Studies on effect of additives on protein profile, microstructure and quality characteristics of pasta

Purnima, C.; Ramasarma, P. R.; Prabhasankar, P.

doi:10.1007/s13197-011-0258-7

Cited by 15 publications

(12 citation statements)

References 23 publications

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“…Jyotsna et al (2004) also observed finer and stretched protein matrix with the use of GMS at 0.5% level. Similar results of improvement in protein network with the use GMS was also reported by Kim et al (1989) and Purnima et al (2012). According to Krog (1981), SSL forms complexes with starch and protein to form huge aggregates and increases the stability of protein matrix.…”

Section: Scanning Electron Microscope Characteristicssupporting

confidence: 80%

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Functionality of Different Surfactants on the Rheological and Micro‐Structural Characteristics of Multigrain Premix Incorporated Wheat Flour

Radha

Sharma

2016

Journal of Texture Studies

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Effect of different surfactants namely, lecithin, glycerol monostearate (GMS), sodium stearoyl‐2 lactylate (SSL) and di‐acetyl tartaric acid esters of monoglycerides (DATEM) at three different levels (0.25, 0.5 and 1%) on the rheological and micro‐structural characteristics of multigrain premix incorporated wheat flour (MWF) were studied. Surfactants significantly (P ≤ 0.05) increased the consistograph water absorption capacity, tolerance, alveograph average abscissa at rupture and mixogram peak value of MWF and decreased the consistograph dough development time, alveograph tenacity, mixograph height and width. DATEM and lecithin decreased the rapid visco analyzer peak viscosity of MWF, whereas GMS and SSL increased the same. The maximum increase in tolerance and peak viscosity were observed with GMS and SSL at 0.5% level when compared to other surfactants at the same level. SEM studies showed thick protein matrix with 0.5% of SSL and DATEM, whereas with GMS, the protein matrix appeared smooth, uniform and continuous. Among the surfactants studied, GMS and SSL at 0.5% level showed maximum effect on the rheological and micro‐structural characteristics of MWF. Practical Applications Rheological characteristics of wheat flour are effective in predicting the behavior of the dough during processing and the quality of end product. Incorporating multigrain premix to wheat flour adversely affected the rheological characteristics of wheat flour. This article discusses the improvement effect of different surfactants on the rheological and micro‐structural characteristics of multigrain premix incorporated wheat flour. This article also suggests the better surfactant for multigrain incorporated wheat flour to improve its rheological characteristics. Knowledge of functionality of different surfactants on multigrain incorporated wheat flour will be very much useful for the industries to develop nutritious multigrain products with desired quality using surfactants.

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Section: Scanning Electron Microscope Characteristicssupporting

confidence: 80%

“…() and Purnima et al . (). According to Krog (), SSL forms complexes with starch and protein to form huge aggregates and increases the stability of protein matrix.…”

Section: Resultsmentioning

confidence: 97%

Functionality of Different Surfactants on the Rheological and Micro‐Structural Characteristics of Multigrain Premix Incorporated Wheat Flour

Radha

Sharma

2016

Journal of Texture Studies

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“…When water at high temperatures is used, the noodles do not absorb as much water because HPMC rapidly gels and develops a coating layer (Lee et al, 2010;Purnima and Ramasarma, 2012). This is also accompanied by lower cooking loss, which is a measure of the elution of solid content during cooking.…”

Section: Texture Profile Analysis Of Rice Noodles Containing Hpmcmentioning

confidence: 99%

“…Purnima et al (2012) reported that HPMC forms a network around the starch granules, possibly encapsulating the starch-protein matrix, and, contributing to the lower cooking loss. H-HPMC recorded the lowest water absorption and cooking loss, as it had the highest viscosity among the 3 types of HPMC, thereby forming the most effective coating layer.…”

Section: Texture Profile Analysis Of Rice Noodles Containing Hpmcmentioning

confidence: 99%

“…This reduces the glycemic index (GI) of the noodles, and, thus, helps prevent obesity and diabetes (Jeya, 2001). Moreover, research is underway for pasta (Purnima, 2012), instant noodles (Farzi et al, 2013), and bread (Marco and Rosell, 2008;Sabanis and Tzia, 2011). HPMC is especially one of the most suitable to improve the volume and texture of the rice bread.…”

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confidence: 99%

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Effects of Hydroxypropyl Methylcellulose and Temperature of Dough Water on the Rice Noodle Quality

Kim

Baek

et al. 2015

FSTR

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Rice noodle is one of the most popular foods consumed in Asia. However, rice flour lacks gluten that forms a cohesive dough structure in wheat-based noodles and for gluten-free, rice-based noodle formulations an alternative to gluten is needed. Therefore, the effects of the addition of hydroxypropyl methylcellulose (HPMC) as a replacement for gluten and the temperature of the water used for rice noodle preparation were investigated. Rice noodle contained high-viscosity HPMC (6%) had the smallest breakdown and the largest setback viscosity, also had better texture and sensory properties. The use of dough water at 80℃ was effective on reducing the cooking loss and improving the textural properties. The overall acceptability score was higher compared to that of the control. The results showed that using high-viscosity HPMC (6%) to replace gluten and kneading with water at 80℃ was the most effective protocol for improving the quality of gluten-free rice noodles.

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Effectual study, statistical optimization, and neural network‐based predictive model of pearl millet and amaranth flours formulations for gluten‐free pasta

Rathore,

Pandey

2023

JSFA Reports

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BackgroundPasta is prepared from high‐gluten wheat flour but poses great harm to gluten‐intolerant population or celiac disease patients. Pearl millet flour is the cheapest among gluten‐free flours and has a high nutritive index. Amaranth is a promising source of protein and fiber and is gluten‐free in nature. Several studies have been done for the development of gluten‐free pasta using different flour blends but an optimized formulation with high nutritive value and consumer satisfaction has not yet been identified.ResultsIn this study, different blends of pearl millet and amaranth flour were used in ratios of 90:10, 80:20, 70:30, 60:40, and 50:50 for pasta preparation and their analysis based on farinographic parameters, cooking quality, and sensory scores was done. Statistical analysis by analysis of variance followed by Duncan's Multiple Range Test was performed to evaluate the optimized formulation. 60:40 blend ratio for pearl millet and amaranth flour displayed comparable farinographic properties and cooking yield to wheat‐based pasta with optimized cooking loss and an overall sensory score of 8.65. Scanning electron microscopy analysis of flours and the cooked and uncooked pasta samples was also performed. Further, a multilayer perceptron neural network was developed to predict the overall quality and grade of pasta. The developed neural network gave high classification accuracy of 90.9% and 100% for training and testing sets, respectively, and can be utilized for pasta quality prediction.ConclusionThis study provides optimized pearl millet and amaranth flour blend formulation to prepare gluten‐free delicious pasta for celiac disease patients.

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Studies on effect of additives on protein profile, microstructure and quality characteristics of pasta

Cited by 15 publications

References 23 publications

Functionality of Different Surfactants on the Rheological and Micro‐Structural Characteristics of Multigrain Premix Incorporated Wheat Flour

Functionality of Different Surfactants on the Rheological and Micro‐Structural Characteristics of Multigrain Premix Incorporated Wheat Flour

Effects of Hydroxypropyl Methylcellulose and Temperature of Dough Water on the Rice Noodle Quality

Effectual study, statistical optimization, and neural network‐based predictive model of pearl millet and amaranth flours formulations for gluten‐free pasta

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