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Using near infrared spectroscopy to determine haloanisoles and halophenols in barrel aged red wines
LWT - Food Science and Technology 46 (2012) 401e405
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Using near infrared spectroscopy to determine haloanisoles and halophenols in barrel aged red wines T. Garde-Cerdán, C. Lorenzo, A. Zalacain, G.L. Alonso, M.R. Salinas* Cátedra de Química Agrícola, E.T.S.I. Agrónomos, Universidad de Castilla-La Mancha, Campus Universitario, 02071 Albacete, Spain
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 February 2010 Received in revised form 13 September 2011 Accepted 7 December 2011
NIR spectroscopy has been applied to determine haloanisoles and halophenols responsible for musty taint defect in barrel aged red wines. Six hundred wines with different ageing time from four different geographic zones were evaluated. Spectra of samples obtained by NIR were co-related with SBSEe GCeMS data using partial least squares (PLS) regression. Favourable calibration results were obtained (R2 > 0.77) for all the compounds studied. Residual predictive deviation (RPD) values over 1.5 in all the compounds were obtained for aged-12 and aged-18 samples as well as for geographical zones 2, 3 and 4, thus indicating the suitability of determining halophenols and haloanisoles with this technique. In conclusion, near infrared spectroscopy can be used as a rapid tool to determine haloanisoles and halophenols in barrel aged red wines, being more effective in the case of the most expensive wines (aged-12 and aged-18 wines). Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: NIR TCA Red wines Ageing SBSEeGCeMS
1. Introduction Musty taint, usually known as “cork taint”, is a serious problem for both wine and cork industries worldwide (Amon, Vandeepeer, & Simpson, 1989; Butzke, Evans, & Ebeler, 1999; Peña-Neira et al., 2000). There are many discrepancies concerning its real incidence as well as the quantification of the economic losses causes. This wine defect has been associated to 2,4,6-trichloroanisole (TCA) and has been found in 7% of contaminated wines (Butzke et al., 1999; Lee & Simpson, 1993; Soleas, Yan, Seaver, & Goldberg, 2002). However, it is known that there are other related compounds belonging to the same chemical family (haloanisoles) and their precursors (halophenols) that are also responsible for this defect (Chatonnet, Bonnet, Boutou, & Labadie, 2004; Rubio-Coque, Álvarez-Rodríguez, Goswami, & Felter, 2006). A recent study by Copete et al. (2009) demostrated that 16% of quality red wines in Spain were affected by TCA and other related compounds. Although halophenols are considered toxic when studied in other matrixes (Armstrong, Galloway, & Ashby, 1993; Jansson & Jansson, 1992), the concentrations found in the contaminated wines do not suppose a risk for consumers (Copete et al., 2009). Different strategies are being carried out to avoid the presence of these compounds in wine (Garde-Cerdán, Zalacain, Lorenzo, Alonso, & Salinas, 2008); and also to find an analytical technique with adequate sensitivity and * Corresponding author. Tel.: þ34 967 599310; fax: þ34 967 599238. E-mail address: [email protected] (M.R. Salinas). 0023-6438/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.lwt.2011.12.012
reproducibility to determine such compounds in liquid matrices (Maggi, Zalacain, Mazzoleni, Alonso, & Salinas, 2008; Zalacain, Alonso, Lorenzo, Iñiguez, & Salinas, 2004). These techniques are quite laborious and require sample manipulation expertise. Since wineries show great interest in controlling the quality of their wines, rapid methods have recently been developed which relate multivariate spectroscopic and chemical data for predicting the concentration of specific chemical constituents with chemometrics (Corbella & Cozzolino, 2006). Near infrared spectroscopy (NIR) has been revealed as a powerful and non-destructive tool requiring minimal sample processing prior to analysis (Cozzolino et al., 2004; Urbano-Cuadrado, Luque de Castro, Pérez-Juan, García-Olmo, & Gómez-Nieto, 2004). This technique has been studied in order to determine different groups of compounds in wines such as phenolic compounds (Cozzolino et al., 2004; Tarantilis, Troianou, Pappas, Kotseridis, & Polissiou, 2008), tannins and dry matter (Cozzolino, Cynkar, Dambergs, Mercurio, & Smith, 2008), fermentative compounds in Riesling wine (Smyth et al., 2008), different elements (Cozzolino et al., 2008), and enological classical parameters (UrbanoCuadrado et al., 2004). In terms of volatiles, recent studies have successfully related NIR technique with fermentative volatile compounds (Lorenzo, Garde-Cerdán, Pedroza, Alonso, & Salinas, 2009) and oak volatile compounds and ethylphenols (GardeCerdán, Lorenzo, Alonso, & Salinas, 2010) in aged red wines. However, no references have been found in the literature in relation to the analysis of haloanisoles and halophenols in wines using NIR spectroscopy. For this reason, the aim of this paper was to relate the
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presence of the compounds responsible of musty taint analyzed by SBSEeGCeMS with the spectrometric data obtained by NIR.
5,0 4,5
2. Material and methods
4,0 3,5
A
2.1. Samples
2.2. NIR analysis Samples were analyzed with Perkin-Elmer Spectrum One FTNIR equipment (Perkin-Elmer, Norwalk, CT, USA) with a 1.0 mm quartz flow cell. Data collection was acquired over a wavelength range of 10000e4000 cm1, although the water absorption regions (5500e4500 and 7800e7000 cm1) were not employed, and the resolution was set at 16 cm1. Fig. 1 shows the NIR spectrum of a wine. The spectra of each sample were collected in duplicate and the average of these two spectra was used for the application of the partial least square (PLS) method. Chemometric analysis was performed using Spectrum Quant þ software (Perkin-Elmer, Norwalk, CT, USA). The spectra were pre-processed using the standard normal variate (SNV) transformation followed by first-derivative transformation to reduce baseline variation and enhance the spectral features (Barnes, Dhanoa & Lister,1989). Calibrations were developed using partial least square regression (PLS), which included the standard error of calibration (SEC), the standard error of prediction (SEP), the coefficient of regression, the number of latent variables (LV), the coefficient of determination in calibration (R2 cal), and the multiple regression coefficient. In order to evaluate how well the calibration model could predict haloanisole and halophenol composition in wines, the full cross validation methodology was used. Statistics calculated for the full cross validation included the coefficient of determination in validation (R2) and the standard error of cross validation (SECV). 2.3. Analysis of haloanisoles and halophenols by gas chromatography The most important compounds responsible for musty taint such as 2,4,6-trichloroanisole (TCA), 2,3,4,6-tetrachloroanisole
3,0 2,5 2,0 1,5 1,0
4000,0
4500
5000
5500
6000
6500
7000
7500
8000
8500
9000
9500
0,5 0,02
10000,0
A planned and randomised sampling program was carried out in order to reflec the widest and most realistic sample distribution from the consumer’s point of view. According to Spanish Ministry of Agriculture, Fisheries and Food (MAPA, 2004), 95% of the Spanish Origin Designations (O.D.) produced more than 30,000 hl. Wine sampling was carried out in different local supermarkets of 8 Spanish cities according to the data obtained, where four O.D. (Rioja, La Mancha, Ribera del Duero and Valdepeñas) represented approximately 72% of all commercialized red wines. The selection of the other Spanish O.D. in this study was based on the information given by MAPA, with an effort to include different geographical wine production zones. For this study, 600 barrel aged red wines from different brands were analyzed (3 bottles from each brand, with each bottle belonging to different lots). In order to simplify the discussion of results, the different O.D. were grouped into the following geographical zones: zone 1 included origin appellations Ribera del Duero, Navarra and Rioja (363 samples); zone 2 included La Mancha and Valdepeñas (117 samples); zone 3 included Jumilla and Valencia (60 samples) and finally zone 4 included Penedés and Somontano (60 samples). Aged red wines were classified, according to their ageing process, into three different categories: aged-6 (red wines with an ageing of at least 6 months in oak barrels), aged-12 (red wines with an ageing time of at least 12 months in barrels), and 18aged (exceptional quality red wines with at least 18 months ageing in oak barrels). The total amount of samples analyzed in this study was: 306 aged-6 wines, 222 aged-12 wines, and 72 aged-18 wines.
cm-1
Fig. 1. Near-infrared spectrum of a typical barrel-aged red wine stored during at least 6 months in oak barrels.
(TeCA), 2,3,4,5,6-pentachloroanisole (PCA), 2,4,6-trichlorophenol (TCP), 2,3,4,6-tetrachlorophenol (TeCP), 2,4,6-tribromoanisole (TBA) and 2,4,6-tribromophenol (TBP) (SigmaeAldrich, Madrid, Spain) were analyzed following the method described by Zalacain et al. (2004). Compounds were extracted by introducing a polydimethylsiloxane coated stir bar (0.5 mm film thickness, 10 mm length, Twister, Gerstel, Mülheim an der Ruhr, Germany) into 10 ml of sample, to which 100 ml of internal standard g-hexalactone solution at 1 ml/ml in absolute ethanol (Merck, Damstard, Germany) was added. Samples were stirred at 700 rpm at room temperature for 60 min. The stir bar was then removed from the sample, rinsed with distilled water and dried with a cellulose tissue, and later transferred into a thermal desorption tube for GCeMS analysis. In the thermal desorption tube, the volatile compounds were desorbed from the stir bar under the following conditions: oven temperature at 330 C; desorption time, 4 min; cold trap temperature, 30 C; helium inlet flow 45 ml/min. The compounds were transferred into the Hewlett-Packard LC 3D mass detector (Palo Alto, USA) with a fused silica capillary column (BP21 stationary phase 50 m length, 0.22 mm i.d., and 0.25 mm film thickness) (SGE, Ringwood, Australia). The chromatographic program was set at 50 C (held for 5 min), raised to 180 C at 2.5 C/ min (held for 2 min) and to 230 C (5 C/min) and held for 20 min. For mass spectrometry analysis, electron impact mode (EI) at 70 eV was used. The mass range varied from 35 to 500 u and the detector temperature was 150 C. Identification was carried out using the NIST library and by comparison with the mass spectrum and retention index of chromatographic standards and data found in the bibliography. Quantification was based on 5-point calibration curves of respective standards (R2 > 0.96) in a 120 ml of ethanol þ 880 ml of water solution at pH 3.6. 3. Results and discussion 3.1. Content of haloanisoles and halophenols in the wines In a previous study (Copete et al., 2009), the presence of haloanisoles and halophenols was related to different wine production zones and other oenological parameters for 966 different wines. Six hundred of these wines were analyzed with SBSEeGCeMS and related to their near infrared spectroscopy spectrum. In one study carried out by our research group on the analysis of these compounds (Zalacain et al., 2004), we observed that the reproducibility was between 0.39 for TBA and 3.12% for TCA (Table 1). SBSEeGCeMS analysis revealed that 13.3% of wines were
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Table 1 Descriptive statistics of haloanisoles and halophenols (ng/l) in aged red wines.
2,4,6-Trichloroanisole (TCA) 2,3,4,6-Tetrachloroanisole (TeCA) 2,3,4,5,6-Pentachloroanisole (PCA) 2,4,6-Trichlorophenol (TCP) 2,4,6-Tribromoanisole (TBA) a b c
Mean value
S. Da
Maximum
Minimum
CV (%)b
Reproducibility (%)c
5.37 7.16 2.55 5.07 5.87
36.47 52.22 24.87 41.78 56.62
546.61 774.15 454.33 521.22 847.35
0.00 0.00 0.00 0.00 0.00
6.79 7.29 9.75 8.25 9.65
3.12 1.83 2.82 2.56 0.39
S. D.: Standard deviation. CV¼[S. D./mean] 100. From Zalacain et al. (2004).
contaminated with one or several halophenols or haloanisoles, with TCA and TeCA being the most frequently found whereas TeCP and TBP were not found at all. An incidence of 80 tainted cases was found, with 37.5% corresponding to aged-6 wines, 50.0% to aged-12 wines and the rest to aged-18 wines (12.5%). This tendency refuted the wineries’ belief that the longer the ageing time in oak barrels, the higher the incidence of such off-flavours. As for geographical zones, 76.3% of the 80 tainted wines corresponded to zone 1 (north of Spain), 11.3% to zone 2 (centre-east of Spain), 5% to zone 3 (east of Spain) and 7.4% to zone 4 (north-east of Spain). The mean concentration of tainted compounds (Table 1) was lower than their respective olfactory thresholds (TCA: 5e10 ng/l, TeCA: 14e25 ng/l, PCA: 4000 ng/l, TCP: unknown, TBA: 8 ng/l) (Liacopoulos et al., 1999). This signifies that consumers would not perceive these compounds and the wines probably wouldn’t be rejected when uncorked. TeCA was the compound that showed the highest mean value, whereas TBA showed the highest maximum value (Table 1). 3.2. Calibration and validation for haloanisoles and halophenols using NIR spectroscopy Table 2 shows the PLS calibration for the target compounds studied. The calibration coefficient (R2) was higher than 0.77 for all compounds (Table 2). As can be observed, the correlation between the values determined by GCeMS and values estimated by NIR calibration was good, with no outliers as no dispersion values were found. TCP was the compound that presented the highest coefficient of correlation and multiple regression coefficient, whereas TBA was the compound that presented the lowest coefficient of correlation and multiple regression coefficient (Table 2). Moreover, the lowest coefficient of regression corresponded to the calibration of PCA, while the highest was found in the calibration of TeCA. The number of latent variables, number of PLS eigen-vectors (factors) used to explain the model, was in all the cases 10 (Table 2). The standard error of calibration (SEC) was between 10.12 in PCA and 27.51 in TBA, and the standard error of prediction (SEP) ranged from 11.66 in PCA to 29.81 in TBA (Table 2). That is, taking into account all the calibration statistics, the worst calibration corresponded to TBA. These results showed that NIR spectroscopy provides a suitable
prediction of haloanisoles and halophenols, indicating that the PLS models based on NIR spectra explains at least the 77% of the variation in data. The full cross validation was carried out in order to validate calibration data consisting of the following: First, sample one in the calibration set is deleted. Then, the calibration is performed on the rest of the samples before being tested on the first sample by comparing the specified value (determined by GCeMS) with the estimated. The first sample is then put back into the calibration set, and the procedure is repeated by deleting sample two. The procedure continues until all samples have been deleted once (Naes, Isaksson, Fearn, & Davies, 2002). Geladi and Kowalski (1986) pointed out that full cross validation is not suitable when a large number of samples are included. This has been corroborated in our study since the cross validation coefficient (R2) ranged between 0.22 in TCA and 0.32 in PCA, and the standard error of cross validation (SECV) was between 20.51 in PCA and 49.82 in TBA (Table 3). In order to improve such full cross validation data, samples were grouped according to ageing time (aged-6, aged-12 and aged-18), and according to geographical zones (1e4). In the case of ageing time, as showed some of the most important statistics (Table 3), the lower the number of samples the better the validation. For example, the coefficient of determination in cross validation (R2) for TCA, considered as the most problematic compound among those studied, was 0.26 for aged-6 wines (N ¼ 306), 0.57 for aged-12 wines (N ¼ 222) and 0.80 for aged-18 wines (N ¼ 72); while SECV was 30.97, 22.58, and 19.94, respectively. When the wine production zone is the differentiation parameter, statistics variables improve in all cases (Table 3). In zone 1 the R2 ranged from 0.34 in PCA to 0.44 in TBA, and SECV was between 12.88 in PCA and 34.50 in TCP; in zone 2 the R2 ranged between 0.58 in TCA, TeCA and TBA and 0.78 in PCA, and SECV was between 19.25 in TCP and 49.18 in TeCA; in zone 3 the R2 ranged from 0.71 in TCA to 0.99 in PCA, and SECV was between 0.07 in PCA and 6.63 in TCA; while in zone 4 the R2 ranged between 0.75 in TCA and TBA and 0.99 in TeCA and PCA, and SECV was between 0.02 in PCA and 56.60 in TBA. The residual predictive deviation (RPD), defined as the ratio between the standard deviation of the population (SD) and the
Table 2 Calibration statistics for haloanisoles and halophenols (ng/l) measured in aged red wines by near infrared spectroscopy.
2,4,6-Trichloroanisole (TCA) 2,3,4,6-Tetrachloroanisole (TeCA) 2,3,4,5,6-Pentachloroanisole (PCA) 2,4,6-Trichlorophenol (TCP) 2,4,6-Tribromoanisole (TBA) a b c d
SEC: Standard error of calibration. SEP: Standard error of prediction. LV: Number of latent variables. R2: Coefficient of correlation.
SECa
SEPb
Coefficient of regression
LVc
R2 cal
d
17.29 25.27 10.12 15.85 27.51
18.42 27.17 11.66 16.34 29.81
171.0 251.7 106.0 194.5 245.7
10 10 10 10 10
0.7789 0.7698 0.8369 0.8585 0.7678
Multiple regression coefficient 0.8826 0.8774 0.9148 0.9265 0.8763
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Table 3 Full cross validation statistics for haloanisoles and halophenols (ng/l) measured in aged red wines by near infrared spectroscopy.
Total (N ¼ 600)
Aged-6 (N ¼ 306)
Aged-12 (N ¼ 222)
Aged-18 (N ¼ 72)
Zone 1 (N ¼ 363)
Zone 2 (N ¼ 117)
Zone 3 (N ¼ 60)
Zone 4 (N ¼ 60)
R2 SECV RPD R2 SECV RPD R2 SECV RPD R2 SECV RPD R2 SECV RPD R2 SECV RPD R2 SECV RPD R2 SECV RPD
2,4,6-Trichloroanisole (TCA)
2,3,4,6-Tetrachloroanisole (TeCA)
2,3,4,5,6-Pentachloroanisole (PCA)
2,4,6-Trichlorophenol (TCP)
2,4,6-Tribromoanisole (TBA)
0.22 32.23 1.13 0.26 30.97 1.16 0.57 22.58 1.52 0.80 19.94 2.25 0.38 22.88 1.27 0.58 22.80 1.53 0.71 6.63 1.84 0.75 38.42 1.97
0.25 45.38 1.15 0.30 33.53 1.19 0.60 29.93 1.58 0.79 43.64 2.18 0.41 32.28 1.30 0.58 49.18 1.52 0.82 2.27 2.36 0.99 0.15 >5
0.32 20.51 1.21 0.53 4.93 1.46 0.54 24.46 1.47 0.77 14.75 2.06 0.34 12.88 1.23 0.78 23.03 2.10 0.99 0.07 >5 0.99 0.02 >5
0.26 35.90 1.16 0.46 28.31 1.36 0.55 24.88 1.48 0.84 25.80 2.49 0.35 34.50 1.24 0.77 19.25 2.05 e e e 0.83 24.86 2.36
0.23 49.82 1.14 0.30 42.81 1.19 0.60 29.49 1.59 0.78 44.34 2.13 0.44 28.88 1.34 0.58 48.53 1.53 e e e 0.75 56.60 1.97
N: Number of samples in validation. R2: coefficient of determination in full cross validation. SECV: standard error in full cross validation. RPD: residual predictive deviation (SD/ SECV).
SECV for the NIR predictions, is a useful parameter commonly applied to evaluate how well a calibration model can predict the volatile compounds concentration (Fearn, 2002). If the SECV is large compared to the range of compositions (as SD), a relatively small RPD value results, and the NIR calibration model is considered to be non robust. An RPD value greater than 3 is considered fair and is recommended for screening purposes (Smyth et al., 2008). This value was higher than 5 for some compounds in the full cross validation done for zones 3 and 4 (Table 3). However, a more qualitative interpretation of the RPD was given by other authors where an RPD value lower than 1.5 was considered insufficient for most applications, since it is only suitable for rough classification, while NIR calibration models with values greater than 2 were considered excellent (Williams, 2001). When the validation was done with all samples, aged-6 wines and samples from zone 1, RPD had values lower than 1.5 probably due to the elevated number of samples. However, when full cross validation was done for zones 2, 3 and 4 and practically for all cases in aged-12 and aged-18 wines, the RPD obtained was higher than 1.5 for all the compounds (Table 3). This is an interesting result since aged-12 and aged-18 are the most expensive wines and wineries could then easily detect occurrence of such compounds before product distribution. 4. Conclusions Calibration using PLS of haloanisoles and halophenols by SBSEeGCeMS and near infrared spectroscopy was good, so these compounds can be determined rapidly and easily by this technique. The full cross validation was adequate for aged-12 and aged-18 wines, and also when it is done with wines from zones 2, 3 and 4. The presence of these compounds in wines may cause rejection by consumers with the resulting economic loss to the wineries, especially in the case of aged-12 and aged-18 wines. Therefore, this method could provide wineries with a valuable tool for the determination of haloanisoles and halophenols in wines, in terms of a fast and reliable analysis that can be performed with no need of a specialist. Moreover, NIR technology requires minimal sample processing prior to analysis.
Acknowledgements Many thanks for the financial support given by Ministerio de Educación y Ciencia to the project AGL2004-04609 and thanks to Kathy Walsh for proofreading the English manuscript. T.G.-C. also wishes to thank Ministerio de Educación y Ciencia for the Juan de la Cierva contract. References Amon, J. M., Vandeepeer, J. M., & Simpson, R. F. (1989). Compounds responsible for cork taint. Australian & New Zealand Wine Industry Journal, 4, 62e69. Armstrong, M. J., Galloway, S. M., & Ashby, J. (1993). 2,4,6-Trichlorophenol (TCP) induces chromosome breakage and aneuploidy in vitro. Mutation Research, 303, 101e108. Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy, 43, 772e777. Butzke, C. E., Evans, T. J., & Ebeler, S. E. (1999). Detection of cork taint in wine using automated solid-phase microextraction in combination with GC/MS-SIM. ACS Symposium Series, 714, 208e216. Chatonnet, P., Bonnet, S., Boutou, S., & Labadie, M. D. (2004). Identification and responsibility of 2,4,6-tribromoanisole in musty, corked odors in wine. Journal of Agricultural and Food Chemistry, 52, 1255e1262. Corbella, E., & Cozzolino, D. (2006). Classification of the floral origin of Uruguayan honeys by chemical and physical characteristics combined with chemometrics. LWT- Food Science and Technology, 39, 534e539. Copete, M. L., Zalacain, A., Lorenzo, C., Carot, J. M., Esteve, M. D., Climent, M. D., et al. (2009). Haloanisole and halophenol contamination in Spanish aged red wines. Food Additives and Contaminants, 26, 32e38. Cozzolino, D., Kwiatkowski, M. J., Parker, M., Cynkar, W. U., Dambergs, R. G., Gishen, M., et al. (2004). Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Analytica Chimica Acta, 513, 73e80. Cozzolino, D., Cynkar, W. U., Dambergs, R. G., Mercurio, M. D., & Smith, P. A. (2008). Measurement of condensed tannins and dry matter in red grape homogenates using near infrared spectroscopy and partial least squares. Journal of Agricultural and Food Chemistry, 56, 7631e7636. Cozzolino, D., Kwiatkowski, M. J., Dambergs, R. G., Cynkar, W. U., Janik, L. J., Skouroumounis, G., & Gishen, M. (2008). Analysis of elements in wine using near infrared spectroscopy and partial least squares regression. Talanta, 74, 711e716. Fearn, T. (2002). Assessing calibration; SEP, RPD, RER and R2. NIR News, 13, 12e14. Garde-Cerdán, T., Zalacain, A., Lorenzo, C., Alonso, J. L., & Salinas, M. R. (2008). Molecularly imprinted polymer-assisted simple clean-up of 2,4,6trichloroanisole and ethylphenols from aged red wines. American Journal of Enology and Viticulture, 59, 396e400.
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Using near infrared spectroscopy to determine haloanisoles and halophenols in barrel aged red wines T. Garde-Cerdán, C. Lorenzo, A. Zalacain, G.L. Alonso, M.R. Salinas* Cátedra de Química Agrícola, E.T.S.I. Agrónomos, Universidad de Castilla-La Mancha, Campus Universitario, 02071 Albacete, Spain
a r t i c l e i n f o
a b s t r a c t
Article history: Received 9 February 2010 Received in revised form 13 September 2011 Accepted 7 December 2011
NIR spectroscopy has been applied to determine haloanisoles and halophenols responsible for musty taint defect in barrel aged red wines. Six hundred wines with different ageing time from four different geographic zones were evaluated. Spectra of samples obtained by NIR were co-related with SBSEe GCeMS data using partial least squares (PLS) regression. Favourable calibration results were obtained (R2 > 0.77) for all the compounds studied. Residual predictive deviation (RPD) values over 1.5 in all the compounds were obtained for aged-12 and aged-18 samples as well as for geographical zones 2, 3 and 4, thus indicating the suitability of determining halophenols and haloanisoles with this technique. In conclusion, near infrared spectroscopy can be used as a rapid tool to determine haloanisoles and halophenols in barrel aged red wines, being more effective in the case of the most expensive wines (aged-12 and aged-18 wines). Ó 2011 Elsevier Ltd. All rights reserved.
Keywords: NIR TCA Red wines Ageing SBSEeGCeMS
1. Introduction Musty taint, usually known as “cork taint”, is a serious problem for both wine and cork industries worldwide (Amon, Vandeepeer, & Simpson, 1989; Butzke, Evans, & Ebeler, 1999; Peña-Neira et al., 2000). There are many discrepancies concerning its real incidence as well as the quantification of the economic losses causes. This wine defect has been associated to 2,4,6-trichloroanisole (TCA) and has been found in 7% of contaminated wines (Butzke et al., 1999; Lee & Simpson, 1993; Soleas, Yan, Seaver, & Goldberg, 2002). However, it is known that there are other related compounds belonging to the same chemical family (haloanisoles) and their precursors (halophenols) that are also responsible for this defect (Chatonnet, Bonnet, Boutou, & Labadie, 2004; Rubio-Coque, Álvarez-Rodríguez, Goswami, & Felter, 2006). A recent study by Copete et al. (2009) demostrated that 16% of quality red wines in Spain were affected by TCA and other related compounds. Although halophenols are considered toxic when studied in other matrixes (Armstrong, Galloway, & Ashby, 1993; Jansson & Jansson, 1992), the concentrations found in the contaminated wines do not suppose a risk for consumers (Copete et al., 2009). Different strategies are being carried out to avoid the presence of these compounds in wine (Garde-Cerdán, Zalacain, Lorenzo, Alonso, & Salinas, 2008); and also to find an analytical technique with adequate sensitivity and * Corresponding author. Tel.: þ34 967 599310; fax: þ34 967 599238. E-mail address: [email protected] (M.R. Salinas). 0023-6438/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.lwt.2011.12.012
reproducibility to determine such compounds in liquid matrices (Maggi, Zalacain, Mazzoleni, Alonso, & Salinas, 2008; Zalacain, Alonso, Lorenzo, Iñiguez, & Salinas, 2004). These techniques are quite laborious and require sample manipulation expertise. Since wineries show great interest in controlling the quality of their wines, rapid methods have recently been developed which relate multivariate spectroscopic and chemical data for predicting the concentration of specific chemical constituents with chemometrics (Corbella & Cozzolino, 2006). Near infrared spectroscopy (NIR) has been revealed as a powerful and non-destructive tool requiring minimal sample processing prior to analysis (Cozzolino et al., 2004; Urbano-Cuadrado, Luque de Castro, Pérez-Juan, García-Olmo, & Gómez-Nieto, 2004). This technique has been studied in order to determine different groups of compounds in wines such as phenolic compounds (Cozzolino et al., 2004; Tarantilis, Troianou, Pappas, Kotseridis, & Polissiou, 2008), tannins and dry matter (Cozzolino, Cynkar, Dambergs, Mercurio, & Smith, 2008), fermentative compounds in Riesling wine (Smyth et al., 2008), different elements (Cozzolino et al., 2008), and enological classical parameters (UrbanoCuadrado et al., 2004). In terms of volatiles, recent studies have successfully related NIR technique with fermentative volatile compounds (Lorenzo, Garde-Cerdán, Pedroza, Alonso, & Salinas, 2009) and oak volatile compounds and ethylphenols (GardeCerdán, Lorenzo, Alonso, & Salinas, 2010) in aged red wines. However, no references have been found in the literature in relation to the analysis of haloanisoles and halophenols in wines using NIR spectroscopy. For this reason, the aim of this paper was to relate the
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presence of the compounds responsible of musty taint analyzed by SBSEeGCeMS with the spectrometric data obtained by NIR.
5,0 4,5
2. Material and methods
4,0 3,5
A
2.1. Samples
2.2. NIR analysis Samples were analyzed with Perkin-Elmer Spectrum One FTNIR equipment (Perkin-Elmer, Norwalk, CT, USA) with a 1.0 mm quartz flow cell. Data collection was acquired over a wavelength range of 10000e4000 cm1, although the water absorption regions (5500e4500 and 7800e7000 cm1) were not employed, and the resolution was set at 16 cm1. Fig. 1 shows the NIR spectrum of a wine. The spectra of each sample were collected in duplicate and the average of these two spectra was used for the application of the partial least square (PLS) method. Chemometric analysis was performed using Spectrum Quant þ software (Perkin-Elmer, Norwalk, CT, USA). The spectra were pre-processed using the standard normal variate (SNV) transformation followed by first-derivative transformation to reduce baseline variation and enhance the spectral features (Barnes, Dhanoa & Lister,1989). Calibrations were developed using partial least square regression (PLS), which included the standard error of calibration (SEC), the standard error of prediction (SEP), the coefficient of regression, the number of latent variables (LV), the coefficient of determination in calibration (R2 cal), and the multiple regression coefficient. In order to evaluate how well the calibration model could predict haloanisole and halophenol composition in wines, the full cross validation methodology was used. Statistics calculated for the full cross validation included the coefficient of determination in validation (R2) and the standard error of cross validation (SECV). 2.3. Analysis of haloanisoles and halophenols by gas chromatography The most important compounds responsible for musty taint such as 2,4,6-trichloroanisole (TCA), 2,3,4,6-tetrachloroanisole
3,0 2,5 2,0 1,5 1,0
4000,0
4500
5000
5500
6000
6500
7000
7500
8000
8500
9000
9500
0,5 0,02
10000,0
A planned and randomised sampling program was carried out in order to reflec the widest and most realistic sample distribution from the consumer’s point of view. According to Spanish Ministry of Agriculture, Fisheries and Food (MAPA, 2004), 95% of the Spanish Origin Designations (O.D.) produced more than 30,000 hl. Wine sampling was carried out in different local supermarkets of 8 Spanish cities according to the data obtained, where four O.D. (Rioja, La Mancha, Ribera del Duero and Valdepeñas) represented approximately 72% of all commercialized red wines. The selection of the other Spanish O.D. in this study was based on the information given by MAPA, with an effort to include different geographical wine production zones. For this study, 600 barrel aged red wines from different brands were analyzed (3 bottles from each brand, with each bottle belonging to different lots). In order to simplify the discussion of results, the different O.D. were grouped into the following geographical zones: zone 1 included origin appellations Ribera del Duero, Navarra and Rioja (363 samples); zone 2 included La Mancha and Valdepeñas (117 samples); zone 3 included Jumilla and Valencia (60 samples) and finally zone 4 included Penedés and Somontano (60 samples). Aged red wines were classified, according to their ageing process, into three different categories: aged-6 (red wines with an ageing of at least 6 months in oak barrels), aged-12 (red wines with an ageing time of at least 12 months in barrels), and 18aged (exceptional quality red wines with at least 18 months ageing in oak barrels). The total amount of samples analyzed in this study was: 306 aged-6 wines, 222 aged-12 wines, and 72 aged-18 wines.
cm-1
Fig. 1. Near-infrared spectrum of a typical barrel-aged red wine stored during at least 6 months in oak barrels.
(TeCA), 2,3,4,5,6-pentachloroanisole (PCA), 2,4,6-trichlorophenol (TCP), 2,3,4,6-tetrachlorophenol (TeCP), 2,4,6-tribromoanisole (TBA) and 2,4,6-tribromophenol (TBP) (SigmaeAldrich, Madrid, Spain) were analyzed following the method described by Zalacain et al. (2004). Compounds were extracted by introducing a polydimethylsiloxane coated stir bar (0.5 mm film thickness, 10 mm length, Twister, Gerstel, Mülheim an der Ruhr, Germany) into 10 ml of sample, to which 100 ml of internal standard g-hexalactone solution at 1 ml/ml in absolute ethanol (Merck, Damstard, Germany) was added. Samples were stirred at 700 rpm at room temperature for 60 min. The stir bar was then removed from the sample, rinsed with distilled water and dried with a cellulose tissue, and later transferred into a thermal desorption tube for GCeMS analysis. In the thermal desorption tube, the volatile compounds were desorbed from the stir bar under the following conditions: oven temperature at 330 C; desorption time, 4 min; cold trap temperature, 30 C; helium inlet flow 45 ml/min. The compounds were transferred into the Hewlett-Packard LC 3D mass detector (Palo Alto, USA) with a fused silica capillary column (BP21 stationary phase 50 m length, 0.22 mm i.d., and 0.25 mm film thickness) (SGE, Ringwood, Australia). The chromatographic program was set at 50 C (held for 5 min), raised to 180 C at 2.5 C/ min (held for 2 min) and to 230 C (5 C/min) and held for 20 min. For mass spectrometry analysis, electron impact mode (EI) at 70 eV was used. The mass range varied from 35 to 500 u and the detector temperature was 150 C. Identification was carried out using the NIST library and by comparison with the mass spectrum and retention index of chromatographic standards and data found in the bibliography. Quantification was based on 5-point calibration curves of respective standards (R2 > 0.96) in a 120 ml of ethanol þ 880 ml of water solution at pH 3.6. 3. Results and discussion 3.1. Content of haloanisoles and halophenols in the wines In a previous study (Copete et al., 2009), the presence of haloanisoles and halophenols was related to different wine production zones and other oenological parameters for 966 different wines. Six hundred of these wines were analyzed with SBSEeGCeMS and related to their near infrared spectroscopy spectrum. In one study carried out by our research group on the analysis of these compounds (Zalacain et al., 2004), we observed that the reproducibility was between 0.39 for TBA and 3.12% for TCA (Table 1). SBSEeGCeMS analysis revealed that 13.3% of wines were
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Table 1 Descriptive statistics of haloanisoles and halophenols (ng/l) in aged red wines.
2,4,6-Trichloroanisole (TCA) 2,3,4,6-Tetrachloroanisole (TeCA) 2,3,4,5,6-Pentachloroanisole (PCA) 2,4,6-Trichlorophenol (TCP) 2,4,6-Tribromoanisole (TBA) a b c
Mean value
S. Da
Maximum
Minimum
CV (%)b
Reproducibility (%)c
5.37 7.16 2.55 5.07 5.87
36.47 52.22 24.87 41.78 56.62
546.61 774.15 454.33 521.22 847.35
0.00 0.00 0.00 0.00 0.00
6.79 7.29 9.75 8.25 9.65
3.12 1.83 2.82 2.56 0.39
S. D.: Standard deviation. CV¼[S. D./mean] 100. From Zalacain et al. (2004).
contaminated with one or several halophenols or haloanisoles, with TCA and TeCA being the most frequently found whereas TeCP and TBP were not found at all. An incidence of 80 tainted cases was found, with 37.5% corresponding to aged-6 wines, 50.0% to aged-12 wines and the rest to aged-18 wines (12.5%). This tendency refuted the wineries’ belief that the longer the ageing time in oak barrels, the higher the incidence of such off-flavours. As for geographical zones, 76.3% of the 80 tainted wines corresponded to zone 1 (north of Spain), 11.3% to zone 2 (centre-east of Spain), 5% to zone 3 (east of Spain) and 7.4% to zone 4 (north-east of Spain). The mean concentration of tainted compounds (Table 1) was lower than their respective olfactory thresholds (TCA: 5e10 ng/l, TeCA: 14e25 ng/l, PCA: 4000 ng/l, TCP: unknown, TBA: 8 ng/l) (Liacopoulos et al., 1999). This signifies that consumers would not perceive these compounds and the wines probably wouldn’t be rejected when uncorked. TeCA was the compound that showed the highest mean value, whereas TBA showed the highest maximum value (Table 1). 3.2. Calibration and validation for haloanisoles and halophenols using NIR spectroscopy Table 2 shows the PLS calibration for the target compounds studied. The calibration coefficient (R2) was higher than 0.77 for all compounds (Table 2). As can be observed, the correlation between the values determined by GCeMS and values estimated by NIR calibration was good, with no outliers as no dispersion values were found. TCP was the compound that presented the highest coefficient of correlation and multiple regression coefficient, whereas TBA was the compound that presented the lowest coefficient of correlation and multiple regression coefficient (Table 2). Moreover, the lowest coefficient of regression corresponded to the calibration of PCA, while the highest was found in the calibration of TeCA. The number of latent variables, number of PLS eigen-vectors (factors) used to explain the model, was in all the cases 10 (Table 2). The standard error of calibration (SEC) was between 10.12 in PCA and 27.51 in TBA, and the standard error of prediction (SEP) ranged from 11.66 in PCA to 29.81 in TBA (Table 2). That is, taking into account all the calibration statistics, the worst calibration corresponded to TBA. These results showed that NIR spectroscopy provides a suitable
prediction of haloanisoles and halophenols, indicating that the PLS models based on NIR spectra explains at least the 77% of the variation in data. The full cross validation was carried out in order to validate calibration data consisting of the following: First, sample one in the calibration set is deleted. Then, the calibration is performed on the rest of the samples before being tested on the first sample by comparing the specified value (determined by GCeMS) with the estimated. The first sample is then put back into the calibration set, and the procedure is repeated by deleting sample two. The procedure continues until all samples have been deleted once (Naes, Isaksson, Fearn, & Davies, 2002). Geladi and Kowalski (1986) pointed out that full cross validation is not suitable when a large number of samples are included. This has been corroborated in our study since the cross validation coefficient (R2) ranged between 0.22 in TCA and 0.32 in PCA, and the standard error of cross validation (SECV) was between 20.51 in PCA and 49.82 in TBA (Table 3). In order to improve such full cross validation data, samples were grouped according to ageing time (aged-6, aged-12 and aged-18), and according to geographical zones (1e4). In the case of ageing time, as showed some of the most important statistics (Table 3), the lower the number of samples the better the validation. For example, the coefficient of determination in cross validation (R2) for TCA, considered as the most problematic compound among those studied, was 0.26 for aged-6 wines (N ¼ 306), 0.57 for aged-12 wines (N ¼ 222) and 0.80 for aged-18 wines (N ¼ 72); while SECV was 30.97, 22.58, and 19.94, respectively. When the wine production zone is the differentiation parameter, statistics variables improve in all cases (Table 3). In zone 1 the R2 ranged from 0.34 in PCA to 0.44 in TBA, and SECV was between 12.88 in PCA and 34.50 in TCP; in zone 2 the R2 ranged between 0.58 in TCA, TeCA and TBA and 0.78 in PCA, and SECV was between 19.25 in TCP and 49.18 in TeCA; in zone 3 the R2 ranged from 0.71 in TCA to 0.99 in PCA, and SECV was between 0.07 in PCA and 6.63 in TCA; while in zone 4 the R2 ranged between 0.75 in TCA and TBA and 0.99 in TeCA and PCA, and SECV was between 0.02 in PCA and 56.60 in TBA. The residual predictive deviation (RPD), defined as the ratio between the standard deviation of the population (SD) and the
Table 2 Calibration statistics for haloanisoles and halophenols (ng/l) measured in aged red wines by near infrared spectroscopy.
2,4,6-Trichloroanisole (TCA) 2,3,4,6-Tetrachloroanisole (TeCA) 2,3,4,5,6-Pentachloroanisole (PCA) 2,4,6-Trichlorophenol (TCP) 2,4,6-Tribromoanisole (TBA) a b c d
SEC: Standard error of calibration. SEP: Standard error of prediction. LV: Number of latent variables. R2: Coefficient of correlation.
SECa
SEPb
Coefficient of regression
LVc
R2 cal
d
17.29 25.27 10.12 15.85 27.51
18.42 27.17 11.66 16.34 29.81
171.0 251.7 106.0 194.5 245.7
10 10 10 10 10
0.7789 0.7698 0.8369 0.8585 0.7678
Multiple regression coefficient 0.8826 0.8774 0.9148 0.9265 0.8763
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Table 3 Full cross validation statistics for haloanisoles and halophenols (ng/l) measured in aged red wines by near infrared spectroscopy.
Total (N ¼ 600)
Aged-6 (N ¼ 306)
Aged-12 (N ¼ 222)
Aged-18 (N ¼ 72)
Zone 1 (N ¼ 363)
Zone 2 (N ¼ 117)
Zone 3 (N ¼ 60)
Zone 4 (N ¼ 60)
R2 SECV RPD R2 SECV RPD R2 SECV RPD R2 SECV RPD R2 SECV RPD R2 SECV RPD R2 SECV RPD R2 SECV RPD
2,4,6-Trichloroanisole (TCA)
2,3,4,6-Tetrachloroanisole (TeCA)
2,3,4,5,6-Pentachloroanisole (PCA)
2,4,6-Trichlorophenol (TCP)
2,4,6-Tribromoanisole (TBA)
0.22 32.23 1.13 0.26 30.97 1.16 0.57 22.58 1.52 0.80 19.94 2.25 0.38 22.88 1.27 0.58 22.80 1.53 0.71 6.63 1.84 0.75 38.42 1.97
0.25 45.38 1.15 0.30 33.53 1.19 0.60 29.93 1.58 0.79 43.64 2.18 0.41 32.28 1.30 0.58 49.18 1.52 0.82 2.27 2.36 0.99 0.15 >5
0.32 20.51 1.21 0.53 4.93 1.46 0.54 24.46 1.47 0.77 14.75 2.06 0.34 12.88 1.23 0.78 23.03 2.10 0.99 0.07 >5 0.99 0.02 >5
0.26 35.90 1.16 0.46 28.31 1.36 0.55 24.88 1.48 0.84 25.80 2.49 0.35 34.50 1.24 0.77 19.25 2.05 e e e 0.83 24.86 2.36
0.23 49.82 1.14 0.30 42.81 1.19 0.60 29.49 1.59 0.78 44.34 2.13 0.44 28.88 1.34 0.58 48.53 1.53 e e e 0.75 56.60 1.97
N: Number of samples in validation. R2: coefficient of determination in full cross validation. SECV: standard error in full cross validation. RPD: residual predictive deviation (SD/ SECV).
SECV for the NIR predictions, is a useful parameter commonly applied to evaluate how well a calibration model can predict the volatile compounds concentration (Fearn, 2002). If the SECV is large compared to the range of compositions (as SD), a relatively small RPD value results, and the NIR calibration model is considered to be non robust. An RPD value greater than 3 is considered fair and is recommended for screening purposes (Smyth et al., 2008). This value was higher than 5 for some compounds in the full cross validation done for zones 3 and 4 (Table 3). However, a more qualitative interpretation of the RPD was given by other authors where an RPD value lower than 1.5 was considered insufficient for most applications, since it is only suitable for rough classification, while NIR calibration models with values greater than 2 were considered excellent (Williams, 2001). When the validation was done with all samples, aged-6 wines and samples from zone 1, RPD had values lower than 1.5 probably due to the elevated number of samples. However, when full cross validation was done for zones 2, 3 and 4 and practically for all cases in aged-12 and aged-18 wines, the RPD obtained was higher than 1.5 for all the compounds (Table 3). This is an interesting result since aged-12 and aged-18 are the most expensive wines and wineries could then easily detect occurrence of such compounds before product distribution. 4. Conclusions Calibration using PLS of haloanisoles and halophenols by SBSEeGCeMS and near infrared spectroscopy was good, so these compounds can be determined rapidly and easily by this technique. The full cross validation was adequate for aged-12 and aged-18 wines, and also when it is done with wines from zones 2, 3 and 4. The presence of these compounds in wines may cause rejection by consumers with the resulting economic loss to the wineries, especially in the case of aged-12 and aged-18 wines. Therefore, this method could provide wineries with a valuable tool for the determination of haloanisoles and halophenols in wines, in terms of a fast and reliable analysis that can be performed with no need of a specialist. Moreover, NIR technology requires minimal sample processing prior to analysis.
Acknowledgements Many thanks for the financial support given by Ministerio de Educación y Ciencia to the project AGL2004-04609 and thanks to Kathy Walsh for proofreading the English manuscript. T.G.-C. also wishes to thank Ministerio de Educación y Ciencia for the Juan de la Cierva contract. References Amon, J. M., Vandeepeer, J. M., & Simpson, R. F. (1989). Compounds responsible for cork taint. Australian & New Zealand Wine Industry Journal, 4, 62e69. Armstrong, M. J., Galloway, S. M., & Ashby, J. (1993). 2,4,6-Trichlorophenol (TCP) induces chromosome breakage and aneuploidy in vitro. Mutation Research, 303, 101e108. Barnes, R. J., Dhanoa, M. S., & Lister, S. J. (1989). Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Applied Spectroscopy, 43, 772e777. Butzke, C. E., Evans, T. J., & Ebeler, S. E. (1999). Detection of cork taint in wine using automated solid-phase microextraction in combination with GC/MS-SIM. ACS Symposium Series, 714, 208e216. Chatonnet, P., Bonnet, S., Boutou, S., & Labadie, M. D. (2004). Identification and responsibility of 2,4,6-tribromoanisole in musty, corked odors in wine. Journal of Agricultural and Food Chemistry, 52, 1255e1262. Corbella, E., & Cozzolino, D. (2006). Classification of the floral origin of Uruguayan honeys by chemical and physical characteristics combined with chemometrics. LWT- Food Science and Technology, 39, 534e539. Copete, M. L., Zalacain, A., Lorenzo, C., Carot, J. M., Esteve, M. D., Climent, M. D., et al. (2009). Haloanisole and halophenol contamination in Spanish aged red wines. Food Additives and Contaminants, 26, 32e38. Cozzolino, D., Kwiatkowski, M. J., Parker, M., Cynkar, W. U., Dambergs, R. G., Gishen, M., et al. (2004). Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy. Analytica Chimica Acta, 513, 73e80. Cozzolino, D., Cynkar, W. U., Dambergs, R. G., Mercurio, M. D., & Smith, P. A. (2008). Measurement of condensed tannins and dry matter in red grape homogenates using near infrared spectroscopy and partial least squares. Journal of Agricultural and Food Chemistry, 56, 7631e7636. Cozzolino, D., Kwiatkowski, M. J., Dambergs, R. G., Cynkar, W. U., Janik, L. J., Skouroumounis, G., & Gishen, M. (2008). Analysis of elements in wine using near infrared spectroscopy and partial least squares regression. Talanta, 74, 711e716. Fearn, T. (2002). Assessing calibration; SEP, RPD, RER and R2. NIR News, 13, 12e14. Garde-Cerdán, T., Zalacain, A., Lorenzo, C., Alonso, J. L., & Salinas, M. R. (2008). Molecularly imprinted polymer-assisted simple clean-up of 2,4,6trichloroanisole and ethylphenols from aged red wines. American Journal of Enology and Viticulture, 59, 396e400.
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