Port wine classification according to their sugar content.
\r\n\t
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He received his post-doctoral training in oncology and cancer proteomics at the Central South University and University of Tennessee Health Science Center (UTHSC). He worked at UTHSC and Cleveland Clinic in USA from 2001 to 2012, and achieved the rank of Associate Professor at UTHSC. After that, he became a Full Professor at the Central South University and Shandong First Medical University, and an Advisor of MS/PhD graduate students and postdoctoral fellows. He is also a Fellow of the Royal Society of Medicine, Fellow of EPMA, European EPMA National Representative, full member of the American Society of Clinical Oncology (ASCO), member of the American Association for the Advancement of Sciences (AAAS), Editor-In-Chief of the International Journal of Chronic Diseases & Therapy, Associate Editor of the EPMA Journal and BMC Medical Genomics, and Guest Editor of Frontiers in Endocrinology and Mass Spectrometry Reviews. 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From chapter submission and review, to approval and revision, copy-editing and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"9742",title:"Ubiquitin",subtitle:"Proteasome Pathway",isOpenForSubmission:!1,hash:"af6880d3a5571da1377ac8f6373b9e82",slug:"ubiquitin-proteasome-pathway",bookSignature:"Xianquan Zhan",coverURL:"https://cdn.intechopen.com/books/images_new/9742.jpg",editedByType:"Edited by",editors:[{id:"223233",title:"Prof.",name:"Xianquan",surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9352",title:"Proteoforms",subtitle:"Concept and Applications in Medical Sciences",isOpenForSubmission:!1,hash:"0f0288da2d32c0c0fcda6be0d4d45d67",slug:"proteoforms-concept-and-applications-in-medical-sciences",bookSignature:"Xianquan 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"74597",title:"Wavelet Transform for Signal Processing in Internet-of-Things (IoT)",doi:"10.5772/intechopen.95384",slug:"wavelet-transform-for-signal-processing-in-internet-of-things-iot-",body:'Internet of Things (IoT) refers to a network of diverse range of smart devices used in the domains of healthcare, industry, vehicles, homes, agriculture, retail, poultry and farming, and many more. Typical equipment supporting the IoT functionality include lightning, thermostats, TVs, sensors, mobile phones, speakers, voice assistants, cameras, video cameras, etc. These devices are basically deployed to facilitate the processes of monitoring and automation by transmitting and receiving information via internet. Undoubtedly, IoT has emerged as a rapidly growing ecosystem that promises to deliver unmatched global coverage, quality-of-service (QoS), scalability, security and flexibility to handle different requirements for a comprehensive list of use-cases. This has resulted in increasing number of IoT devices (relays, sensors, transceiver, actuators etc.) being deployed in in all types of urban, suburban and rural environments to cater to the innovative and emerging applications.
Since more devices and appliances have been transforming into their smarter version, we now have the applications such as smart cars with features of smart dashboards, GPS, smart doors and auto-route designed to reduce the accidents. Such applications clearly require high number of connected devices; in fact, it has been forecasted by International Energy Agency that the estimated number of connected devices which was 15 billion in 2018 shall reach 46 billion in 2030 [1]. In addition to the IoT devices, the evolution of IoT networking technologies has also been remarkable over the past decade, where more and more IoT devices have been shifting from using Long Term Evolution (LTE) to Narrowband-IoT (NB-IoT) which offers a cost-effective and energy efficient solution for continued operation of these systems. Naturally, the connected devices are expected to transmit large volumes of heterogeneous data at high data rates, and we will be required to deal with ever-increasing radio frequency noise.
The signals carrying IoT data are highly likely to face numerous obstacles and can be corrupted by significant amount of noise present in the environment. White Gaussian model has been commonly been used to quantify the noise faced by [1]. The types of noise which have been found to degrading the quality of IoT signals vary from the impact noise resulting from high frequency interference and instantaneous disturbance on the initialization of large equipment to changing connections around the participating IoT devices [2]. All these kinds of noise negatively influence the multi-device information fusion system [3]. Such noises should be filtered out and the transmitted signal should be reconstructed back to its actual form to ensure the accuracy and reliability of the transmitted information. Here, accuracy of IoT solutions is measured in terms of the number of packets reporting correct information, deviation between the reported and actual results and the delivery to correct destination timely. Similarly, the reliability of IoT is measured using information such as failure rate of the IoT devices, average time between two consecutive failures, average repair time and probability for needing to change a component within a certain time-frame.
Although this chapter mainly deals with algorithms for signal denoising, they can be also be applied for image denoising, as images can be represented as two-dimensional signals. Consequently, signal processing techniques applicable to signals can be modified for images.
The process of removing the noise while retaining and not distorting the quality of the received signal or image is referred to as denoising. The traditional way of denoising is to use a low or band-pass filter with cut-off frequencies. However, the traditional filtering techniques are able to remove out-of-band noise. Therefore many denoising techniques are proposed to overcome this problem.
Denoising is also an indispensable link in speech signal processing owing to the varying origins and non-stationarity, and difficulty in modeling the noise affecting the signal. Assuming that the received signal is affected by white additive Gaussian noise (AWGN) which is also stationary in nature, the received signal
where
Another common denoising method is the modulus maxima method [4]. It is based on the concept that signal and noise exhibit different characteristics when projected to their maxima in space divided in multiple scales. Magnitude scales increasing with decreasing extreme value points are filtered out to remove noise and the extreme value points themselves are reconstructed back [5]. The modulus maxima method in addition to the noise effect is better than any other method when mixed with white noise and singular information is significant, but the computational complexity is quite high. However, Fourier transform based denoising is restricted due to its weakness in obtaining partial characteristic of the transmit signals and possible Gibbs phenomenon [6]. If the signal has the same frequency as the noise, filtering out those frequency components will cause noticeable loss of information of the desired signal when considering the frequency representation of the signal.
Wavelet Transform (WT) has emerged as a powerful tool for signal and image denoising and processing, that have been successfully used in many scientific fields such as signal processing, image compression, computer graphics and pattern recognition [7, 8]. On contrary to the traditional Fourier transform, WT is particularly suitable for application of non-stationary signals which may instantaneously vary in time. Primarily, the received signal is divided into different frequency components using wavelets. The basis function of WT is scaled based on frequency and a subset of small waves (known as mother wavelet) is used for implementing WT [9]. The mother wavelet is a time-varying window function used for decomposition of
Since different frequency levels are used for WT, it is quite convenient for analyzing the signal characteristics at different frequencies and detecting removing corrupting noise. Broadly, there are two types of WT, Continuous WT (CWT) and Discrete WT (DWT).
CWT measures the congruence between an analyzing function and actual signal by calculating the inner product and then integrating the product. The mother wavelet window function can be shifted and moved over the time-axis by changing scale and position parameters, thereby including different frequency components at the different locations. Mathematically CWT can be represented as,
where
If suitable transformation is applied to a group of selected wavelet, a collection of orthogonal real-valued wavelets will be generated, a representation of the received signal referred to as wavelet expansion. In this case, the properties of the generated wavelets depend on the features of the mother wavelet. Since the newly generated wavelets are a group of orthogonal wavelets, they provide a time-frequency localization of the actual input signal, thereby concentrating the signal energy over a few frequency coefficients. Scaling and translation of the mother wavelet generated. If the scaling factor is a power of two, the wavelet transform technique is referred to as the dyadic-orthonormal wavelet transform [10]. If the chosen mother wavelet has orthonormal properties, there is no redundancy in the discrete wavelet transforms. In addition, this provides the multiresolution algorithm decomposing a signal into scales with different time and frequency resolution [9].
DWT is an implementation of WT using mutually orthogonal set of wavelets defined by carefully chosen scaling and translation parameters (
where
The process is repeated at multiple levels, a technique equivalent to consecutive iterations of low pass and high pass filtering. As a result, the low frequency and high frequency components of
Where
The approximation and the detailed coefficients are compared by applying FIR filter bank. The filter bank uses a low-pass filtering
The DWT decomposition and reconstruction steps of a 1D signal for level of 2; (a) decomposition, (b) reconstruction.
The wavelet packet decomposition and reconstruction steps of a 1D signal for level of 2; (a) decomposition, (b) reconstruction.
Wavelet Packet Transform (WPT) is another powerful denoising tool. WPT is a generalized form of DWT, in which both smooth and details parts are subject to further transforms. A full transformed matrix contains
for which
Wavelet packet transform (WPT) has several advantages over WT (continuous and discrete) as it sets no requirements of mother wavelet windowing function [15], wavelet packet basis function [16], and selection of the number of decomposition levels [17] and threshold [18]. WPT is introduced in [19] for denoising and harmonic detection by computing the difference between the noise and the desired signal. The effectiveness is also experimentally verified in [20] and tested against dynamics of Electro-encephalogram (EEG) and Electro-cardiogram (ECG) measurements in [21]. Image denoising is implemented by using an adaptive anisotropic dual-tree complex WPT on a bivariate stochastic signal model in [21].
DWT has become a powerful tool for denoising experimental data over the past few years. Original data is decomposed into a series of wavelets at different scales and intensities. Using WT, where the signal is multiplied by a transformation matrix; the detailed and the smooth parts are separated and the process is repeated over
The DWT denoising procedure consists of three steps. In the first step, if the length of the data stream is of length of the order of power of two, it is transformed to the wavelet domain. In the second step, coefficients with either zero magnitude or criterion-based minimized values are selected. In the third or final step, the minimized coefficients are reverted back to the original domain from the wavelet domain to extract the denoised data. DWT-based denoising techniques can be broadly classified into two categories - linear and non-linear. In linear DWT, signal and noise are assumed to be belonging to the smooth and the detailed part of the wavelet domain, where high frequency components are attenuated. While in non-linear DWT, the filter removes the coefficients selected in the second stage with amplitudes less than the threshold. In practicality, non-linear DWT is always preferred over linear DWT, as linear DWT introduces error due to the retention of noise components and loss of signal components owing to wavelet filtering.
Whether linear or non-linear DWT denoising technique is used, performance depends on the choice of the wavelet family and the length of the filter. The traditional way for making this choice is based on visual inspection of the data, for example, daublets are implemented when the data appears smooth in the wavelet domain, while Haar or other wavelets are used when the data appears bursty and discontinuous in the wavelet domain. In order to overcome the problems with DWT denoising, correlation denoising method was introduced in [11]. Correlation denoising method implements wavelet transformation and filtering in a way such that the correlation between wavelet coefficients of the signal part and the noise part is different at each level. However, correlation denoising in its original form is computationally complex. In order to reduce computational complexity, wavelet threshold denoising method was proposed by [12]. The method is simple to calculate and the noise can be suppressed to a large extent. At the same time, singular information of the original signal can be preferred well, so it is a simple and effective method. A brief overview of what happens when DWT is applied for denoising is demonstrated in Figure 3.
Denoising with DWT.
The four major components of the DWT denoising technique are: wavelet-type selection, threshold selection, threshold function selection and threshold application to the wavelet coefficients.
Wavelet Selection - There is a wide variety of wavelets that can be used for denoising. Selecting the optimum one depends on the selection of the matching wavelet filter. Out of different wavelet transform based denoising methods, only minimum description length (MDL) method has the flexibility of choosing the filter type.
Threshold Selection - There are four basic types of threshold selection, mini-max, Stein’s unbiased estimate of risk (SURE), and minimum description length (MDL). The Universal threshold is computed using,
for which
In the case of Minimax criterion using the estimates of the minimax risk bounds for the transformed wavelets, a table is generated for threshold values corresponding to each set of given data lengths. These threshold values are always smaller than the universal threshold. The noise level estimates are calculated using (8) and signal components are retained along with a few number of noise components.
Stein’s unbiased estimate of risk (SURE) is used to obtain an unbiased estimate of the variance between the filtered and unfiltered data. SURE is defined as
for which
The Minimum description length (MDL) method for threshold computation can be expressed as,
for which
3.Selecting threshold function - whether wavelet threshold denoising method is good or bad depends on two decisive factors; one is the threshold
Comparative hard and soft thresholding when implemented for DWT.
The Hard Threshold Function (HTF) nullifies the decomposition coefficients to zero if they are less than the threshold and retains the coefficients if they are more than the threshold [22]. The HTF preserves the local properties of a signal with a few discontinuities introduced by the variations in the reconstructed signals. HTF can be expressed as,
The Soft Threshold Function (STF) [23] selects the threshold value such that all decomposition coefficients are nullified to zero. A major drawback with this technique is that a part of the high frequency components is lost owing to their location above threshold. STF can be mathematically expressed as,
where
Garrote Threshold Function is proposed in [25] to improve the drawbacks of HTF and STF, whose denoising effect is better than the above two methods with respect to continuity of expressions,
The continuity in the soft threshold function is much better, but it has a constant deviation. So, in order to overcome its shortcomings, the soft and hard threshold algorithms are compromised process by the literature; the semisoft threshold function [26].
It is worth-mentioning here, that the values of the threshold
Another variation is the Improved Threshold Function which can be given by,
The adjustment factor of the new function is different from the semisoft threshold function. It consists of a complex exponential function
4.Thresholding or threshold application - thresholding is defined as the ways in which threshold is applied for modifying wavelet coefficients. DWT is a multi-level wavelet transform technique with different thresholds being applied at different level of coefficients
Global Thresholding - This technique assumes the corrupting noise as Gaussian distributed with amplitude and frequency distributions same for all orthogonal bases for the entire data space. Global thresholding can be implemented using either hard, soft, Garrote or firm-threshold functions, expressed as,
Hard:
Soft:
Garrote:
Firm:
for which
Level-Dependent Thresholding - This technique uses different thresholds at each level of wavelet transformations. It uses a combination of SURE and global thresholding techniques to initiate a hybrid method. In this case, if the sample variance at each level is sparse, global thresholding is applied, while SURE thresholding is applied otherwise.
Data-Dependent Thresholding - A Data-dependent threshold (DDT) technique selects a threshold such that empirical wavelet coefficients are shrunk. The thresholding is achieved through statistical tests of hypotheses like linear regression. The level of this statistical test is adjusted to control the smoothness of the resulting estimator such that a good mean-squared error (MSE) performance is achieved for different data analysis settings with smoothness in estimator response. The main aim of this technique is to eliminate a group of wavelet coefficients that exhibit characteristics of pure noise.
Cycle-Spin Thresholding - It combines the process of subspace identification, projecting denoising and averaging of the projections. The subspace mentioned here refers to the region where most of the energy of the signal is concentrated and signal corrupted with noise is projected on to this subspace.
The huge amount of sensor data generated in an IoT network are used to take decisions on a certain observation/ phenomenon based on real-time processing. The decision-making procedure often involves detecting the signal energy level transmitted from the sensors. If the received energy level is higher than a predefined threshold, the target is detected to be present phenomenon and vice-versa. However, the sensor data gets crippled with noise contributed by the wireless environment and the internal electronics of the sensors, on its way to the data center for processing. The WPT method will be the best option in this case for denoising the sensor data, where the original signal coefficients are preserved while removing the noise within the signal. The WPT method can decompose a signal in both scale and wavelet space thereby revealing more details about both the sensor signals and the crippling noise. If energy correlation analysis is used in conjunction with WPT, signal energy from the sensor data can be analyzed and noise can be eliminated by zooming into the signal characteristics at different time scales. Advantages of WPT over WT is evident in Figure 5. Hence, in this section, a universal framework is presented for denoising sensor signals in IoT networks. The framework is based on energy correlation analysis and combines the processes of WP decomposition, coefficient modification and WP reconstruction. The functional block diagram for this framework is presented in Figure 6.
Comparative performance of WPT and WT.
Architecture of the universal framework.
In WPT for IoT networks, for a given for a given orthonormal scaling function
where
When
In the process of WP decomposition, scale space
From
where,
During the procedure of multi-resolution analysis, objective function is decomposed into the subspace
Consequently,
Finally, the wavelet packet coefficients can be computed [27] as follows:
where
Following this technique of WPT, the efficiency of the denoising process improves quite a bit over the case where just WT is used for denoising the signals, as is evident in Figure 5.
Digital signal energy computation is achieved by extracting and squaring signal amplitude at different locations in the time domain and then adding them together [28]. The influence of relative large energy is eliminated using normalization technique [29]. This normalization can be avoided by selecting the sum of absolute values of amplitudes at each sampling points as approximations for evaluating energy; the mathematical formulation for which can be represented as:
Any kind of non-deterministic relationship existing between two or more variables can be exploited and formalized using correlation analysis. Thus, different kinds of signals can be differentiated by exploring the internal relation with correlation analysis.
where
The correlation coefficient
The closer the absolute value of Pearson’s
The polarity of the coefficient determines the direction of correlation, with plus-sign representing positive and minus-sign representing negative correlation.
An online filtering process capable of denoising both Gaussian and impact noise is presented below based on the energy correlation between signal components reconstructed from WP coefficients.
Step 1 - Obtain WP decomposition coefficients through the application of appropriate decomposition level and mother wavelet.
Step 2 - Compare WP coefficients in each subspace to eliminate singular data based on a pre-selected threshold through the application of multi-resolution analysis.
Step 3 - After reconstructing WP node signals from real coefficients, compute the ratios of the energy of the reconstructed signal components to the actual signal components to obtain the correlation between them. Subspace unsatisfied coefficients are processed through the use of a different threshold resulting in a series of new coefficients.
Step 4 - Using the new set of modified coefficients on each node, signal components are reconstructed and noise is eliminated. If the filtering requirements are not satisfied, repeat steps to step 4 after increasing the decomposition level. A flow-diagram for energy correlation analysis based WP coefficient processing is depicted in Figure 7.
Flowchart of wavelet packet coefficients based on energy-correlation analysis.
The best way to denoise a signal is to assume that the noise signal is Gaussian distributed with values that are independent and identical real values. The performance of the denoising process can be evaluated by comparing the quality of the denoised signal with that of the original transmit signal. A variety of methods have been proposed over years to measure the performance of denoising; the most common of which are the metrics of SNR and the peak SNR (PSNR), generally accepted to measure the quality of signal and images respectively. For 1-D signal, measuring the performance of the denoising method by calculating the residual SNR is given by,
In order to measure the quality of image, PSNR is generally used, which is given by
Decomposition in time and frequency domain for Fourier Transform is replaced by decomposition in space domain for WT thereby removing any ambiguity related to time and frequency and offering high flexibility and quality to the overall denoising process. Different threshold estimation methods, wavelet types, threshold types and thresholding functions can be used for implementing WT depending on the application scenario, network architecture, the kind of signal transmitted and the kind of noise commonly observed in the considered application scenario. However, comparing performances of different thresholding methods, wavelet types or threshold types when applied for the WT reveal that the number of decomposition levels are more crucial to the denoising performance than the types of wavelets or thresholds.
If the application scenario is considered to be an industrial IoT network, WPT method is preferred over simple WT for denoising sensor signals. This is because in WPT, signal is decomposed into an approximation and a detail component at each layer of each decomposition level, therefore resulting in
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Example MATLAB codeset for signal denoising
....................................................................................................................................
—————————————-
Signal Generation
—————————————-
N = 2048*2;
name = ’piece-regular’;
f0 = loadsignal(name, N);
f0 = rescale(f0,.05,.95);
sigma = 0.05;
f = f0 + randn(size(f0))*sigma;
figure(1)
subplot(2,1,1); plot(f0); axis([1 N 0 1]);
title(’Clean signal’);
subplot(2,1,2);
plot(f); axis([1 N 0 1]);
title(’Noisy signal’);
—————————————-
Thresholding
—————————————-
Theta0 = @(x,T)x.* (abs(x)¿T);
Theta1 = @(x,T)max(0, 1-T./max(abs(x),1e-9)).* x;
t = linspace(-3,3,1024)’; T = 1;
figure(2)
plot( t, [Theta0(t,T), Theta1(t,T)], ’LineWidth’, 2 );
axis(’equal’); axis(’tight’);
legend(’Θ0’, ’Θ1’);
—————————————-
Wavelet-Threholding
—————————————-
options.ti = 0; Jmin = 4;
W = @(f) performwavelettransf(f,Jmin,+1,options);
Wi = @(fw)performwavelettransf(fw,Jmin,-1,options);
x = W(f);
x1 = Theta0(x, 3*sigma);
figure(3)
subplot(2,1,1);
plotwavelet(x,Jmin); axis([1N -11]);
title(’W(f)’);
subplot(2,1,2);
plotwavelet(Theta0(W(f),T),Jmin); axis([1N -11]);
title(’Θ0(W(f))’);
f1 = Wi(x1);
figure(4)
subplot(2,1,1);
plot(f); axis([1 N 0 1]);
title(’f’);
subplot(2,1,2);
plot(f1); axis([1 N 0 1]);
title(’
x = W(f);
reinject = @(x1)assign(x1, 1:2Jmin, x(1:2Jmin));
Theta0W = @(f,T)Wi(Theta0(W(f),T));
Theta1W = @(f,T)Wi(reinject(Theta1(W(f),T)));
—————————————-
TI WT
—————————————-
options.ti = 1;
W = @(f) performwavelettransf(f,Jmin,+1,options);
Wi = @(fw)performwavelettransf(fw,Jmin,-1,options);
fw = W(f);
nJ = size(fw,3)-4;
figure(5)
subplot(5,1, 1);
plot(f0); axis(’tight’);
title(’Signal’);
i = 0;
for j=1:3
i = i+1;
subplot(5,1,i+1);
plot(fw(:,1,nJ-i+1)); axis(’tight’);
title(strcat([’Scale=’ num2str(j)]));
end
subplot(5,1, 5);
plot(fw(:,1,1)); axis(’tight’);
title(’Low scale’);
Port wine is a traditional fortified wine produced in the Douro Demarcated Region (Northeast of Portugal in the Douro Valley, Figure 1) under very specific conditions. There are several Port wine styles being related to the winemaking and ageing process and also to the ageing time, which enhances uniqueness to the wines and recognition throughout the world. The Douro Demarcated Region is located within the Douro River basin, surrounded by mountains, having a total area of approximately 250,000 hectares. This region has singular climate and soil characteristics for the production of selected grape varieties for the Port winemaking that contributes to the distinctive characteristics of Port wines and guarantee that these wines are irreproducible elsewhere. This area is divided into three naturally distinct sub-regions (Figure 1) concerning the climatic as well as socio-economic factors, “Baixo Corgo” (Lower Corgo), “Cima Corgo” (Upper Corgo), and “Douro Superior” (Upper Douro) [1].
Location of the Douro Demarcated Region in the northeast of Portugal and of the three sub-regions: “Baixo Corgo”, “Cima Corgo” and “Douro Superior” according to [1].
Different grape varieties are used in the production of Port wine, being usually produced by their blend. There is evidence that the grape varieties determines the wine character, even after the extended ageing process. The grape varieties that may be cultivated in the Douro Demarcated Region are regulated by Decree-Law n°104/85, 10th of April, 1985 [2]. Accordingly, Port wine is produced only from the authorised grape varieties, being the principal red grape varieties recommended for Port wine production “Touriga Nacional”, “Mourisco Tinto”, “Bastardo”, “Tinta Roriz”, “Tinta Cão”, “Tinta Amarela”, “Tinta Barroca”, “Touriga Franca” and “Tinta Francisca”, since these grape varieties produce wines with stable colour, fruity aroma and sugar content, characteristics required to produce good quality Port wines. The white grape varieties used for White Port wine production are “Malvasia Fina”, “Viosinho”, “Donzelinho Branco”, “Gouveio”, “Rabigato”, and “Códega” [3].
In the Port wine vinification process, the alcoholic fermentation is stopped according to the desired residual sugar content by the addition of a wine spirit known as “aguardente vínica” (with an alcohol content of about 77% (v/v)), to an alcohol content up to 18–22% (v/v) of the final product. Therefore, Port wine is a naturally sweet fortified wine since the natural sugar from the grapes is not completely transformed into alcohol. After the vinification process, Port wine is usually stored and aged in wood barrels of different sizes, from 2 years to many decades in accordance with the intended Port wine style. The Port wine ageing process can take place either in the Douro Valley or in Vila Nova de Gaia (Porto), in order to qualify for a Certificate of Origin from the “Instituto dos Vinhos do Douro e Porto” (IVDP).
Port wine is subjected to an extensive set of legislation and regulations. According to the Decree-Law n°173/2009 of 3rd of August [4], the IVDP, located in Oporto city, has the responsibility of promoting and perform the quality control of Port wine, as well as the amount of Port wine that can be produced annually, regulating all the production process, and the protection of the denominations of origin Douro and Port and the geographical indication of the Douro Region. The panel of expert tasters of IVDP is responsible for the certification and approval of wines and wine spirits, as well as the granting of the guarantee seal [5].
In the Port wine vinification process, the alcoholic fermentation is stopped, between 6% and 9% (v/v) alcohol content, according to the Port wine style and sweetness desired. Therefore, the wine is runoff from the skins, and it is fortified with a wine spirit containing 77% (v/v) of ethanol to raise the alcohol concentration to 18–22% (v/v). The average proportions of wine spirit added are 115 L for each 435 L of fermenting wine. The wine spirit allowed to be used in Port wine production required rigorous quality standards regulated by the laboratories and panel of tasters of IVDP. The sensory characteristics evaluated by the panel are turbidity, colour, aroma, and taste. The analytical parameters (ethyl carbamate, total higher alcohols, acetaldehyde (ethanal), ethyl acetate, methanol, 2-butanol, 1-butanol, allylic acid, cyanidric acid, calcium, copper, iron, alcohol content, total acidity, and density) must be below of the allowed limits described in Regulation n° 84/2010 [6]. Contrasting with most other fortifying spirits, the wine spirit used in Port wine production is not highly rectified; therefore, it contains many flavourants, especially higher alcohols, and aldehydes and this fortification process results in a high concentration of acetaldehyde in these initial wines.
Port wine can be extra dry, dry, semi-dry, sweet or very sweet, according to the levels of unfermented sugars remaining (Table 1) that is dependent on the time of wine spirit addition to stop the alcoholic fermentation [1].
Sweetness | Specific gravity (g/cm3, 20°C) | °Baumé (ºBé) | Sugar content (g/L) |
---|---|---|---|
Extra dry | <0.9980 | 0.0 | <40 |
Dry | 0.9980–1.0079 | 0.0–1.3 | 40–65 |
Semi-dry | 1.0080–1.0179 | 1.4–2.7 | 65–85 |
Sweet | 1.0180–1.0339 | 2.8–5.0 | 85–130 |
Very sweet | >1.0340 | > 5.0 | >130 |
Port wine classification according to their sugar content.
There are four main different styles of Port wine, designated as Tawny, Ruby, White and Rosé (Figure 2). Port wine intended for Tawny style are obtained from different wines in different stages of ageing. During the ageing in the wood barrels, the red colour of the wines gradually develops into tawny, medium tawny or light tawny, with an aroma of dried fruits and wood [1]. In this style, there are some special categories like Tawny Reserve, Tawny with Indication of Age (10, 20, 30 and 40 years) and “Colheita”. This last category is an exception, as these wines are from a single vintage [7]. All these wines are ready to drink when they are bottled [1]. Port wines that belong to Ruby style are wines that the evolution of their deep red colour is limited and the fruity character is maintained [1]. Within this Port style, special categories can be found like Crusted, Reserve, Late Bottled Vintage (LBV) and Vintage [7]. Crusted Ports are high quality, very full-bodied, deep coloured wines, obtained by blending wines from numerous vintages, aged for a minimum of 2 years in wood barrels and then bottled and aged during further 3 years. The year in which the wine was bottled must be indicated on the bottle label [1, 7]. LBV is a special single harvest and it is bottled after 4 to 6 years from the harvest, with the previous ageing in vats (wood or stainless steel) and has a deep ruby red colour, extremely full body and rich in the mouth. To be recognised as Vintage, the wines must present an outstanding quality and with a very full bodied and have a deep colour. Vintage and LBV are good for storing since they age well in the bottle [1]. The types of Port wines made from red grape varieties vary in colour from deep purple to light gold, with transitional hues like tawny, golden tawny, golden and light gold. White Port wines differ according to shorter or longer periods of ageing and different degrees of sweetness. The colour of White Port wines varies from pale yellow, straw to golden white. However, when aged in wood barrels for many years, white Port wines develop, through natural oxidation, a golden hue that is very similar to that of a very old Tawny Port wine made from red grapes [1]. The special categories inside this style are similar to those of Tawny Port wines [7]. White Port wine is a Port wine style with increasing market expression. Nowadays it represents 33% of the total Port wine sales with a higher market value (+6.2%) when compared to Tawny Port wine, and nearly 80% of the production is exported [1]. Rosé Port wine is a recent Port wine style, first released in the market in 2008 by Croft, part of the Taylor Fladgate Partnership. It is technically a Ruby Port but fermented in a similar way to a Rosé wine, with limited grape skin maceration, thus producing the pink colour. Croft came up with Rosé Port as a way to introduce the pleasures of Port wine to a younger market. The colour of this type of Port wine may go from light ruby to pale salmon. This style is commonly described as a light and fresh style of Port wine that is very fruity and enjoyable [1]. Different ageing processes leads to numerous Port wines quality categories, presenting different colours (from white to deep purple), sweetness (sweet to dry) and a wide range of flavours. The Port wines styles and categories are summarised in Figure 2.
Port wines styles and categories according to [1, 7].
The ageing process is an important period for this wine and includes storage, ageing in wood barrels or vat tanks and/or bottle ageing. When aged in old wood barrels their size depends on the Port wine style. Wine intended for Ruby and Vintage Port wine production will be aged in large wood barrels and that intended for Tawnies will be aged in small ones. The Tawny Port wine undergoes an oxidative ageing process, while Ruby and Vintage Port have a much less oxidative ageing termed reductive ageing process [1, 7].
Ruby, Reserve and LBV Port wines usually age in large wood barrels for two, three years or even six years (LBV) and have a deep red youthful colour and intense fruity flavours, evocative of cherry and blackberry. Tawny Port wines (10, 20, 30 and 40 years old) age for longer periods in small wood barrels and show nuttiness and aroma of butterscotch. White Port wines usually age for two or three years in large wood barrels. Traditionally, White Port wines are fermented with skin contact like Red Port wines; in this case, the wines are aged in conditions that results in its oxidation. Nevertheless, the trend is for a shorter maceration period, to obtain White Port wines with a pale colour and fresh aromas [1]. Like Red Port wines, most White Port wines are fortified when half of the grape sugar concentration has been fermented. Semi-dry and dry White Port wines are fortified later, or when alcoholic fermentation is finished [8].
The fortification process gives a high concentration of acetaldehyde to the wines. Acetaldehyde is probably responsible for the colour stability by favouring the production of anthocyanin-tannin polymers (discussed below) [9, 10]. The high sugar concentration retained tends to mask the bitterness of small flavanols, but not their astringency [8, 11]. Young Port wines are generally sweet, intensely red with a high concentration in tannins and with a fruity aroma. The colour, aroma and flavour of young Port wines are due to compounds from the grape, from the alcoholic fermentation and from the wine spirit used for the fortification. These wines need to age to develop the complex sensory attributes typically associated with the several Port wine styles. Port wines can be aged for a minimum of three years to a decade or more in old wood barrels to develop their character; normally it is aged in an old wood barrel, ranging from 525 or 600 L capacity up to 200,000 L. The type and length of the ageing process, as well as the capacity of the ageing barrel, and the oxygenation during racking, influenced the Port wine style that will be developed depending mainly on the wine style planned. Therefore, wines destined for Ruby and Vintage Port wines will be usually aged in large wood barrels, while those intended for the production of Tawny Port wines will be aged in small wood barrels (Figure 2). Racking is a very important operation during Port wine ageing and may be performed periodically. Slight fortification after each racking operation to adjust the alcoholic content up to 22% (v/v), compensating the volume lost via evaporation from the wood barrels [8].
The value of aged Tawny Port wine is linked to the characteristic aroma compounds developed during the ageing process in small wood barrels that allow the admission of oxygen. This oxidative ageing is influenced by factors such as oxygen levels, temperature, and pH. The high quality Tawny Port wines generally have an ‘indication of age (10, 20, 30, or over 40 years old)’ on the bottle, and are a blend of wines aged in wood barrels from different years. The age indicated on the label corresponds to a wine that has the sensory characteristics recognised by the IVDP of a wine aged in wood with 10, 20, 30, or over 40 years, obtained by blending wines with different ages. Tawny Port wines produced from a single vintage are referred to as ‘Colheita’ Port wines, aged in wood barrels for a minimum of 7 years [1].
Ruby Port wines have red colour, full-bodied structure and often still quite fruity in character when the wines are ready to drink. Ruby Port wines are aged between 3 to 5 years before blending and bottling in old large wood barrels known as “balseiros” of larger capacity, between 10.000 and 100.000 L, and do not usually have any wood-aged characteristics. The flavour modifies from an intensely fruity, even spirity character when the wines are very young to a rich fruity ruby wine after 3 to 5 years ageing in wood. They are used to age full-bodies and fruity wines such as Ruby, LBV and Vintage Port wine. These wines age more slowly than those aged in smaller wood barrels, retaining their structure and fresh fruity aromas that are the main characteristics of these wines. Some special Ruby Port wines (the so called Vintage Port wines) have a considerable bottle ageing process, giving lighter red wines, with often a very fruity character, despite having aged for two decades or more. Vintage Port wines are aged in wood barrels for two or three years, followed by a considerable ageing time in a bottle in the so called reductive ageing (10 to 50 years or more before consumption), and so it develops a different character from those wines aged exclusively in wood barrels. These wines remain fruity and with a red colour. Consequently, Vintage Port wines develop much of its distinctive bouquet from a long process of reductive ageing in bottle.
After the initial period in wood, LBV wines are aged in dark glass bottles in cool dark cellars with controlled temperature, ventilation and relative humidity. The vintage year is always indicated on the label [1].
White Port wine is made in the same way as red Port wines. However, there is a tendency to reduce the skin contact time, and even to ferment clarified grape juice at a lower temperature (18–20°C), to obtain wines with fruity aromas. The wines are aged in small size old wood barrels for a minimum of three years before its commercialisation depending on the desired White Port wine colour type.
The colour of red and white Port wines is one of the main quality parameters of the different Port wines styles. For Port wines made from red grape varieties, the initial wine colour is mainly due to the anthocyanins extracted from grape skins during vinification. Nevertheless, in a young Port wine, the percentage of colour due to the so called polymeric pigments is already 23 to 30% indicating that changes in the compounds responsible for the colour have already started during the short alcoholic fermentation and wine spirit addition to stop the alcoholic fermentation. The red Port wine colour increases up to 80% during the first months of ageing depending on the concentration of free acetaldehyde present in the young wine. After 46 weeks of ageing, the polymeric pigments can make up 78 and 98% of the wine colour [12]. The colour evolution during ageing is explained by the involvement of anthocyanins in different equilibria in solution and their simultaneous transformation through various concurring chemical reactions to a range of other pigments, many of them still unknown (Figure 3). These changes are dependent on the wine composition like anthocyanin, flavonol and tannin concentrations, different processing parameters like temperature, oxygen level, pH and the presence of other compounds either produced during alcoholic fermentation, added during processing or formed during the ageing process. On the other hand, no studies have been reported about the colour changes occurring during White Port wine ageing.
Colour evolution during ageing by the involvement of anthocyanins in different equilibria and their simultaneous transformation through various concurring chemical reactions to a range of other pigments (references are listed in Table 2).
Anthocyanins in aqueous solution, depending on the pH, occur in different forms present in equilibria [13, 14, 15]. At pH < 2, the red flavylium cation is the main structure present (I in Figure 3). With increasing pH, for values between 3 and 6, after hydration of the flavylium cation, the colourless hemiketal (II) structure is formed, this last being in equilibrium with the pale yellow cis-chalcone (III) through tautomerisation. This chalcone isomer is also in equilibrium with the trans-chalcone isomer (IV). With the pH increase, the flavylium cation is deprotonated to the corresponding violet neutral quinoidal bases (V and VI) that at higher pH yields the blue anionic quinoidal bases after further deprotonation (VII and VIII, Figure 3) [15]. When sulphur dioxide (SO2) is present, there is observed reversible bleaching of anthocyanins that occurs due to the formation of the colourless anthocyanin-4-bisulphite adducts [16] (IX).
Considering all these equilibriums, at wine pH (3–4) these pigments would be expected to be present mainly in their non-coloured hemiketal form (II). However, the flavylium cation (I) is the main form present in young red wines. This is the result of its stabilisation by different copigmentation mechanisms such as self-association and interaction with other wine components [17, 18, 19, 20]. In the copigmentation process, anthocyanins and other colourless organic compounds, such as flavonoids, amino acids, organic acids, polysaccharides, anthocyanins, or metallic ions, form molecular or complex associations [21]. The copigmentation is based in two effects [22]: (1) the formation of the π–π complex which causes changes in the spectral properties of the molecules in the flavylium ion, increasing the absorption intensity (hyperchromic effect) and its wavelength (bathochromic shift); and (2) the stabilisation of the flavylium form by the π complex displaces the equilibrium in such way that the red colour increases. This association also gives protection for the water nucleophilic attack in the 2 position of the flavylium cation [23] and for other species such as peroxides and sulphur dioxide in the 4 position [24, 25], so that the balance is displaced from hydrated forms towards the red flavylium cations. If the copigment is other anthocyanin, a self-association is formed (X); in the case of copigments with free electron pairs, an intermolecular copigmentation takes place (XI) finally, in the most complex case, the copigmentation can be carried out by a part of the structure itself (usually one of the aromatic acyl group substituents) (Figure 3).
During wine ageing, the concentration of monomeric anthocyanins starts to decrease leading to the formation of new anthocyanin derived pigments with different colour features and greater colour expression at high pH, important for the long-term colour stability of aged red wines [21]. The formation of most of the anthocyanin-derived pigments occurs in the first months of ageing, as the oxidative conditions in oak barrels favour their formation [26, 27]. Copigmentation has been hypothesised as the first mechanism involved in the formation of polymeric anthocyanin-derived pigments in red wines during ageing [19]. Numerous pigments have been characterised in wines and wine-like model solutions, and can be classified into three groups with respect to their formation pathways: 1) Direct condensation between anthocyanins and flavonols; 2) Condensation between anthocyanins and flavonols mediated by aldehydes, mainly acetaldehyde; and 3) Pyroanthocyanins (Figure 3). Although some of these pigments have only been detected in very small quantities in red wines, they have unique spectroscopic features that may, in some way, contribute together to the overall colour of aged red wines. In the first case, free anthocyanins can condense directly with flavan-3-ols and oligomeric proanthocyanins generating tannin-anthocyanins condensation products (T-A+, XII) or anthocyanin-tannin condensation products (A+-T, XIII) [8, 28, 29, 30, 31, 32, 33]. The T-A+ formation begins with the acid cleavage of the interflavanic bond of a procyanidin, giving a carbocation T+ which reacts with the hydrated form of the anthocyanin (II). This mechanism leads to a colourless compound (T-AOH) which easily dehydrates to the coloured flavylium form T-A+ [34]. In the formation of A+-T pigments, nucleophilic addition of the flavanol takes place onto the flavylium form of the anthocyanin, yielding a colourless compound with the anthocyanin in flavene form. This flavene can be oxidised, resulting in a coloured flavylium A+-T pigment (XIII) or in a colourless compound A(-O-)T with a type-A bond (XIV) [31] (Figure 3). As described for the monomeric anthocyanins, these pigments can also occur in a dynamic equilibrium among some molecular forms, mainly the quinoidal base, the flavylium cation and the hemiketal or carbinol pseudobase [30]. Both T-A+ pigments and colourless A(-O-)T have been detected in wines [35]. Dimeric anthocyanins (XVI) consisting of one unit under flavylium cation and the other one under hydrated hemiketal form (A+-AOH) were also characterised by mass spectrometry in wine like solutions [36] (Figure 3).
The A+-T adducts can generate yellow-orange xanthylium pigments (XV) by further structural rearrangements. After the dehydration, a new heterocyclic pyran ring is formed and the xanthylium structure is generated [17, 37, 38, 39, 40] (Figure 3). However, xanthylium pigments are also proposed to be formed directly from oligomeric flavan-3-ols [41, 42].
On the other hand, the acetaldehyde-mediated polymerisation between either only flavanols or with anthocyanins is the most well documented reaction in the literature [31, 37, 43, 44, 45, 46, 47, 48, 49, 50, 51]. Acetaldehyde is the main aldehyde (90%) present in wines as a result of yeast metabolism during the first stages of alcoholic fermentation, being also produced throughout the wine ageing process from ethanol oxidation [52]. In fortified wines like Port wines, this compound and other aldehydes (propionaldehyde, 2-methylbutyraldehyde, isovaleraldehyde, methylglyoxal, benzaldehyde) are present in higher amounts due to the addition of wine spirit (40–260 mg/L of acetaldehyde) to stop the alcoholic fermentation [53]. Ethyl-linked products, including ethyl-linked flavanols [54, 55] and ethyl-linked anthocyanin-flavanol pigments (XVII) [55] have been detected in wines (Figure 2). The formation of ethyl-linked anthocyanin oligomers (A+-Et-AOH, XVIII) was also shown to occur both in model solution and in wine [56]. The ethyl-linked 8,8-malvidin-3-glucoside dimer was characterised by NMR under biflavylium cation forms [56]. However, physicochemical studies carried out on this pigment showed that the dimer under monoflavylium cation is the most abundant form at wine pH [57].
Another important group of anthocyanin derived pigments formed during ageing, also found in red Port wines, are the pyranoanthocyanins (XIX) (Figure 3). Pyranoanthocyanins are a group of anthocyanin-derived pigments [58, 59], which were first discovered in red wine by Cameira-dos-Santos et al. [60]. Pyranoanthocyanins are structurally characterised by the presence of a fourth ring between C-4 and the 5-hydroxyl group of an anthocyanin moiety, differing from each other on the type of group or molecule linked to the C-10 of the new ring [58, 61, 62]. The pyranic ring in pyranoanthocyanins provides protection against the nucleophilic attack from water or bisulphite, increasing their stability [63], making these compounds exceptionally stable pigments towards sulphite bleaching and pH variations. Both anthocyanin-flavanol derived pigments, direct ones and ethyl-linked ones, show less stability during ageing than pyranoanthocyanins. Through the reaction of anthocyanins with acetaldehyde [61, 63], pyruvic acid [58, 64], cinnamic acids [65, 66], acetoacetic acid [64], and procyanidins in the presence of acetaldehyde [67], several different classes of these pigments have been identified in the past decade such as vitisins [58, 59, 61, 68, 69], hydroxyphenyl-pyranoanthocyanins (pinotins) [59, 64, 70, 71, 72], methylpyranoanthocyanins [59, 73], vinylflavanol-pyranoanthocyanins [59], portisins [58, 59, 61, 66, 67, 74, 75], and more recently a new family of pyranoanthocyanin dimers [28, 73, 76, 77] (Table 2).
Pyroanthocyanins | Precursors | References |
---|---|---|
R1 and R2 = OCH3; R3 = Glucose | ||
(I) In Figure 3 | ||
Malvidin-3-glucoside (Oenin) | [13, 14, 15] | |
(XIX) In Figure 3 | ||
R4 = H | ||
Non-substituted pyroanthocyanins (Vitisin B) | Oenin+acetaldehyde | [58, 59, 60, 61, 68, 69] |
R4 = COOH | ||
Carboxypyroanthocyanins (Vitisin A) | Oenin+pyruvic acid | [58, 59, 60, 61, 68, 69] |
R4 = CH3 | ||
Methylpyroanthocyanins | Oenin+acetoacetic acid or acetone | [59, 73] |
R4 = COCH3 | ||
Acetylpyroanthocyanins | Oenin+diacetyl | [8, 28, 29, 30, 31, 32, 33] |
R4 = hydroxyphenyl | ||
Hidroxyphenylpyroanthocyanins | Oenin+p-coumaric acid or vinylphenol | [59, 64, 70, 71, 72] |
R4 = dihydroxyphenyl | ||
Pinotin A | Oenin + caffeic acid or vinylcatechol | [59, 64, 70, 71, 72] |
R4 = flavanol | ||
Flavanol-pyroanthocyanins | Oenin+vinylflavanols or flavan-3-ols + acetaldehyde | [59] |
(XX) In Figure 3 | ||
Pyranone-anthocyanins (oxovitisins) | Carboxypyroanthocyanins + water | [90] |
(XXI) In Figure 3 | ||
R6 = hydroxyphenyl | ||
Vinylphenyl-pyroanthocyanins (Portisin B) | Carboxypyroanthocyanins+hydroxycinnamic acids or vinylphenols | [58, 59, 61, 66, 67, 74, 75] |
R6 = flavanol | ||
Vinylflavanol-pyroanthocyanins (Portisin A) | Carboxypyroanthocyanins + vinylflavanols or flavan-3-ols and acetaldehyde | [58, 59, 61, 66, 67, 74, 75] |
(XXII) in Figure 3 | ||
Pyroanthocyanins dimers | Carboxypyroanthocyanins + methylpyroanthocyanins | [28, 73, 76, 77] |
Pyranoanthocyanins identified in wines and precursors.
Pyruvic acid leads to the major pyranoanthocyanins determined in wines, i.e. carboxy-pyranoanthocyanins (R = COOH), sometimes referred to as vitisin A [58, 59, 61, 68, 69]. In red Port wines, it is the main pigment found during ageing. Due to its particular vinification process, the concentration of vitisin A is very high: 51.2 mg/L for Touriga Nacional Port wines, for example [78]. Indeed, wine fortification after alcoholic fermentation allows greater availability of pyruvic acid [79], which leads to reaching the highest contents shortly after fermentation and during the first year of ageing, followed by a slow decline [80]. After one year of ageing in barrels, the contents decrease by about 15–25% and about 70% after two years, whereas it is not so much important during bottle ageing (9–18%). Romero and Bakker [81] have demonstrated that the addition of pyruvic acid to finished Port wines from four different grape varieties resulted in an increase of malvidin-pyruvic acid adducts. It was also found that the concentration of anthocyanin-pyruvic acid adducts in wines was directly related to the original grape anthocyanin profile; the higher the initial anthocyanin precursor forms, the higher the concentration of corresponding adducts [81, 82, 83]. Morata et al. [84] have reported that the yeast strain used in the alcoholic fermentation (inoculated or not) also affects the production of malvidin-3-glucoside-pyruvate, existing a direct relation between the concentration of the pigment and the production of pyruvic acid by the yeast.
Moreover, the content of SO2 in must can also influence the production of malvidin-3-glucoside-pyruvate, since SO2 regulates the concentration of pyruvic acid through the formation of a weak bisulphite addition compound [85].
Romero and Bakker [68] have reported that malvidin-derived pyruvic acids adduct in model solutions provided approximately 11-fold (at pH 3) and 14-fold (at pH 2) more colour than grape anthocyanins.
Flavanol pyranoanthocyanins are formed by the cycloaddition between anthocyanins and 8-vinylflavanol adducts initially derived from the cleavage of ethyl-linked flavanol oligomers [46] or pigments [86, 87]. In red Port wines, pyranoanthocyanin-procyanidin dimers were identified in higher concentrations than the corresponding pyranoanthocyanin-catechins, representing up to 80% of the total pyranomalvidin-flavanols. This postulate is concordant with the fact that procyanidin dimers are more abundant than catechin monomers in grapes and wines from the Douro region [88, 89]. Furthermore, their concentrations decreased in older wines for both malvidin-3-glucoside derived-pigments (10.59 mg/L in 3 year aged wines, 9.16 mg/L in 4 year aged wines and 7.86 mg/L in 6 year aged wines) and associated coumaroyl pigments (6.62, 5.51 and 3.33 mg/L in 3, 4 and 6 year aged wines, respectively).
A second generation of pyranoanthocyanins can be formed by the reaction between a vitisin A and other metabolites. For example, oxovitisins (XX) are neutral yellowish pyranone structures involving the nucleophilic attack of water at the C-10 position of vitisin A [90].
In 2003 Mateus et al. [74] reported a new group of pyranoanthocyanins-vinylpyranoanthocyanins-which were named portisins (XXI), because of their occurrence in aged red Port wine [61, 67, 74, 75], Figure 3. The structure of these compounds consists of a pyranoanthocyanin moiety linked through a vinyl bridge to a flavanol or phenol unit. Their pathway of formation involves the carboxypyranoanthocyanins and vinylphenolic compounds. The first of these compounds reported in the literature arise from reaction of 8-vinylflavanol with carbon C-10 of the carboxypyranoanthocyanins, followed by loss of a formic acid group yielding the vinyl bridge. Portisins have been shown to have very high colouring capacity, much higher than that of their anthocyanin or pyruvic acid adduct counterparts [91, 92, 93]. Later, other portisins (B type) were detected in aged Port wines. In these, the flavanol moiety is replaced by a phenolic moiety with different hydroxylation and methoxylation patterns [61, 67, 74, 75]. These compounds were reported to result from the reaction of carboxypyranoanthocyanins with vinylphenols and cinnamic acids, following a mechanism similar to that of vinylflavanols and involving a further decarboxylation. However, the colour features of these portisins are different from those of the portisins discussed above because they have a λmax hypsochromically shifted from that of vinylpyranoanthocyanin-catechins, and are only slightly affected by the substitution pattern of the new phenolic ring (between 533 and 540 nm at aqueous pH 1) [91].
The condensation between A-type vitisins and methylpyroanthocyanins results in the formation of pyranoanthocyanin dimers (XXII), Figure 3. These turquoise blue pigments were found in a 9 year aged Port wine [73].
The volatile compounds present in Port wines have their origin on the grapes used, are produced during the alcoholic fermentation and being also added as part of the wine spirit used for Port wine production that contains trace volatile compounds such as esters (ethyl hexanoate, ethyl octanoate, ethyl decanoate) and terpenes (α-terpineol, linalool) that can affect the quality of the Port wines, contributing to a fruity, balsamic and spicy aroma [94]. In addition, wine spirits are rich in aldehydes such as acetaldehyde, propionaldehyde, isovaleraldehyde, isobutyraldehyde, and benzaldehyde [94]. The volatile profile of young Port wines is significantly different from that of aged Tawny Port wines or bottle-aged Port wines. Producers blend wines from several vintages and vineyards to produce wines with a consistent character. The final aroma character of the Port wine is to a considerable extent determined by the processes that take place during the oxidative ageing process of these wines, such as oxidation, carbohydrate degradation, formation and hydrolysis of esters, formation of acetals and to a lesser extent extraction of components from wood [11]. More than 200 volatile components have been detected in Port wines, 141 of which have been entirely or partially identified, however, the sensory importance of the various groups of volatile compounds does not entirely explain the sensory properties of Ruby or Tawny Port wines [95]. For the Ruby Port wine sensory profile, the attributes are ‘Ruby’, ‘Persistence’, ‘Red fruits’, ‘Fruity flavour’, ‘Astringency’ and ‘Floral’ were dominant, whereas in the White Port wine attributes like ‘Honey’, ‘Sweet taste’, ‘Alcoholic sensation’, ‘Balance’, ‘Acid taste’ and ‘Moscatel’ are the ones that better characterise these wines, Tawny Port wines are characterised by the attributes ‘Dried fruits flavour’, ‘Dried fruits’, ‘Spices’, ‘Wood’ and ‘Sweet/Honey’ [96, 97]. The Pink Port wines sensory attributes are characterised by the attributes ‘Red fruit aroma’, ‘Body’, ‘Fruit aroma’, ‘Fruity flavour’, ‘Spicy sensation’ and ‘Persistence’ [98].
Norisoprenoids have been found to contribute significantly to the aroma of young and aged Port wines [76, 99, 100, 101]. In a one year aged Port wine produced from Touriga Franca and Touriga Nacional grape varieties the norisoprenoid, 2,6,6-trimethylcyclohex-2-ene-1,4-dione, described as having sweet honey aroma, was identified by Rogerson et al. [102]. In a young Port wine produced from Tinto Cão and Tinta Barroca grape varieties, Rogerson et al. [103] identified the 1,3-dimethoxybenzene and 2-ene-1,4-dione. Falqué-Lopez et al. [104] characterised a one year aged Touriga Nacional monovarietal Port wine as having ‘plum brandy’, ‘mulberry’, ‘cherry’, ‘wild fruits’ and ‘dry raisin’ aromas and Guedes de Pinho et al. [105] identified linalool and linalyl acetate as being the responsible for the bergamot descriptor.
Ferreira et al. [100] have studied the influence of several factors on the levels of norisoprenoid in Port wines such as dissolved oxygen levels, free sulphur dioxide concentration, pH, and time/temperature of ageing. These authors observed that temperature and pH had a major influence on norisoprenoids levels and oxygen saturation reduced these compounds.
The concentration of several norisoprenoids increases during ageing, as for example β-ionone and β-damascenone in Vintage Port wines, and vitispirane, 2,2,6-trimethylcyclohexanone (TCH) and 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN) in Tawny Port wines [99]. Ferreira and Pinho [99] showed that the occurrence of β-damascenone, β-ionone, TCH, TDN, and vitispirane was distinct in young or aged Port wines. It was observed that in wood barrel ageing TDN, vitispirane, and TCH increased, however, the concentration of β-ionone and β-damascenone decreased. Freitas et al. [106] described that TCH was responsible for the “rock-rose-like” aroma. According to several authors, in Port wines due to the short fermentation time precursors of norisoprenoids such as carotenoids, β-carotene, lutein, neoxanthin and violaxanthin can be present [104, 107, 108]. Carotenoids are the precursor of norisoprenoids and in Port wine carotenoids persist after the vinification process [108]. Grape varieties used for Port wine production are rich in certain carotenoids and viticultural practices, such as bunch shading [108, 109] and grapevine water status [110] can influence the concentration of carotenoids in the grape berries.
Acetals, derived from glycerol and acetaldehyde, also appear to be involved in the flavour of aged Tawny Port wines [111]. The levels of aldehydes and methyl ketones increase during the oxidative ageing of Port wines. The major aliphatic aldehyde is acetaldehyde with a clear trend of increasing with the time of storage in wood barrels. Glycerol is present in wines in large amounts, in particular with concentration from 4 to 8 g/L in Port wines and therefore the formation of acetal can be high. At wine pH, four isomers are formed by condensation of glycerol and acetaldehyde: cis- and trans-5-hydroxy-2-methyl-1,3-dioxane and cis- and trans-4-hydroxymethyl-2-methyl-1,3-dioxolane. These four acetals have been studied in more detail in order to understand their impact on wine aroma and if these substances can be used as indicators of Port wines age with oxidative ageing conditions. These four isomers are found in Port wine at high concentrations. Nevertheless, this reaction is strongly dependent on free sulphur dioxide levels. When there is no free sulphur dioxide, the level of four isomers increases with the extent of ageing. On the other hand, when sulphur dioxide combines with acetaldehyde, the acetals cannot be formed because of the formation of the acetaldehyde-bisulphite adduct. The concentrations of the four acetals increases consistently with age due to the constant increase of acetaldehyde content and the nonexistence of free sulphur dioxide during Port wine storage. The acetal with the highest intensity aroma described as sweet and Port-like is trans-5-hydroxy-2- methyl-1,3-dioxane and the aroma threshold limit of the total concentration of the four acetals was determined as 100 mg/L [111]. Many acetals have been isolated from Tawny Port wines, but their contribution to the oxidised character of the wine is unclear [8].
Port wines with extensive wood-ageing have higher concentrations of diethyl and other succinate esters that contribute to the Port wine bouquet. Oak lactones (β-methyl-γ-octalactone isomers) and other oxygen-containing heterocycles have also been isolated. Some of the latter are furan derivatives, such as dihydro-2-(3H)-furanone and may contribute to a sugary oxidised bouquet [8]. Esters of 2-phenylethanol may contribute to the fruity, sweet bouquet of Port wines, and diacetyl can contribute to its caramel odour [99]. Some aldehydes and ketones are associated with the oxidative aged Port wines, conferring “rancio” odour to wines [112, 113].
However, in wood barrel aged Port wine the 3-hydroxy-4,5-dimethyl-2(5H)-furanone (sotolon) seems to be the most significant volatile compound [112, 113]. Some works suggested that sotolon contributes to the characteristic barrel aged aroma of Port wines [114], being the fundamental molecule to understand the “perceived age” of Port wines. The levels of sotolon were measured in “Colheita” and Tawny categories and were shown that it increases with ageing time being present in a range of concentration of some dozen μg/L in a young wine, to about 100 μg/L in wines with 10 years ageing, and to about 1000 μg/L in Port wines older than 50 years [112, 113, 114]. Albeit being a compound with an apparently important role in Port wine aged aroma, the mechanism of sotolon formation in wine is not yet fully understood. However, different pathways have been proposed, such as aldol condensation between α-ketobutyric acid and acetaldehyde [115, 116, 117] (Pathway 1 – Figure 4) and the reaction between ethanol and ascorbic acid [118] (Pathway 2 – Figure 4). The formation of sotolon in Port wines is dependent on the temperature and oxygen levels [112, 113], which are crucial parameters during the oxidative ageing of Tawny Port wines. The sensory threshold limit of sotolon was determined as 19 μg/L, which is above the amount present in Port wines older than 10 years [112, 113].
Pathway 1: Reaction between α-ketobutyric acid (1) and acetaldehyde (2); pathway 2: Reaction between ascorbic acid (3) and ethanol (3). These reactions can lead to the formation of sotolon (8) according to [115, 116, 117, 118].
Young Port wines show higher levels of volatile sulphur compounds than aged Port wines [119]. Sulphur compounds, such as 2-mercaptoethanol, 2-(methylthio) ethanol, ethyl 3-(methylthio) propionate, 3-(methylthio)-1-propanol, cis-(odourless), and trans-2-methyltetrahydrothiophen-3-ol, 3-(ethylthio)-1-propanol, 4-(methylthio)-1-butanol, dimethyl sulfone, benzothiazole, 3-(methylthio)-1-propionic acid and N-3-(methylthiopropyl) acetamide are not present or are present in lower concentrations in aged Tawny Port wines when compared to young Tawny Port wines [119].
The changes observed in the volatile composition of Port wines during the oxidative ageing results in a complex, oxidised character defined as nutty, nuts, raisins and crisp apples with a slightly oaky note’ giving an impression of dryness. According to Falqué et al. [104] and Ferreira et al. [120] floral, bergamot-like, violet or jasmine notes are present in young Port wines that are changed during the ageing process in wood barrels. Freitas et al. [106] and Ferreira et al. [121] have described the flavours developed during ageing of Port wines as woody, burnt, dry fruit, nutty, and spicy.
Port wine is one of the most famous and old fortified wine in the world, produced in the Douro Demarcated Region (Portugal), a region with a singular “terroir”. It is a traditional product with more than 250 years old and commercialised all around the world for many centuries. However, more knowledge is still needed in order to understand and control its composition and its evolution during the wine ageing process. Port wine presents a complex physicochemical matrix that results from complex and concurrent chemical reactions that occur during the ageing process significantly changing its sensory profile. Different Port wine styles are obtained using different ageing processes, related to different oxygen levels, temperature and sulphur dioxide variations. The two main Port wine styles are the Ruby and Tawny Port wines, with the first style being obtained by a reductive ageing process resulting in a ruby colour and fruity aroma, while Tawnies Port wine styles shows a brown colour and an intense dry fruit aroma. The colour of the different Port wines styles is one of their main quality parameters. For the Port wines produced with red grapes, colour changes are related to the changes in the anthocyanins composition during the ageing process. The colour attributes of the Port wines made with white grapes is still largely unknown. Sotolon plays an important role in aged Port wines aroma obtained by oxidative ageing, due to its low olfactory threshold and pleasant and potent aroma. Sotolon levels increase during Port wine oxidative ageing. The intense government legislation and specific production rules protect this important product produced in a world protected region (Unesco) in order to reduce adulterations or even imitations. However, more studies are still needed to deepen our knowledge in order to understand and control the reactions involved in Port wine ageing process that contribute to its uniqueness.
We appreciate the financial support provided to the Research Unit in Vila Real (UIDB/00616/2020 and UIDP/00616/2020) by FCT and COMPETE and JM acknowledges the financial support provided by the FCT grant PD/BD/135331/2017.
The authors declare no conflict of interest.
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