Aortic Aneurysm - Clinical Findings, Diagnostic, Treatment and Special Situations

*Edited by Ana Terezinha Guillaumon and Daniel Emilio Dalledone Siqueira*

Published in London, United Kingdom

### *Supporting open minds since 2005*

Aortic Aneurysm - Clinical Findings, Diagnostic, Treatment and Special Situations http://dx.doi.org/10.5772/intechopen.91488 Edited by Ana Terezinha Guillaumon and Daniel Emilio Dalledone Siqueira

#### Contributors

Akshay Kumar, Dor Mordehay Avishay, Jennifer Chin, Marguerite Lisa Bartholomew, Enrique Ortiz Herrasti, Jorge Armando Martinez Martinez, Raúl Alberto Bacelis, Pedro Manuel Córdova, Ingrid Estrella Diaz, Kuldeep Singh, Zachary Chadnick, Luciano Izzo, Virgilio Nicolanti, Paolo Izzo, Emiliano Venditti, Sara Izzo, Daniela Messineo, Ana Terezinha Guillaumon, Daniel Emilio Dalledone Siqueira, Purnadeo Persaud, Ana Francesca Leite, Nathaniel Aoyama, Ruan Matos, Nimisha Shiwalkar, Vinit Garg, Juber Dastagir Shaikh, Roopvir Kaur, Asim Kichloo

#### © The Editor(s) and the Author(s) 2021

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**4. Simulation results**

#### **4.1. Simulation of continuous review model**

To illustrate numerically the results obtained, firstly we present some simulations for opti‐ mal control of the continuous-review integrated production-forecasting system with stockdependent demand and deteriorating items. The data used in this simulation is presented in Table 1.

Using the MATLAB software, we implemented the results of the previous section and ob‐ tained the graphs below. Figures 1, 2 and 3 show the variations of Δ*I*(*t*), Δ*D*(*t*), and Δ*P*(*t*). We observe that they all converge toward zero, as desired.

Using equation (3), the optimal cost is found to be *J* = 3047.93. A sensitivity analysis is per‐ formed in order to assess the effect of some of the system parameters on the optimal cost. The analysis is conducted by keeping the values of the parameters at the base values shown in Table 1 and varying successively one parameter at a time. We were interested in the effect on the value of the optimal objective function *J* of the parameters *a*, *b*, and *θ*, that we varied from 0.1 to 0.9. Table 2 summarizes the results of the sensitivity analysis.

**Figure 2.** Variations of the optimal demand rate

Optimal Control of Integrated Production – Forecasting System 131

**Figure 3.** Variations of the optimal production rate

**Figure 1.** Variation of the optimal inventory level

**Figure 2.** Variations of the optimal demand rate

**4. Simulation results**

130 Decision Support Systems

Table 1.

**4.1. Simulation of continuous review model**

**Figure 1.** Variation of the optimal inventory level

We observe that they all converge toward zero, as desired.

from 0.1 to 0.9. Table 2 summarizes the results of the sensitivity analysis.

To illustrate numerically the results obtained, firstly we present some simulations for opti‐ mal control of the continuous-review integrated production-forecasting system with stockdependent demand and deteriorating items. The data used in this simulation is presented in

Using the MATLAB software, we implemented the results of the previous section and ob‐ tained the graphs below. Figures 1, 2 and 3 show the variations of Δ*I*(*t*), Δ*D*(*t*), and Δ*P*(*t*).

Using equation (3), the optimal cost is found to be *J* = 3047.93. A sensitivity analysis is per‐ formed in order to assess the effect of some of the system parameters on the optimal cost. The analysis is conducted by keeping the values of the parameters at the base values shown in Table 1 and varying successively one parameter at a time. We were interested in the effect on the value of the optimal objective function *J* of the parameters *a*, *b*, and *θ*, that we varied

**Figure 3.** Variations of the optimal production rate

**Parameters Values planning horizon length** *T* 10 **number of subintervals** *N* 51 **coefficient for demand dynamics** *a* 0.1 **coefficient for demand dynamics** *b* 0.2 **deterioration rate θ** 0.01 **deviation cost for inventory level** *qI* 20 **deviation cost for demand rate** *qD* 15 **deviation cost for production rate** *r* 0.01

For the periodic review case, the simulation results are shown in the graphs below. Figure 4 shows the variations of the optimal inventory level and the inventory goal level. We observe

**I(k) and Ihat(k)**

0 5 10 15 20 25 30 35 40 45 50 3.5

*Time k*

^

(*k*)very closely.

Optimal Control of Integrated Production – Forecasting System 133

I(k) Ihat(k)

**Table 3.** Data for periodic-review model

4

**Figure 4.** Optimal and inventory goal levels

4.5

5

5.5

6

6.5

7

7.5

that except for the early transient periods, *I*(*k*) follows *I*

**Table 2.** Sensitivity analysis

As can be seen, the objective function decreases as any of the three parameters increases.

#### **4.2. Simulation of periodic review model**

In this second part of the simulation, we illustrate the results obtained on the optimal con‐ trol of the periodic review integrated production-forecasting system with stock-dependent demand and deteriorating items. Thus, consider the production planning problem for a firm for the next *T* units of time. Divide this interval into *N* subintervals of equal length. Assume the product in stock deteriorates at the rate *θ*. Assume also the variations of the demand rate occur according to the dynamics (10). The firm has set the following targets. For *k* =1, ⋯, *N* , the goal inventory level and goal demand rate are assumed to be as follows:

*I* ^ (*k*)=5 + 1.5 *sign*(*sin*( 2*πk* 40 )) and *D* ^ (*k*)=2 + 0.5 *sign*(*sin*( 2*πk* 15 ))

where the sign function of a real number *x* is defined by

$$
\operatorname{sign}(\mathbf{x}) = \begin{vmatrix}
\cdot \mathbf{1} & \text{if } \mathbf{x} \le \mathbf{0}, \\
\mathbf{0} & \text{if } \mathbf{x} = \mathbf{0}, \\
\mathbf{1} & \text{if } \mathbf{x} \ge \mathbf{0}.
\end{vmatrix}
$$

We have to note that the goal inventory level and the goal demand rate were constant in the continuous review case.

The goal production rate is then computed using

$$
\stackrel{\frown}{P}(k) = \stackrel{\frown}{D}(k) + \stackrel{\frown}{\partial}\stackrel{\frown}{I}(k),
$$

where we assume that the inventory goal level is constant over a certain range. The penalties for deviating from these targets are *qI* for the inventory level, *qD* for the demand rate, and *r* for the production rate. The data are summarized in Table 3.

**Table 3.** Data for periodic-review model

*a* / *b* / θ *J*(*a*) *J*(*b*) *J*(θ)

0.1 5822.40 23426.21 3047.93 0.2 489.77 7648.73 463.79 0.3 485.62 5560.00 461.53 0.4 481.57 740.12 459.30 0.5 477.58 659.34 457.09 0.6 473.67 600.85 454.91 0.7 469.84 556.02 452.76 0.8 466.08 520.24 450.63 0.9 462.38 490.83 448.52

As can be seen, the objective function decreases as any of the three parameters increases.

In this second part of the simulation, we illustrate the results obtained on the optimal con‐ trol of the periodic review integrated production-forecasting system with stock-dependent demand and deteriorating items. Thus, consider the production planning problem for a firm for the next *T* units of time. Divide this interval into *N* subintervals of equal length. Assume the product in stock deteriorates at the rate *θ*. Assume also the variations of the demand rate occur according to the dynamics (10). The firm has set the following targets. For *k* =1, ⋯, *N* , the goal inventory level and goal demand rate are assumed to be as follows:

^ (*k*)=2 + 0.5 *sign*(*sin*( 2*πk*

We have to note that the goal inventory level and the goal demand rate were constant in the

where we assume that the inventory goal level is constant over a certain range. The penalties for deviating from these targets are *qI* for the inventory level, *qD* for the demand rate, and *r*

15 ))

**Table 2.** Sensitivity analysis

132 Decision Support Systems

(*k*)=5 + 1.5 *sign*(*sin*( 2*πk*

continuous review case.

^ (*k*) + *θ I* ^ (*k*)

*I* ^

*P* ^(*k*)=*D*

*sign*(*x*)={

**4.2. Simulation of periodic review model**

40 )) and *D*

where the sign function of a real number *x* is defined by

The goal production rate is then computed using

for the production rate. The data are summarized in Table 3.

For the periodic review case, the simulation results are shown in the graphs below. Figure 4 shows the variations of the optimal inventory level and the inventory goal level. We observe that except for the early transient periods, *I*(*k*) follows *I* ^ (*k*)very closely.

**Figure 4.** Optimal and inventory goal levels

Figure 5 shows the variations of the optimal demand rate and the demand goal rate. We ob‐ serve that except for the early transient periods, *D*(*k*) follows *D* ^ (*k*) very closely.

Finally, Figure 6 shows the variations of the optimal production rate and the production goal rate. We again observe that except for the early transient periods, *P*(*k*) follows *P* ^ (*k*) very closely.

The optimal cost is found to be *J* =10.3081. Here also a sensitivity analysis is performed in order to assess the effect of some of the system parameters on the optimal cost. The analysis is conducted by keeping the values of the parameters at the base values shown in Table 3 and varying successively one parameter at a time. We were interested in the effect on *J* of the parameters *a*, *b*, and *θ*, that we varied from 0.1 to 0.9. Table 4 summarizes the results of the sensitivity analysis.

0 5 10 15 20 25 30 35 40 45 50 1.4

**a/b/θ J(a) J(b) J(θ)**

**0.1** 10.3081 10.0807 10.3072

**0.2** 10.3173 10.3081 10.3064

**0.3** 10.3339 10.6868 10.3056

**0.4** 10.3601 11.2166 10.3050

**0.5** 10.4004 11.8973 10.3044

**0.6** 10.4630 12.7284 10.3036

**0.7** 10.5651 13.7098 10.3040

**0.8** 10.7444 14.8413 10.3033

**0.9** 11.0880 16.1226 10.3032

**Table 4.** Effect of the parameters *a*, *b* and θ on the optimal cost *J*

**Time k**

P(k) and Phat(k)

P(k) Phat(k)

Optimal Control of Integrated Production – Forecasting System 135

1.6

**Figure 6.** Optimal and production goal rates

1.8

2

2.2

2.4

2.6

2.8

**Figure 5.** Optimal and demand goal levels

**Figure 6.** Optimal and production goal rates

Figure 5 shows the variations of the optimal demand rate and the demand goal rate. We ob‐

Finally, Figure 6 shows the variations of the optimal production rate and the production goal rate. We again observe that except for the early transient periods, *P*(*k*) follows *P*

The optimal cost is found to be *J* =10.3081. Here also a sensitivity analysis is performed in order to assess the effect of some of the system parameters on the optimal cost. The analysis is conducted by keeping the values of the parameters at the base values shown in Table 3 and varying successively one parameter at a time. We were interested in the effect on *J* of the parameters *a*, *b*, and *θ*, that we varied from 0.1 to 0.9. Table 4 summarizes the results of

**D(k) and Dhat(k)**

0 5 10 15 20 25 30 35 40 45 50 1.4

**Time k**

^ (*k*) very closely.

^ (*k*)

D(k) Dhat(k)

serve that except for the early transient periods, *D*(*k*) follows *D*

very closely.

134 Decision Support Systems

the sensitivity analysis.

1.6

**Figure 5.** Optimal and demand goal levels

1.8

2

2.2

2.4

2.6

2.8

**Table 4.** Effect of the parameters *a*, *b* and θ on the optimal cost *J*

The second column of Table 4 shows that the optimal cost increases as *a* increases. The third column of Table 4 shows that the optimal cost increases also as *b* increases. The effect of *b* on *J* is however more significant than the effect of *a*. Finally, column 4 of Table 4 shows that the optimal cost decreases as *θ* increases. The effect of *θ* is however almost negligible.

NMPC has gained significant interest over the past decade. Various NMPC strategies that lead to stability of the closed-loop have been developed in recent years and key questions such as the efficient solution of the occurring open-loop control problem have been exten‐

Optimal Control of Integrated Production – Forecasting System 137

The case combining unknown coefficients and a nonlinear relationship between the demand rate and the on-hand inventory yields a very complex, highly nonlinear process for which there is no simple mathematical model. The use of fuzzy control seems particularly well ap‐ propriate. Fuzzy control is a technique that should be seen as an extension to existing con‐ trol methods and not their replacement. It provides an extra set of tools which the control engineer has to learn how to use where it makes sense. Nonlinear and partially known sys‐ tems that pose problems to conventional control techniques can be tackled using fuzzy con‐

This work has been supported by the Research Center of College of Computer and Informa‐

1 King Saud University, College of Computer and Information Sciences, Department of

2 Dalhousie University, Faculty of Management, School of Business Administration, Halifax,

3 American University in Dubai, College of Business Administration, Department of Man‐

[1] Strijbosch, L.W.G., Syntetos, A.A., Boylan, J.E., and Janssen, E. On the interaction be‐ tween forecasting and stock control: The case of non-stationary demand. Internation‐

al Journal of Production Economics2011; 133:1, 470-480.

sively studied.

trol.

**Acknowledgement**

**Author details**

Nova Scotia, Canada

agement, Dubai, UAE

**References**

R. Hedjar1

tion Sciences, King Saud University.

, L. Tadj2\* and C. Abid3

\*Address all correspondence to<EMAIL_ADDRESS>
Computer Engineering, Riyadh, Saudi Arabia


**2. Study on the ecology of S***. cerevisiae* **in the Rioja qualified designation of origin**

#### **2.1 Material and methods**

#### *2.1.1 Sample collection*

Samples were taken from 11 wineries (A–K) located in three different sub-zones of the Rioja designation of origin (Rioja Alta, Rioja Oriental, and Rioja Alavesa)

**119**

identified.

the following equation:

**2.2 Results and discussion**

*sub-zone*

*Biodiversity of Saccharomyces cerevisiae Yeasts in Spontaneous Alcoholic Fermentations: Typical…*

over a period of 3 or 4 consecutive years (**Figure 1**). None of the wineries studied had ever used a commercial starter yeast. Wineries A and B were new; wineries C,

Alcoholic fermentation (AF) was carried out by the destemming and crushing method in stainless steel (wineries A, B, C, D, and F) or wooden vats (winery H). In the other wineries, AF was carried out following the traditional carbonic maceration method (whole grape) in open concrete vats (wineries E, G, I, J, and K). The wines underwent spontaneous AF with the indigenous microbiota in all cases. In each winery, one fermentation tank was monitored in each year studied. The sampling was carried out 24 hours after vatting, in tumultuous AF (density

Samples collected in sterile bottles were taken to the laboratory and processed as follows: serial decimal dilutions were performed, and the samples were seeded onto plates containing a chloramphenicol glucose agar medium (CGA). The plates were incubated at 28°C for 48 h. Plates containing between 30 and 300 colonies were examined, and 10 colonies were randomly isolated from each CGA plate. The colonies were analyzed in order to identify *Saccharomyces* and non-*Saccharomyces* yeast and the clonal distribution of the *S. cerevisiae* by mitochondrial DNA (mtDNA) restriction analysis. Yeast cells were grown overnight in a culture of 5 ml YPD. DNA extraction and mtDNA restriction were determined by the method described by Querol and Barrio [12]. The DNA was digested with the restriction endonucleases Alu I, RSa I, and Hinf I, in accordance with the supplier's instructions (Boehringer Mannheim). The restriction fragments were separated by electrophoresis in agarose 1% gels and visualized on a UV transilluminator after ethidium bromide staining. The different clones isolated in each fermentation were named with the letter of

the cellar (A–K), followed by a Roman numeral and year of harvest (1–4).

I.D. = 1 − ∑3 *nj* (*nj* − 1) \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

*cerevisiae* strains with the same electrophoretic profile.

tion took place after destemming and crushing the grapes.

The clonal variability of each fermentation was determined as a percentage of different *S. cerevisiae* genotypes compared to the total colonies *S. cerevisiae*

The index of diversity (I.D.) [13] was calculated with the different *S. cerevisiae* strains identified in the tumultuous and final stages of fermentation, according to

where N is the total number of *S. cerevisiae* strains and *nj* is the number of *S.*

*2.2.1 Ecology of the yeast population during alcoholic fermentation in Rioja Oriental*

In this sub-zone, four wineries were sampled (A–D). In all of them, the vinifica-

As expected, and concurring with previous studies on the ecology of alcoholic fermentation (FA) [14, 15], non-*Saccharomyces* yeasts were detected mainly in the first stages of the fermentation (data not shown). The rest of the yeasts isolated in this sub-zone (316 colonies) were identified as belonging to the *S. cerevisiae* species.

N (N − 1) (1)

E, and K were about 20 years old, while the others were over 50 years old.

*DOI: http://dx.doi.org/10.5772/intechopen.84870*

*2.1.2 Microbial analyses: strain typing of S. cerevisiae*

1025 g/L) and final AF.

*Biodiversity of Saccharomyces cerevisiae Yeasts in Spontaneous Alcoholic Fermentations: Typical… DOI: http://dx.doi.org/10.5772/intechopen.84870*

over a period of 3 or 4 consecutive years (**Figure 1**). None of the wineries studied had ever used a commercial starter yeast. Wineries A and B were new; wineries C, E, and K were about 20 years old, while the others were over 50 years old.

Alcoholic fermentation (AF) was carried out by the destemming and crushing method in stainless steel (wineries A, B, C, D, and F) or wooden vats (winery H). In the other wineries, AF was carried out following the traditional carbonic maceration method (whole grape) in open concrete vats (wineries E, G, I, J, and K). The wines underwent spontaneous AF with the indigenous microbiota in all cases.

In each winery, one fermentation tank was monitored in each year studied. The sampling was carried out 24 hours after vatting, in tumultuous AF (density 1025 g/L) and final AF.

#### *2.1.2 Microbial analyses: strain typing of S. cerevisiae*

Samples collected in sterile bottles were taken to the laboratory and processed as follows: serial decimal dilutions were performed, and the samples were seeded onto plates containing a chloramphenicol glucose agar medium (CGA). The plates were incubated at 28°C for 48 h. Plates containing between 30 and 300 colonies were examined, and 10 colonies were randomly isolated from each CGA plate. The colonies were analyzed in order to identify *Saccharomyces* and non-*Saccharomyces* yeast and the clonal distribution of the *S. cerevisiae* by mitochondrial DNA (mtDNA) restriction analysis. Yeast cells were grown overnight in a culture of 5 ml YPD. DNA extraction and mtDNA restriction were determined by the method described by Querol and Barrio [12]. The DNA was digested with the restriction endonucleases Alu I, RSa I, and Hinf I, in accordance with the supplier's instructions (Boehringer Mannheim). The restriction fragments were separated by electrophoresis in agarose 1% gels and visualized on a UV transilluminator after ethidium bromide staining.

The different clones isolated in each fermentation were named with the letter of the cellar (A–K), followed by a Roman numeral and year of harvest (1–4).

The clonal variability of each fermentation was determined as a percentage of different *S. cerevisiae* genotypes compared to the total colonies *S. cerevisiae* identified.

The index of diversity (I.D.) [13] was calculated with the different *S. cerevisiae* strains identified in the tumultuous and final stages of fermentation, according to the following equation:

$$\text{LD.} = \mathbf{1} - \frac{\sum \mathbf{3} \, n\_f \, (n\_f - 1)}{\mathbf{N} \, (\mathbf{N} - 1)} \tag{1}$$

where N is the total number of *S. cerevisiae* strains and *nj* is the number of *S. cerevisiae* strains with the same electrophoretic profile.

#### **2.2 Results and discussion**

### *2.2.1 Ecology of the yeast population during alcoholic fermentation in Rioja Oriental sub-zone*

In this sub-zone, four wineries were sampled (A–D). In all of them, the vinification took place after destemming and crushing the grapes.

As expected, and concurring with previous studies on the ecology of alcoholic fermentation (FA) [14, 15], non-*Saccharomyces* yeasts were detected mainly in the first stages of the fermentation (data not shown). The rest of the yeasts isolated in this sub-zone (316 colonies) were identified as belonging to the *S. cerevisiae* species.

*Advances in Grape and Wine Biotechnology*

yeasts in all the winegrowing regions of the world can cause a loss of typicity and have a negative effect on the biodiversity of natural yeasts present in the wineries [2]. *S. cerevisiae* is the predominant yeast species in alcoholic fermentation and the main element responsible for the characteristics of the wines. Many surveys carried out with spontaneous fermentations in different wine-producing regions have demonstrated that there is high genetic diversity within this species in each vinification [3]. However, in most cases, only a small number of strains of *S. cerevisiae* are dominant, mainly in the tumultuous and final fermentation stages, representing a

Earlier studies have shown that some strains of *S. cerevisiae* have been isolated in several consecutive years in the same winery, which is why some authors have suggested the term "winery effect" [4], and also some strains of these species were detected in different wineries of the same wine-producing area, suggesting that they were representative of a specific enological ecosystem [5, 6]. Knight et al. [7] even found specific genotypes from a particular region. These findings suggest that specific native *S. cerevisiae* strains could be associated with a *terroir* and have an influence on terroir-associated wine characteristics [8].These authors found a correlation between genotypic and phenotypic groups and the geographical origin of the strains, supporting the concept that there can be a microbial aspect to *terroir*. Nowadays, many wineries and regions are carrying out strain selection programs with yeasts isolated from their vineyard/winery ecosystems, based on the idea that these yeasts are better adapted to their musts, which have characteristics determined by the grape varieties and the *terroir* [9, 10]. Thus, the use of these typical strains as starter yeasts could provide wines with distinctive characteristics of a particular winery or region. For this reason, studying the existence of strains which are specific to one winery or enological area is very interesting for the wine industry [11]. Rioja is a wine region in Spain with qualified designation of origin status. It is subdivided into three sub-zones: Rioja Alta, Rioja Oriental, and Rioja Alavesa. Rioja Alta is located on the western edge of the region and at higher elevations with an Atlantic climate. Rioja Oriental, the eastern section, is strongly influenced by a Mediterranean climate which makes this area the warmest and driest part of the region. Rioja Alavesa, with a similar climate to Rioja Alta, produces different wines due to the relatively poor condition of the soil. Each sub-zone has its own character, which results in different wines derived from the different compositions and origins of the soils and the climate conditions. The study of the ecology and biodiversity of the yeast population during alcoholic fermentation is an interesting and important step in the research into, and understanding of, a winegrowing area and should be a step prior to the selection and subsequent employment of the yeasts isolated from that area as starters. The aim of this work was to study the ecology of spontaneous fermentations in 11 wineries from all over the Rioja Designation of Origin during 3–4 consecutive years in order to establish the existence of typical strains belonging to wineries, sub-zones, and regional ecosystems. The wineries under study were distributed throughout the three sub-zones of the Rioja designation of origin.

**2. Study on the ecology of S***. cerevisiae* **in the Rioja qualified designation**

Samples were taken from 11 wineries (A–K) located in three different sub-zones of the Rioja designation of origin (Rioja Alta, Rioja Oriental, and Rioja Alavesa)

high percentage out of the total number of strains identified.

**118**

**of origin**

**2.1 Material and methods**

*2.1.1 Sample collection*

**Figure 1.** *Location of the wineries in the three different sub-zones of the Rioja designation of origin.*

The clonal variability in this species was different depending on the winery and the year studied within each winery (**Table 1**). Thus, while in cellars A and B the total clonal variability was 13.8 and 21.8%, respectively, in the other two (C and D), it was around 35%. Because of the lower variability in the first two wineries, the index of diversity was also smaller.

A percentage of the isolated microbiota in each harvest had already been identified in previous years (**Table 1**). This percentage was higher in wineries A and B, while in the others, this proportion was lower. However, the evolution in the population of the strains during fermentation was similar in all the wineries, since in all of them the fermentation in general was carried out by one or two majority yeasts (**Table 2**) associated with a variable number of minority strains (**Table 3**). This majority strain(s) represented between 50 and 82% of the total isolated in each vinification (**Table 2**). The only exception was found in vintage 2 of cellar C, in which there were three major strains with a percentage of 14.3% each. Previous research on the *S. cerevisiae* population in spontaneous fermentations also showed the existence of a great diversity of strains. Among them, one or two represented more than 50% of the total [16, 17].

Nonetheless, only in wineries A and B, there was one common majority strain in the vinifications studied in the 3–4 years (it is highlighted in the same color in **Table 2**). The fact that the same strains were predominant in successive campaigns in the same winery has already been described in other works [4, 18], for which reason they considered that these yeasts were representative of the winery or the processing area. In contrast, in other studies, common yeasts were not found from one campaign to another or were only found in small proportions [19, 20].

The results obtained could suggest that in wineries A and B, there are yeasts characteristic of the winery that lead the fermentation, in proportions that vary depending on the characteristics of the vintage. However, in the other two wineries, the fermentations were carried out by a succession of strains, among which one or two were majority, but different from one vintage to another.

The main difference between the four wineries studied in the Rioja Oriental sub-zone was their age. While wineries A and B were new (this study began in its third and first harvest, respectively), cellar C had been producing wine for 15 years and D for 50. This means that the latter two had more complex ecosystems, which have been formed over successive harvests, as evidenced by the greater number of clones that participated in the fermentations and the higher diversity index (**Table 1**).

**121**

**Table 1.**

*Biodiversity of Saccharomyces cerevisiae Yeasts in Spontaneous Alcoholic Fermentations: Typical…*

**A** 1 29 6 20.7 0.65 – –

**B** 1 – – – – – –

**C** 1 – – – – – –

**D** 1 – – – – – –

**Clonal variability (%)**

2 28 7 25.0 0.77 3 71.4 3 22 4 18.2 0.33 2 86.3 4 30 6 20.0 0.46 3 83.2

2 20 5 25.0 0.76 – – 3 30 9 30.0 0.84 2 36.6 4 28 8 28.6 0.77 3 63.8

2 21 11 53.4 0.94 **–** – 3 22 8 36.4 0.77 2 13.6 4 24 11 45.8 0.87 3 45.8

2 20 10 50.0 0.86 – – 3 22 5 22.7 0,58 1 9.1 4 20 8 40.0 0.82 2 10.0

**Index of diversity** **Genotypes detected in previous years Number %**

**Number of different clones**

**Total 109 15 13.8 0.64**

**Total 78 17 21.8 0.83**

**Total 67 25 37.3 0.92**

**Total 62 20 32.3 0.91**

*Clonal diversity of S. cerevisiae yeasts in Rioja Oriental wineries in 4 consecutive years (1–4).*

In a later study, carried out by our research group in cellar B [21], 72 colonies of the species *S. cerevisiae* were isolated in three fermentation tanks analyzed. The results showed 41 different clones, which provided a clonal variability of 56.9% and a biodiversity index of 0.98. These data would indicate that the fermentations were carried out by the succession of different clones and that there were no dominant ones. On the other hand, the comparison of the restriction profiles of this year's clones with those identified in the years specified in **Tables 1** and **2** showed that there was no correspondence with any of them, and therefore the majority strain (**Table 2**) that was isolated in the first three vintages as representative of the winery did not appear years later. Therefore, the ecology of the fermentations of this last year in cellar B was similar to those found in cellars C and D in the period shown previously. The situation in winery A could be similar, but it could not be proven since in the following years, commercial yeasts were inoculated, and the study could not be carried out. Taking into account the results shown, the presence of a dominant strain in the elaborations of wineries A and B could be due to the fact that

they are new wineries and not to the existence of representative yeasts.

On the other hand, the comparison of the different restriction profiles obtained in the four wineries during the 3–4 years of study showed that only two yeasts were common among the different wineries (**Table 3**). Therefore, the results obtained in this study did not show the existence of typical strain/s of this sub-zone. In the wine

*DOI: http://dx.doi.org/10.5772/intechopen.84870*

*S. cerevisiae* **analyzed**

**Winery Years Isolates of**

*Biodiversity of Saccharomyces cerevisiae Yeasts in Spontaneous Alcoholic Fermentations: Typical… DOI: http://dx.doi.org/10.5772/intechopen.84870*

#### **Table 1.**

*Advances in Grape and Wine Biotechnology*

index of diversity was also smaller.

**Figure 1.**

more than 50% of the total [16, 17].

The clonal variability in this species was different depending on the winery and the year studied within each winery (**Table 1**). Thus, while in cellars A and B the total clonal variability was 13.8 and 21.8%, respectively, in the other two (C and D), it was around 35%. Because of the lower variability in the first two wineries, the

A percentage of the isolated microbiota in each harvest had already been identi-

Nonetheless, only in wineries A and B, there was one common majority strain in the vinifications studied in the 3–4 years (it is highlighted in the same color in **Table 2**). The fact that the same strains were predominant in successive campaigns in the same winery has already been described in other works [4, 18], for which reason they considered that these yeasts were representative of the winery or the processing area. In contrast, in other studies, common yeasts were not found from one

The results obtained could suggest that in wineries A and B, there are yeasts characteristic of the winery that lead the fermentation, in proportions that vary depending on the characteristics of the vintage. However, in the other two wineries, the fermentations were carried out by a succession of strains, among which one or

The main difference between the four wineries studied in the Rioja Oriental sub-zone was their age. While wineries A and B were new (this study began in its third and first harvest, respectively), cellar C had been producing wine for 15 years and D for 50. This means that the latter two had more complex ecosystems, which have been formed over successive harvests, as evidenced by the greater number of clones that participated in the fermentations and the higher diversity

campaign to another or were only found in small proportions [19, 20].

two were majority, but different from one vintage to another.

fied in previous years (**Table 1**). This percentage was higher in wineries A and B, while in the others, this proportion was lower. However, the evolution in the population of the strains during fermentation was similar in all the wineries, since in all of them the fermentation in general was carried out by one or two majority yeasts (**Table 2**) associated with a variable number of minority strains (**Table 3**). This majority strain(s) represented between 50 and 82% of the total isolated in each vinification (**Table 2**). The only exception was found in vintage 2 of cellar C, in which there were three major strains with a percentage of 14.3% each. Previous research on the *S. cerevisiae* population in spontaneous fermentations also showed the existence of a great diversity of strains. Among them, one or two represented

*Location of the wineries in the three different sub-zones of the Rioja designation of origin.*

**120**

index (**Table 1**).

*Clonal diversity of S. cerevisiae yeasts in Rioja Oriental wineries in 4 consecutive years (1–4).*

In a later study, carried out by our research group in cellar B [21], 72 colonies of the species *S. cerevisiae* were isolated in three fermentation tanks analyzed. The results showed 41 different clones, which provided a clonal variability of 56.9% and a biodiversity index of 0.98. These data would indicate that the fermentations were carried out by the succession of different clones and that there were no dominant ones. On the other hand, the comparison of the restriction profiles of this year's clones with those identified in the years specified in **Tables 1** and **2** showed that there was no correspondence with any of them, and therefore the majority strain (**Table 2**) that was isolated in the first three vintages as representative of the winery did not appear years later. Therefore, the ecology of the fermentations of this last year in cellar B was similar to those found in cellars C and D in the period shown previously. The situation in winery A could be similar, but it could not be proven since in the following years, commercial yeasts were inoculated, and the study could not be carried out. Taking into account the results shown, the presence of a dominant strain in the elaborations of wineries A and B could be due to the fact that they are new wineries and not to the existence of representative yeasts.

On the other hand, the comparison of the different restriction profiles obtained in the four wineries during the 3–4 years of study showed that only two yeasts were common among the different wineries (**Table 3**). Therefore, the results obtained in this study did not show the existence of typical strain/s of this sub-zone. In the wine

*Clones with box in gray within the same winery indicate that they are the same strain, according to their mtDNA restriction profiles*

#### **Table 2.**

*Major S. cerevisiae strains in the fermentations of Rioja Oriental in 4 consecutive years (1–4).*

*Clones in bold and with box in gray in different columns show the same clones in different cellars*

#### **Table 3.**

*S. cerevisiae clones identified in each elaboration of Rioja Oriental in 4 consecutive years (1-4).*

area of Charentes in Cognac, a strain widely distributed throughout the area had been described, which was considered representative of the wine region. But unlike our data, this strain was the dominant one in all the samples where it appeared [18]. Likewise, in other ecological studies, widely disseminated strains were isolated in an area, and it was thought that they may be typical strains of that area [22].

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*Biodiversity of Saccharomyces cerevisiae Yeasts in Spontaneous Alcoholic Fermentations: Typical…*

In this sub-zone, five wineries were sampled. Vinification was by both the destemming and crushing (F and H) and the carbonic maceration methods (E, G, and I). As happened in the Rioja Oriental, alcoholic fermentation in the Rioja Alta sub-zone was carried out by yeasts of the *S. cerevisiae* species, since the non-*Saccharomyces* group was identified mainly in the early stages of the process. However, in the third harvest of cellars E and F, three colonies belonging to non-*Saccharomyces* genera were isolated in tumultuous fermentation (data not shown). The detection of these yeasts in advanced stages of winemaking had also been reported in other works

*2.2.2 Ecology of the yeast population during alcoholic fermentation in Rioja Alta*

[23, 24]. In total, 450 yeasts belonging to the *S. cerevisiae* species were studied.

Clonal variability was high in this sub-zone in all the wineries (**Table 4**). From the 450 isolated colonies of *S. cerevisiae*, 177 different clones were identified, resulting in high clonal variability in Rioja Alta, which ranged between 35% in winery H and 39% in winery F, with the exception of cellar I, in which 50% was reached. It is noteworthy that clonal variability depended on the year analyzed, being generally lower the first year and higher the third. These results could be related to the

The index of diversity was high and similar for all the wineries with a value of 0.95–0.96. These results were due to the high number of different strains that participated in each fermentation, which in the case of wineries F and I was favored by the low presence of common clones in different campaigns within the same winery (**Table 4**). In wineries E, G, and H, although there were more strains that appeared in different campaigns, they did so in a low percentage, and, therefore, the index remained high. The population evolution in this sub-zone was similar to Rioja Oriental, since the alcoholic fermentation was carried out by different clones during the fermentation process. In most of the vinifications, 1–3 majority clones were detected, representing at least 43% of the population (**Table 5**). However, in this sub-zone certain exceptions were found. Thus, in the first year analyzed in winery F, one clone represented 65% of the total and so was therefore dominant and responsible for the fermentation. This could be due to the special characteristics of this campaign (frosts at the end of April, high rainfall and strong winds in spring, hail storms and low temperatures in summer) that negatively influenced the grape ripening and meant that few yeasts were able to adapt and develop to carry out the fermentation. These conditions were more adverse in winery F, due to its geographic situation. It is

the winery located at higher altitude and closer to the mountains.

On the other hand, in wineries G and I, there were vinification processes in which no major clones were isolated, which could be related to the high number of different clones that participated in the fermentations. What these wineries have in common is that they follow the carbonic maceration method and have been making wine for more than 100 years. Santamaría et al. [15] showed that the number of different S*. cerevisiae* strains and the frequency of their appearance varied according to age. When comparing the clones found in the five wineries (**Table 6**), only pattern I, coming from the last year studied in the four wineries, was common in all of them. When comparing the restriction profiles of the four strains, all of them showed the

The presence of the same strain in four wineries could be due to the fact that the villages where wineries G, H, and I are located are geographically very close to each other (**Figure 1**). In cellar F, a little further away, a part of the grapes came from the same area as the other three. In this fourth year of study, strong winds were reported by grape growers in September and October. These winds could be responsible for transporting yeasts from one area to another, which would explain the appearance

*DOI: http://dx.doi.org/10.5772/intechopen.84870*

climatological characteristics of the harvest.

same electrophoretic pattern (**Figure 2**).

*sub-zone*

*Biodiversity of Saccharomyces cerevisiae Yeasts in Spontaneous Alcoholic Fermentations: Typical… DOI: http://dx.doi.org/10.5772/intechopen.84870*

## *2.2.2 Ecology of the yeast population during alcoholic fermentation in Rioja Alta sub-zone*

In this sub-zone, five wineries were sampled. Vinification was by both the destemming and crushing (F and H) and the carbonic maceration methods (E, G, and I).

As happened in the Rioja Oriental, alcoholic fermentation in the Rioja Alta sub-zone was carried out by yeasts of the *S. cerevisiae* species, since the non-*Saccharomyces* group was identified mainly in the early stages of the process. However, in the third harvest of cellars E and F, three colonies belonging to non-*Saccharomyces* genera were isolated in tumultuous fermentation (data not shown). The detection of these yeasts in advanced stages of winemaking had also been reported in other works [23, 24]. In total, 450 yeasts belonging to the *S. cerevisiae* species were studied.

Clonal variability was high in this sub-zone in all the wineries (**Table 4**). From the 450 isolated colonies of *S. cerevisiae*, 177 different clones were identified, resulting in high clonal variability in Rioja Alta, which ranged between 35% in winery H and 39% in winery F, with the exception of cellar I, in which 50% was reached. It is noteworthy that clonal variability depended on the year analyzed, being generally lower the first year and higher the third. These results could be related to the climatological characteristics of the harvest.

The index of diversity was high and similar for all the wineries with a value of 0.95–0.96. These results were due to the high number of different strains that participated in each fermentation, which in the case of wineries F and I was favored by the low presence of common clones in different campaigns within the same winery (**Table 4**). In wineries E, G, and H, although there were more strains that appeared in different campaigns, they did so in a low percentage, and, therefore, the index remained high.

The population evolution in this sub-zone was similar to Rioja Oriental, since the alcoholic fermentation was carried out by different clones during the fermentation process. In most of the vinifications, 1–3 majority clones were detected, representing at least 43% of the population (**Table 5**). However, in this sub-zone certain exceptions were found. Thus, in the first year analyzed in winery F, one clone represented 65% of the total and so was therefore dominant and responsible for the fermentation. This could be due to the special characteristics of this campaign (frosts at the end of April, high rainfall and strong winds in spring, hail storms and low temperatures in summer) that negatively influenced the grape ripening and meant that few yeasts were able to adapt and develop to carry out the fermentation. These conditions were more adverse in winery F, due to its geographic situation. It is the winery located at higher altitude and closer to the mountains.

On the other hand, in wineries G and I, there were vinification processes in which no major clones were isolated, which could be related to the high number of different clones that participated in the fermentations. What these wineries have in common is that they follow the carbonic maceration method and have been making wine for more than 100 years. Santamaría et al. [15] showed that the number of different S*. cerevisiae* strains and the frequency of their appearance varied according to age.

When comparing the clones found in the five wineries (**Table 6**), only pattern I, coming from the last year studied in the four wineries, was common in all of them. When comparing the restriction profiles of the four strains, all of them showed the same electrophoretic pattern (**Figure 2**).

The presence of the same strain in four wineries could be due to the fact that the villages where wineries G, H, and I are located are geographically very close to each other (**Figure 1**). In cellar F, a little further away, a part of the grapes came from the same area as the other three. In this fourth year of study, strong winds were reported by grape growers in September and October. These winds could be responsible for transporting yeasts from one area to another, which would explain the appearance

*Advances in Grape and Wine Biotechnology*

**122**

**Table 3.**

*restriction profiles*

**Table 2.**

area of Charentes in Cognac, a strain widely distributed throughout the area had been described, which was considered representative of the wine region. But unlike our data, this strain was the dominant one in all the samples where it appeared [18]. Likewise, in other ecological studies, widely disseminated strains were isolated in an area, and it was thought that they may be typical strains of that area [22].

*Clones in bold and with box in gray in different columns show the same clones in different cellars*

*S. cerevisiae clones identified in each elaboration of Rioja Oriental in 4 consecutive years (1-4).*

**Winery A Winery B Winery C Winery D**

*Major S. cerevisiae strains in the fermentations of Rioja Oriental in 4 consecutive years (1–4).*

**Years 1 2 3 4**

A-II1 24.1 A-II2 21.4

**Winery Strain % Strain % Strain % Strain % A** A-I1 55.2 A-I2 42.9 A-IV3 81.8 A-I4 73.3

**B** – – B-I2 35.0 B-I3 33.3 B-II4 21.4

**C** – – C-IV2 14.3 C-I3 40.9 C-I4 33.3

**D** – – D-V2 35.0 D-II3 63.6 D-I4 35.0

*Clones with box in gray within the same winery indicate that they are the same strain, according to their mtDNA*

C-X2 14.3

**Total 79.3 64.3 81.8 73.3**

– – B-IV2 35.0 B-VIII3 20.0 B-IV4 42.3 **Total** – **70.0** 53.3 **63.7**

– – C-V2 14.3 C-IV3 27.3 C-IX4 16.7

**Total** – **42.9 68.2 50.0**

– – D-VI2 15.0 D-III4 25.0 **Total** – **50.0 63.6 60.0**

**1 2 3 4 2 3 4 2 3 4 2 3 4** A-I1 A-I2 A-I3 A-I4 **B-I2** B-I3 B-I4 C-I2 C-I3 C-I4 D-I2 D-I3 D-I4 A-II1 **A-II2** A-II3 **A-II4** B-II2 B-II3 B-II4 C-II2 C-II3 **C-II4** D-II2 D-II3 D-II4 A-III1 A-III2 A-III3 A-III4 B-III2 B-III3 B-III4 C-III2 C-III3 C-III4 D-III2 D-III3 D-III4 A-IV1 A-IV2 A-IV3 A-IV4 B-IV2 B-IV3 B-IV4 C-IV2 C-IV3 C-IV4 D-IV2 D-IV3 D-IV4 A-V1 A-V2 *A-V4* B-V2 B-V3 B-V4 C-V2 C-V3 C-V4 D-V2 D-V3 D-V4 A-VI1 A-VI2 A-VI4 B-VI3 **B-VI4** C-VI2 C-VI3 C-VI4 D-VI2 D-VI4 A-VII2 B-VII3 B-VII4 C-VII2 C-VII3 C-VII4 D-VII2 D-VII4

B-VIII3 B-VIII4 C-VIII2 C-VIII3 C-VIII4 D-VIII2 D-VIII4

C-X2 C-X4 D-X2

C-XI2 C-XI4

B-IX3 C-IX2 C-IX4 D-IX2

#### *Advances in Grape and Wine Biotechnology*

#### **Table 4.**

*Clonal diversity of S. cerevisiae yeasts in Rioja Alta wineries in 4 consecutive years (1–4).*

of the same strain in the four wineries. This yeast could come from the grapes, since in none of the three previous vintages had this strain been isolated in any of the cellars. This strain dominated the beginning of the fermentations and was later replaced by other indigenous strains, since in no case was it isolated at the end of the fermentation processes. These results would concur with those of Schütz and Gafner [19], who consider that the population of yeasts can be considered dependent on the harvest and the vineyard, and also with Le Jeune et al. [25], who indicated that the populations involved in spontaneous alcoholic fermentation result from a balance between the *S. cerevisiae* strains present in the grape and in the cellar.

The data obtained would show the existence of extensive microbiota in each winery, which, together with the microflora that accompanies the grapes, will develop according to the characteristics of the harvest. The fermentations would be the result of the sequence of different yeasts, and despite having found a widely spread yeast in the sub-zone 1 year, it did not appear in any other winery in the

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*Biodiversity of Saccharomyces cerevisiae Yeasts in Spontaneous Alcoholic Fermentations: Typical…*

**Years 1 2 3 4**

**Winery Strain % Strain % Strain % Strain % E** E-I1 40.0 E-I2 13.3 E-I3 25.0 E-I4 20.0

**F** F-III1 65.0 F-II2 13.0 F-I3 25.0 F-I4 45.0

**G** G-I1 20.0 G-V2 20.0 G-I3 14.3 G-I4 33.3

**H** H-II1 25 H-III2 20.0 H-I3 30.0 H-I4 43.3

**I** – – I-I2 18.2 I-XV3 15.0 I-I4 50.0 I-IV2 36.4

F-VIII2 17.4

E-II1 20.0 E-IV2 16.7 E-III3 25.0 E-IV4 25.0

**Total 60.0 43.3 50.0 65.0**

F-III2 21.8 F-V3 30.0

**Total 65.0 52.2 55.0 45.0**

**Total 20.0 50.0 42.9 70.7**

H-III1 20 H-X2 13.3 H-II3 20.0 H-XI4 13.3 **Total 55 33.3 50.0 56.6**

**Total** – **54.6 15.0 50.0**

E-XI2 13.3 E-V4 20.0

G-VII2 15.0 G-IV3 28.6 G-III4 20.0 G-VII2 15.0 G-VI4 17.4

other three campaigns studied. So, we do not consider that in Rioja Alta, there are

In this sub-zone, two wineries (J and K) were sampled, where wines were produced by the carbonic maceration method. As in the other two sub-zones, the non-*Saccharomyces* yeasts participated at the beginning of the alcoholic fermentation, and later they were replaced by *S. cerevisiae* strains, which were the ones that directed and carried out the process until the end. In total, 149 colonies of these

The level of clonal variability was very high in both cellars, especially in winery K, where it reached 60% (**Table 7**). It is noteworthy that the highest percentage of clonal variability in both wineries was obtained in the third year, as in the vinifications in the Rioja Alta, which confirms the importance of the characteristics of the

As in the rest of the sub-zones, the high number of different clones identified in each fermentation and the few common yeasts between campaigns (**Table 7**) provided high indexes of diversity, which reached values of 0.95 and 0.98 for cellars J and K, respectively. These indices were of the same order as those found in Rioja Alta and higher than those of Rioja Oriental. Likewise, the number of yeasts that participated in more than one campaign was small in the two wineries, with the

*2.2.3 Ecology of the yeast population during alcoholic fermentation in the Rioja*

representative strains from either the wineries or the sub-zone.

*Major S. cerevisiae strains in the fermentations of Rioja Alta in 4 consecutive years (1–4).*

harvest in the microbiota that drives the fermentations.

*Alavesa sub-zone*

**Table 5.**

species were isolated.

*DOI: http://dx.doi.org/10.5772/intechopen.84870*

*Biodiversity of Saccharomyces cerevisiae Yeasts in Spontaneous Alcoholic Fermentations: Typical… DOI: http://dx.doi.org/10.5772/intechopen.84870*

#### **Table 5.**

*Advances in Grape and Wine Biotechnology*

*S. cerevisiae* **analyzed**

**Number of different clones**

**Total 92 34 37.0 0.96 F** 1 20 5 25.0 0.57 –

**Total 82 32 39.0 0.95**

**Total 104 40 38.5 0.95**

**Total 100 35 35.0 0.95**

**Total 72 36 50.0 0.96**

*Clonal diversity of S. cerevisiae yeasts in Rioja Alta wineries in 4 consecutive years (1–4).*

**G** 1 30 13 43.3 0.93 – –

**H** 1 20 9 45.0 0.88 – –

**I** 1 – – – – – –

**E** 1 30 10 33.3 0.80 – –

**Clonal variability (%)**

2 30 14 46.7 0.93 2 6.6 3 12 8 66.7 0.91 3 41.6 4 20 8 40.0 0.87 1 5.0

2 23 10 43.5 0.91 0 0 3 19 9 47.4 0.85 0 0 4 20 9 45.0 0.79 1 5.0

2 23 11 47.8 0.92 0 0 3 21 12 57.1 0.92 5 57.3 4 30 12 40.0 0.85 3 40.0

2 30 13 43.3 0.92 3 36.6 3 20 11 55.0 0.88 4 50.0 4 30 13 43.3 0.80 4 23.2

2 22 9 40.9 0.82 – – 3 30 20 66.7 0.97 3 13.3 4 20 11 55.0 0.76 1 5.0

**Index of diversity** **Genotypes detected in previous years Number %**

**Winery Years Isolates of**

of the same strain in the four wineries. This yeast could come from the grapes, since in none of the three previous vintages had this strain been isolated in any of the cellars. This strain dominated the beginning of the fermentations and was later replaced by other indigenous strains, since in no case was it isolated at the end of the fermentation processes. These results would concur with those of Schütz and Gafner [19], who consider that the population of yeasts can be considered dependent on the harvest and the vineyard, and also with Le Jeune et al. [25], who indicated that the populations involved in spontaneous alcoholic fermentation result from a balance

The data obtained would show the existence of extensive microbiota in each winery, which, together with the microflora that accompanies the grapes, will develop according to the characteristics of the harvest. The fermentations would be the result of the sequence of different yeasts, and despite having found a widely spread yeast in the sub-zone 1 year, it did not appear in any other winery in the

between the *S. cerevisiae* strains present in the grape and in the cellar.

**124**

**Table 4.**

*Major S. cerevisiae strains in the fermentations of Rioja Alta in 4 consecutive years (1–4).*

other three campaigns studied. So, we do not consider that in Rioja Alta, there are representative strains from either the wineries or the sub-zone.

## *2.2.3 Ecology of the yeast population during alcoholic fermentation in the Rioja Alavesa sub-zone*

In this sub-zone, two wineries (J and K) were sampled, where wines were produced by the carbonic maceration method. As in the other two sub-zones, the non-*Saccharomyces* yeasts participated at the beginning of the alcoholic fermentation, and later they were replaced by *S. cerevisiae* strains, which were the ones that directed and carried out the process until the end. In total, 149 colonies of these species were isolated.

The level of clonal variability was very high in both cellars, especially in winery K, where it reached 60% (**Table 7**). It is noteworthy that the highest percentage of clonal variability in both wineries was obtained in the third year, as in the vinifications in the Rioja Alta, which confirms the importance of the characteristics of the harvest in the microbiota that drives the fermentations.

As in the rest of the sub-zones, the high number of different clones identified in each fermentation and the few common yeasts between campaigns (**Table 7**) provided high indexes of diversity, which reached values of 0.95 and 0.98 for cellars J and K, respectively. These indices were of the same order as those found in Rioja Alta and higher than those of Rioja Oriental. Likewise, the number of yeasts that participated in more than one campaign was small in the two wineries, with the

*Advances in Grape and Wine Biotechnology*

**Table 6.**

**127**

**Figure 2.**

**J**

**K**

**Table 7.**

*Alta wineries in year 4.*

**Winery Years Isolates of**

*S. cerevisiae* **analyzed**

*Biodiversity of Saccharomyces cerevisiae Yeasts in Spontaneous Alcoholic Fermentations: Typical…*

*Restriction patterns obtained with different restriction endonucleases, from the patterns I isolated in four Rioja*

**Clonal variability (%)**

**Index of diversity** **Genotypes detected in previous years**

3 29.9

5 50.0

3 15.0

**%**

–

–

0

**Number**

–

–

0

**Number of different clones**

1 29 15 51.7 0.93

2 30 13 43.3 0.92

3 20 14 70.0 0.94

1 20 12 60.0 0.95

2 30 14 46.7 0.92

3 20 19 95.0 0.99

**Total 70 42 60.0 0.98**

*Clonal diversity of S. cerevisiae yeasts in Rioja Alavesa wineries in 3 consecutive years (1–3).*

**Total 79 34 43.0 0.95**

exception of the third year of study in winery J. Therefore, the microbiota respon

responding to the fermentations of the second year. Therefore, the data obtained would show the existence of extensive microbiota in each winery. The fermenta

tions would be the result of the sequence of different populations of yeasts, as there

Regarding the population dynamics of the fermentation, it was observed that in the two wineries, the fermentations were carried out by different *S. cerevisiae* strains that followed each other during the different fermentative phases. In each vinification process, several major clones were detected, which represented at least 30% of the yeasts that carried out the fermentation (**Table 8**). The exception was the third year, in which most of the strains that were found in a fermentative phase were replaced by others in the following stage, particularly in winery K. This fact was also found in some of the vinifications in wineries G and I in Rioja Alta. All four wineries are over a hundred years old and conduct vinifications using the carbonic maceration method. The comparison of the different strains found in the two cellars during the 3 years of study (**Table 9**) showed the existence of a single common clone, cor

sible for the fermentation was different in each harvest.

are no representative strains of the sub-zone.

*DOI: http://dx.doi.org/10.5772/intechopen.84870*

*Biodiversity of Saccharomyces cerevisiae Yeasts in Spontaneous Alcoholic Fermentations: Typical… DOI: http://dx.doi.org/10.5772/intechopen.84870*

#### **Figure 2.**

*Advances in Grape and Wine Biotechnology*

**126**

**Winery E**

**1** E-I1 E-II1 E-III1 E-IV1

E-V1 E-VI1 E-VII1 E-VIII1

E-IX1

E-X1

E-X2 E-XI2 E-XII2 E-XIII2 E-XIV2

E-IX2

E-VIII2

E-VIII3

E-VIII4

F-VIII2

F-IX2 F-X2

F-IX3

F-IX4

G-IX1

G-X1 G-XI1 G-XII1 G-XIII1

G-XII3

G-XII4

H-XII2 H-XIII2

H-XIII4

H-XII4

G-XI2

G-XI3

G-XI4

H-XI2

H-XI3

H-XI4

G-X2

G-X3

G-X4

H-X2

H-X3

H-X4

G-IX2

G-IX3

G-IX4

H-IX1

H-IX2

H-IX3

H-IX4

I-IX2

I-IX3

I-X3 I-XI3 I-XII3

I-XIII3

I-XIV3

I-XV3

I-XVI3

I-XVII3

I-XVIII3

I-XIX3

I-XX3

*Clones with box in gray in different columns show the same clone in different cellars*

*S. cerevisiae clones identified in each elaboration of Rioja Alta in 4 consecutive years (1–4).*

**Table 6.**

II-XI4

II-X4

II-IX4

F-VIII3

F-VIII4

G-VIII1

G-VIII2

G-VIII3

G-VIII4

H-VIII1

H-VIII2

H-VIII3

H-VIII4

I-VIII2

I-VIII3

II-VIII4

E-VII2

E-VII3

E-VII4

F-VII98

F-VII3

F-VII4

G-VII1

G-VII2

G-VII3

G-VII4

H-VII1

H-VII2

H-VII3

H-VII4

I-VII2

I-VII3

II-VII4

E-VI2

E-VI3

E-VI4

F-VI2

F-VI3

F-VI4

G-VI1

G-VI2

G-VI3

G-VI4

H-VI1

H-VI2

H-VI3

H-VI4

I-VI2

I-VI3

II-VI4

E-V2

E-V3

E-V4

F-V1

F-V2

F-V3

F-V4

G-V1

G-V2

G-V3

G-V4

H-V1

H-V2

H-V3

H-V4

I-V2

I-V3

II-V4

E-IV2

E-IV3

E-IV4

F-IV1

F-IV2

F-IV3

F-IV4

G-IV1

G-IV2

G-IV3

G-IV4

H-IV1

H-IV2

H-IV3

H-IV4

I-IV2

I-IV3

II-IV4

E-III2

E-III3

E-III4

F-III1

F-III2

F-III3

F-III4

G-III1

G-III2

G-III3

G-III4

H-III1

H-III2

H-III3

H-III4

I-III2

I-III3

II-III4

E-II2

E-II3

E-II4

F-II1

F-II2

F-II3

F-II4

G-II1

G-II2

G-II3

G-II4

H-II1

H-II2

H-II3

H-II4

I-II2

I-II3

II-II4

E-I2

E-I3

E-I4

F-I1

F-I2

F-I3

F-I4

G-I1

G-I2

G-I3

G-I4

H-I1

H-I2

H-I3

H-I4

I-I2

I-I3

I-I4

**2**

**3**

**4**

**1**

**2**

**3**

**4**

**1**

**2**

**3**

**4**

**1**

**2**

**3**

**4**

**2**

**3**

**4**

**Winery F**

**Winery G**

**Winery H**

**Winery I**

*Restriction patterns obtained with different restriction endonucleases, from the patterns I isolated in four Rioja Alta wineries in year 4.*

#### **Table 7.**

*Clonal diversity of S. cerevisiae yeasts in Rioja Alavesa wineries in 3 consecutive years (1–3).*

exception of the third year of study in winery J. Therefore, the microbiota responsible for the fermentation was different in each harvest.

Regarding the population dynamics of the fermentation, it was observed that in the two wineries, the fermentations were carried out by different *S. cerevisiae* strains that followed each other during the different fermentative phases. In each vinification process, several major clones were detected, which represented at least 30% of the yeasts that carried out the fermentation (**Table 8**). The exception was the third year, in which most of the strains that were found in a fermentative phase were replaced by others in the following stage, particularly in winery K. This fact was also found in some of the vinifications in wineries G and I in Rioja Alta. All four wineries are over a hundred years old and conduct vinifications using the carbonic maceration method.

The comparison of the different strains found in the two cellars during the 3 years of study (**Table 9**) showed the existence of a single common clone, corresponding to the fermentations of the second year. Therefore, the data obtained would show the existence of extensive microbiota in each winery. The fermentations would be the result of the sequence of different populations of yeasts, as there are no representative strains of the sub-zone.

#### *Advances in Grape and Wine Biotechnology*

#### **Table 8.**

*Major S. cerevisiae strains in the fermentations of Rioja Alavesa in 3 consecutive years (1–3).*

*Clones In bold in different columns show the same clone in different cellars*

#### **Table 9.**

*S. cerevisiae clones identified in each elaboration of Rioja Alavesa in 3 consecutive years (1–3).*


**10. Current stent biomaterial design**

Over the years, attempts have been made at preventing stent associated symptoms by targeting either bacterial adhesion and encrustation or inhibition of bacterial proliferation. Drug eluting technology to prevent bacterial adhesion has previously been used in a triclosan-eluting ureteral stent. Triclosan is an antimicrobial found in over 800 commercially available products such as soaps, hand scrubs, and toothpaste. This stent proved to be successful at eliminating bacterial loads *in vitro* (Chew, Cadieux et al. 2006) as well as a *Proteus mirabilis* urinary tract infection in a rabbit model (Cadieux, Chew et al. 2006), but did not show any significant differences in long term clinical trials (Cadieux, Chew et al. 2009). Similarly a heparin- coated stent was designed to prevent bacterial adhesion given the material's highly negative charge. This stent was shown to decrease encrustation in patients (Hildebrandt, Sayyad et al. 2001; Riedl, Witkowski et al. 2002; Cauda, Cauda et al. 2008), however was unable to prevent bacterial adhesion (Lange, Elwood et al. 2009). The use of diamond-like amorphous carbon as a coating on stents is a new technology that has shown some promise in terms of inhibiting encrustation (Laube, Bradenahl et al. 2006; Laube, Kleinen et al. 2007), however experiments aimed at determining its ability to inhibit bacterial adhesion is lacking. One of the drawbacks of these new technologies is the fact that they are susceptible to blockage by the deposition of the urinary conditioning film, which covers any coating and blocks elution of drugs from the stent, rendering it ineffective and promoting bacterial adhesion and encrustation via mechanisms discussed above.

Biomaterials in Urology - Beyond Drug Eluting

**12. Conclusions**

**13. References**

and Degradable - A Rational Approach to Ureteral Stent Design 469

the stent surface form a base layer to which other proteins such as serum albumin attach. Such a mechanism of deposition would be favorable for the purpose of rational stent design, as the proteins forming the base layer would make excellent targets for adhesion prevention. If temporal aspects can be determined in addition to the various layers of proteins, potential targets could be identified to prevent the initiating events of encrustation and infection.

We propose that the principles used by our colleagues in pharmaceutical research in the pursuit of rational drug design can be transferred to the design of novel ureteral stent biomaterials: 1) To understand the potential targets in ureteral stent encrustation and infection and 2) Develop biomaterials to limit these processes. Current research to date has focused on the prevention of bacterial adherence and encrustation; however, we propose that research interests should shift to the initial primary steps in conditioning film formation, thus perhaps preventing the whole cascade of events from occurring. Once these processes have been more clearly defined, the pursuit of highly specific engineered biomaterials can be started. Identification of specific targets would help direct the development of new materials and hopefully succeed where previous work has failed.

Allen, D. J., S. E. Longhorn, et al. (2010). "Percutaneous urinary drainage and ureteric stenting in malignant disease." Clin Oncol (R Coll Radiol) 22(9): 733-739. Appelgren, P., U. Ransjo, et al. (1996). "Surface heparinization of central venous catheters

Barbalias, G. A., E. N. Liatsikos, et al. (2002). "Ureteropelvic junction obstruction: an

Barbalias, G. A., E. N. Liatsikos, et al. (2002). "Externally coated ureteral metallic stents: an

Beiko, D. T., J. D. Watterson, et al. (2004). "Double-blind randomized controlled trial

Blaschko, S. D., L. A. Deane, et al. (2007). "In-vivo evaluation of flow characteristics of novel

Borboroglu, P. G., C. L. Amling, et al. (2001). "Ureteral stenting after ureteroscopy for distal

assessing pain, outcomes and complications." J Urol 166(5): 1651-1657. Borin, J. F., O. Melamud, et al. (2006). "Initial experience with full-length metal stent to

relieve malignant ureteral obstruction." J Endourol 20(5): 300-304.

after extracorporeal shockwave lithotripsy." J Endourol 18(8): 723-730. Benson, A. D., E. R. Taylor, et al. (2011). "Metal ureteral stent for benign and malignant

randomized trial." Crit Care Med 24(9): 1482-1489.

ureteral obstruction." J Urol 185(6): 2217-2222.

metal ureteral stent." J Endourol 21(7): 780-783.

unfavorable clinical experience." Eur Urol 42(3): 276-280.

168(6): 2383-2386; discussion 2386.

reduces microbial colonization in vitro and in vivo: results from a prospective,

innovative approach combining metallic stenting and virtual endoscopy." J Urol

assessing the safety and efficacy of intravesical agents for ureteral stent symptoms

ureteral calculi: a multi-institutional prospective randomized controlled study


**Table 8.**

*Instructional design approaches.*

**13**

its development.

*Gamification: A Necessary Element for Designing Privacy Training Programs*

Based on [34], the main aim is to identify which the learners are and the priorities of each curriculum. Afterwards, the assessment framework should be developed, which, significantly, includes the selection of tasks, tests and quizzes. The last phase concerns the creation of the learning activities, considering that the context should engage users on educating. The ASSURE model, published in 2019 [35], named after its phases, as the ADDIE model. The described parts are the a) analyzation of learners' characteristics b) definition of objectives, c) selection and design of learning materials and strategies d) employment of technologies and learning media e) implementation of the material and f) the evaluation and revision of the

In [36], similar steps with the above models, have been identified. The main difference is identified on the separation of some phases, which previously were presented as the step one. In particular, the identification of needs, the definition and documentation of the objectives, the analyzation of learners and contexts are the four distinct steps in this approach. Similarly, the development of assessment instruments, instructional strategy and materials concern the three next phases. Afterwards, the aim is to evaluate the product in order to revise it, based on the results of the evaluation and finally, to design and re-evaluate the product.

The repetition of the assessment process is recommended in order to improve more

An equivalent approach has been published in 2015 by [37], where the first parts concern the identification of the instructional program, purposes, users' needs and context of program. Next, the already defined program has to be planned and developed in order to be implemented. As it is suggested in most of the instructional models, the evaluation process is needed in order to improve it based on the assessment results. The recommended approach in 2011 [38], is relative to the first one of the above described models. This approach consists of six steps, where at first the same identifications about the users' needs, characteristics, learning objectives and materials are included. Next, the design and application of the material, based on the previous analyzations, are described. The last step is the evaluation of the material based on the recorded feedback. In 2007 [39], Hrvoje Stančić et al. presented general steps, which should be implemented for developing the instructional simulation approaches. In this work, the first step is to identify the scope of the program and therefore, to record the needed information, e.g. development timeframe, in order to design the conceptual model of the program. The design of the program and the examination of each validity are

Based on the described models, several steps with similarities are identified. Most of them include the definition of objectives, users' needs, concept of program and instructional materials during the first phases. Afterwards, the design phase is included where the interface and the context of the program is illustrated in order to be developed. The developed programed is implemented, where users interact with it. In order for the system to be improved, it is crucial to record users' feedback. Thus, an evaluation stage is needed, where the evaluation criteria are specifically defined. Few differences have been recorded among the models. For instance, one difference that could be mentioned concerns the model ARCS, which focuses more on motivating users, so as to be educated. Furthermore, one of them, i.e. the ARCS+G model [33], is totally different, since its concept is not only to develop an educational product, but also to gamify it by incorporating game elements during

Such approaches should be enhanced and as it was aforementioned, it could be interesting and useful to be combined with gamification attributes in order for, by implementing them, attractive training programs to be developed. Gamification has

*DOI: http://dx.doi.org/10.5772/intechopen.97420*

program.

the final product.

considered as the final steps.

#### *Gamification: A Necessary Element for Designing Privacy Training Programs DOI: http://dx.doi.org/10.5772/intechopen.97420*

*The Role of Gamification in Software Development Lifecycle*

Chapter

Google

**12**

**Instructional Design Approaches**

**Publication** An ASSURE-Model Instructional Design Based

2019

Instructional design

1.Analyze learners

2.State standards and objectives

3.Select strategies, technology, media and materials

4.Utilize technology, media and materials.

5.Require learner participations

6.Evaluate and revise

1.Formulation of the problem

2.Collection of the information and construction of the

conceptual model

3.Checking the validity of the conceptual model

4.Model programming

5.Checking the validity of the programmed model

6.Design, conduction and analysis of the simulation

Documentation and presentation of the simulation

10

on Active Learning Strategies and its Effect for 1st

Intermediate Student's Higher Order Thinking Skills

in Teaching Science Text Book

(Sami Hameed Kadhim Al-Khattat et al.)

Simulation Models in Education

2007

Simulation Model for

e-learning

(Hrvoje Stančić et al.)

Total number of studies

**Table 8.**

*Instructional design approaches.*

**Year**

**Target**

**Context**

**Type of**

**Database**

**publication**

Journal

Google

Based on [34], the main aim is to identify which the learners are and the priorities of each curriculum. Afterwards, the assessment framework should be developed, which, significantly, includes the selection of tasks, tests and quizzes. The last phase concerns the creation of the learning activities, considering that the context should engage users on educating. The ASSURE model, published in 2019 [35], named after its phases, as the ADDIE model. The described parts are the a) analyzation of learners' characteristics b) definition of objectives, c) selection and design of learning materials and strategies d) employment of technologies and learning media e) implementation of the material and f) the evaluation and revision of the program.

In [36], similar steps with the above models, have been identified. The main difference is identified on the separation of some phases, which previously were presented as the step one. In particular, the identification of needs, the definition and documentation of the objectives, the analyzation of learners and contexts are the four distinct steps in this approach. Similarly, the development of assessment instruments, instructional strategy and materials concern the three next phases. Afterwards, the aim is to evaluate the product in order to revise it, based on the results of the evaluation and finally, to design and re-evaluate the product. The repetition of the assessment process is recommended in order to improve more the final product.

An equivalent approach has been published in 2015 by [37], where the first parts concern the identification of the instructional program, purposes, users' needs and context of program. Next, the already defined program has to be planned and developed in order to be implemented. As it is suggested in most of the instructional models, the evaluation process is needed in order to improve it based on the assessment results. The recommended approach in 2011 [38], is relative to the first one of the above described models. This approach consists of six steps, where at first the same identifications about the users' needs, characteristics, learning objectives and materials are included. Next, the design and application of the material, based on the previous analyzations, are described. The last step is the evaluation of the material based on the recorded feedback. In 2007 [39], Hrvoje Stančić et al. presented general steps, which should be implemented for developing the instructional simulation approaches. In this work, the first step is to identify the scope of the program and therefore, to record the needed information, e.g. development timeframe, in order to design the conceptual model of the program. The design of the program and the examination of each validity are considered as the final steps.

Based on the described models, several steps with similarities are identified. Most of them include the definition of objectives, users' needs, concept of program and instructional materials during the first phases. Afterwards, the design phase is included where the interface and the context of the program is illustrated in order to be developed. The developed programed is implemented, where users interact with it. In order for the system to be improved, it is crucial to record users' feedback. Thus, an evaluation stage is needed, where the evaluation criteria are specifically defined. Few differences have been recorded among the models. For instance, one difference that could be mentioned concerns the model ARCS, which focuses more on motivating users, so as to be educated. Furthermore, one of them, i.e. the ARCS+G model [33], is totally different, since its concept is not only to develop an educational product, but also to gamify it by incorporating game elements during its development.

Such approaches should be enhanced and as it was aforementioned, it could be interesting and useful to be combined with gamification attributes in order for, by implementing them, attractive training programs to be developed. Gamification has been mostly utilized as a tool for designing gamified educational programs. Various of such programs have been recorded in previous literature [40, 41] offering a more entertaining educational process and users are more engaged to be trained on several issues. Despite the usefulness of gamification in education domain, its report in educational models is missing. A correlation could be defined among these elements and the processes of instructional models, based on the concept of each element and the aim of each process. Thus, game elements will be included in each step of a model and not only in the design phases as it is customary, according to the gamification models.

#### **5.2 Privacy awareness topics**

According to the results, recorded during the search for the RQ2, several privacy topics have been identified, which could be considered during designing educational programs for increasing users' awareness on privacy issues. Based on their concept, they were classified into four main categories, as presented in **Table 9**. Landau [42] recommends that users could be educated on privacy social aspects, i.e. privacy regulations and laws, psychology and economics. Specifically, regarding law issues on information privacy, it would be interesting for users to be trained on the existing privacy regulations, on how data has to be protected by each type of organization based on these regulations, and on which rights the privacy policies have been based [42].

Additionally, more technical subjects are suggested, which could be used for example in order to educate software developers on designing tools, which analyze security and privacy concepts in systems or on developing privacy and security aware services [42]. Furthermore, one subject concerns the anonymization tools, e.g. k-anonymity, while others relative to anonymity – one of the privacy requirements –, concern security issues, cryptography technique and privacy techniques. In general, for users is important to be aware on the "Privacy by Design" philosophy, since they will be able to understand either the importance of protecting their privacy or to recognize what is needed to be implemented to ensure privacy protection [43]. Privacy threats is another issue on which users would be educated, as it is useful to be aware on possible threats in order to be able to protect their personal information while using each type of technology.

Besides them, a sequence of questions has been recorded, recommended as possible topics for privacy awareness. First of all, the audience of an educational program would be interested to know what privacy means and its importance, why it should be aware on privacy issues [44] in order to be able to understand more specified issues. Such issues could be the type of data that should be protected, the way of organizing data, the importance of saving and backing up critical data and the protocols of sharing data [45]. Another privacy awareness topic could be to learn how data can be controlled in order to avoid possible attacks [46]. As, frequently, new products are provided by organizations, the education on privacy issues which arise with new services would be useful for users, as they will be able to identify them while using technologies [43].

While many organizations restore data, individuals should be informed about their rights, which data are used, by whom and the reason of each use. In a similar way, organizations should be aware on their responsibilities regarding the collection, processing and sharing of this users' data [47]. Further to this, responsible departments of an organization could be educated on strategies regarding the training of the staff on protecting security and privacy in order the education process to be more effective [48]. Additionally, they could be

**15**

**Table 9.**

*Gamification: A Necessary Element for Designing Privacy Training Programs*

Privacy law in commercial practice, health information, and communications Privacy and data protection, including the international aspects of this

Anonymization tools (pseudonymity, Tor, k-anonymity, and differential privacy) and attacks on and failures

*DOI: http://dx.doi.org/10.5772/intechopen.97420*

Psychology (including human–computer interaction)

What are the consequences of not being #PrivacyAware? Why should I care about privacy if I have nothing to hide?

Rights that individuals have regarding their personal data

Organization's responsibilities when collecting and processing personal data

How to identify the privacy issues that can arise with new products or services

The responsibilities organizations have when sharing data internally, externally, and across borders

What are the best tactics for educating staff about their roles in and the importance of privacy and data

What should organizations look out for to make sure their vendors and partners are privacy aware?

Wouldn't we all be better off if all information was always recorded and visible?

**Law issues on information privacy** Development of privacy within the law

Regulatory frameworks for privacy **Technical issues on information privacy**

What data needs to be protected?

How to dispose of data no longer needed The importance of backing up critical data Differences between privacy and security

Minimize What Data You Collect and Release

Understand the "Privacy by Design" philosophy

How to label data How to organize data Protocols on sharing data

Trust

security?

*Privacy awareness topics.*

Controlling Data Access

Cryptography Anonymity Security

Privacy techniques

of those tools Privacy threats **General issues** Why privacy?

**Privacy's social aspects**

Law Regulation Economics