Cooking quality, color, and texture profile analysis of a quinoa and lentil pasta

Quinoa (Chenopodium quinoa willd) and lentils (Lens culinaris) are ingredients used to enrich or substitute gluten in pasta manufacture due to their high nutritional content. The objective of this work was to develop quinoa noodles with lentils that have similar or superior attributes compared to the product with gluten. Therefore, we evaluated the cooking properties (cooking quality, hydration, rheology), color, and texture profile of noodles developed with different concentrations (10%, 20%, and 30%) of lentil flour (LF) in comparison to commercial wheat pasta (control). ANOVA comparisons were performed on cooking and texture profile attributes, with the best treatment being the one with values that did not significantly differ from the control sample (T0). Thus, T3 (70% quinoa grits and 30% LF) is the formulation that presents better and/or similar attributes to those of the control sample.

Index terms:
Quinoa: treatment; control sample

Authorship

Elizabeth Vargas Huamán

Methodology design

Data collection

Data analysis and interpretation

Writing and editing

Universidad Peruana Unión, Facultad de Ingeniería y Arquitectura, Escuela Profesional de Ingeniería de Industrias Alimentarias, Juliaca, Puno, Perú Universidad Peruana UniónPeruJuliaca, Puno, PeruUniversidad Peruana Unión, Facultad de Ingeniería y Arquitectura, Escuela Profesional de Ingeniería de Industrias Alimentarias, Juliaca, Puno, Perú

https://orcid.org/0009-0008-2882-6214

Vilma Pinto Hurtado

Methodology design

Data collection

Data analysis and interpretation

Writing and editing

https://orcid.org/0009-0002-3155-7576

Jose M. Prieto

Data analysis and interpretation

https://orcid.org/0000-0001-6280-7844

Edgar Mayta Pinto ^**Corresponding author: edgar.mayta@upeu.edu.pe

Conceptual idea

Methodology design

Data analysis and interpretation

Writing and editing

https://orcid.org/0000-0001-5973-8577

^*Corresponding author: edgar.mayta@upeu.edu.pe

Editor: Renato Paiva

SCIMAGO INSTITUTIONS RANKINGS

Figures | Tables | Formulas

Figures (4)
Tables (5)
Formulas (1)

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Figure 1:
Paste hydration kinetics as a function of temperature. The lines represent data fitted with the Peleg model. T0: control sample; T1: 90% QG -10% LF; T2: 80% QG - 20% LF; T3: 70% QG - 30% LF; exp: experimental data; pred: predicted data.

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Figure 2:
Storage module (G’) of quinoa pasta with lentils.

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Figure 3:
Loss modulus (G’’) of quinoa pasta with lentils.

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Figure 4:
Location of the CIEL*a*b* scale on the raw and cooked pasta color discs.

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Table 1:
Nutritional composition of quinoa pasta with lentils.

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Table 2:
Water absorption properties of formulations under the effect of temperature, according to the Peleg model.

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Table 3:
Pasta cooking quality.

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Table 4:
Texture profile of cooked pasta.

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Table 5:
Color attributes in raw and cooked pasta.

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Figure 1: Paste hydration kinetics as a function of temperature. The lines represent data fitted with the Peleg model. T0: control sample; T1: 90% QG -10% LF; T2: 80% QG - 20% LF; T3: 70% QG - 30% LF; exp: experimental data; pred: predicted data.

Figure 2: Storage module (G’) of quinoa pasta with lentils.

Figure 3: Loss modulus (G’’) of quinoa pasta with lentils.

Figure 4: Location of the CIELab* scale on the raw and cooked pasta color discs.

Table 1: Nutritional composition of quinoa pasta with lentils.

	T0	T1	T2	T3
Carbohydrates (%)	78.04 ± 1.02^a	74.45 ± 0.87^b	73.32 ± 0.98^c	73.25 ± 0.79^d
Energy (Kcal/100 g)	362.28 ± 0.84^a	358.77 ± 0.93^b	358.84 ± 0.91^c	368.11 ± 1.04^d
Protein (%)	12.26 ± 0.92^a	12.7 ± 0.87^a	13.69 ± 0.95^a	15.02 ± 0.77^b
Fat (%)	0.12 ± 0.05^a	1.13 ± 0.24^b	1.2 ± 0.25^c	1.67 ± 0.47^d
Ash (%)	0.45 ± 0.36^a	4.21 ± 0.82^b	3.69 ± 0.98^c	3.45 ± 0.57^d
Moisture (%)	9.13 ± 0.87^a	7.51 ± 0.46^b	8.1 ± 0.96^a	6.61 ± 0.77^c
Crude fiber (%)	1.08 ± 0.55^a	1.05 ± 0.32^a	1.03 ± 0.53^a	1± 0.21^a

Data are means ± standard deviation (n = 3). Significant differences (ANOVA, p <0.05) among treatments are indicated by letters. T0: control sample; T1: 90% QG -10% LF; T2: 80% QG - 20% LF; T3: 70% QG - 30% LF.

Table 2: Water absorption properties of formulations under the effect of temperature, according to the Peleg model.

	Kinetic parameter	Temperature
Formulation	Kinetic parameter	60 °C	70 °C	80 °C
T0	K₁ (min g bs/g H₂O)	9.72 ± 0.13	9.20 ± 0.18	3.45 ± 0.21
	K₂ (g bs/g H₂O)	0.87 ± 0.28	0.40 ± 0.16	1.03 ± 0.18
	H_pred (g H₂O/g bs)	7.53 ± 0.52	8.89 ± 0.20	7.35 ± 0.33
	R²	0.98	0.98	0.94
	t (min)	2.45 ± 0.33	5.76 ± 0.19	7.21 ± 0.13
T1	K₁ (min g bs/g H₂O)	9.53 ± 0.61	5.51 ± 0.64	5.68 ± 0.45
	K₂ (g bs/g H₂O)	1.07 ± 0.15	0.87 ± 0.81	0.52 ± 0.31
	H_pred (g H₂O/g bs)	6.77 ± 0.42	6.99 ± 0.12	7.76 ± 0.22
	R²	0.96	0.97	0.96
	t (min)	7.56 ± 0.53	8.65 ± 0.28	6.04 ± 0.17
T2	K₁ (min g bs/g H₂O)	16.28 ± 0.29	24.61 ± 0.32	9.52 ± 0.25
	K₂ (g bs/g H₂O)	1.15 ± 0.40	0.27 ± 0.24	0.60 ± 0.31
	H_pred (g H₂O /g bs)	8.86 ± 0.16	11.62 ± 0.11	9.65 ± 0.20
	R²	0.92	0.97	0.96
	t (min)	7.21 ± 0.32	6.92 ± 0.15	3.46 ± 0.24
T3	K₁ (min g bs/g H₂O)	0.19 ± 0.34	3.82 ± 0.22	4.47 ± 0.30
	K₂ (g bs/g H₂O)	0.09 ± 0.41	0.77 ± 0.37	0.94 ± 0.44
	H_pred (g H₂O/g bs)	11.38 ± 0.10	8.34 ± 0.08	7.34 ± 0.13
	R²	0.89	0.96	0.98
	t (min)	3.96 ± 0.16	7.56 ± 0.28	7.21 ± 0.26

Data are means ± standard deviation (n = 3). T0: control sample; T1: 90% QG -10% LF; T2: 80% QG - 20% LF; T3: 70% QG - 30% LF), H_pred: predicted humidity.

Table 3: Pasta cooking quality.

Variables	T0	T1	T2	T3
OCT (min)	8.40 ± 0.09^a	5.35 ± 0.09^b	6.22 ± 0.06^b	7.13 ± 0.39^b
WA (g/100 g)	80.103 ± 2.09^a	76.77 ± 7.4^a	73.64 ± 6.0^b	82.93 ± 7.3^a
CL (%)	1.35 ± 0.25^a	0.97 ± 0.15^a	1.02 ± 0.37^b	1.26 ± 0.25^b
IH (g w/g ps)	1.6 ± 0.04^a	1.08 ± 0.06^a	0.95 ± 0.07^a	1.1 ± 0.18^a

Data are means ± standard deviation (n = 3). Significant differences (ANOVA, p <0.05) among treatments are indicated by letters. T0: control sample; T1: 90% QG - 10% LF; T2: 80% QG - 20% LF; T3: 70% QG - 30% LF;. OCT: optimal cooking time; WA: water absorption; CL: cooking losses; and IH: swelling index.

Table 4: Texture profile of cooked pasta.

Form.	Hardness (N)	Cohesiveness	Elasticity	Chewiness (N)	Gumminess (N)	Fracture ability (N)
T0	0.51 ± 0.20^a	0.52 ± 0.06^a	0.99 ± 0.02^a	0.26 ± 0.08^a	0.26 ± 0.08^a	0.51 ± 0.20^a
T1	0.76 ± 0.11^b	0.11 ± 0.06^a	0.79 ± 0.37^a	0.06 ± 0.02^a	0.09 ± 0.05^a	0.76 ± 0.11^a
T2	1.28 ± 0.27^b	0.06 ± 0.01^a	1 ± 0.00^a	0.08 ± 0.03^a	0.08 ± 0.03^a	1.24 ± 0.37^b
T3	0.69 ± 0.13^a	0.03 ± 0.03^a	1 ± 0.00^a	0.02 ± 0.03^a	0.02 ± 0.03^a	0.76 ± 0.04^b

Data are means ± standard deviation (n = 3). Significant differences (ANOVA, p < 0.05) among treatments are indicated by letters. T0: control sample; T1: 90% QG -10% LF; T2: 80% QG - 20% LF; T3: 70% QG - 30% LF).

Table 5: Color attributes in raw and cooked pasta.

Form.	Raw Pasta				Cooked Pasta
Form.	L*	a*	b*	ΔE	L*	a*	b*	ΔE
T0	56.46 ± 0.87	3.29 ± 0.1	14.06 ± 0.3		64.46 ± 3.85	1.27 ± 0.19	10.25 ± 0.62
T1	65.76 ± 2.33	9.31± 0.64	23.28 ± 1.16	14.41	64.18 ± 1.89	7.26 ± 0.88	23.28 ± 1.16	14.34
T2	64.22 ± 2.03	9.65 ± 0.37	22.88 ± 0.88	13.36	65.58 ± 1.32	5.68 ± 0.58	16.54 ± 1.16	7.76
T3	63.9 ± 2.66	8.61 ± 0.65	21.01 ± 0.76	11.49	63.15 ± 1.04	5.36 ± 0.35	15.2 ± 0.53	6.55

Data are means ± standard deviation (n = 6). T0: control sample control; T1: 90% QG -10% LF; T2: 80% QG - 20% LF; T3: 70% QG - 30% LF. L: luminosity, a: red-green coordinates, b: yellow-blue coordinates, ΔE: total color difference.

M (t) = M o + \frac{t}{K_{1} + K_{2} x t}

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Cooking quality, color, and texture profile analysis of a quinoa and lentil pasta

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[1] ^*Corresponding author: edgar.mayta@upeu.edu.pe