Noodles are ready to eat product that are popular among several group of consumer. However, the presently available noodles provided low protein quality. With the constituents of good quality protein and phytochemicals (isoflavonon), soybean has been accepted as the functional foods that have a potentially positive effect on health beyond basic nutrition. Proponents of functional foods say that they promote optimal health and help to reduce the risk of disease. Thus the study aimed to develop protein enriched soy noodles with Full Fat Soy Flour (FFSF), Defatted Soy Flour (DFSF), and Soy Protein Isolate (SPI) that has acceptable characteristics and contained soy protein 47.0%, 43.2% and 90% respectively of the amount recommended by ICMR/NIN per serving. The DFSF, FFSF and SPI were substituted in wheat flour using 5%, 10%, 15% and 20%, where SPI was also substitute at 8%. It was observed that the level of DFSF, FFSF and SPI substitution was acceptable from the panelist at 10%, 10% and 8% containing 16.91%, 16.69% and 16.87% protein respectively. The noodles packaged in PET-MET/LDPE and MET-MET/LDPE was not affected by moisture and the product was highly acceptable in comparison to LDPE and PP. To lead a nutritious and healthy life, consumer should take soy fortified noodles into their daily diet.
A great interest has raised in the development of functional foods products that may provide a health benefit beyond the traditional nutrients [1]. Noodles are widely consumed throughout the world and a fast growing sector of the noodle industry, second to bread [2,3]. Study indicated that dried noodles or instant noodles contain about 9 g protein per 100 g of product [4]. This is due to low protein content cereals (rice and wheat flour) [5]. Several studies reported that malnutrition at early age causes impaired physical as well as mental development in children [6]. Also, researches carried out in India have confirmed that protein, energy and iron deficiency among school children has increased [7]. It is worthy to note that consumers worldwide are increasingly at risk of premature death from both cardiovascular disease and diabetes due to overweight, elevated cholesterol, high blood pressure and abnormal blood sugar. These risk factors are partially influenced by a diet low in fiber and high in refined grains, sugars and saturated fats. So, for gaining popularity as well as rendering noodles having high in protein and low in fat content in view of curbing varieties of ailment on our body with respect to changing lifestyle, research for alternative raw materials are still in continuing. Today soybeans are one of the most economicalcereal-based diets [8] and valuable agricultural commodities because of its unique chemical composition and multiple uses as food, feed and industrial materials. Soy bean (Glycine max) is an important source of oil (17-25%) and protein (35-45%) [9-11]. Soybeans have the highest protein content (40%) among cereal and other legume species, and the second highest oil content among all food legumes. Soy protein contains most the essential amino acids, most of which are present in amounts that closely match those required for humans or animals [12,13]. Furthermore, soybeans also contain many biological active components, including isoflavones, lecithin, saponins, oligosaccharides, and phytosterols [14]. Mangaraj et al., [15] stated that soybeans are good source of B6, foliate and vitamin E, whole soybeans have the highest levels of phytic acid, an organic acid and mineral cheater present in many plant tissues, especially bran and seed, which binds to certain ingested minerals; calcium, magnesium, iron and especially zinc-in intestinal tract and reduces the amount the body assimilates. It has been suggested that the high intakes of soy may explain, in part, the lower incidence of certain cancers in Asian countries, where soy consumption is high, when compared to Europe or America [16,17]. Nagarajan [18] suggested that soy isoflavonon may inhibit the effect of endothelial cell activation associated to chronicle diseases such as atherosclerosis by blocking the activation of inflammatory cells and the adhesion to the vascular endothelium. On 1999, the US Food and Drug Administration announced that food containing soy protein may reduce the risk of Coronary Heart Disease (CHD). The health claim is based on the US FDA’s determination that 25 grams of soy protein per day, as part of a diet low in saturated fat and cholesterol may reduce the risk of heart disease by reducing blood cholesterol levels. Apart from noodles, enabling fast food items to be more nutritious is to fortify them with protein, vitamins and minerals etc., to compete with other non-nutritional, yet popular, fast food items. Singh et al., [19] prepared noodles from semolina of durum and aestivum wheat and flour of aestivum with or without 10% of different types of full fat soy flour (i.e., enzyme active, conventional and roasted soy flour) and defatted soy flour and reported the product made from durum semolina containing 10% of defatted soy flour was as good as that from durum semolina alone. Osorio [20] studied the quality parameters of noodles made with various supplements (extruded maize, maize, defatted soy flour and maize/soy flour blends, lecithin and wheat straw). The noodles made with extruded maize flour, maize flour, and wheat straw supplements had the highest total sensory score. Young Soo Kim [21] prepared wet noodles from wheat flour with 3, 5 and 7% oyster mushroom and oak mushroom with improved protein and fiber contents having better acceptability.
A number of research studies have been reported on baked products, extruded products and deep fat fried snacks, noodles made from rice, wheat, ragi and legume flours supplemented with soya flour, defatted corn germ meal, soybean meal etc. [Buiet; Rathi; Marques; Osorio; Nielsen; Sudha; Kaur; Hou and Kruk; Fu] [20,22-28]. The main object of the present study is to provide soy based low-fat and high protein nutritious food in accordance to ICME/NIN guidelines [29]. It is a particular object of the invention to provide novel fast food item in which the proteins are balanced to provide optimum nutrition (i.e., up to 13-17g/100g of product). This study will provide an additional source of utilization of soy flour in fast food formulation.
Raw Ingredient |
Protein (%) |
Fat (%) |
Ash (%) |
Fiber (%) |
Carbohydrate (%) |
Energy (k Cal) |
Wheat |
12.1 |
1.7 |
2.7 |
1.9 |
69.4 |
341.0 |
Refined Wheat Flour (RWF) |
11.3 |
0.9 |
- |
- |
- |
73.9 |
Defatted Soy Flour (DFSF) |
47.0 |
1.2 |
- |
4.3 |
38.4 |
329.0 |
Full Fat Soy Flour (FFSF) |
43.2 |
19.5 |
4.6 |
3.7 |
20.9 |
432.0 |
Soy Protein Isolate (SPI) |
90 |
0.1 |
0.1 |
0 |
- |
- |
Table 1: Chemical composition of raw material (per 100gm).
|
Input Parameters |
Output Parameters |
||||
Run |
WF |
RWF |
SPI |
Protein (%) |
Fat (%) |
Energy |
1 |
19.90878 |
75.09122 |
5 |
14.15463 |
0.99876 |
340.765 |
2 |
24.33079 |
70.66921 |
5 |
13.94568 |
1.00034 |
338.675 |
3 |
12.33789 |
80 |
7.66211 |
15.13245 |
0.89743 |
343.321 |
4 |
21.22119 |
68.78556 |
9.993249 |
17.98453 |
0.94879 |
342.135 |
5 |
17.86295 |
72.28429 |
9.85276 |
17.54637 |
0.93654 |
341.875 |
6 |
10.60555 |
79.39445 |
10 |
17.87568 |
0.87436 |
343.256 |
7 |
14.24135 |
75.86167 |
9.896988 |
16.32452 |
0.90654 |
339.565 |
8 |
16.3651 |
78.6349 |
5 |
14.13002 |
0.95765 |
342.321 |
9 |
25 |
65.00111 |
9.998892 |
17.98734 |
0.99876 |
338.216 |
10 |
24.33079 |
70.66921 |
5 |
13.87523 |
1.00065 |
344.546 |
11 |
12.33789 |
80 |
7.66211 |
16.32765 |
0.96453 |
343.674 |
12 |
21.33811 |
71.6892 |
6.972685 |
14.94653 |
0.99564 |
339.165 |
13 |
16.3651 |
78.6349 |
5 |
13.54362 |
0.96345 |
347.321 |
14 |
10.60555 |
79.39445 |
10 |
17.13245 |
0.87965 |
345.34 |
15 |
25 |
65.00111 |
9.998892 |
16.98765 |
0.98765 |
341.321 |
16 |
25 |
67.76714 |
7.232864 |
14.56743 |
1.00324 |
339.1342 |
Input Parameter |
Output Parameter |
|||||
Run |
WF |
RWF |
DFSF |
Protein (%) |
Fat (%) |
Energy (Kcal) |
1 |
10.6244 |
74.3756 |
15 |
15.512 |
1.00232 |
387.345 |
2 |
10.26805 |
80 |
9.731949 |
14.101 |
0.978723 |
338.3712 |
3 |
25 |
66.51373 |
8.486266 |
13.341 |
1.00542 |
340.9536 |
4 |
25 |
66.51373 |
8.486266 |
13.654 |
0.999872 |
341.9564 |
5 |
17.73888 |
74.26112 |
8 |
14.122 |
0.986543 |
339.3452 |
6 |
24.99689 |
60.00311 |
15 |
15.325 |
0.985436 |
341.9456 |
7 |
21.88654 |
64.74022 |
13.37324 |
15 |
0.985342 |
343.1321 |
8 |
13.42041 |
75.85916 |
10.72043 |
14.12 |
0.97543 |
337.4231 |
9 |
17.73888 |
74.26112 |
8 |
13.342 |
0.96903 |
335.9876 |
10 |
24.99689 |
60.00311 |
15 |
15.342 |
1.00543 |
339.3215 |
11 |
14.35615 |
70.64385 |
15 |
15 |
0.98765 |
333.1432 |
12 |
18.62192 |
69.39567 |
11.9824 |
14.894 |
0.97654 |
332.9856 |
13 |
18.51129 |
66.48871 |
15 |
15.324 |
0.98765 |
338.1432 |
14 |
10.26805 |
80 |
9.731949 |
14.444 |
0.97987 |
336.2435 |
15 |
21.68259 |
70.31741 |
8 |
14.346 |
1.00834 |
341.4356 |
16 |
10.6244 |
74.3756 |
15 |
14.543 |
1.00765 |
332.8796 |
*** Mixture Component Coding is U_Pseudo. *** |
||||||
Analysis of variance table [Partial sum of squares-Type III] |
||||||
Source |
Sum of Squares |
Df |
Mean Square |
F Value |
p-value Prob>F |
|
Model |
37.02865 |
2 |
18.51433 |
59.54066 |
<0.0001 |
Significant |
Linear mixture |
37.02865 |
2 |
18.51433 |
59.54066 |
<0.0001 |
|
Residual |
4.042385 |
13 |
0.310953 |
|||
Lack of fit |
2.377834 |
8 |
0.297229 |
0.892821 |
0.5788 |
Not significant |
Pure error |
1.664551 |
5 |
0.33291 |
|||
Cor total |
41.07104 |
15 |
||||
Std. dev. |
0.557631 |
R-Squared |
0.901576 |
|||
Mean |
15.77886 |
Adj R-Squared |
0.886434 |
|||
CV% |
3.53404 |
Pred R-Squared |
0.853106 |
|||
Press |
6.033076 |
Adeq Precision |
14.76095 |
*** Mixture Component Coding is U_Pseudo*** |
||||||
Analysis of variance table [Partial sum of squares-Type III] |
||||||
Source |
Sum of Squares |
Df |
Mean Square |
F Value |
p-value Prob>F |
|
Model |
5.774653 |
2 |
2.887327 |
20.42253 |
<0.0001 |
Significant |
Linear mixture |
5.774653 |
2 |
2.887327 |
20.42253 |
<0.0001 |
|
Residual |
1.837933 |
13 |
0.141379 |
|
|
|
Lack of fit |
0.956299 |
8 |
0.119537 |
0.677931 |
0.7027 |
Not significant |
Pure error |
0.881634 |
5 |
0.176327 |
|
|
|
Cor total |
7.612586 |
15 |
|
|
|
|
Std. dev. |
0.376005 |
|
R-Squared |
0.758567 |
|
|
Mean |
14.52563 |
|
Adj R-Squared |
0.721423 |
|
|
CV% |
2.58856 |
|
Pred R-Squared |
0.623935 |
|
|
Press |
2.86283 |
|
Adeq Precision |
8.937561 |
|
|
*** Mixture Component Coding is U_Pseudo. *** |
||||||
Analysis of variance table [Partial sum of squares-Type III] |
||||||
Source |
Sum of Squares |
Df |
Mean Square |
F Value |
p-value Prob>F |
|
Model |
0.027184 |
2 |
0.013592 |
42.58544 |
<0.0001 |
Significant |
Linear mixture |
0.027184 |
2 |
0.013592 |
42.58544 |
<0.0001 |
|
Residual |
0.004149 |
13 |
0.000319 |
|
|
|
Lack of fit |
0.001806 |
8 |
0.000226 |
0.481461 |
0.829 |
Not significant |
Pure error |
0.002344 |
5 |
0.000469 |
|
|
|
Cor total |
0.031334 |
15 |
|
|
|
|
Std. dev. |
0.017865 |
|
R-Squared |
0.867578 |
|
|
Mean |
0.957124 |
|
Adj R-Squared |
0.847205 |
|
|
CV% |
1.866581 |
|
Pred R-Squared |
0.805794 |
|
|
*** Mixture Component Coding is UPseudo. *** |
||||||
Analysis of variance table [Partial sum of squares-Type III] |
||||||
Source |
Sum of Squares |
Df |
Mean Square |
F Value |
p-value Prob>F |
|
Model |
0.001822 |
5 |
0.000364 |
5.359067 |
0.0118 |
Significant |
Linear mixture |
0.000321 |
2 |
0.00016 |
2.358509 |
0.1448 |
|
WF*RWF |
0.000471 |
1 |
0.000471 |
6.927213 |
0.0251 |
|
WF*DFSF |
0.000537 |
1 |
0.000537 |
7.905854 |
0.0184 |
|
WF*DFSF |
0.000144 |
1 |
0.000144 |
2.112546 |
0.1767 |
|
Residual |
0.00068 |
10 |
6.80E-05 |
|
|
|
Lack of fit |
0.000296 |
5 |
5.93E-05 |
0.77272 |
0.6079 |
Not significant |
Pure error |
0.000383 |
5 |
7.67E-05 |
|
|
|
Cor total |
0.002501 |
15 |
|
|
|
|
Std. dev. |
0.008245 |
|
R-Squared |
0.728226 |
|
|
Mean |
0.990078 |
|
Adj R-Squared |
0.59234 |
|
|
CV% |
0.83277 |
|
Pred R-Squared |
0.305582 |
|
|
*** Mixture Component Coding is U_Pseudo. *** |
||||||
Analysis of variance table [Partial sum of squares-Type III] |
||||||
Source |
Sum of Squares |
Df |
Mean Square |
F Value |
p-value Prob>F |
|
Model |
38.47702 |
2 |
19.23851 |
3.95992 |
0.0454 |
Significant |
Linear mixture |
38.47702 |
2 |
19.23851 |
3.95992 |
0.0454 |
|
Residual |
63.15801 |
13 |
4.858309 |
|
|
|
Lack of fit |
26.36935 |
8 |
3.296168 |
0.447987 |
0.8504 |
Not significant |
Pure error |
36.78867 |
5 |
7.357733 |
|
|
|
Cor total |
101.635 |
15 |
|
|
|
|
Std. dev. |
2.204157 |
|
R-Squared |
0.37858 |
|
|
Mean |
341.9144 |
|
Adj R-Squared |
0.282977 |
|
|
CV% |
0.644652 |
|
Pred R-Squared |
0.041856 |
|
|
Source |
Sum of Squares |
Df |
Mean Square |
F Value |
p-value Prob>F |
|
Model |
31.81132 |
5 |
6.362263 |
6.532988 |
0.006 |
Significant |
Linear mixture |
14.95817 |
2 |
7.479085 |
7.679778 |
0.0095 |
|
AB |
15.47507 |
1 |
15.47507 |
15.89033 |
0.0026 |
|
AC |
0.354093 |
1 |
0.354093 |
0.363594 |
0.5599 |
|
BC |
0.171868 |
1 |
0.171868 |
0.17648 |
0.6833 |
|
Residual |
9.738673 |
10 |
0.973867 |
|
|
|
Lack of fit |
5.605641 |
5 |
1.121128 |
1.356302 |
0.3731 |
Not significant |
Pure error |
4.133032 |
5 |
0.826606 |
|
|
|
Cor total |
41.54999 |
15 |
|
|
|
|
Std. dev. |
0.986847 |
|
R-Squared |
0.765616 |
|
|
Mean |
337.9824 |
|
Adj R-Squared |
0.648423 |
|
|
CV% |
0.291982 |
|
Pred R-Squared |
0.427936 |
|
|
Formulations |
Protein Content (%) |
Fat Content (%) |
Moisture Content (%) |
Ash Content (%) |
Carbohydrate Content (%) |
Energy Contents (kcal.) |
Control |
8.27 |
4.78 |
6.2 |
1.96 |
79.59 |
394.46 |
S-1 |
16.91 |
6.85 |
6.9 |
2.26 |
67.08 |
397.61 |
S-2 |
16.69 |
8.80 |
6.1 |
2.72 |
66.14 |
410.52 |
S-3 |
16.87 |
5.59 |
5.9 |
2.23 |
68.71 |
392.63 |
Table 10: Initial quality of most accepted soy fortified and control noodles.
Efforts have been made to develop nutritious protein enriched healthy noodles by incorporating soy flour at 10% DFSF, 10% FFSF and 8% SPI as per ICMR/NIN requirement for soy health claim. All three (DFSF, FFSF and SPI) noodles contained more protein when compared with control noodles. The increased substitution of soy flour (FFSF, DFSF and SPI) caused brittle and hard cooked noodles strand which reduced the chewiness and increased the firmness respectively. All types of soy fortified noodles give significant difference (P<0.05) in the term of general appearance, color and overall acceptability from control formula. On the other hand, there were no significant difference (F<0.05) among control sample and DFSF, FFSF and SPI formulas in term of stickiness and texture/mouth feel, respectively. FFSF, DFSF and SPI were used as soy protein sources in soy protein enriched noodles because of their low cost and functional food characteristics. Even FFSF cost was lower than other soy protein source (DFSF and SPI). Defatted soy flour and SPI increased the protein, ash content of the noodles keeping the fat at optimum level. It is concluded that SPI and DFSF based noodles provides the best technological responses for getting high energy content and low fat content noodles products with more favourable sensory evaluation. This study focuses to develop the process for production of nutritionally balanced formulated and functional noodles in meeting the intended nutritional requirements and is also accessible to the young children at minimum possible cost. The new products were highly appreciated by the school children when it was given after mid-day-meal.
Citation: Mangaraj S, Swain S, Deshpande SS (2018) Development of Nutritious Healthy Noodles Incorporating Soy Based Functional Food Ingredients. J Food Sci Nut 4: 028.
Copyright: © 2018 Mangaraj S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.