Análise de qualidade de cozimento, cor e perfil de textura de uma pasta de quinoa e lentilha

Quinoa (Chenopodium quinoa willd) e lentilhas (Lens culinaris) são ingredientes utilizados para enriquecer ou substituir o glúten em massas devido ao seu alto conteúdo nutricional. O objetivo deste trabalho foi desenvolver um macarrão de quinoa com lentilha com atributos semelhantes ou superiores ao produto com glúten. Para tanto, realizamos análise proximal e avaliamos as propriedades de cozimento (qualidade de cozimento, hidratação e reología), perfil de cor e textura de massas desenvolvidas com diferentes concentrações (10%, 20% e 30%) de farinha de lentilha (LF). Comparações de ANOVA foram realizadas nos atributos de cozimento e perfil de textura, sendo o melhor tratamento aquele com valores que não diferiram significativamente da amostra controle (T0). Assim, T3 é a formulação que apresenta atributos melhores e/ou semelhantes aos da amostra controle.

Termos para indexação:
Quinoa: tratamento; amostra controle

Autoria

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

Figuras | Tabelas | Fórmulas

Figuras (4)
Tabelas (5)
Fórmulas (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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Análise de qualidade de cozimento, cor e perfil de textura de uma pasta de quinoa e lentilha

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