Abstract
Purpose
Methods to assess the authenticity and traceability of wines have been extensively studied as enhancers of food quality, allowing producers to obtain market recognition and premium prices. Among analytical techniques, the volatilome profile attained by gas chromatography coupled with mass spectrometry is acquiring more and more attention by the scientific community, together with the use of chemometrics
Design/methodology/approach
The volatilome profile of three varieties of blanc wines from the Collio area (namely Ribolla Gialla, Malvasia and Friulano) between Italy and Slovenia, was determined by head space-solid phase micro extraction-gas chromatography-mass spectrometry, enhancing the carbonyl compounds identification with O-(2, 3, 4, 5, 6-pentafluorobenzyl)-hydroxylamine with the aim of identifying the autochthonous Friulano variety.
Findings
A two-step chemometric approach based on an unsupervised technique (PCA) followed by a supervised one (PLS-DA) allowed to identify possible markers for discriminating the Friulano Collio variety from the others, in particular two chemical classes were identified by PCA (ketones and long chain esters). PLS-DA showed 87% accuracy in classification. A correct classification (i.e. non-Friulano Collio) of a group of wines obtained from the same grape variety but produced in an extra-Collio area was obtained as well. The results confirmed the benefits of using a derivatization step prior to volatile organic compounds analysis.
Research limitations/implications
Among methods to assess the authenticity and traceability of wines, volatilome profile of wines determined by head space-solid phase micro extraction-gas chromatography-mass spectrometry, enhanced by the carbonyl compound identifications with O-(2, 3, 4, 5, 6-pentafluorobenzyl)-hydroxylamine, may have a key role in conjunction with chemometrics and, in particular with principal component analysis and partial least square discriminant analysis.
Practical implications
Among methods to assess the authenticity and traceability of Friulano wine, volatilome profile of wines determined by head space-solid phase micro extraction-gas chromatography-mass spectrometry, enhanced by the carbonyl compound identifications with O-(2, 3, 4, 5, 6-Pentafluorobenzyl)Hydroxylamine hydrochloride, may have a key role in conjunction with chemometrics.
Originality/value
Few works investigated both wine traceability with a volatilome enhancer and chemometrics of the Friulano wine variety obtaining such an improvement in this wine variety discrimination.
Keywords
Citation
Licen, S., Muzic, E., Briguglio, S., Tolloi, A., Barbieri, P. and Giungato, P. (2021), "Derivatized volatile organic compound characterization of Friulano wine from Collio (Italy–Slovenia) by HS-SPME-GC-MS and discrimination from other varieties by chemometrics", British Food Journal, Vol. 123 No. 8, pp. 2844-2855. https://doi.org/10.1108/BFJ-08-2020-0690
Publisher
:Emerald Publishing Limited
Copyright © 2021, Sabina Licen, Elija Muzic, Sara Briguglio, Arianna Tolloi, Pierluigi Barbieri and Pasquale Giungato
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Methods to assess the authenticity of wines have been extensively studied as using geographical indications allows producers to obtain market recognition and premium prices (Berna et al., 2009). Authenticity allows consumers to be aware of the importance of diet to ensure a healthy living, to make correct choices. Sustainability among Italian wine consumers seems not to be the main criterion of the consumer's choices, although it is increasing, Italian consumers are mainly attracted by organoleptic properties and terroir characteristics (Mastroberardino et al., 2019). Moreover, consumers are aware that regular and moderate intake of wine reduces the incidence of heart disease, diabetes mellitus and hormonal issues, increasing longevity, with a recently improvement of the aspects of personal hedonistic well-being and social relations (Fiore et al., 2019). For these reasons, the development of new selective techniques for determining the geographical origin of agricultural products is of ongoing concern. Among classification techniques, molecular spectroscopy (Chandra et al., 2017; Giannetti et al., 2016; Guzmán et al., 2015) and chemical characterization of wine's aroma (Francis and Newton, 2005) are of increasing interest. Wine aroma is determined by a broad pool of volatile organic compounds (VOCs), basically ethyl esters, acetates, cinnamic esters, acids, alcohols, phenols, lactones, norisoprenoids and sulfur compounds (Styger et al., 2011). VOCs are present in wine in a very low concentration, nevertheless they significantly influence the olfactory peculiarities. The aroma of wines is influenced by a variety of factors: grapes employed, production area, climate, fermentation conditions, yeast strain, winemaking production steps and storage conditions (Cordente et al., 2012; Ebeler, 2001; Styger et al., 2011). A technique currently and widely used to analyze the volatiles in wine is HS-SPME-GC-MS (head space-solid phase micro extraction-gas chromatography-mass spectrometry). It limits the manipulation of the sample and avoids the use of solvent and its parameters can be tailored to detect a wide variety of analytes in wine (Azzi-Achkouty et al., 2017; Perestrelo et al., 2014). Moreover, the use of derivatizing agents has proved to enhance the detection of specific compounds such as carbonyls (Panighel and Flamini, 2014; Zapata et al., 2010), phenols (Pizarro et al., 2007) and thiols (Coetzee and du Toit, 2012; Rodríguez-Bencomo et al., 2009). Progress in authentication and traceability of grapes and wines by using data mining and analysis with chemometric approaches has been the subject of a recent review (Versari et al., 2014). Liu et al., used PCA (principal component analysis) to disclose realtions between different volatile compounds detected in Danish Solaris white wines (Liu et al., 2015). South African wines were classified analyzing the volatilome by using FA (Factor Analysis), PCA and LDA (linear discriminant analysis) (Weldegergis et al., 2011). Markers of typical red wine varieties from the Valley of Tulum (San Juan–Argentina) were identified analyzing the volatilome by HS-SPME-GC-MS using PCA and S-LDA (stepwise linear discriminant analysis) (Fabani et al., 2013). HS-SPME-GC-MS analysis of VOCs of samples of Montepulciano monovarietal red wines from two different regions (Marche and Abruzzo, Italy) combined with CA (cluster analysis) and PCA was used to identify markers of Montepulciano wines (Sagratini et al., 2012). Springer et al. (2014) proposed a discrimination model for German white wine varietal authentication based on exploratory data analysis by PCA of the VOCs data set obtained by an untargeted analytical approach followed by a classification performed by PLS-DA (partial least square discriminant analysis) (Springer et al., 2014). Ríos-Reina et al. (2019) used PLS-DA approach to obtain a high performance in the classification volatile profiles of three Spanish wine vinegar PDOs (Ríos-Reina et al., 2019). The aim of this study was to make a first survey of the volatile organic compound profiles (volatilome) of three varieties of blanc wines from the Collio area (Italy and Slovenia), namely Ribolla Gialla, Malvasia and Friulano by using a derivatizing agent (PFBHA) on samples before performing HS-SPME-GC-MS analysis to foster the carbonyl compound identification. Our main goal was to verify if these different wine varieties, produced in a relatively small territory, could be differentiated from their VOCs profiles, particularly focusing on the very little studied Friulano VOCs profiles.
Only two studies concerning these wine varieties have been published before, but they were not focused on Collio area wines. One reported few data on Friulano (formerly named Tocai) VOCs from extra-Collio areas (Tocai di Lison–Veneto region and Tocai Grave–Friuli Venezia Giulia region) (Moret et al., 1994) and one reported on VOCs of Malvasia and Ribolla Gialla wines produced in laboratory for research purposes on the influence on final wine aroma of grape skin contact during the fermentation process (Bavčar et al., 2011). Both studies lacked in the carbonyl compounds information. A two step chemometric approach based on an unsupervised technique (PCA) followed by a supervised one (PLS-DA) allowed to identify possible markers for discriminating the Friulano Collio variety from the others. We also compared the Friulano wines with a group of wines obtained from the same grape variety but produced in an extra-Collio area.
2. Materials and methods
2.1 Wine samples
The Collio wine-producing area (Figure 1) is a hilly area of Friuli Venezia Giulia region–northeast of Italy, which extended between Isonzo river and Iudrio stream, divided in an Italian side and in a Slovenian one. The Italian side is included in the Gorizia province (Figure 1a) and divided in the municipalities of San Floriano del Collio, Gorizia, Cormons, Dolegna del Collio, Farra d'Isonzo and Capriva d'Isonzo (Figure 1b). The whole area where grapes were cultivated has obtained the Italian quality assurance Denominazione di Origine Controllata (designation of origin) and it is well-known world-wide to produce excellent local white wines. Among these wines, we chose three varieties (Friulano, Ribolla Gialla and Malvasia) which are the older autochthonous and most present ones in the Collio area, thus representing the best indicators of local flavor characteristics.
According to the designation of origin protocol the grape varieties permitted were (all Vitis Vinifera): Friulano (100%), Ribolla Gialla (100%) and Malvasia (100%), respectively. The Collio wine samples (n = 15, five for each variety, vintage 2014) used in this study were with certified origin and directly collected at the wineries of local producers. Three additional Friulano samples produced in extra-Collio areas (Friuli Annia, Colli Orientali del Friuli, Friuli Grave, vintage 2014) were further analyzed for comparison. All the samples were collected in wineries, which operated as similar as possible winemaking processes in terms of maceration, pressing, must draining, fermentation conditions, refinement and bottling.
2.2 Volatile organic compounds analysis
2.2.1 Sample preparation and derivatization
Samples were transported to the laboratory in the commercial 750 mL glass bottles which had been opened right before analysis. A sample of diluted wine (10 mL, 1:5 sample dilution with deionized water) with addition of 2 mg of O-(2, 3, 4, 5, 6-Pentafluorobenzyl) Hydroxylamine hydrochloride (PFBHA, CAS 57981-02-9, Sigma–Aldrich S.r.l., Milan, Italy) as derivatizing agent (Figure 2a) and 2 g of anhydrous NaCl (JT Baker, Phillipsburg, NJ, USA) was directly prepared in a 20 mL vial successively crimp sealed with a PTFE septum cap. The derivatization process was carried out at 50 °C for 15 min at 500 rpm using the automatic sampling system Gerstel MPS2-Twister (Gerstel GmbH and Co., Mülheim, Germany).
2.2.2 Sample analysis
The analysis was conducted by a GC–MS system (Agilent 6890/5973 Inert, Agilent, Santa Clara, CA, United States) equipped with the abovementioned autosampler (Gerstel MPS2-Twister) with helium as carrier gas (1.5 ml min-1). A coated Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CAR/PDMS) fiber (Sigma–Aldrich S.r.l., Milan, Italy) was exposed to the headspace of the derivatized sample for 15.00 min at 50 °C and 500 rpm. The fiber was desorbed for 4.00 min at 250 °C in the injection unit. The separation was carried out in an Agilent DB 5ms UI capillary column (30 m × 0.25 mm i.d. x 0.25 μm film thickness). The GC oven temperature program started at 45 °C for 5 min and then was ramped to 100 °C at 2 °C/min and to 270 °C at 15°C/min with a final isothermal stage held for 4 min. The mass spectrometer operated with ion source at 230 °C, quadrupole temperature of 170 °C, 70 eV electron energy, acquiring in SIM (m/z 161, 181, 195, 239) and TIC mode from m/z 35–400 amu. The SPME fiber was thermally cleaned prior to each analysis at 265 °C for 15.00 min. Analysis were conducted on three replicates. All chemicals were analytical reagent grade unless otherwise stated, and water was obtained from a Milli-Q purification system (Merck-Millipore, Darmstadt, Germany).
2.3 Chemometric data analysis
The VOCs selected are reported in Figure 2b, and their chromatogram areas were organized in a dataset according to variety (Ribolla, Friulano and Malvasia) of wines. The calculations have been performed in R software environment (R Foundation for Statistical Computing, Vienna, 2016).
The data set has been normalized to have zero mean and unit variance (z-score normalization) to give to all the variables the same importance independently from their absolute value ranges. Then the dataset has been mined by a two steps chemometric analysis. First the variables have been grouped according to their chemical class as in (Giungato et al., 2019) and elaborated by PCA using pcaMethods package (Stacklies et al., 2007). Then the supervised method PLS-DA has been applied to a subset of variables according to the findings obtained by PCA. For performing PLS-DA the mdatools package (Kucheryavskiy, 2020) has been used.
2.4 VOCs composition of wines and dataset selection for classification purposes
In total, 40 VOCs were detected in the sample chromatograms and, among these, 25 compounds were selected for their higher peak area variability across the samples (Figure 2b). The selected compounds (aldehydes, esters, ketones and one alcohol) were identified (Table 1) by comparison of their mass spectra with the NIST 09 MS library. Among the selected compounds ethyl decanoate, 2-butanone, ethyl octanoate and acetone, showed the highest variations in peak area (Figure 3).
For every sample, the chromatographic peak areas averaged on three replicates of the 25 selected VOCs were used for chemometric analysis. We obtained a matrix of 15 × 25 elements to be analyzed for the classification among Collio wine varieties. Before chemometric analysis, the data sets were subjected to an in-depth analysis following the best practices in statistical analysis applied in food science (Granato et al., 2014).
3. Results and discussion
3.1 Principal component analysis
The data set has been z-score normalized and the variables have been grouped according to their chemical class as follows: alcohols (h1), aldehydes (from a1 to a5), long chain aldehydes (a6), diesters (e6), esters (from e1 to e4), aromatic esters (e8), long chain esters (e7, e9, e10, e11), ketones (from k1 to k5, k7, k8), saccharides (k6). Then PCA has been performed on a 15 × 9 data set.
PC1, PC2 and PC3 accounted for 74.27 % of the total variance (36.28 %, 23.61 % and 14.38 %, respectively.) Bi-plots of PC1 vs PC2 and PC2 vs PC3 are reported in Figure 4.
As it can be observed in Figure 4, in PC1 all the loadings are positive except for a6 compound. Therefore, we can say that the samples that showed the higher values for this component are those which contained higher relative total VOC concentrations.
Friulano samples showed negative values both for PC2 and PC3 except for one of the samples that showed a positive value for PC2 and a nearly zero value for PC3. Only one non Friulano wine (Malvasia) had similar values. Considering the graph representing PC2 vs PC3 it appears that all the Friulano samples are pulled in the both components negative quadrant by ketones and long chain esters classes, except for the Friulano sample pulled in the both components positive quadrant by a6 compound.
The Friulano extra-Collio samples data set, not used for building the PCA model, has been normalized by variable with the same parameters as the previous data set and with the same class grouping. The resulting data set has been projected into the PCA model obtaining the following component values for the scores: Friuli Annia (14.66, 7.46, −11.95), Colli Orientali del Friuli (−3.28, 1.17, 0.15), Friuli Grave (−1.03, 1.72, 1.52). It can be observed that no one of them lies in the both PC2 and PC3 negative quadrant, with Friuli Annia vary far apart from all the other samples. The graphs are presented in the Supplementary Materials. Therefore we considered that the compounds present in the ketones and long chain esters classes could be good candidates to discriminate Friulano Collio wines from the other Collio wines and Friulano extra-Collio wines.
3.2 Partial least square discriminant analysis
Considering the outcomes obtained by PCA exploratory analysis we decided to perform PLS-DA on a new 15 × 10 data set containing the following variables: e7, e9, e10, e11, from k1 to k5, k7, k8. A one class model (Friulano vs. non Friulano) and a leave-one-out cross-validation method have been used. One component was selected based on the minimum cross-validation error. The results of calibration and cross-validation are presented in Table 2.
Both in classification and cross-validation one of the Friulano samples was misclassified, namely the outlier shown in the PCA model. Additionally, both in classification and cross-validation one of the non Friulano wines was misclassified (Malvasia). Nevertheless the model showed a high sensitivity and accuracy in cross-validation and, moreover, the Friulano extra-Collio samples, not used for building the model, were correctly classified as non Friulano Collio wines (Table 2).
4. Conclusions
This study provides a first survey of the volatile organic compound profiles of three varieties of blanc wine from the Collio area (Italy and Slovenia), namely Ribolla Gialla, Malvasia and Friulano and tested the effectiveness of the chemometrics in fostering the identification of Friulano samples by using a variable selection method. The carbonyl compound identification has been fostered using a derivatizing agent (PFBHA) before performing HS-SPME-GC-MS analysis. Chemometric analysis has been applied to the obtained data set to identify possible markers of discrimination between varieties. PCA data set reduced by grouping VOCs belonging to the same chemical family, revealed good classification capabilities for the Friulano wine samples. PCA showed that compounds belonging to the ketones and long chain esters fostered the separation of Friulano samples. Therefore, a PLS-DA model based on these predictors was built. It showed a very good discrimination (87 % accuracy) between Friulano and other Collio varieties as well as between the former and other Friulano extra-Collio wines (100% accuracy).
Most of the useful predictors were carbonyl compounds, confirming the benefit in the use of the derivatizing agent. Improvements in the applicability of the authentication/discrimination model we presented could be achieved analyzing Collio wines with diverse wine-making processes and of different vintages.
Figures
Volatile organic compounds identified by analysis of the wine samples with HS-SPME-GC/MS
| Code | Name |
|---|---|
| h1 | 3-methyl-1-butanol |
| e1 | Ethyl butanoate |
| e2 | 3-methyl-ethyl butanoate |
| e3 | Isopentyl acetate |
| e4 | Ethyl hexanoate |
| k1 | Acetone |
| e6 | Diethyl succinate |
| a1 | Propionaldehyde |
| e7 | Ethyl octanoate |
| k2 | 2-butanone |
| e8 | 2-phenylethyl acetate |
| a2 | n-Butanal |
| k3 | 3-methyl-2-butanone |
| e9 | Ethyl nonanoate |
| k4 | 3-pentanone |
| a3 | 2-methyl-butanal |
| k5 | 2-pentanone |
| a4 | 3-metil-butanal |
| k6 | Di-hydroxy-acetone |
| a5 | Pentanal |
| k7 | 3-hexanone |
| e10 | Ethyl decanoate |
| k8 | 3-heptanone |
| e11 | Ethyl dodecanoate |
| a6 | Decanal |
Specificity, sensitivity and accuracy of PLS-DA of the Collio and extra-Collio wine samples
| TP | FP | TN | FN | Specificity | Sensitivity | Accuracy | |
|---|---|---|---|---|---|---|---|
| Collio wines (calib.) | 4 | 1 | 9 | 1 | 0.9 | 0.8 | 0.867 |
| Collio wines (cross val.) | 4 | 1 | 9 | 1 | 0.9 | 0.8 | 0.867 |
| Extra Collio wines | 0 | 0 | 3 | 0 | 1 | – | 1 |
Note(s): TP = true positive; FP = false positive; TN = true negative; FN = false negative
Abbreviations
- VOC
Volatile organic compound
- DVB/CAR/PDMS
Divinylbenzene/carboxen/polydimethylsiloxane
- FVG
Friuli-Venezia Giulia
- HS-SPME-GC-MS
Head space-Solid phase micro extraction-gas chromatography-mass spectrometry
- PFBHA
O-(2, 3, 4, 5, 6-pentafluorobenzyl) -Hydroxylamine hydrochloride
- FA
Factor analysis
- CA
Cluster analysis
- LDA
Linear discriminant analysis
- PCA
Principal component analysis
- PLS-DA
Partial least square discriminant analysis
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Acknowledgements
The authors acknowledge the University of Bari Aldo Moro for financial support by means of Athenaeum Fund for scientific research 2017/2018, identification number: UPB 102180105, and the University of Trieste for financial support by means of Atheneum Fund for scientific research 2017/2018.The APC of the present paper has been entirely covered by the CRUI-CARE/Emerald contract subscribed by the University of Bari Aldo MoroEthical statement: This article does not contain any study with human participants or animals performed by any of the authors. Informed consent is not applicable.Authors' contribution: Pasquale Giungato and Pierluigi Barbieri participated in study conception, supervision and coordination. Pasquale Giungato and Sabina Licen participated in data analysis, bibliographic research and drafted the manuscript. Sara Carmela Briguglio, Arianna Tolloi and Elija Muzic participated in collecting samples and setting-up the analytical method. All authors carefully read and approved the final manuscript.Conflict-of-interest statement: All authors disclose any actual or potential conflict of interest including any financial, personal or other relationships with other people or organizations within three years of beginning the submitted work that could inappropriately influence, or be perceived to influence, their work.