Open Access
Issue
BIO Web Conf.
Volume 9, 2017
40th World Congress of Vine and Wine
Article Number 02005
Number of page(s) 5
Section Oenology
DOI https://doi.org/10.1051/bioconf/20170902005
Published online 04 July 2017

© The Authors, published by EDP Sciences 2017

Licence Creative Commons
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).

1. Introduction

Wine is an ancient beverage spread all over the world and one of the most known alcoholic drinks, which has the benefit to reduce the risk of cardiovascular diseases, thanks to the many compounds playing a role of great importance human health-wise [1].

For the past several years there has been a steady increase of the alcoholic degree of wines because of climate change. Because of global warming, grapes that have a higher sugar concentration are produced. For this reason the cultivation of grape varieties is being moved from Southern to cooler areas. This changes every year, depending on various circumstances like occasionally unfavorable climate conditions [2].

An important aspect of wine production is the process of obtaining a certain lower ethanol concentration, which has gained considerable attention over the last 10 years. Increase consumer demand for wines that are perceived as healthier, changing attitudes of consumers regarding the social consequences of excessive ethanol consumption and more favorable excise rates for lower alcohol beverage are just some of the reasons for lowering ethanol levels in wine. There is a significant consumer demand for the beverages with lower ethanol levels [3].

Metals in wine occur at the mg/L level or less. Though not directly related to the taste of the wine, the metal content should be determined because excess is undesirable, and in some cases prohibited, due to potential toxicity. Lead content in wine, to guarantee consumer health protection is restricted in several states by legislation because toxicity for human health [4].

For reason of direct correlation with soil composition, metals appear to be the best way to identify the geographical origin of the wine [5]. Moreover, knowledge of the metallic ion concentration of wine is of interest because of their influence on the wine-making process where metals such as calcium, potassium, copper and iron can cause cloudiness or precipitates.

The international community set some low acceptance levels for some metals, due to their importance in nutrition and their potentially toxic effects [6].

In grapes, the total acid content normally reaches a maximum content during growth and decreases during ripening. [7] Although having the same genotype, grapes harvested from different climates, have different organic acid contents. [8] During grape ripening, in continuous warm conditions a lower acid content at maturity results mostly due to increasing degradation of malic acid [9]. Malic and tartaric acids are dominant organic acids and account for 90% or more of the total acids in grapes [10].

All organic acids play an important role in wine, the taste being influenced by the concentration of different organic acids. Wine’s organic acids in wine are malic, tartaric, citric, acetic, lactic, succinic and others. Concentration of organic acids varies depending on different factors such as pH, temperature, sulfur dioxide and oxygen concentration. The ratio of tartaric acid to malic acid mainly influences the taste of the wine. When the ratio of these acids is about 2 or less, the wine will not be harmonious and will have a sour aftertaste. The wine with best flavor and bouquet will be obtained at a ratio of tartaric to malic acid equal to 3 and more. For example succinic acid has a salty-bitter taste, citric acid gives freshness to wine and malic acid gives the taste of green apples [11].

A complete analysis of wine composition is a time- consuming process, but it brings necessary information for both obtaining a quality product and its maintenance under proper conditions. Therefore, only a few parameters are checked periodically. Among them, the most common are: alcoholic degree, reducing sugars, pH, sulfur dioxide, total and volatile acidity.

The classical methods used for monitoring the wine composition with high robustness and precision are recognized by the international community as official methods of analysis, even though more recent methods may be based on modern automated instrumental techniques [12].

2. Materials and method

2.1. Grape sample and winemaking

Muscat Ottonel grapes from Iasi vineyard were harvested in 2015 at optimal maturity. The grapes were destemmed and crushed, and must was transferred in stainless steel containers. The grape must was concentrated until 308.3 g/L sugar using a device for reverse osmosis, Bucher Vaslin. The obtained permeate has 8.032 g/L sugar.

There were obtained ten variants of beverages (wines) with low alcoholic concentration, by using known quantities of the two phases resulting from the reverse osmosis process. These beverages (wines) had an alcoholic concentration starting from 2.5% (v/v) in the first variant, up to 7% (v/v) in the tenth variant (VSA1 to VSA10). Alcoholic concentration varies for each variant by 0.5% (v/v). After fermentation in 50 L stainless steel tanks (Zymaflore X16® yeast), the samples were filtered with 0.45 μm sterile membrane, and bottled in 0.75 L glass bottles.

After 2 months of storage at constant temperature, the beverage samples were analyzed to determine the metal content (using an Agilent MP-AES 4200), organic acids concentration (HPLC method), and other physical- chemical characteristics (OIV standard methods).

2.2. Physical-chemical analysis

The main physico-chemical parameters analyzed were alcoholic strength, reducing sugars, total acidity (TA), volatile acidity (VA), real acidity (pH), sulfur dioxide and density (O.I.V., 2016).

The results are presented in Table 1.

Table 1.

Classical enological parameters of low alcoholic wine obtained by reverse osmosis.

2.3. Analysis of the metals content in wines

All analyzed samples were diluted to reduce the salinity of the samples under 1%. If this technique is used, the mineralization and ionization of the sample is not necessary. The measurements were performed using an Agilent MP-AES 4200 instrument equipped with the standard sample introduction system consisting of the OneNeb nebulizer, double pass cyclonic spray chamber and easy fit torch.

The Agilent SPS-3 auto-sampler was used to deliver samples to the instrument allowing automatic operation. Selection of optimal lines depended on wavelengths that were free from spectral interference and matched the appropriate sensitivity. Spectral and background interfer- ences were simultaneously and accurately corrected using the MP software.

Each sample was analyzed for three times and the registration time was 3 seconds. In Table 2 are shown the instrumental parameters and operating conditions of the equipment.

Table 2.

Instrumental parameters.

2.4. Analysis of the organic acids in wines using HPLC-High-performance liquid chromatography method

For the analysis of the organic acids content a Shimadzu series Proeminence LC20 device was used. The sample was filtered through a 28 mm diameter nylon 0.45 μm syringe cartridge. This method is described in the MA-E- AS313-04-ACIORG by O.I.V. standards.

Using a 5 μL volume of standard, a sample was injected through two analytical columns (YMC-Triart C18 multi-stage hybrid group’s 3 μm 150 × 4.6mm 120 A˚) at a flow rate of 0.9 mL/min., using a solution of sulphuric acid adjusted to a value of 1.3 pH.

Columns’ temperatures were maintained at 45 ◦C for the entire period of analysis.

3. Results and discussion

Statistical evaluation of the data was performed using Statgraphics Centurion XVI@ software, (StatPoint Tech- nologies, Inc, U.S.A.). In this study, a one-way ANOVA procedure was applied.

3.1. Physical-chemical characteristics of wine

For alcoholic strength, there was significant difference between all samples. In the first variant, were obtained wines with 2.5% alcohol degree. Then every sample has an alcoholic degree with 0.5% volume more than the previous one.

Total acidity (TA) also showed similar mean values for all samples, with a minimum in the first sample and with a maximum in the last variant.

All other parameters showed average values with variability to a smaller or larger degree depending on the impact of different quantities of permeate and concentrate used for every wine sample.

All of the wine samples were dry.

3.2. Metals content of wine sample

The values of Zn, Fe, Cu, Ni, Pb, Mn, Mg, Ca, K, Na, cations content identified in samples of low alcoholic wine, as determined by AES, are shown in Table 3.

Table 3.

Zn, Fe, Cu, Ni, Pb, Mn, Mg cations concentration in μg/L and Ca, K, Na cations concentration in mg/L of low alcoholic wine obtained by reverse osmosis.

The measured concentration of zinc, iron, copper, nickel show that these varied with a minimum content in the first sample VSA and then increased gradually in other samples.

Magnesium and calcium concentration in wine have an insignificant change in all analyzed samples.

The lead value progressively increases from the first VSA1 sample to VSA5 with a minimum content of 66.73 μg/L and a maximum content of 138.21 μg/L. This increase is then found again starting with VSA6 with a minimum content of 94.75 μg/L and a maximum content in the 10th VSA10 sample of 547.51 μg/L.

The content of manganese fluctuates in large limits, with a minimum content in VSA3 sample of 269.97 μg/L and a maximum content in VSA10, of 857.29 μg/L.

Potassium cations concentration values present small differences from one sample to another.

Sodium concentrations fluctuates, registering a min- imum content in the case of the seventh sample VSA7 178.51 mg/L and a maximum of 586.07 mg/L in the fourth sample VSA4. This increase was not linear since the first sample, but fluctuated.

3.3. Wine organic acids analysis

The descendent pH values of the analyzed samples are concordance whit the weight of each acid, in accordance with the literature [13].

ANOVA table decomposes the variability for all acids identified (Table 4) into contributions due to various factors. Since Type III sums of squares (the default) have been chosen, the contribution of each factor is measured having removed the effects of all other factors.

Table 4.

Organic acid content (g/L), (mg/L).

The P-values test the statistical significance of each of the factors. Since one P-value (0.00) is less than 0.05, this factor has a statistically significant effect on all acids identified at the 95.0% confidence level.

For example, in Table 5 a multiple comparison procedure was applied to determine which means are significantly different from others in the case of acid malic. The bottom half of the output shows the estimated difference between each pair of means. An asterisk has been placed next to 42 pairs, indicating that these pairs show statistically significant differences at the 95.0% confidence level.

Table 5.

Multiple Range Tests for Malic Acid by Sample.

In Table 6, 7 homogenous groups are identified using columns of X’s. Within each column, the levels containing X’s form a group of means within which there are no statistically significant differences. The method currently being used to discriminate among the means is Fisher’s least significant difference (LSD) procedure. With this method, there is a 5.0% risk of calling each pair of means significantly different when the actual difference equals 0. In Table 7 are shown the mean malic acid for each level of the factors. It also shows the standard error of each mean, which is a measure of its sampling variability. The rightmost two columns show 95.0% confidence intervals for each of the means.

Table 6.

The homogenous groups that were identified.

Table 7.

Table of Least Squares Means for Malic Acid with 95.0% confidence Intervals.

For all other parameters, the main effects are statistically significant at the 95.0% confidence level. This rule is available for all identified acids in all samples.

4. Conclusion

The results obtained indicate that the very complex physical-chemical composition of the low alcoholic beverages analyzed is influenced by the specific chemical composition of a given grape must, as well as by the use of products obtained from reverse osmosis.

We found moderate correlation between quantities of permeate and concentrate regarding metals content. The content of metals has not increased in large limits.

The study show the influence of used quantities of the two phases resulting from the reverse osmosis process (permeate/concentrate). These had a statistically significant effect on all acids identified at the 95.0% confidence level.

References

  • L. Liguori, J. Food Chem. 140 , 68–75 (2013) [CrossRef] (In the text)
  • E. Duchene, C. Schneider, J. Dev. Agron. Sustain. 25 , 93–99 (2005) [CrossRef] [EDP Sciences] (In the text)
  • B. Saha, J. Oenoviti Int. Net. Fr. 1 , 78–86 (2013) (In the text)
  • M. Aceto, O. Abollino, M. C. Bruzzonti, E. Mentasti, C. Sarzanini and M. Malandrino, J. Food Addit. Contam. 19 , 126–133 (2002) [CrossRef] [PubMed] (In the text)
  • P.R. Ashurst & M. J. Dennis, Food authentication , 60–107 (EDP Chapman & Hall, 1996) (In the text)
  • H. Eschneauer, Am J Enol Vitic 33 , 226–230 (1982) (In the text)
  • N. A. M. Eskin, H. M. Henderson & R. J. Townsend, N. A. M. Eskin Biochemistry of foods , 31–63 (EDP Academic Press, 1971) (In the text)
  • T. Fuleki, E. Pelayo & R. Palabay, J. AOAC Int. 76 , 591–600 (1993) (In the text)
  • Seymour, G.B., Taylor, J.E., Tucker, Gregory A., Biochemistry of fruit ripening , 189–220 (EDP Chapman & Hall 1993) (In the text)
  • T. Philip & F. E. Nelson. J. Food Sci. 38 , 18–20 (1973) (In the text)
  • V. N. Bayraktar, J. Biotech. Acta 6 , 97–106 (2013) [CrossRef] (In the text)
  • M. D. Luque de Castro, J. González-Rodríguez, P. Pérez-Juan, J.Food Sci. Technol. 21 , 231–265 (2005) (In the text)
  • G. Odageriu, Evaluarea solubilitatii compusilor tartrici din vinuri , 169–187 (EDP. “Ion Ionescu de la Brad” Iasi, 2006) (In the text)

All Tables

Table 1.

Classical enological parameters of low alcoholic wine obtained by reverse osmosis.

Table 2.

Instrumental parameters.

Table 3.

Zn, Fe, Cu, Ni, Pb, Mn, Mg cations concentration in μg/L and Ca, K, Na cations concentration in mg/L of low alcoholic wine obtained by reverse osmosis.

Table 4.

Organic acid content (g/L), (mg/L).

Table 5.

Multiple Range Tests for Malic Acid by Sample.

Table 6.

The homogenous groups that were identified.

Table 7.

Table of Least Squares Means for Malic Acid with 95.0% confidence Intervals.

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.