2.4.3. Dietary Fiber Content (DF)

An Infrared FOSS 6500 NIR (Foss, Silver Springs, FL, USA) device was employed to estimate the DF content. Ground pasta was subjected to analysis at room temperature and the spectrometer was calibrated by using INGOT calibrations (AUNIR, Towcester, UK). Three measurements were performed for each experiment and the results were reported on dry pasta as it is.

#### *2.5. Synergistic Effects of HMT and GPF on Cooked Pasta Properties*

Pasta was boiled according to the optimum cooking time considered when the white core of uncooked starch disappeared.

#### 2.5.1. Cooked Pasta Texture

Pasta texture was evaluated by TPA, firmness and gumminess parameters being registered in triplicate. For this purpose, one piece of pasta was subjected to double compression with a cylindrical probe of 35 mm, at a height of 50%, a speed of 5.0 mm/s, and a trigger force of 20 g [28]. Measurements were performed on a Perten TVT-6700 device (Perten Instruments, Hägersten, Sweden).

#### 2.5.2. Resistant Starch (RS) Determination

Fresh boiled pasta resistant starch content was determined by using a Megazyme kit (Cat. No. K-DSTRS; Megazyme, Bray, Ireland), according to the standard method AOAC 2017.16. The principle of the method was to digest the sample with α-amylase and a-myloglucosidase for 4 h and to quantify spectrophotometrically (at 510 nm) the resultant glucose by using GOPOD reagent.

#### *2.6. Optimization of HMT Regime and GPF Addition*

The Design Expert software (Stat-Ease, Inc., Minneapolis, MN, USA) trial version was employed to create the experimental matrix in order to evaluate the effects of factors (temperature, time, moisture, GPF level) on the considered responses (dough G', G", firmness, pasta color, TPC, DF, firmness, gumminess and RS content). The study was carried out on 30 resulting experiments (Table 1). Response surface methodology (RSM) with a central composite design (CCD) was used for experiment planning and data processing. The option of face centered at α = 1 with six points at the center and three repetitions for each experiment was selected. Mathematical model fittings were evaluated trough *F* sequential test, coefficient of determination (*R*2), and adjusted coefficients of determination (*Adj.-R*2). Analysis of Variance (ANOVA) performed by Design Expert software was used to establish the significant effects at *p* < 0.05 of the factors and their interactions on the responses. The optimization of HMT regime and GPF level was done by means of desirability function. The constraints applied consisted of maximization of G', G", TPC, RS, DF, and pasta firmness, while *L*\*, dough hardness, and pasta gumminess were kept within the range.

RSM is a well-known tool successfully employed in different industries, with various applications in food formulation, quality, and processing technologies optimization. RSM is based on simple algebraic equations although they present an advantage regarding ease of use [31].



#### **3. Results**

Dough elastic and viscous moduli, firmness, pasta color, polyphenolics contents, dietary fiber content, pasta firmness, gumminess, and resistant starch content responses were fitted to the quadratic polynomial regression model. The quadratic models (Equation (1)) were selected because they presented the highest *Adj.-R*<sup>2</sup> values compared to other mathematical models proposed:

Y = x0 + x1A + x2B + x3C + x4D + x5AB + x6AC + x7BC + x8AD + x9CD + x10BD + x11A2 + x12B2 + x13C2 + x14D2 (1)

where Y is the response, x0-x9 are the regression coefficients, and A, B, C, and D are the factors.

#### *3.1. Pasta Dough Properties*

Dough pasta rheological properties are affected by HMT and the addition of fiber-rich ingredients such as GPF. The variation on the elastic modulus G' was explained by the quadratic model (*R*<sup>2</sup> = 0.93, *p* < 0.01), temperature, time, moisture, and GPF level factors significantly influencing this parameter (Table 2).



A—temperature, B—time, C—moisture, D—GPF level, G'—elastic modulus, G"—viscous modulus, \*—significant at *p* < 0.05, \*\*—significant at *p* < 0.01.

The response surface plot showed (Figure 2a,b) that G' significantly (*p* < 0.01) increased with an increase in factor levels, indicating a strengthening effect of HMT and GPF on wheat dough. The biggest significant (*p* < 0.01) positive influence was observed for temperature factor, while the interaction between moisture and GPF level presented the highest significant negative effect on G'. The variation of the viscous modulus (G") was successfully described by the quadratic model (*R*<sup>2</sup> = 0.86, *p* < 0.01). Temperature, time, moisture, and GPF level rise led to higher G" values (Figure 2c,d), with all the considered factors presenting significant (*p* < 0.01) influence (Table 2).

**Figure 2.** Three-dimensional response surface plots presenting the synergistic effects of factors on dough elastic G' (**a**,**b**) and viscous G" (**c**,**d**) moduli.

HMT temperature showed the biggest positive influence, while the most important negative effect on G" was observed for the quadratic term of temperature, considering a significance level of *p* < 0.01. Higher elastic and viscous moduli are desirable for pasta shape keeping. G' and G" values (Figure 2) were significantly higher compared to the data obtained by Fanari et al. [32] for semolina dough, probably due to the difference in dough moisture used.

Wheat flour HMT and GPF addition affected dough firmness, the quadratic model describing 71% of data variation at *p* < 0.01. Only temperature and GPF level factors influenced significantly (*p* < 0.05) dough firmness (Table 2). The highest positive effect was observed for the linear term of temperature, while the interaction between HMT time and moisture showed a significant (*p* < 0.01) negative effect. Dough firmness increase was directly proportional with temperature level increase, while GPF determined higher dough firmness at additions up to 5% (Figure 3). Dough firmness is an important technological property that is directly related to pasta handling and modeling. Soft doughs are not desirable for short pasta due to their low capacity to keep the shape and the issues that may appear during drying. These results showed that both HMT and GPF addition had beneficial effects on dough firmness and on further pasta quality.

**Figure 3.** Three-dimensional response surface plots presenting the synergistic effects of factors: GPF level–temperature (**a**) and moisture-time (**b**) on dough firmness.
