsoa subject of analysis in this work. In fact, ethanol, organic acids and other compounds, present in high concentrations in wine, can producemajor interferences in the analysis for compounds such as volatile acid and sugars in dry wines, when their strong infrared absorption bandsdo not differ significantly from other abundant compounds.© 2003 Elsevier B.V. All rights reserved.

Keywords:Wine; Analysis; FTIR; Spectrometry; Interferences

1. Introduction

Fourier transform infrared (FTIR) spectrometry hasbrought countless analytical advantages, such as time sav-ing and high resolution[1]. Its application to wine analysisprovides excellent results in terms of precision and accuracy[2]. The application of FTIR spectrometry to wine analysisis an indirect analytical method, once an alternative methodof analysis is required[3].

Both organic and inorganic compounds can be deter-mined if a calibration that correlates the IR spectrum andthe analytical reference results is obtained. According to theBeer–Lambert law[1], the concentration of the compoundto be determined is directly proportional to its absorbance atgiven wavelength. This relation is affected by the presenceof other compounds which absorb in the same wavelength.This is the case for wine analysis by FTIR technology.Therefore, a multiple-wavelength measurement is necessaryin order to compensate for the interferences[4]. This meansthat the characteristic wavelengths of the compounds to be

∗ Corresponding author. Tel.:+351-2-25081682;fax: +351-2-25081449.

E-mail address:jluis@fe.up.pt (J.L. Moreira).

analysed have to be measured, as well as the characteristicwavelengths of the interferents which absorb in the sameIR region[2].

An algorithm that allows this kind of correlation is par-tial least squares (PLS). The first step is to determine whichIR spectral ranges can be used to obtain the correlation foreach analytical parameter. It is therefore necessary to definen filters, which can be a single wavenumber or a range ofwavenumbers. The second step is to obtain the PLS coeffi-cients, so that the parameter result can be calculated. Thesesteps are performed using a set of samples analysed in theFTIR equipment and the analytical reference results for eachsample:

FTIR result= A1 × B1 + A2 × B2 + · · · + An × Bn + B0

where i is the filter number,Ai the absorbance at filteri,Bi the PLS coefficient for filteri, n the number of filtersselected for calibration.

Despite the enhancement brought to the analytical proce-dures of wine laboratories, the application of FTIR method-ology can still be limited for many parameters. For instance,chloride ions and some other inorganic species are impossi-ble to analyse directly by FTIR spectrometry because theydo not absorb in the IR region. Furthermore, the analysis

0003-2670/$ – see front matter © 2003 Elsevier B.V. All rights reserved.doi:10.1016/j.aca.2003.09.029

264 J.L. Moreira, L. Santos / Analytica Chimica Acta 513 (2004) 263–268

of ash for fraud control also constitutes an example of suchlimitations.

In order to evaluate the interferences in the analysis ofseveral wine control parameters, recovery experiments wereperformed using a FTIR wine analyser. A spectroscopicanalysis for each parameter was also done in order to ex-plain the obtained results.

2. Experimental

2.1. Reagents

Ethanol was p.a. from Riedel-de-Haën, glucose was forbiochemical purposes from Merck, tartaric acid and aceticacid were p.a. from Merck and sulfuric acid was p.a. fromPronalab.

2.2. Equipment

The equipment used was a Winescan FT 120 FTIRanalyser (Foss). The infrared measurement range was 926–5012 cm−1.

2.3. Calibration

A calibration for the analysis of several wine control pa-rameters using a PLS algorithm was obtained. The calibratedparameters were ethanol, total acid, volatile acid, total sug-ars in dry wines (total sugars< 5 g l−1) and in sweet wines(total sugars≥ 5 g l−1) and sulfate.

A set of 897 wines and their respective analytical ref-erence results (obtained by an alternative analysis method)were used.

For the PLS algorithm, 15 filters for each parameter wereselected. Subsequently, the PLS coefficients were calculatedusing the same set of samples.

2.4. Analytical reference results

The analytical reference results for all parameters, exceptfor sulfate, were obtained according to the European Com-munity Regulation No. 2676/90[5]: ethanol by distillationand hydrometry, total acid by titrimetry, volatile acid by dis-tillation and titrimetry, total sugars by iodometry and sulfateby continuous flow analysis (spectrophotometric detection).

2.5. Recovery procedure

Wine samples were spiked with ethanol, glucose, tartaricacid, acetic acid and sulfuric acid and the analyte recoverywas measured according to the equipment response. Sampleswere analysed before and after the analyte addition. For eachparameter, three to four concentration ranges were testedusing the same wine matrix. Recovery was calculated foreach concentration range.

3. Results and discussion

The IR region used in the measurement of each analysedparameter (expressed by the filters), the explained variationof results and whether the PLS coefficients are positive ornegative are presented inTable 1.

The absolute value of the PLS coefficient (Bi) is an indi-cation of the contribution (positive or negative) in the PLSregression, depending on whether theBi value is positive ornegative, respectively. The explained variation (EV) for eachfilter is a numerical parameter that measures how much ofthe statistical variation in the sample set has been describedby the mathematical model.

The recovery experiments were then performed and theresults are shown inTable 2.

It is expected that compounds with identical or veryclose absorption bands should be affected by those of oth-ers present in higher concentrations. It is expected that theanalytical calibration of compounds present in low con-centrations, should be affected by other compounds withidentical or very close IR absorption bands, present inhigher concentrations.

In order to analyse the effect of the interferences inFTIR wine analysis and its responsibilities for poor recov-ery results,Figs. 1 and 2present, respectively, the averageabsorbance and the standard deviation of the absorbanceregarding the samples used for the analytical calibration.According toFig. 2there is a noticeably large IR absorbancevariance at 1100, 1700 and 3000–3500 cm−1.

In terms of recovery, ethanol presents a good recovery,very close to 100%, as it is the main component in wine,besides water. Interferences are therefore negligible.

Total acid also presents a good recovery after the additionof tartaric acid. The same did not occur for the addition ofacetic and sulfuric acid. Total acid main components are gen-erally (in decreasing concentration order) tartaric acid, malicand lactic acids, succinic acid, citric and acetic acids[6].Succinic acid and acetic acid do not have the alcohol func-tional group, and therefore are lacking the C–O and the O–Hbond from the secondary alcohol that the other organic acidshave. These bonds should absorb at approximately 1060–1150 cm−1 (C–O stretch) and at 1320–1420 cm−1 (O–Hbend)[1], which should correspond, according toTable 1, tofilter no. 2 (1111–1115 cm−1) and no. 4 (1373–1377 cm−1)of total acid, both with an high explained variation (61.2and 5.0%, respectively). An addition of a compound withthese bonds, but slightly different absorption frequencies(C–O and O–H only from the carboxylic acid and notfrom the secondary alcohol), should therefore give a lowerequipment response, as happened in the case of acetic acidaddition.

It was observed that the addition of sulfuric acid slightlydecreased the pH of the wine. Attending to the pKa1 andpKa2 values of the major organic acids in wines (between3 and 4)[6], an equilibrium between acid (protonated) andbasic species of the acids is expected. A decrease in the pH

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Table 1Infrared spectrum ranges and PLS parameters used in the calibration for wine analysisa

Filter no. i Ethanol Total acid Volatile acid Total sugars (dry wines) Total sugars (sweet wines) Sulfate

Fi (cm−1) Bi EV (%) Fi (cm−1) Bi EV (%) Fi (cm−1) Bi EV (%) Fi (cm−1) Bi EV (%) Fi (cm−1) Bi EV (%) Fi (cm−1) Bi EV (%)

1 2894–2897 + 38.73 1728–1732 + 5.59 1107–1107 − 2.12 1111–1111 − 92.81 1111–1111 − 92.81 1123–1123 + 14.362 1107–1111 − 56.19 1111–1115 − 61.22 1373–1373 + 2.99 1377–1377 − 1.82 1377–1377 − 1.82 1065–1065 − 5.173 1728–1732 − 1.20 2894–2897 + 14.03 2894–2897 − 20.94 1003–1003 − 1.71 1003–1003 + 1.71 1397–1397 + 7.994 1011–1015 − 0.34 1373–1377 + 5.02 1732–1732 − 2.44 1728–1728 + 0.25 1728–1728 + 0.25 1728–1728 − 4.265 1524–1524 + 0.85 1516–1520 + 1.34 1343–1343 − 16.92 1516–1516 − 0.59 1516–1516 − 0.59 1096–1107 + 32.226 999–1003 + 0.61 1007–1007 + 1.25 999–999 − 0.88 1358–1362 + 1.90 1358–1362 + 1.90 1088–1088 + 6.567 1470–1470 − 0.31 1481–1481 + 1.66 1130–1134 + 3.09 2897–2901 − 0.21 2897–2901 + 0.21 2886–2890 − 3.418 1377–1385 + 0.18 1127–1130 − 0.05 1520–1520 − 2.79 1323–1327 − 0.15 1323–1327 − 0.15 1273–1273 + 1.519 1350–1354 − 0.03 1470–1470 − 0.33 1277–1277 + 0.28 1069–1069 + 0.04 1069–1069 + 0.04 1443–1443 − 1.58

10 1408–1412 − 0.01 1346–1350 − 0.21 1470–1470 + 1.38 1470–1474 + 0.01 1470–1474 − 0.01 1335–1346 + 2.9511 1458–1458 + 0.05 1184–1184 + 0.67 1065–1065 + 0.70 1130–1134 − 0.12 1130–1134 − 0.12 1154–1154 − 2.9512 1130–1134 + 0.09 1277–1277 − 0.08 1532–1532 + 0.74 1015–1015 + 0.01 1015–1015 + 0.01 1030–1030 − 0.8713 1065–1065 − 0.07 1065–1065 − 0.07 1447–1447 − 0.60 1215–1215 − 0.01 1215–1215 − 0.01 1208–1208 + 0.4614 1532–1535 − 0.01 1235–1238 + 0.34 1015–1019 − 0.37 972–976 − 0.00 972–976 − 0.00 1744–1744 − 0.4415 1262–1289 − 0.02 1443–1443 + 0.06 1312–1319 − 0.23 1258–1262 + 0.02 1258–1262 + 0.02 1508–1508 − 0.15

AcEV (%) 98.69 91.92 56.47 99.65 99.65 84.88

a Fi: filter measured;Bi: PLS coefficient (+: positive contribution;−: negative contribution); EV: explained variance; AcEV: accumulated explained variance.

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Table 2Recovery results

Addition Parametermeasured (g l−1)

Recovery results

Concentrationrange

Recovery(%)

Ethanol Ethanola 11.2–11.6 96.511.2–12.1 96.511.2–12.5 94.8

Tartaric acid Total acid 5.3–5.8 93.05.3–6.3 96.15.3–6.8 95.65.3–7.3 97.6

Acetic acid Total acid 4.4–4.5 58.44.4–4.6 68.44.4–4.7 70.8

Sulfuric acid Total acid 4.5–5.3 78.74.5–6.2 76.34.5–7.0 71.4

Acetic acid Volatile acid 0.25–0.36 61.80.25–0.46 66.50.25–0.56 65.4

Glucose Total sugarsb 1.22–1.79 72.91.22–2.26 78.91.22–2.73 73.01.22–3.24 81.3

Glucose Total sugarsc 22.3–27.4 94.422.3–32.3 93.722.3–37.3 93.822.3–42.3 93.6

Sulfuric acid Sulfate 0.54–1.09 90.30.54–1.64 81.90.54–2.19 70.4

a Measured as % (v/v).b Dry wine (total sugars< 5 g l−1).c Sweet wine (total sugars≥ 5 g l−1).

Fig. 1. Average absorbance of the 897 wine samples used for calibration (absorbance presents negative results because every IR spectrum is subtractedfrom the IR spectrum of a blank solution, in order to eliminate the background absorbance of water).

drives the equilibrium towards the formation of protonatedspecies, which have the O–H bond of the carboxylic acid.The rise in the absorbance of this IR band should then in-crease the response of the FTIR equipment for total acid.

The calibration for total sugars in sweet wines achievedvery good recovery results on the addition of glucose. Thesame did not happen for total sugars in dry wines. This couldbe due to the low concentration of sugars in these wines.Glucose and fructose, main components of total sugars[6],have similar IR absorption bands to those of the organic acids(C–O and O–H), which are in higher concentrations thansugars in dry wines. Therefore, only the calibration of totalsugars in dry wines should be affected by the interferences.

Acetic acid represents generally 90% of the volatile acidin wines[6] and was therefore chosen for the recovery ex-periment. These results are the poorest of all obtained withinall the parameters, and could be caused by the low speci-ficity of the IR spectrum of the acetic acid. This compoundhas C–H bonds from CH3, just like ethanol. All the absorp-tion bands from the carboxylic acid (C=O, C–O and O–H)should also be similar to those of the same bonds in the or-ganic acids present in wines. In all the samples used for cal-ibration, volatile acid appeared in concentrations<1 g l−1,much lower than the other organic acids and, of course,ethanol. Hence, the interferences should overtake the PLSregression power. This is also proven by the accumulatedexplained variance of 56.5% (for the volatile acid), mean-ing that only this small percentage of the variance has beendescribed by the PLS mathematical model.

For sulfate it is evident that compounds at low concen-trations (<1 g l−1) can attain better recovery results than

J.L. Moreira, L. Santos / Analytica Chimica Acta 513 (2004) 263–268 267

Fig. 2. Standard deviation of the absorbance of the 897 wine samples used for calibration.

others at the same levels. Such is the case for total sugars indry wines. This could be explained by the use of a differentIR region, not employed for most of the other organic com-pounds. For sulfuric acid, due to the high values of pKa1(total dissociation) and pKa2 (1.99 at 25◦C) [7] and to theaverage pH of wine (between 3 and 3.5), the sulfuric acidshould all be dissociated to sulfate ions. These ions existas a resonance hybrid, presenting absorption bands of S=Oand S–O that absorb at 1060–1150 cm−1 [1]. This probablycorresponds to some of the most important filters in thePLS regression (nos. 1, 2, 5 and 6).

The decrease in recovery percentage with increase in theconcentration range could be due to the original analyticalreference results, in the range 0.2–1 g l−1.

4. Conclusions

Good recovery results were obtained for the determina-tion of ethanol (addition of ethanol), total acid (addition oftartaric acid), total sugars in sweet wines (addition of glu-cose) and sulfates (addition of sulfuric acid). Poor recoveryresults were obtained for total acid (addition of acetic andsulfuric acids), volatile acid (addition of acetic acid) and to-tal sugars in dry wines (addition of glucose).

These results could be explained by spectroscopic inter-ferences. The major compounds in wine, such as ethanoland organic acids, may affect the analytical calibration ofthose which are present in low concentrations and have ab-sorption frequencies very close to those mentioned above.Some examples are acetic acid and sugars (in dry wines),

once they are present in lower concentrations and their IRspectrum is not remarkably different from other abundantorganic compounds.

The use of narrow, low concentration ranges in the analyt-ical calibration increases the magnitude of the spectroscopicinterferences whenever weak sensitivity occurs towards thecompound. These interferences cause poor recoveries andresults centred around the average values used for calibra-tion, when high concentration levels are analysed (a verycommon problem in daily routine analysis).

A solution to diminish the referred problems is performingspecific calibrations using different sample sets grouped bywine types (red/white or dry/sweet, for example), in orderto minimise the variations between samples and thereforedecrease the spectroscopic interferences.

Acknowledgements

The authors wish to acknowledge ALABE (Associaçãode Laboratórios de Enologia) for its financial support andIVV (Instituto da Vinha e do Vinho) for providing the FTIRequipment.

References

[1] R.M. Silverstein, G.C. Bassler, T.C. Morrill, Spectrometric Identifica-tion of Organic Compounds, 5th ed., Wiley, New York, 1991.

[2] J.L. Moreira, A.M. Marcos, P. Barros, Ciencia Téc. Vitiv. 17 (2002)27.

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[3] C.-D. Patz, A. David, K. Thente, P. Kürbel, H. Dietrich, Vinicult.Enol. Sci. 54 (1999) 80.

[4] M. Dubernet, M. Dubernet, Rev. Fra. Oenol. 181 (2000) 10–13.[5] European Community Regulation No. 2676/90, dated 17 September

1990, Off. J. Eur. Commun. No. L272 of 3 October 1990.

[6] A.S. Curvelo Garcia, Controlo de Qualidade dos Vinhos—QuımicaEnológica, Métodos Analıticos, Instituto da Vinha e do Vinho,1988.

[7] Handbook of Chemistry and Physics, 83rd ed., CRC Press, BocaRaton, FL, 2002.