Preparation and Evaluation Gluten Free pasta

doi:10.21203/rs.3.rs-4899888/v1

Download PDF

Research Article

Preparation and Evaluation Gluten Free pasta

https://doi.org/10.21203/rs.3.rs-4899888/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

This study aimed to prepare gluten-free pasta for several patients who were gluten sensitive. Therefore, several sources of free gluten raw materials have been evaluated and compared with those of semolina (S) to produce pasta characterized by its high nutritional and sensory properties. For this reason, brown rice flour (BRF),quinoa (QF) and chickpea flour (CPF) have been used to produce high-quality gluten - free pasta. The chemical composition of the raw materials revealed that higher protein, carbohydrate and fat contents were present in the CPF (23.19%),S (74.63%), and QF (6.07%) samples, respectively. Additionally, CPF had relatively high percentages of Ca, K and Fe, while zinc was relatively high in the BRF. Furthermore, the amino acid composition of the tested samples indicated that the total essential amino acid content of CPF, PRF and QF ranged from 38.9–34.04%, whereas that of Semolina flour decreased to 29.95%.

On the other hand, different selected formulas have been evaluated chemically, physically and sensorally. Higher crude protein, fat, ash, and fiber contents were found in the BRQ5 formula (40% BRF, 50% QF, and 10% CPF) than in the100% BRF or 100% semolina samples. Additionally, the cooking quality of the BRQ5 paste was relatively high, whereas its weight and volume increased to 300% and 215%, respectively. A color evaluation of the pasta for different formula showed that lightness (L*) decreased as the QF increased. This result was confirmed during sensory evaluation, where a relatively high color value was observed in the control sample and then declined gradually as the percentage of QF increased. The same trend was observed for the overall acceptability of different pasta formulas, where greater acceptability was found in the control sample (100% semolina), followed by BR and then BRQ1. Furthermore, texture profile analysis was used to evaluate the hardness and hardness parameters in different pasta formula for uncooked or cooked pasta.

From the previous results that the Pasta formula BRQ1 can be used as a functional past product.

Food Science & Technology

Chemical analysis

cooking quality

color attributes

texture

organoleptic properties and gluten-free pasta

Pasta is one of the most popular dishes worldwide because it is easy to prepare, is delicious, has a long shelf life, and is inexpensive. Over the past ten years, their output has increased from 7 to 12 million tons annually (Marti andPagani., 2013; Li et al.,2014; Carini et al.,2014; Sobota et al., 2015). The high gluten content of durum wheat flour, which is principally responsible for creating the matrix structure, imparts distinctive qualities, such as decreased cooking loss, high hardness, adhesiveness, and tolerance to overcooking (Marti et al., 2013). There is an increasing demand for the production of gluten-free (GF) food products for those with gluten allergy (such as those with celiac disease) (Rosell et al.,2014 and Taşkin and Savlak,2020). The gluten-free (GF) products on the market are based on starch, primarily from riceand maize (Marti and Pagani., 2013), which produces goods with low nutritional value. The technological improvement of gluten-free pasta aims to reproduce a network of polymers, of various protein types (mainly eggs, pseudocereals and legumes), starches (flour and/or isolated starches) and hydrocolloids (gum) (Scarton et al., 2021). There are deficiencies in the consumption of proteins, fiber, minerals (calcium, magnesium, iron, and zinc), and vitamins (folate, niacin, vitamin B12, and riboflavin). Quinoa-based pasta is a good alternative because of its outstanding nutritional value (such as its balanced amino acid composition) and bioactive elements (such as vitamins such as folate, tocopherols, and flavonoids such as kaempferol and quercetin) (Wang and Zhu.,2016; Nascimento et al., 2014). Many studies have focused on how to make pasta without the gluten network. To improve the quality of GF pasta, a number of additional and texturizing chemicals, including protein isolates, hydrocolloids, emulsifiers, and modified starches, are routinely used (Ibrikci et al.,2003); moreover, prior to starch gelatinization and subsequent retrogradation, dough heating and cooling procedures are the mainstays of GF technology(Padalino et al., 2016). Brown rice has a wide range of functional and nutritional elements, including dietary fiber, gamma-aminobutyric acid (GABA), phytosterols, minerals, lipids, amino acids, vitamin E, and phenolic compounds (Cho and Lim, 2015). Furthermore, brown rice has more nutrients than milled white rice since only the hull, or the outermost layer, is removed during the milling process. Moreover, several studies have documented positive health outcomes associated with brown rice consumption. Brown rice can help middle-aged men’s and women's blood pressure decrease and may also be an excellent way to help control body weight (Baek and Lee 2014). According to research by Sun et al. (2010), brown rice may lower the incidence of type 2 diabetes. Therefore, the main focus of brown rice research has been its physiological advantages and functional elements. Quinoa protein has a balance of amino acids, including relatively high amounts of lysine (5.1–6.4%) and methionine (0.4–1.0%). Quinoa proteins are a source of bioactive peptides, and potential bioactive peptides might be used as new ingredients in the development of nutraceuticals or functional foods to reduce oxidative stress-associated diseases, like cancer(Vilcacundo et al.,2018). Unfortunately, the outer layers of the quinoa seed coat contain bitter-tasting compounds (accepting water-soluble saponins), which can have a bitter effect on food. For this reason, the coating of the majority of commercially consumed quinoa seeds has been removed via water washing and milling to remove antinutritional compounds before consumption (Rosero et al., 2013). The World Health Organization (WHO) has evaluated quinoa as being comparable to milk in terms of nutrition due to its high levels of riboflavin, potassium, B6, thiamine, and niacin as well as zinc, magnesium, manganese, and copper. Chickpea is a significant food legume and a key component of many people's diets. After common bean (Phaseolus vulgaris L.) and pea (Pisum sativum L.), chickpea (Cicer arietinum L.) is the third most significant edible legume farmed worldwide (Ibrikci et al., 2003). It is well known to be a superior source of dietary fiber, protein, vitamins, and minerals, and it is an important part of many countries' daily diets (Aguilera et al.,2009 and Zaky et al., 2022). Reduced risks of diabetes, cardiovascular disease, and cancer are among the health advantages of chickpea consumption (Zaky et al., 2022). Among edible legumes, chickpea is a hypo cholesterol emic agent (Zia-Ul-Haq et al., 2007).

Previous studies have reported that brown rice flour (BRF), quinoa flour (QF) and chickpea flour (CPF) are free from gluten and have high nutritional value. Therefore, this study aimed to evaluate the possibility of using formulas from brown rice flour (BRF), quinoa flour (QF) and chickpea flour (CPF) to produce high-quality gluten-free pasta.

2.1. Materials:

Semolina was obtained from the North Cairo Flour Mills Company, Egypt. Brown Rice (Oryza sativa), quinoa seeds (Chenopodium quinoa Willd. ), and Chickpeas (Cicer arietinum) were purchased from a local market in Giza, Egypt.

2.2. Methods:

2.2.1. Preparation of Raw Materials.

Chickpea flour and rice flour were prepared according to Prasad et al., (2012) as follows: Chickpea grains and rice were sorted separately to remove extraneous contaminants such as stones, damaged and discoloured grains, and sticks. Then, they were ground using a small Quadrumat mill (Brabender, Germany), and the flour was obtained after sieving through 40 mesh. The flour was subsequently packed in polyethylene bags until use.

The quinoa seeds were cleaned to eliminate undesired materials such as mud, dirt, stone, dust, and leaves and then soaked in water for 2 hours at room temperature to remove saponin materials. Quinoa seeds were washed with tap water until the froth disappeared from the water and then dried at 50°C for 12 h to eliminate saponins. To make the full meal, the quinoa seeds were ground to a fine powder in a Kenwood electrical blender.

2.2. Rheological properties of the dough.

The rheological properties of the different pasta formulations were evaluated via the following methods:

2.2.1 Rapid viscoanalyzer(RVA).

The RVA was determined via different parameters (Rheolab QC, Anton Paar, made in Austria, in the Food Technology Department, National Research Centre). Accordance with AACC (2000).

Gross chemical composition.

The gross chemical composition of the different samples was determined according to the methods of the AACC (2000). The total carbohydrate content was determined by the following equation:

Total carbohydrate content = 100 – (protein + ash + fat + crude fiber). Moreover, total calories were calculated according to James (1995) using the following equation:

Total calories = Fat × 9 + Protein × 4 + Available carbohydrate × 4

Mineral.

The mineral contents of semolina, brown rice, quinoa and chickpea were determined by using flame photometry for sodium and potassium and flame atomic absorption spectrometry for calcium, iron, phosphorus and zinc (A.O.A.C 2019).

Amino acids.

The method was used to assess the amino acid profiles of different flours (semolina, brown rice, quinoa and chickpea) on a dry weight basis; amino acids are presented as g/100 g protein and were evaluated according to the methods of (AOAC 2012).

The chemical score (CS) was calculated according to the FAO/WHO. mg of essential amino acids in g test protein C. S mg of essential amino acids in the requirement pattern

The computed protein efficiency ratio (C-PER) C-PER was calculated as described by Alsmeyer et al. (1974) with the following equation: C-PER = 0.684 + 0.456 (leucine) − 0.047 (proline).

The computed biological value (BV) Biological value was calculated as described by Farag et al. (1996) according to the following equation: Computed Biological Value (BV) = 49.9 + 10.53C-PER

A/E ratio: essential amino acid content in the food protein (A)/total essential amino acid content (E) ratio; PER: protein efficiency ratio; B.V.: biological value.

Pasta preparation

A standard pasta sample was prepared using semolina flour as described previously (AACC 2000). The flour sample was mixed and formed using a lab pasta maker (Matic 1000 Simac Machine Corporation, Millano, Italy) in the Food Technology Department, National Research Centre. The modified formula used for preparing free gluten pasta is clearly shown in Table (1). Moreover, 1% glycerol monostearate (GMS) and 2% carboxy methyl cellulose were used to improve the textural properties of gluten-free pasta. Pasta was dehydrated for 15 minutes in ambient air and dried for 10 hours in a cabinet dryer at 70°C (Hussein et al., 2021). The sample was subsequently cooled to room temperature, packaged in polyethylene bags, and stored until use.

2.2.7. Cooking quality.

Cooking quality was determined via weight increase, volume, and cooking loss according to AOAC (2000).

2.2.8. Color.

Pasta color was determined by using a Hunter, Lab Scan XE (Reston, VA, USA). The color value is expressed digitally through L* whiteness (black to white, 0 -100; a* (positive = redness, greenness = negative) and b* (positive = yellowness, negative = blueness).

2.2.9. Sensory evaluation of pasta.

Twenty trained person in Laboratory of Food Technology, National Research Centre, evaluated the quality of the sensory parameters. Each pasta sample was cooked separately in boiling distilled water. After cooking, the pasta was directly drained, coded and served (65 and 70°C) in randomized order. The panelists evaluated the products for color, flavor, appearance, tenderness, stickness and general acceptability (Hussein et al., 2021).

2.2.10. Textural profile analysis (TPA).

The texture of the pasta was evaluated before and after cooking using an instrument (Brookfield model-CT3-10 kg). The dimensions of the uncooked pasta samples were 25 mm length and 8 mm diameter. The test methods for uncooked pasta were performed as follows: fixture TA-SBA, test type Rupture, Correction 7 N, Trigger - load 7 N, and test speed 2 mm/s. double compression test to penetrate to 50% depth, at a 1 mm/s speed test. While the texture of the pasta after cooking was evaluated using Texture Profile Analysis (TPA), the trigger load to reach the target distance was 9 N, and the test speed was 2 mm/s. The dimensions of the uncooked pasta were 36 mm length and 13 mm diameter. The test method was carried out as follows: the test type was TPA, or Probe TA. AACC36, trigger load 9 N, Target 3 mm, test speed 2 mm/min

2.2.11. Statistical analysis.

The statistical analysis (means ± SDs) of three replicates for each experiment were statistically analyzed using one-way analysis of variance (ANOVA), and Duncan's test was used to obtain the SEs. SAS Systems for Windows software, mean version 6.12 TS020 (SAS, Statistical Analysis System, Institute, Inc., Cary, NC, 1996), was used to statistically analyze all pasta and raw material findings. We employed analysis of variance (ANOVA) and the least significant difference (LSD) test (P˂ 0.05) to determine significant differences between the treatment means.

Table 1

**Formulas of gluten-free prepared pasta**.
Sample Code	Semolina (%)	Brown rice flour (%)	Quinoa flour (%)	Chickpea flour (%)	Glycerol monostearate (GMS) (%)	Carboxy methyl cellulose (%)
Control	100	-	-	-	-	-
BR	-	100	-	-	1	2
BRQ1	-	80	10	10	1	2
BRQ2	-	70	20	10	1	2
BRQ3	-	60	30	10	1	2
BRQ4	-	50	40	10	1	2
BRQ5	-	40	50	10	1	2
Control: 100% Semolina; BR: brown rice; BRQ1: brown rice with 10% quinoa flour; BRQ2: brown rice with 20% quinoa flour; BRQ3: brown rice with 30% quinoa flour; BRQ4: brown rice with 40% quinoa flour; BRQ5: brown rice with 50% quinoa flour

3.1. Chemical composition of the tested flour:

Brown rice (BRF), quinoa (QF) and chickpea flour (CPF) were selected for the preparation of gluten-free pasta. The gluten-free flour samples were compared chemically with the semolina flour samples (flour of control pasta), as shown in Table (2). The moisture content of all the flour samples ranged from 11.53–13.28%. Compared with semolina, CPF was characterized by greater protein (23.19%) and fat (6.04%) contents (8.60% protein and 1.22% fat). The lowest total carbohydrate content was found in QF and CPF, where the total carbohydrate content decreased to 57.15 and 57.52.71%, respectively. The results obtained for semolina flour agreed with those of Raffo et al. (2003) and Graziano et al. (2019). A greater fat content was detected in QF (6.07%) and CPF (6.04%), while the fat content decreased in BRF and Semolina to 2.3 and 1.22%, respectively. Additionally, QF and CPF contained higher fiber contents than did BRF and Semolina. These results are in agreement with those of Abugoch et al. (2008), and Roushdi et al. (2016) reported that rice flour contains 6.65% protein, 0.38% crude fiber, 0.54% ash and 83.33% carbohydrates. Additionally, Motawei et al. (2022) reported that rice flour contains 7.80% crude protein, 0.85% ash, 0.45% fiber and 90.20% available carbohydrates. Furthermore, several investigators have evaluated the chemical composition of previously selected free gluten flour, i.e., Abou Arab et al., (2010), El-Hadidy et al. (2020), Karla and Claudia (2020) and Yegrem (2021).

In addition to the chemical composition, the total caloric content was calculated to evaluate the expected caloric content from the selected gluten-free flour. The results indicated that the total energy of the tested gluten-free flour ranged between 357 and 339.71%.

Table 2

Gross chemical constituents of the tested flour.
	Semolina	BRF	QF	CPF	L.S.D at 0.05
Moisture (%)	11.65^b ± 0.11	12.01^ab ± 0.17	13.28^a ± 0.29	11.53^b ± 0.28	0.733
Protein (%)	10.90^c ± 0.19	8.60^d ± 0.27	14.12^b ± 0.25	23.19^a ± 0.48	0.066
Fat (%)	1.22^c ± 0.05	2.30^b ± 0.05	6.07^a ± 0.03	6.04^a ± 0.07	0.075
Ash (%)	0.79^d ± 0.02	1.25^c ± 0.07	2.38^b ± 0.01	2.48^a ± 0.03	0.067
Fiber (%)	0.81^d ± 0.03	1.49^c ± 0.32	7.0^a ± 0.37	4.05^b ± 0.02	0.336
T. Carbohydrate (%)	74.63^a ± 0.72	74.35^b ± 0.56	57.15^c ± 0.85	52.71^d ± 0.75	0.090
Total calories K Cal	353.1	353.22	339.71	357.96

Mean ± SD Brown rice flour (BRF). Quinoa flour (QF). Chickpea flour (CPF),

The obtained results represent the mean value of three representative samples. The same letter in a row indicates no significant difference.

3.2. Mineral contents of the tested flour.

The mineral contents of the semolina flour, brown rice flour, quinoa flour and chickpea flour are shown in Table 3. The results indicated that, the highest calcium, potassium and iron contents were found in chickpea flour, followed by quinoa flour and Semolina flour. Brown rice flour is characterized by a higher zinc content than the other selected flours. The results obtained for brown rice flour agreed with those of Gunzer (2018), quinoa flour agreed with those of Al-Sayed et al. (2019), and chickpea flour agreed with that of Vilod et al. (2023).

Table 3

Mineral contents of the tested flour (mg/100 g).
Sample	Semolina (S)	BRF	QF	CPF	LSD at 0.05
Calcium	50.89^b ± 0.11	31^c ± 0.09	47^b ± 0.13	105^a ± 0.17	4.71514
Phosphor	120^c ± 0.15	32.4^d ± 0.19	457^a ± 1.12	366^b ± 0.82	5.35837
Potassium	205.33^c ± 0.52	182^d ± 0.33	563^b ± 0.65	875^a ± 2.13	5.07138
Sodium	630.18^a ± 0.52	22.9^b ± 0.13	5^c ± 0.03	24^b ± 0.05	4.59139
Iron	2.35^c ± 0.05	1.6^d ± 0.01	4.57^b ± 0.05	6.24^a ± 0.09	0.22838
Zinc	3.36^b ± 0.03	12.15^a ± 0.10	3.1^b ± 0.07	3.43^b ± 0.03	0.27996

* Values are the mean ± SD (n = 3). a-d Means in the same row with different superscripts are significantly different.

3.3. Total amino acid contents of semolina, brown rice, quinoa and chickpea flour.

The amino acid content of foods plays an important role in evaluating their nutritional value. Therefore, the amino acid contents of selected gluten-free flour samples were determined and are presented in Table 4. The results indicated that, compared with the selected free gluten flour, the semolina flour had a lower total essential amino acid content (29.95 g/100 g). The total essential amino acid content was highest in chickpea (38.9 g/100 g) and brown rice (38.1 g/100 g), followed by quinoa (34.04 g/100 g). Leucine was the first limited amino acid in chickpea flour (8.70 g/100 g), and lysine was the second limited amino acid. Furthermore, the biological value (BV) was highest for chickpea flour, followed by brown rice flour, quinoa flour and semolina, where the values reached 97, 90.13, 84.89 and 77.29%, respectively. The computed protein efficiency ratio (C-PER) also exhibited the same trend as that of the BV (Ibrahim, 2022).

Table 4

Amino acid composition of the tested flour (g/100 g protein).
Amino acids	Semolina (S)	BRF	QF	CPF
Essential amino acids (NEAA)
Tryptophan (TRP)	1.14	0.15	1.10	0.9
Therionine(THR)	3.36	4.71	3.35	3.1
Valine(VAL)	2.85	6.60	4.40	4.6
Methionine	1.33	1.72	2.75	1.1
Isoleucine(ILE)	3.90	5.63	3.55	4.8
Leucine(LEU)	5.15	7.39	6.20	8.7
Phenylalanine(PHE)	4.22	5.54	2.75	5.5
Hisitidine(HIS)	2.33	2.84	5.90	3.0
Lysine(LYS)	2.02	3.52	4.0	7.2
Total EAA	29.95	38.1	34.04	38.9
Nonessential amino acids(NEAA)
Serine(SER)	4.95	3.91	4.70	3.7
Glutamic(GLU)	28.11	13.85	14.30	17.3
Proline(PRO)	9.22	4.95	4.00	3.8
Glycine(GLY)	3. 37	5.51	5.50	3.7
Alanine(ALA)	3.38	5.30	4.45	4.8
Aspartic (ASP)	10.02	10.51	9.50	11.0
Tyrosine(TYR)	2.33	3.20	2.65	3.0
Argnine(ARG)	4.6	8.30	8.60	8.3
Cystine(CYS)	1.11	1.50	2.15	0.60
Total NEAA	73.80	57.03	55.85	56.2
Computed B.V. (%)	77.267	90.135	84.891	97.00
C-PER (g)	2.599	3.821	3.323	4.473

C-PER = Computed protein efficiency ratio calculated as follows: C-PER = 0.684 + 0.456 (leucine) − 0.047 (proline).

BV = the computed biological value was calculated using the following equation: BV = 49.9 + 10.53C-PER

3.4. Pasting profile (RVA) of the raw materials.

The pasting properties of rapid- viscoanalyzer (RVA) for the gluten-free flour samples were determined and are presented in Table 5. Five formulas of brown rice, quinoa, and chickpea flour mixtures were prepared as shown in Table (1) and compared with control samples of 100% semolina and 100% brown rice. The peak viscosity decreased gradually with increasing percentage of quinoa flour, where the highest peak viscosity reached 3516 and 3462 cP in the control samples of semolina and brown rice, respectively. However, the lowest peak viscosity decreased to 2620 cP in PRQ5. The same trend was also observed for the Trough, breakdown, Final viscosity Setback and Pasting temperature parameters. Additionally, the obtained peak viscosity data indicated that, compared with those of the control sample, the brown rice, BRQ1 and PRQ2 samples were the best formulas (semolina), where the percentages of decrease in peak viscosity compared with that of the control sample were 1.5, 2.1, and 13.9%, respectively. The obtained results agreed with those of Gómez et al. (2008), who stated that peak viscosity, breakdown, and setback decreased when chickpea flour was used instead of wheat flour. The low carbohydrate content and different carbohydrate and protein compositions of chickpea flour likely affect viscosity measurements (Adebowale et al., 2005). The pasting temperature may be related to the material's capacity to bind water. The lowest peak, trough, breakdown, final, and setback viscosities and lowest pasting temperatures were found for the flour blends, according to the RVA properties of the flour samples (Hallén et al., 2004 and Muralikrishna and Nirmala 2005). Because gluten-free flour was used in place of regular flour, the amylose/amylopectin ratios of various starches changed, and a decrease in gluten content was observed (Renzetti and Arendt et al., 2009).

Table 5

RVA parameters of blends from Semolina, brown rice, quinoa flour and chickpea flour.
Samples	Peak Vis. (cP)		Trough1 (cP)	breakdown (cP)	Final Vis. (cP)	Setback (cP)	Peak Time (min)	Pasting Temp. (°C)	Peak Temp. (°C)
Samples	cP	Decline %	Trough1 (cP)	breakdown (cP)	Final Vis. (cP)	Setback (cP)	Peak Time (min)	Pasting Temp. (°C)	Peak Temp. (°C)
Control	3516	---	1470	1700	3399	458	12.0	60.2	94.8
BR	3462	1.5	1150	1770	3185	445	10.8	64.5	94.7
BRQ1	3389	2.1	1121	1890	3047	342	10.9	62.0	94.7
BRQ2	3027	13.9	840	1742	2882	330	10.7	61.6	94.7
BRQ3	2890	17.8	795	1650	2750	305	10.10	60.0	93.5
BRQ4	2750	21.8	750	1580	2630	280	11.5	58.5	92.0
BRQ5	2620	25.5	680	1470	2410	265	10.9	56.5	89.5

Decline (%) = Decline in the percentage of peak viscosity compared to that of the control sample (Semolina).

BR: Brown Rice; BRQ1: Brown Rice with 10% quinoa flour; BRQ2: Brown Rice with 20% quinoa flour; BRQ3: Brown Rice with 30% quinoa flour; BRQ4: Brown Rice with 40% quinoa flour; BRQ5: Brown Rice with 50% quinoa flour.

3.5. Chemical composition of gluten-free pasta.

The chemical compositions of pasta prepared from 100% semolina flour, 100% brown rice flour (BRF) and blends of BRF and quinoa flour (QF) at different levels ( 10 to 50%) with 10% chickpea flour were studied, and the obtained results are presented in Table 6. In comparison to pasta made from 100% semolina (the control) and 100% BRF, all the pasta blends contained more crude protein, oil, ash, and crude fiber. On the other hand, compared to pasta made from 100% semolina or BRF, the mean value of total carbs decreases in all pasta blends. This might be because semolina flour and BRF contain less crude protein than chickpea flour and QF. These results are in accordance with Olaoye et al. (2007), Osorio-Díaz et al. (2008), Wani et al. (2014), Makpoul & Ibrahem (2015) and Abdelazim et al. (2019).

Table 6

Proximate analysis (%) of gluten-free brown rice pasta formulated with quinoa flour and chickpea flour.
Samples	Moisture	Protein	Oil	Ash	Fiber	Carbohydrate	Total calories
Control	6.50^b ± 0.20	11.00^bc ± 0.41	1.05^g ± 0.05	0.92^f ± 0.05	0.83^g ± 0.03	86.20^a ± 0.72	398.25
BR	6.90^b ± 0.07	8.50^e ± 0.25	2.35^f ± 0.02	1.35^e ± 0.03	1.25^f ± 0.05	86.55^a ± 0.55	401.35
BRQ1	7.20^ab ± 0.08	10.70^d ± 0.32	2.70^e ± 0.06	1.65^d ± 0.09	1.80^e ± 0.07	83.15^b ± 0.72	399.7
BRQ2	7.40^b ± 0.13	11.10^cd ± 0.44	3.00^d ± 0.09	1.80^c ± 0.11	2.40^d ± 0.13	81.70^c ± 0.79	398.2
BRQ3	7.65^b ± 0.11	11.75^b ± 0.27	3.50^c ± 0.05	2.05^b ± 0.13	2.80^c ± 0.11	79.90^d ± 0.85	398.1
BRQ4	7.80^a ± 0.09	12.30^a ± 0.32	4.00^b ± 0.03	2.30^a ± 0.15	3.40^b ± 0.15	78.00^d ± 0.69	397.2
BRQ5	7.90^a ± 0.10	12.75^a ± 0.38	4.50^a ± 0.11	2.42^a ± 0.19	4.10^a ± 0.17	76.23^e ± 0.84	396.42
L.S.D at 0.05	0.80396	0.54598	0.12382	0.11862	0.07673	1.06002
* Values are the mean ± SD (n = 3). a-d Means in the same row with different superscripts are significantly different
BR: Brown Rice; BRQ1: Brown Rice with 10% quinoa flour; BRQ2: Brown Rice with 20% quinoa flour; BRQ3: Brown Rice with 30% quinoa flour; BRQ4: Brown Rice with 40% quinoa flour; BRQ5: Brown Rice with 50% quinoa flour

3.6. Cooking Quality of Pasta.

The quality of pasta may be impacted by cooking variables such as weight increase, volume increase, and cooking loss. The pasta gained 210 to 300% more weight and 160 to 215% more volume. The results revealed that for different blends of BRF with QF, the weight and volume increased in comparison with those of the control sample (Table 7). This may be because the protein content of QF and chickpea flour increased, resulting an increase in the weight of the cooked pasta (Mercier et al., 2016). A high level of protein and fiber in the QF may be responsible for the improvement in cooking quality indicators.

One of the primary criteria used to assess the quality of pasta cooking is cooking loss (Zaky et al., 2022). This value indicates the amount of dry matter lost into the cooking water (Sozer et al. 2007). As the QF increased, cooking losses decreased. This result might be the result of a dough matrix reinforced by gluten and QF, which can trap starch in the resulting network; this matrix is also associated with the amount of dry matter lost in the cooking water (Özyurt et al.,2015). Cooking losses of various types of semolina pasta range from 6 to 11% (Cacak-Pietrzak et al.,1997). The cooking losses for the pasta control sample, BR sample and BRQ5 sample were 2.5%, 1.65% and 1.30%, respectively. The cooking loss of the pasta samples was ≤ 8%, which is regarded as acceptable technologically (Dick and Youngs 1998). The cooking water thickens as a result of certain soluble starch and non-starch polysaccharides leaching into it during the cooking of the pasta. The findings of Özyurt et al. (2015), Hussein et al. (2021) and Fouad et al. (2022) agree with the findings of the present study.

There were significant differences in cooking loss among the samples (P > 0.05); therefore, adding quinoa and chickpea at these levels affected cooking loss.

Table 7

Cooking quality of gluten-free brown rice formulated with quinoa flour and chickpea flour.
Samples	Weight increase (%)	Volume increase (%)	Cooking loss (%)
Control	210^f ± 1.11	160^f ± 1.61	2.50^a ± 0.11
BR	235^e ± 1.42	170^e ± 1.75	1.65^b ± 0.15
BRQ1	250^d ± 2.15	180^d ± 1.65	1.50^c ± 0.17
BRQ2	270^c ± 2.25	190^c ± 1.50	1.45^cd ± 0.21
BRQ3	280^b ± 1.55	200^b ± 1.75	1.40^de ± 0.25
BRQ4	285^b ± 2.20	210^a ± 2.35	1.35^de ± 0.22
BRQ5	300^a ± 2.45	215^a ± 2.65	1.30^e ± 0.19
L.S.D at 0.05	9.52489	7.35281	0.12246
* Values are the mean ± SD (n = 3). a-d Means in the same row with different superscripts are significantly different
BR: Brown Rice; BRQ1: Brown Rice with 10% quinoa flour; BRQ2: Brown Rice with 20% quinoa flour; BRQ3: Brown Rice with 30% quinoa flour; BRQ4: Brown Rice with 40% quinoa flour; BRQ5: Brown Rice with 50% quinoa flour

3.7. Color parameters of pasta.

The color of the pasta is an essential indicator for determining its quality (Petitot et al., 2010). In Table 8, the color profiles of the raw and cooked pasta samples are shown. The dry pasta made from BRF, QF and chickpea flour was significantly darker than was the dry brown rice pasta and dry semolina pasta (p ˂0.05). As the amount of QF in the recipe increased, the lightness of the dried pasta samples decreased. For pasta that has 50% more QF added, this observation is more obvious (p ˂0.001). As the quantity of QF increased, the a* values decreased (greenness decreased, and redness increased). The yellow color of the quinoa variety employed in our study was responsible for the large increase in b* (yellowness) for dry pasta that had been enriched with 50 g/100 g of QF. Additionally, pasta enriched with 50 g/100 QF presented the most yellow color (b*). The yellow QF employed in the present study has a bearing on this value. Compared with dry pasta, cooked pasta was characterized by greater lightness, greater greenness, and reduced yellowness. The degradation and dissolution of pigments by hot water may be the cause of the decreased yellow color of cooked pasta (Giuberti et al., 2015). Regardless of the type of quinoa utilized, differences in color coordinates were less than they were for dried pasta.

Table 8

Color quality of uncooked and cooked gluten-free brown rice pasta formulated with quinoa flour and chickpea flour.
Pasta samples	Uncooked Pasta Sample			Cooked Pasta Sample
Pasta samples	L*	a*	b*	L*	a*	b*
Control	73.02^a ± 1.15	4.25^a ± 0.05	13.85^g ± 0.22	78.55^a ± 0.97	2.20^g ± 0.10	18.30^a ± 0.32
BR	71.15^b ± 0.65	3.30^b ± 0.07	16.30^f ± 0.16	70.20^b ± 0.30	2.70^f ± 0.07	18.15^b ± 0.29
BRQ1	68.10^c ± 0.29	3.10^c ± 0.03	20.89^e ± 0.25	67.18^c ± 0.22	2.90^e ± 0.05	17.85^c ± 0.24
BRQ2	65.15^d ± 0.48	2.85^d ± 0.09	22.35^d ± 0.19	64.30^d ± 0.23	3.10^d ± 0.11	17.65^d ± 0.20
BRQ3	63.22^e ± 0.60	2.50^e ± 0.11	23.40^c ± 0.15	62.35^e ± 0.35	3.31^c ± 0.13	16.70^e ± 0.25
BRQ4	61.19^f ± 0.40	2.35^f ± 0.04	24.52^b ± 0.22	60.60^f ± 0.35	3.50^b ± 0.17	15.90^f ± 0.17
BRQ5	59.18^g ± 0.35	2.28^f ± 0.03	25.60^a ± 0.15	58.55^g ± 0.35	3.70^a ± 0.12	15.15^g ± 0.13
L.S.D at 0.05	0.10592	0.10273	0.08432	0.08797	0.06831	0.10091

* Values are the mean ± SD (n = 3). a-d Means in the same row with different superscripts are significantly different.

3.8. Sensory properties.

Table 9, shows the color, flavor, appearance, tenderness, stickiness, and overall acceptability of pasta made from semolina, BRF, and BRF supplemented with QF at different concentrations (10.0, 20.0, 30.0, 40.0, and 50.0%) in combination with 10% chickpea flour. As shown in Table 9, consumers preferred pasta with a lower QF substitution percentage; as the QF mixing level increased, the pasta color significantly decreased. The color of the cooked pasta varied from 9.80 for the control group to 9.80 for the BR treatment group, with significant differences among them; the worst score was 6.60 for BRQ5. This result agreed with the obtained whiteness (L) of the Hunter color parameter, as shown in Table (8), where if the amount of QF increased, the lightness of the dried pasta or cooked pasta decreased. Flavor scores ranged from 9.65–6.90. The lowest qualification score was obtained for the BRQ5, with a score of 6.90. Similarly, surface stickiness was analyzed; in this case, even though the scores ranged from 9.80 to 8.81, significant differences were found among all the samples. All formulations were acceptable, as they received scores ranging from 48.45 to 35.21. There were significant differences in overall acceptability for all the samples compared with the control (48.45%). The overall acceptable results indicated that all pasta samples had good sensory scores, but the most preferable score was that the pasta was enriched with 10–20% QF (BRQ1-BRQ2). It should be mentioned that younger generations may be more willing to accept higher QF ratios because they are more inclined to accept others and are more interested in tasting new meals. Therefore, QF has potential use in pasta applications.

Table 9

Mean values of the sensory characteristics of cooked gluten-free pasta.
Pasta samples	Color (10)	Flavor (10)	Appearance (10)	Tenderness (10)	Stickiness (10)	overall acceptability (50)
Control	9.80^a ± 0.35	9.65^a ± 0.31	9.60^a ± 0.38	9.60^a ± 0.54	9.80^a ± 0.65	48.45^a ± 1.19
BR	9.10^b ± 0.41	9.30^b ± 0.42	8.65^b ± 0.42	9.11^b ± 0.45	9.50^c ± 0.56	46.02^b ± 1.28
BRQ1	8.60^c ± 0.32	8.40^c ± 0.39	7.90^c ± 0.40	8.80^c ± 0. 40	9.40^d ± 0.48	43.10^c ± 1.51
BRQ2	7.70^d ± 0.60	8.05^d ± 0.51	7.55^d ± 0.50	7.70^d ± 0.50	9.15^e ± 0.65	40.15^d ± 1.32
BRQ3	7.40^e ± 0.50	7.80^e ± 0.45	7.02^e ± 0.60	7.10^e ± 0.60	9.65^b ± 0.63	38.97^e ± 1.12
BRQ4	7.00^f ± 0.35	7.50^f ± 0.52	6.60^f ± 0.45	6.80^f ± 0.53	9.17^e ± 0.52	37.07^f ± 1.22
BRQ5	6.60^g ± 0.28	6.90^g ± 0.45	6.40^g ± 0.35	6.50^g ± 0.60	8.81^f ± 0.52	35.21^g ± 1.19
L.S.D at 0.05	0.07018	0.07527	0.07769	0.06991	0.06858	0.07911
* Values are the mean ± SD (n = 3). a-d Means in the same row with different superscripts are significantly different; BR: Brown Rice; BRQ1: Brown Rice with 10% quinoa flour; BRQ2: Brown Rice with 20% quinoa flour; BRQ3: Brown Rice with 30% quinoa flour
quinoa flour; BRQ4: Brown Rice with 40% quinoa flour; BRQ5: Brown Rice with 50% quinoa flour

3.9. Texture Parameters of Pasta.

The textural characteristics of pasta prepared from semolina, BRF, and BRF augmented with QF at different concentrations (10, 20, 30, 40, and 50%) with 10% chickpea flour before and after cooking are shown in Tables 10 and 11. The texture parameters of the pasta and the pasta enhanced with QF were determined using the sample's maximum shearing force in a Brookfield Texture Analyzer device. The hardness (N) of the uncooked control and BR pasta samples reached 104.02 and 93.8, respectively. The amount of uncooked pasta with added QF ranged from 10 to 50%, ranging from 40.06 to 25.03 N. In contrast, pasta without QF was associated with increased effort and increased hardness due to its low moisture content. Consumers can tell when pasta is firm, and regardless of moisture content, this could be related to the product's cell structure and expansion. In general, consumers want pasta that is firm, chewy, and nonsticky. These findings agree with those of Coello et al. (2021) and Pasqualone et al. (2017). According to the texture profile results, mixing with QF at varying concentrations (10 to 50%) decreased the pasta hardness (N), deformation at hardness (mm), deformation at hardness (%), hardness work (mJ), and fracturability (N) with a 1% load sensitivity. On the other hand, the hardness decreased as the QF concentration in the pasta formulations increased. The matrix structure network of starch, gluten, other proteins, and other ingredients has a significant effect on the texture properties of pasta (Chang et al., 2008).

Table 10

Texture profile analysis of uncooked gluten-free brown rice pasta formulated with quinoa flour and chickpea flour.
Pasta samples	Hardness (N)	Springiness Index	Chewiness Index
Control	104.02	-1.07	0.18
BR	93.80	0.18	0.20
BRQ1	40.06	0.50	0.22
BRQ2	35.60	0.60	0.45
BRQ3	30.70	0.82	0.60
BRQ4	28.50	0.92	0.75
BRQ5	25.03	1.02	0.90

Table 11

Texture profile analysis of cooked gluten-free brown rice pasta formulated with quinoa flour and chickpea flour.
	Control	BR	BRQ1	BRQ2	BRQ3	BRQ4	BRQ5
Hardness (N)	0.89	0.60	1.33	1.40	1.50	1.60	1.72
Deformation at hardness (mm)	4.25	4.40	4.60	4.70	4.90	5.05	5.35
% Deformation at hardness (%)	13.30	14.45	18.30	20.50	22.18	24.50	27.20
Hardness work (mJ)	37.00	33.00	40.00	50.00	60.00	65.00	70.00
Load at Target (N)	0.85	0.70	1.0	1.25	1.50	1.75	1.90
Peak Stress (N/m2)	51600	44962	48584	55133	57160	59275	61940
Strain at Peak Load	0.18	0.16	0.19	0.22	0.25	0.27	0.30
Fracturability (N)	0.08	0.10	0.35	0.50	0.65	0.80	1.05
Fracture Load Drop Off (N)	0.02	0.01	0.03	0.05	0.07	0.09	0.10
Cohesiveness	0.35	0.22	0.42	0.44	0.47	0.52	0.55
Springiness	6.10	5.50	5.60	5.80	6.10	6.30	6.50
Gumminess	0.32	0.28	0.35	0.40	0.60	0.72	0.85
Chewiness	18.00	16.00	19.00	20.00	23.00	25.00	28.00
Adhesiveness	1.00	1.00	2.00	3.00	4.00	5.00	6.00
Resilience	0.18	0.15	0.18	0.22	0.30	0.35	0.40

This study confirmed that the use of QF (10–50%) together with 10% chickpea flour to create pasta was associated with acceptable nutritional, rheological, color, texture and sensory properties. The present work's nutritional, rheological, color, and textural results demonstrate the potential of using QF as a nourishing component to produce high-quality pasta. According to the judges' total acceptance score, 10% of the pasta enhanced with QF was acceptable in terms of sensory and texture properties.

Competing interests

The authors declare no competing financial or nonfinancial interests.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. I do not have any research data outside the submitted manuscript file.

A.A.C.C. (2000). Approved Method of the AACC. 10th^ed., American Association of Cereal Chemists, INC. st., Paul, Minnesota, USA.
A.O.A.C. (2012) Official Methods of Analysis of the Association of Official Analytical Chemists.18th edition, Washington DC.
AOAC (2019): Association of Official Analytical Chemists AOAC Official Method 2015.01 Heavy Metals in Food. Inductively Coupled Plasma‒Mass Spectrometry, First Action 2015.
Abdelazim, S. A. A., Sohair, T. El-Hadidie and Kamel, M, A. (2019) Chemical and Technological Evaluation of Some Varieties Naked Barley. Acta Scientific Nutritional Health, 3, (11), 114-123.
Abou Arab E.A., Helmy I.M.F. & Bareh, G.F. (2010). Nutritional evaluation and functional properties of chickpea (Cicer arietinum L.) flour and the improvement of spaghetti produced from it. Journal of American Science, 6: 1055–1072.
Abugoch L., Romero N., Tapia C., Silva J. and Rivera M. (2008). Study of some physicochemical and functional properties of quinoa (Chenopodium quinoa Willd.) protein isolates, J. Agric. Food Chem., 56: 4745-4750.
Adebowale, Y. A., Adeyemi, I. A., & Oshodi, A. A. (2005). Functional and physicochemical properties of flours of six Mucuna species. African Journal of Biotechnology, 4(12).
Aguilera, Y., Esteban, R.M., Benitez, V., Molia, E. & Martin-Cabrejas, M. (2009). Starch, functional properties, and microstructural characteristics in chickpea and lentils as affected by thermal processing. Journal of Agriculture and Food Chemistry, 57, 10682–10688.
Alsmeyer, R. H., Cuningham, A. E. and Happich, M. L. (1974) Equations predict PER from amino acid analysis. Food Techno., 28 (7), 34-40.
Baek, J. J., & Lee, S. (2014). Functional characterization of brown rice flour in an extruded noodle system. Journal of the Korean Society for Applied Biological Chemistry, 57, 435-440.
Cacak-Pietrzak G., Haber T., Lewczuk J. and Madrias M. (1997). Quality evaluation of selected extra pasta on the Warsaw market. Przeg. Zboz-Mlyn, 41: 26–29.
Carini E., Curti E., Minucciani M., Antoniazzi F. and Vittadini E. (2014) Pasta. In: Guiné RPF, Correia PMR (eds) Engineering aspects of cereal and cereal-based products. CRC Press, Boca Raton p, pp 211–238
Chang H.C., Wu L.C.(2008). Texture and quality properties of Chinese fresh egg noodles formulated with green seaweed (Monostroma nitidum) powder. J. Food Sci. 79: S398–S404.
Chapman D. and Pratt F. (1978) Methods of Analysis for Soil, Plants and Water. Univ. of California, Div Agric Sci., Priced Publication.
Cho, D.-H. and Lim, S.-T. (2015). Germinated brown rice and its bio functional compounds. Food Chemistry, 196, 259–271
Coello, K.E.; Peñas, E.; Martinez-Villaluenga, C.; Cartea, M.E.; Velasco, P.; Frias,(2021) J. Pasta products enriched with moringa sprout powder as nutritive dense foods with bioactive potential. Food Chem. 360, 130032.
Dick J.W. and Youngs V.L. (1998). Evaluation of durum wheat, semolina and pasta in the United States. In Durum Wheat Chemistry and Technology; Fabriani, G., Lintas, C., Eds.; American Association of Cereal Chemists Inc.: St. Paul, MN, USA: 237–248.
El-Hadidy, G. S., Rizk, E.A and El-Dreny, E. G.(2020). Improvement of Nutritional Value, Physical and Sensory Properties of Biscuits Using Quinoa, Naked Barley and Carrot. Egypt. J. Food. Sci., 48 (1): 147-157.
FAO (1965) Food and Agriculture Organization of United Nations. Protein Requirement. FAO Nutrition Meetings Report Series, No. 37, Rome.
Farag, S. A.; El-Shirbeeny, A. and Nassef, A. E. (1996) Physicochemical studies for preparing quick cooking rice by using gamma irradiation. Annals of Agric Sci., Moshtohor, 34, 641-652
Fouad Mohamed T., Ahmed M. S. Hussein and Moustafa A. El-Shenawy (2022). Production of pasta using tiger nut and fermented permeate with some probiotic bacteria. Egypt. J. Chem. 65 (4): 569 – 578.
Giuberti G., Gallo A., Fiorentini L., Fortunati P. & Masoero F. (2015). In vitro starch digestibility and quality attributes of gluten free ‘tagliatelle’ prepared with teff flour and increasing levels of a new developed bean cultivar. Starch-Starke € , 66: 1–5.
Gómez M., Oliete B., Rosell C.M. Pando V. and Fernández E. (2008). Studies on cake quality made of wheat chicken pea flour blends. LWT – Food Sci. Tech. 41, 1701–1709.
Graziano S., Marando S., Prandi B., Boukid F., Marmiroli N., Francia E., Pecchioni N., Sforza S., Visioli G. and Gullì M. (2019). Technological quality and nutritional value of two durum wheat varieties depend on both genetic and environmental factors. J.Agric.Food Chem, 67, 2384-2395.
Hallén E.; Ibano˘glu S. and Ainsworth P. (2004). Effect of fermented/germinated cowpea flour addition on the rheological and baking ˙properties of wheat flour. J. Food Eng. 63: 177–184.
Hussein A.M.S., Gamil Ibrahim, Mohie Kamil, Marwa El-Shamarka,Sayed Mostafa and Doha Mohamed (2021). Spirulina-Enriched Pasta as Functional Food Rich in Protein and Antioxidant. Biointerface Research in Applied Chemistry 11(6):14736-14750.
Ibrahim, R. M. (2022). Utilization of chickpea split (Cicer arietinum L.) in preparing some gluten-free casein-free food products for autism children. Food and Nutrition Sciences, 13(3), 284-315.‏
Ibrikci H., Knewtson S.J.B. & Grusak, M.A. (2003). Chickpea leaves as a vegetable green for humans: evaluation of mineral composition. Journal of the Science of Food and Agriculture, 83: 945–950.
James C.S. (1995) General Food Studies. In: Analytical Chemistry of Foods, Chapter 6, Blachie Academic & Professional: 137-171.
Karla Carmen Miranda-Ramos and Claudia Monika Haros (2020) Combined Effect of Chia, Quinoa and Amaranth Incorporation on the Physico-Chemical, Nutritional and Functional Quality of Fresh Bread. Foods 9, 1859:1-22
Li, M., Zhu, K. X., Guo, X. N., Brijs, K., & Zhou, H. M. (2014). Natural additives in wheat‐based pasta and noodle products: opportunities for enhanced nutritional and functional properties. Comprehensive Reviews in Food Science and Food Safety, 13(4), 347-357.
Makpoul K, R. and Ibrahem A. A. (2015). Improving Biscuits nutritional value using quinoa flour, J. Food and Dairy Sci., 6 (12):771- 780.
Marti A. and Pagani M.A. (2013). What can play the role of gluten in gluten free pasta? Trends Food Sci Technol 31:63–71.
Marti A., Seetharaman K. and Pagani M.A. (2013). Rheological approaches suitable for investigating starch and protein properties related to cooking quality of durum wheat pasta. J Food Quality 36(2):133–138
Mercier S., Moresoli C., Mondor M., Villeneuve S. and Marcos B. (2016) A meta-analysis of enriched pasta: what are the effects of enrichment and process specifications on the quality attributes of pasta? Comprehensive reviews in food science and food safety. Inst Food Technol 15:685–704
Motawei, A. M., Hussien, M. M., & Yousef, E. A. (2022). Preparation of Gluten Free Biscuits from Quinoa, Rice and Chickpeas for Celiac Disease Patients. Journal of Food and Dairy Sciences, 13(3), 41-45.
Muralikrishna G. and Nirmala M. (2005).Cereal α-amylases-an overview. Carbohydr. Polym. 60: 163–173.
Nascimento C.A., Mota C., Coelho I., Gueifao S., Santos M., Matos A.S., Gimenez A., Lobo M., Samman N. and Castanheira I. (2014). Characterization of nutrient profile of quinoa (Chenopodium quinoa), amaranth (Amaranthus caudatus), and purple corn (Zea mays L.) consumed in the north of Argentina: proximate, minerals and trace elements. Food Chem 148:420–426.
Olaoye O. A., Onilude A. A. and Oladoye C. O. (2007). Breadfruit flour in biscuit making: Effects on product quality. African Journal of Food Science, 1(2): 021- 023.
Osorio-Díaz P., Agama-Acevedo E., Mendoza-Vinalay M., Tovar J. and Bello-Pérez L. A.(2008). Pasta added with chickpea flour: chemical composition, in vitro starch digestibility and predicted glycemic index. Cienc. Tecnol. Aliment. 6(1): 6-12.
Özyurt G., Uslu L., Yuvka I., Gökdo˘gan S., Atci G., Ak B. I and Sik O.(2015). Evaluation of the cooking quality characteristics of pasta enriched with Spirulina platensis. J. Food Qual., 38: 268–272.
Padalino L., Conte A. & Del Nobile M.A. (2016). Overview on the general approaches to improve gluten-free pasta and bread. Foods, 5, 87.
Pasqualone A., Punzi R., Trani A., Summo C., Paradiso V.M., Caponio F. and Gambacorta G. (2017). Enrichment of fresh pasta with antioxidant extracts obtained from artichoke canning byproducts by ultrasound-assisted technology and quality characterization of the end product. Int. J. Food Sci. Technol. , 52: 2078–2087.
Petitot M., Boyer L., Minier C. and Micard V. (2010). Fortification of pasta with split pea and faba beans flours: pasta processing and quality evaluation. Food Res Int 43:634–641
Prasad, K., Singh, Y. and Anil, A. (2012) Effects of Grinding Methods on the Characteristics of Pusa 1121 Rice Flour. Journal of Tropical Agriculture and Food Science., 40: 193-201.
Raffo A., Pasqualone A., Sinesio F., Paoletti F., Quaglia G. and Simeone R. (2003). Influence of durum wheat cultivar on the sensory profile and staling rate of Altamura bread. European Food Research and Technology, 218, 49-55
Renzetti, S. and Arendt E.K. (2009). Effect of protease treatment on the baking quality of brown rice bread: From textural and rheological properties to biochemistry and microstructure. J. Cereal Sci. 50: 22–28.
Rosell C.M., Barro F., Sousa C. and Mena M.C. (2014). Cereals for developing gluten-free products and analytical tools for gluten detection. J Cereal Sci 59:354–364.
Rosero, O., Marounek, M. Bğeğová, N. and Lukešova, D. (2013) Phytase activity and comparison of chemical composition, phytic acid content of four varieties of quinoa grain (Chenopodium quinoa Willd.) Acta Agronómica., 62, (1), 13 - 20.
Roushdi M., Ragaa I. Zain, Osman M.F. and Hassan M.H. (2016). Effect of autoclaving treatment on increasing of rice and corn resistant starches J. Agric. Res. Kafr El-Sheikh Univ., 42: 976-996.
Scarton, M. Ribeiro, T. Godoy, H. T. Behrens, J. H. Campel, P. H. and Clerici, M. T. P. S. (2021)."Gluten free pasta with natural ingredient of color and carotene source." Research, Society and Development 10 (4), e21310413959-e21310413959
Sobota A., Rzedzicki Z., Zarzycki P. & Kuzawinska E. (2015). Application of common wheat bran for the industrial production of high-fiber pasta. International Journal of Food Science & Technology, 50, 111–119.
Sun Q, Spiegelman D, van Dam RM, Holmes MD, Malik VS, Willett WC et al. (2010) White rice, brown rice, and risk of type 2 diabetes in US men and women. Arch Intern Med 170, 961–9.
Sozer, N., Dalgic, A.C. and Kaya, A. 2007. Thermal, textural and cooking properties of spaghetti enriched with resistant starch. J. Food Eng. 81, 476–484
Taşkin, B. and Savlak, N. (2020) "Public awareness, knowledge and sensitivity toward celiac disease and gluten-free diet is insufficient: a survey from Turkey." Food Science and Technology41 218-224.
Vilcacundo, R. Miralles, B. Carrillo, W. and Herna´ndezLedesma, B.(2018). In vitro chemo preventive properties of peptides released from quinoa (Chenopodium quinoa Willd.) protein under simulated gastrointestinal digestion. Food Research International 105, 403-411
Wang S. and Zhu F. (2016). Formulation and quality attributes of quinoa food products. Food Bioprocess Technol 9:49–68.
Wani S. A. and Kumar P. (2014). Comparative study of chickpea and green pea flour based on chemical composition, functional and pasting properties. Journal of Food Research and Technology, 2(3): 124-129
Yegrem L. (2021). Nutritional composition, antinutritional factors, and utilization trends of Ethiopian chickpea (Cicer arietinum L.). International Journal of Food Science, 2021, 5570753.
Zaky, A. A. Hussein, A. S. Mostafa S. and Abd El-Aty, A. M. (2022).Impact of Sunflower Meal Protein Isolate Supplementation on Pasta Quality. Separations,9 (12), 429
Zia-Ul-Haq, M., Iqbal, S., Ahmad, S., Imran, M., Niaz, A. & Bhanger, M.I. (2007). Nutritional and compositional study of Desi chickpea (Cicer arietinum L.) cultivars grown in Punjab, Pakistan. Food Chemistry, 105, 1357–1363.

The authors declare no competing interests.

Download PDF

Version 1

posted

You are reading this latest preprint version

Preparation and Evaluation Gluten Free pasta

Status:

Version 1

Abstract

1. Introduction

2. Materials and methods

2.1. Materials:

2.2. Methods:

2.2.1. Preparation of Raw Materials.

2.2. Rheological properties of the dough.

2.2.1 Rapid viscoanalyzer(RVA).

2.2.7. Cooking quality.

2.2.8. Color.

2.2.9. Sensory evaluation of pasta.

2.2.10. Textural profile analysis (TPA).

2.2.11. Statistical analysis.

3. Results and discussion

3.1. Chemical composition of the tested flour:

3.2. Mineral contents of the tested flour.

3.3. Total amino acid contents of semolina, brown rice, quinoa and chickpea flour.

3.4. Pasting profile (RVA) of the raw materials.

3.5. Chemical composition of gluten-free pasta.

3.6. Cooking Quality of Pasta.

3.7. Color parameters of pasta.

3.8. Sensory properties.

3.9. Texture Parameters of Pasta.

4. Conclusions

Declarations

Competing interests

Data availability

References

Additional Declarations

Status:

Version 1