Applications of the Rapid Visco Analyser (RVA) in the Food Industry: a broader view

Mario M. Martínez
Food Technology Area, College of Agricultural Engineering, University of Valladolid, 34004 Palencia, Spain
mario.martinez@iaf.uva.es

Introduction

The vast majority of cereal-based food matrices are systems resulting from cooking starch in excess water. Therefore, the study of the rheology during heating-cooling cycles could be interesting with a view to elucidating many useful indicators for quality assessment of final products and raw materials. The Rapid Visco Analyser (RVA) has been widely used and is well known for assessing the pasting properties of flour or starch. However, it is important to highlight its versatility due to its capability of analyzing the viscosity in heating-cooling cycles. These features also make it suitable for simulating step processes on a small scale under controlled conditions, as well as monitoring changes in the viscosity as the manufacturing process progresses. Therefore, the RVA can serve as a useful tool for elucidating multiple quality indicators which could help to optimize many different food step processes.

Importance of the measurement of viscosity in heating-cooling cycles

Rheology is the study of the flow and deformation of materials. Generally, to measure rheological behavior, a controlled, well-defined deformation or strain is applied to a material over a given time and the resulting force response is measured (or vice versa) to give an indication of material parameters such as stiffness, modulus, viscosity, hardness, strength or toughness of the material. The general aims of rheological measurements are:

• to obtain a quantitative description of the material’s mechanical properties
• to obtain information related to the molecular structure and composition of the material
• to characterize and simulate the material’s performance during processing
• to attempt to predict final product quality.

Controlled tests on well-defined small samples of food in the laboratory can be related to the larger, more complex multi-component situations found in practical processing conditions.

The main techniques used for measuring cereal properties have traditionally been divided into descriptive empirical techniques and fundamental measurements. In fundamental rheological tests the sample geometry is constant and well defined, the stress and strain states are controlled and uniform, and it is therefore possible to define any rheological parameters such as stress, strain, strain rate, modulus or viscosity. However, such fundamental tests require complex instrumentation (which is expensive), which is time consuming, difficult to maintain in an industrial environment and requires high levels of technical skill. In addition, often inappropriate deformation conditions and slip and edge effects during testing might hinder the interpretation of results. Meanwhile, empirical tests are easy to perform and are often used in practical factory situations, providing data that is useful in evaluating performance during processing and for quality control. The instruments are often robust and capable of withstanding demanding factory environments, and do not require highly skilled or technically trained personnel.

The vast majority of cereal-based food matrices are systems resulting from cooking starch in excess water. Moreover, they could also be composed of hydrocolloids, fibers and proteins. Therefore, the study of the rheology during heating-cooling cycles could be interesting. In order to analyze the rheology in such conditions, the combination of a container capable of stirring the ingredients under controlled temperature, the possibility of controlling the temperature and the heating-cooling rate as well as the applied shear stress could turn a viscometer into a powerful and versatile tool to elucidate viscosity indicators. Several specific examples of elucidating and characterizing viscosity indicators in heating-cooling cycles will be described in this paper. However, it is important to highlight that there are infinite applications of these devices regarding industrial processes and the type of product.

Indicators in cereal-based products

Rice quality
Rice is mainly consumed as cooked rice. However there are also a number of products to which rice is added as an ingredient to improve their organoleptic properties. Rice can be mainly classified according to:

• the growing area: Indica or Japonica varieties
• the size of the grain: long grain (> 6.6 mm), medium grain (5.5–6.6 mm) and short grain (<5.5 mm)
• amylose content: waxy rice (0–5% amylose), very low (5–12%), low (12–20%), intermediate (20–25%), and high (25–33%) (Juliano, 1992). Added to that, amylose extended mutants known as ‘amylotype starches’ contains from 50 to 70% amylose (Kahraman & Köksel, 2013).

The different grains will show different functional properties specially related to their different amylose content, and therefore to their degree of retrogradation. Moreover, retrogradation is related to the degree of stickiness of cooked grains, a crucial parameter in the culinary quality of the different grains. Typically, long grains show high Pasting Temperature (PT) and low Peak Viscosity (PV) as a consequence of their high amylose content. Nevertheless, some short grains present long grain properties. Therefore the length of the grain itself cannot be used to represent the functional properties of rice. Several analytical methods, such as amylose/amylopectin (AM/AP) ratio, hydration properties, damaged starch, differential scanning calorimetry (DSC), and crystalline polymorphism among others, have been used with the aim of characterizing the functional properties of rice. However, they are time consuming (AM/AP ratio), expensive (crystalline polymorphism, DSC), or do not provide enough information. Taking into account that the compounds of milled rice flour are the same as those of rice grains, the measurement of the pasting properties (PP) of rice flour could be a quick and easy method for elucidating rice grain properties. Thus, the determination of the PP of rice with the RVA has been made an official method (61-02.01) by the American Association of Cereal Chemists International (AACCI). de la Hera, et al. (2013) characterized the PP of rice flours of different particle sizes from long and short rice grain varieties appreciating important differences among them.

Apparent viscosity of cake batters and quality of flour for cake making

Cake batters can be considered a complex food system in which air is mechanically dispersed in a continuous liquid phase containing dissolved or suspended dry ingredients, such as sugar and flour. The incorporation of air cells during mixing generates a wet foam that is converted by baking into a solid foam (cake). The quality of cakes depends on the balanced formulae, aeration of cake batters, stability of fluid batters in the early stage of baking and thermal-setting stage. Starch represents the main ingredient acting in two ways: during batter mixing, starch with the other components of flour hinders fat coalescence by increasing the viscosity of the aqueous phase, while during baking starch is responsible for the transformation of an aqueous, fluid batter into a solid, porous cake structure. Therefore, the measurement of the apparent viscosity of the batter after mixing all the ingredients by the RVA can be an interesting indicator related to the quality of the final product. Apparent viscosity has been correlated with some quality attributes of cakes, such as volume and texture (Edoura-Gaena, et al., 2007, Turabi, et al., 2008).

One of the most crucial parameters in flour for cake making is the temperature of starch gelatinization, which has a strong influence on the expansion of air bubbles during the baking stage before the cake sets. In many cases, starch gelatinization is related to the PT assessed by the standard RVA method (mixing 3 g of flour into 25 mL of water). Kweon, et al. (2010) observed a better correlation between the pasting properties of flour dispersed in 50% sucrose solution with the final product than dispersed only in water. Therefore, it is important to note that, depending on the ingredients, the process and especially the desired indicators for the quality of the final product, optimizing the conditions of the RVA analysis could be an easy and effective way to improve the relevance of the data.

Interactions between starch and other compounds

Starch and flour are among the most widely used ingredients in the food industry. Besides being the key ingredients in bread and other bakery products, they are used as thickening agents in numerous dishes. These thickening properties are based on the capacity of starch granules to absorb water and swell. These events constitute the basis for the use of starch and flour in the preparation of fillings, sauces, creams and dairy desserts, and other products. However, occasionally the properties of native flours/starches are not suitable for their use in certain products and they must be modified or supplemented with other ingredients or additives, such as hydrocolloids, sugars and polyols, proteins or lipids.

The assessment of the viscosity over heating-cooling cycles by the RVA provided valuable information on the interactions between starch and hydrocolloids (Martínez, Macias, et al., 2015), sugar and polyols (Martínez, Pico, et al., 2015a) and fatty acids (Blazek, et al., 2011). The knowledge of these interactions could be the basis for optimizing food industrial processes and achieving final products with desired properties.

Extruded products and the measurement of the degree of cook

Extrusion is a high-temperature-short-time (HTST) physical treatment during which the ingredients are subjected to high temperatures and mechanical shear at relatively low levels of moisture. Extrusion allows starch gelatinization, denaturation of protein, microbial reduction, enzyme (in)activation and color changes, the extent of which are dependent on the conditions of the extrusion. Due to the fact that starch is fully or partially gelatinized, the curve follows a different pattern and therefore should be interpreted according to the three sections displayed in Figure 1 (Whalen, 2007):

1. Left section. This is the cold viscosity. Swelling starch granules or products of high shear systems display elevated viscosities.
2. Middle section (transition products). The transition from the cold viscosity section to the middle section reflects raw features or less-cooked characteristics in a product.
3. Final viscosity (setback). This is the gel set of the cooked RVA sample and is the recombination of the starch in the sample.

These results could provide valuable viscosity indicators with a view to optimizing the extrusion process as well as the extrusion conditions as a function of the final utility of these flours. In fact, Martínez, Calviño, et al. (2014) and Martínez, Rosell, et al. (2014) characterized the cooking degree of wheat- and rice-extruded flours respectively. In addition, the quality or the cooking degree of final extruded products, such as textured vegetable proteins, ready-to-eat breakfast cereals and direct expanded and third generation snacks, could also be assessed by measuring the PP of their milled counterpart (Whalen, 2007). Therefore, the RVA could serve as a useful tool to characterize extruded products and their degree of cook.


Figure 1: Wheat flours subjected to different extrusion conditions. Discontinuous black line depicts fully gelatinized wheat flour (the most severe extrusion conditions). Temperature profile (discontinuous points).

Quality indicators for hydrocolloids and fibers

Hydrocolloids are water soluble macromolecules of high molecular weight which, by binding a large quantity of water, modify the rheology of aqueous systems to which they are added. Their functionality depends on their molecular weight, monosaccharide distribution, degree of acetylation, substitution or esterification, and process conditions among others. Hydrocolloids such as agar have strong gelling properties, great water holding capacity and their gels are thermo-reversible. Two key quality parameters of agar are the gelling and melting temperatures, usually analyzed by traditional time-consuming methods (Freile-Pelegrín & Robledo, 1997). However, the proper selection of the RVA setting parameters, such as heating and cooling rate, could serve as a tool to analyze the gelling and melting temperature by assessing changes in the viscosity profile (Figure 2).

Figure 2: RVA heating-cooling cycle displaying the gelling and melting temperatures of agar.

Simulation and monitoring of processes

Many food products are subjected to multi-step processes whose control will be crucial not only for improving the quality of the final product, but also for optimizing process efficiency by increasing yield or diminishing costs. Simulation of these processes on a small scale can be a great way of testing without wasting large amounts of raw materials or energy. In this way, Trivedi, et al. (2008) used the RVA to manufacture rennet-casein-based processed cheese spreads studying the effect of different starches. The RVA was also used to cook pudding mix and to monitor the viscosity by assessing its pasting properties in the step process (Alamri, et al., 2014). In addition, Krasaekoopt, et al. (2004) measured the viscosity of yogurt during fermentation at 45°C and observed clearly the onset of gelation. Therefore, the RVA could be interesting both as a tool to simulate and monitor step processes at a small scale where variables such as mixing rate and the temperature profiles would be well controlled all through the step.

Enzymatic reactions

The enzymatic hydrolysis of starch is one of the most important enzymatic reactions. Liquefaction is a key step in the starch hydrolysis process used to produce syrups, including those used in the industrial production of microbial fermentation products such as alcohol, organic acids, amino acids and antibiotics. Liquefaction is commonly achieved through the dispersion of insoluble starch granules in an aqueous solution, followed by partial hydrolysis at a relatively high temperature using thermostable α-amylases, which are endoglucanases that catalyze the hydrolysis of internal a-1,4-glycosidic linkages in starch. These reactions must be controlled and with that purpose several methods have been used. The degree of liquefaction is usually monitored by measuring the amount of reducing sugars of the starch hydrolysate and expressed as dextrose equivalent (DE), which is defined as the total reducing sugars expressed as dextrose and calculated as a percentage of the dry substance. However DE determination requires the use of chemical reagents and can be time consuming, thus alternative ways of process monitoring could be very useful.

Morphological and structural changes in starch granules during liquefaction give rise to variations in the viscosity of starch slurries as the reaction progresses. Changes in the viscosity as a function of time is an important indicator to monitor the enzymatic reactions (Li, et al., 2015). Besides assessing the viscosity, the RVA can also be used as a container in which enzymatic reactions can be carried out under controlled conditions of temperature and shear. In addition, the measurement of the pasting properties of flours modified by enzymatic amylolysis can provide useful indicators of not only flour functionality but also the degree of starch amylolysis (Martínez, Pico, et al., 2015b).

Conclusions

The RVA has been widely used and is well known for assessing the pasting properties of flour or starch. It is important to highlight its versatility taking into account that the measure of the viscosity in heating-cooling cycles can provide many useful indicators for quality assessment of final products and raw materials. In addition, its features make it suitable for simulating step processes at a small scale under controlled conditions, as well as monitoring changes in the viscosity as the manufacturing process progresses. Therefore, the RVA can serve as a versatile tool for elucidating multiple quality indicators which could help to optimize many different food processes.

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