*3.2. Dry Pasta Properties*

Pasta color is one of the quality characteristics of pasta and impacts directly the consumer perception and purchase intention. The ANOVA results for the quadratic models fitted to the dry pasta properties are presented in Table 3. The quadratic regression model explained 87% of *L\** parameter variation, being significant at *p* < 0.01 (Table 3). Temperature, moisture, and GPF level showed significant (*p* < 0.01) influence on *L\**, while time factor had a non-significant influence (*p* > 0.05).

**Table 3.** ANOVA results for quadratic model fitted to dry pasta parameters.


A—temperature, B—time, C—moisture, D—GPF level, *L\**—luminosity, DF—dietary fiber, TPC—total polyphenolics content, \*—significant at *p* < 0.05, \*\*—significant at *p* < 0.01.

The highest positive effect was observed for the linear term of temperature, while the most important negative effect (*p* < 0.01) was that of GPF level. The decrease of *L\** was proportional with GPF level increase, while the opposite trend was obtained with temperature (Figure 4).

**Figure 4.** Three-dimensional response surface plots presenting the synergistic effects of factors: GPF level–temperature (**a**) and moisture-time (**b**) on dry pasta luminosity (*L\**).

Grape peels are known to increase DF content of foods since they are an important source of insoluble and soluble fibers. The quadratic model was correctly chosen (*p* < 0.01) to describe 97% of DF content data variation. HMT conditions and GPF addition level showed significant effects (*p* < 0.01) on DF. The linear term of temperature presented the biggest positive influence, while its quadratic term had the highest negative effect on DF. The response surface plots describing the synergistic effects of factors on pasta DF are given in Figure 5. These results are confirming the enhancement of pasta nutritional value by HMT and GPF addition.

**Figure 5.** Three-dimensional response surface plots presenting the synergistic effects of factors: GPF level–temperature (**a**) and moisture-time (**b**) on dietary fiber content (DF).

Pasta polyphenolics content is affected by HMT regime and the addition of GPF, which is a rich source of bioactive compounds. The quadratic model was adequate (*p* < 0.01) to describe 92% of TPC data variation. HMT temperature and time and GPF addition level showed significant (*p* < 0.01) effects on TPC (Table 3). GPF level linear term had the highest positive influence, while the quadratic term of temperature showed the most important negative effect at *p* < 0.01. The negative impact of HMT given by the decrease of TPC with

temperature and time increase was countered by the addition of GPF, which determined raised TPC values as the level was higher (Figure 6).

**Figure 6.** Three-dimensional response surface plots presenting the synergistic effects of factors: GPF level–temperature (**a**) and moisture-time (**b**) on total polyphenolics content (TPC).

#### *3.3. Cooked Pasta Properties*

Pasta texture could be a predictor for some of the sensory characteristics of the product. Cooked pasta firmness is another important quality parameter that was significantly influenced (*p* < 0.05) by wheat HMT temperature and time (Table 4).


**Table 4.** ANOVA results for quadratic model fitted to cooked pasta parameters.

A—temperature, B—time, C—moisture, D—GPF level, RS—resistant starch, \*—significant at *p* < 0.05, \*\* significant at *p* < 0.01.

The quadratic regression model explained 79% of data variation at *p* < 0.01. The highest negative effect was that of the linear term of temperature, while the interaction between moisture and GPF level showed the larger positive effect (*p* < 0.01). As can be seen in Figure 7, pasta firmness decreased with temperature and time increase, while moisture and GPF level had a non-significant effect (*p* > 0.05).

**Figure 7.** Three-dimensional response surface plots presenting the synergistic effects of factors: GPF level–temperature (**a**) and moisture-time (**b**) on pasta firmness.

Pasta gumminess data were fitted to the quadratic regression model, which presented a coefficient of determination of 0.79 and a significance of *p* < 0.01. All the considered factors showed significant effects (*p* < 0.01) on pasta gumminess, while their interactions and quadratic terms had a non-significant effect (*p* > 0.05). The highest negative influence was obtained for HMT moisture factor (Table 4). HMT regime in terms of temperature, time, moisture, and the GPF level determined a proportional decrease of pasta gumminess, as it is shown in Figure 8.

**Figure 8.** Three-dimensional response surface plots presenting the synergistic effects of factors: GPF level–temperature (**a**) and moisture-time (**b**) on pasta gumminess.

The formation of RS during HMT led to improved nutritional and functional value of pasta. The quadratic model chosen to describe RS content data variation was suitable (*p* < 0.01), since a high determination coefficient (*R*<sup>2</sup> = 0.96) was obtained. All the factors presented significant (*p* < 0.01) influence on past RS content (Table 3). The quadratic term of temperature had the biggest positive influence at *p* < 0.01, while its linear term had the highest negative effect on RS. GPF addition level, HMT time, and moisture increase

determined increases of RS content, while temperature showed an opposite trend up to 80 ◦C (Figure 9).

**Figure 9.** Three-dimensional response surface plots presenting the synergistic effects of factors: GPF level–temperature (**a**) and moisture-time (**b**) on pasta resistant starch (RS).

HMT temperature increase up to 80 ◦C determined RS increase. A similar trend was observed for time factor and moisture up to 22%. GPF addition level increase resulted in a proportional RS contents rise (Figure 9).

#### *3.4. Optimization*

The optimal values of the factors and the predicted values of the responses are shown in Table 5.


**Table 5.** Minimum, maximum, and optimal values of the variables.

G'—elastic modulus, G"—viscous modulus, *L\**—luminosity, DF—dietary fiber, TPC—total polyphenolics content, RS—resistant starch.

The results of the optimization revealed a recommended HMT regime for wheat flour of 87.56 ◦C temperature applied for 3 h at a moisture of 26.01%. GPF can be incorporated in wheat pasta at a level of 4.81% without major negative impact on quality parameters and with considerable nutritional and functional benefits.

#### *3.5. Correlations between Dough and Pasta Properties*

Correlations between dough and pasta properties were obtained and are presented in Table 6. Pasta luminosity *L\** was significantly (*p* < 0.05) negatively correlated with TPC and RS contents. Dough firmness was positively correlated (*p* < 0.01) with pasta firmness, G', G", TPC, and DF contents. Pasta gumminess showed a negative correlation at *p* < 0.01 with G', G", and DF, while with RS the correlation was significant at *p* < 0.05. The elastic (G') and viscous (G") moduli were significantly (*p* < 0.01) correlated (*p* < 0.01) with TPC and DF contents.


**Table 6.** Correlations between dough and pasta properties.

*L\**—luminosity, G'—elastic modulus, G"—viscous modulus, TPC—total polyphenolics content, DF—dietary fiber, RS—resistant starch, \*—significant at *p* < 0.05, \*\*—significant at *p* < 0.01.
