Value-Added Foods: Characteristic, Benefits, and Physical Properties

Abstract

The growth of diet-related diseases (obesity, diabetes, osteoporosis, and cardiovascular diseases) is becoming an important societal concern and a challenge for a more sustainable society. This has developed important trends in food consumption, including the increasing demand for innovative food with natural attributes and with health claims (foods with added value, enriched foods, and functional foods). The physical properties of food are essential for scientists and engineers at solving the problems in food preservation, processing, storage, marketing, consumption, and even after consumption. In this chapter, we introduce the utilization of physical properties in enriched foods evaluation. The correlations were found between physical properties and other characteristics of foods.

1. Introduction

Nowadays, people think deeply about how can their diet influence health and prefer to eat foods that can help them to get a better lifestyle. This situation support development of important trends in food consumption, which has seen, among others, the growing consumer interest in foods with natural and health properties. The category of food with health benefits includes enriched food with bioactive compounds, such as phenolics, mineral compounds, vitamins, and natural colorants, while healthy food is food without synthetic additives and human interventions, considered by consumers harmful for their organism [1]. The main goal of functional foods is to support the human body, strengthen immunity, or reduce the body’s susceptibility to civilization diseases. This concept of functional foods is originated from Japan (1984) [2]. The Japanese government defined a new product category, Food for Specific Health Uses (FOSHU), as “food containing an ingredient with functions for health and officially approved to claim their physiological effects on the human body” and produced a dedicated legislative framework. Japan was followed by the United States, which in the ‘90s developed the first health claim act, but without providing a formal definition of functional food. European countries acquired the functional food concept more than 10 years later when the European Parliament and Council introduced the regulation on nutritional and health claims (Reg. (EU) n. 1924/2006); but also, in this case no formal definition was mentioned. The research interest in functional food experienced a steep increase only in the 21st century and this globally growing attention has tremendously influenced their market, the size of which was estimated at USD 162 billion in 2018 and was projected to reach USD 280 billion by 2025 with an annual growth rate of around 8% [3]. On the other hand, the term “nutraceuticals” is a combination of two words—nutrition and pharmaceutical; it is a substance that can be considered as food, which provides health benefits in that it can prevent or cure diseases. Nutraceuticals are health-promoting compounds or products that have been isolated or purified from food, providing a positive effect against some chronic diseases [4] and are generally sold in medical form (pills). Functional food products can be consumed as part of a daily diet, and medical food is sold under the supervision of doctors.

In recent years, value-added produce has gained increasing interest among small-scale produce growers. Agriculture defines one approach to adding value to a raw agricultural commodity as changing its physical state or form by processing and transforming it into a sellable product with a higher value. Some examples include making fruit jams from fruits or making fermented or pickled vegetables from vegetables. Previous studies also reported that growers were interested in adding value to their products by making jams and jellies. Value-added production enables growers to utilize excess fresh produce to make finished products to capture losses from unmarketable produce, provide more customer choice, and increase profitability [4, 5].

1.1 Plant-derived foods with added value

Plant-derived foods with added value are divided into primary and secondary metabolites; primary metabolites are plant components important for growth, while secondary metabolites are not essential for growth, but are important for plant survival mechanisms. Primary metabolites include proteins, beta-glucans, and omega-3 fatty acids. Plant proteins include texturized vegetable protein, soy protein isolate, and amino acids; these proteins act as functional ingredients in foods that can help to decrease the amount of meat consumption, connecting with positive effects to decreasing fat and cholesterol in the diet. Beta-glucans acting as foods with added value due to decreasing cholesterol absorption can be especially found in higher amounts in oat and barley. Omega-3 fatty acids, found in higher amounts in flaxseed and chia seed also act as a functional food due to their properties, such as reducing platelet aggregation. Phytoestrogens, antioxidants, vitamins, tocopherols, steroids, and gamma-linolenic acid are considered as other very important secondary metabolites. Phytoestrogens, compounds in plants, especially found in soybeans, flaxseed, and alfalfa plants, are used more to produce enriched foods due to the possibility of decreased post-menopausal cancer development. Antioxidants, such as flavonoids, act as functional compounds by quenching reactive oxygen species. Vitamins act as functional compounds by preventing deficiencies and are rich in fruits and vegetables; certain vitamins, such as vitamins C and E, also act as quenchers of reactive oxygen species. Tocopherols, which belong to vitamin E compounds found in oilseeds, act as quenchers of reactive oxygen species. In oilseeds, steroids are found that act as functional components and reduce the absorption of cholesterol [5, 6].

Agricultural and agri-food systems are influenced by trends that could jeopardize their future sustainability. The population is growing very quickly and people have started getting afraid about their food sources and are changing their dietary preferences. Persistent poverty, inequality, and unemployment constrain access to food and hamper the achievement of food security and nutrition goals. Agricultural production is limited by the increasing scarcity and diminishing quality of land and water resources, as well as by insufficient investment in sustainable agriculture. Climate change is increasing, which has a negative impact, affecting yields and rural livelihoods, while agriculture continues to emit large amounts of greenhouse gases [7]. The global increase in demand for food and the limited land area available prompt the search for alternative food sources, rich in biologically active compounds. Medicinal herbs, edible flowers, and wastes from food industries (coffee skin, and cacao shell) can be interesting for the production of foods with added value. Edible flowers have become a culinary trend, referred to in international culinary magazines, such as Bon appetite “How to use edible flowers in salads, cocktails, and more” and Food and Wine “The Edible Flower.” The clients of edible flowers are gourmet restaurants and their associated food service operations, and grocery stores. So, eating edible flowers is a new trend, described as one of the “six trends of food and drinks in gastronomy” [8, 9]. Edible flowers possess nutritional value—being rich in moisture, carbohydrates, and protein, and being low in lipids. They also contain interesting amounts of ash, including dietary minerals, such as calcium, iron, potassium, magnesium, phosphorous, and zinc. Furthermore, they contain bioactive components, such as phenolic compounds, which contribute to their high antioxidant activity, while also conferring color and aroma. Other biological effects include antimicrobial and anti-inflammatory activities that are also reported to inhibit cell proliferation, turning them into a potential ally for cancer treatment and prevention. Still, it is important to bear in mind what amounts need to be ingested for these health effects to be effective on the human body. From this point of view, many of these possible health claims are not yet established through recommended intake dosages [9, 10].

1.2 Animal-derived foods with added value

Animal-derived foods with added value are enriched mainly with bioactive compounds, such as omega-3 and six fatty acids, conjugated linolenic acid, small peptides, whey and casein, and glucosamine and chondroitin sulfate. Omega-3 fatty acids include alpha-linolenic, docosahexaenoic, and eicosapentaenoic fatty acids. Soy and canola oils, walnuts, and flaxseed belong to one of the best sources of alpha-linolenic acid. The main animal source of these fatty acids is fatty fish, such as salmon, tuna, and cod. Omega-3 and six fatty acids have a positive impact on immunity, modulating inflammation, and protecting against neurodegenerative diseases. Omega-6 fatty acids include linolenic, gamma-linolenic, and arachidonic fatty acids. Vegetable oils, nuts, and whole grains are considered as main plant sources. Conjugated linoleic acid is a fatty acid present in milk and related products that reportedly acts as a functional compound due to its properties to reduce cancer risks and adipose differentiation; however, a fatty liver may develop as a side effect. Whey and casein are milk proteins that act as functional ingredients by being easily digested and absorbed, and help in building muscle mass; small peptides function in the same manner. For collagen formation are required glucosamine and chondroitin sulfate, which as functional compounds, can alleviate pain associated with osteoarthritis; however, this claim must be supported by more observations [5]. Attractive source for the production of animal-derived foods in the food industry can be edible insects.

Edible insects as an alternative protein source for human food are interesting in terms of low greenhouse gas emissions, high feed conversion efficiency, low land use, and their ability to transform low-value organic side streams into high-value protein products. More than 2000 insect species are eaten mainly in tropical regions [11]. Eating insects by humans is not a new concept; it occurs globally but is still rare in Europe. Why not eat insects? Is it worth it? The answer is simple—definitely yes. Entomophagy has several advantages. First of all, insects are a good source of protein, essential fats, and antioxidant peptides. Many insects are rich in microelements, such as iron, calcium, zinc, and vitamins. Secondly, insect breeding is environmentally friendly. Insects emit significantly fewer greenhouse gases and ammonia than most livestock. Moreover, insects require less space, feed, and water for breeding than livestock. Economic factors are also important. Insect rearing can be low-tech or very sophisticated, depending on the level of investment. For these three main reasons, insects have been highlighted as an important food source in response to the growing concerns about the future of world food security [12, 13].

1.3 Microbial-derived foods with added value

Value-added foods derived from microbial sources include probiotics, prebiotics, symbiotics, and synbiotics. Probiotics are natural microflora that occurs in the gut, such as Lactobacillus casei or numerous Bifidobacterium species, which improve health. Prebiotics are dietary compounds that promote the growth of probiotic bacteria. Symbiotics contain both—probiotics and prebiotics, combined randomly, while synbiotics contain specific probiotics and prebiotics mixed together to benefit one another. Value-added foods derived from microbial sources promote the growth of probiotic bacteria so that the growth of pathogenic bacteria is limited and these properties can improve the immune system of the human body [5].

1.4 Miscellaneous-derived foods with added value

Some sources and foods with added values are derived from miscellaneous compounds, such as algae and mushrooms [5]. Nowadays global demand for foods enriched by macroalgal and microalgal components is growing, and algae are increasingly being consumed for health benefits beyond the traditional considerations of nutrition and health [14]. Algae contain a large number of bioactive components with several positive biological activities, such as antioxidant, antimicrobial, and anticarcinogenic. Algae are also considered as a source of fiber in the form of sulfated galactans or carragenates in red algae, fucans, alginates, and laminarin. A diet rich in dietary fiber has a positive impact on human health that reduces the risks of cancer, cardiovascular diseases, diabetes, obesity, hypercholesterolemia, and digestive problems. The diet with higher dietary fiber content showed good immunological activity. Alginate in Undaria pinnatifida showed a positive effect on cardiovascular disease, and alginic acid was demonstrated to reduce hypertension in hypertensive rats. Alginic acid, xyloglucans in Sargassum vulgare and sulfated fucans in Undaria pinnatifida have shown powerful anti-viral activity against herpes type-1 and cytomegalovirus in humans, which can be used in future medicine and pharmacy [15].

The rising demand for functional food free from synthetic chemicals indicates the awareness of people on quality food. The excellent texture and unique flavor of edible and medicinal mushrooms make them universally accepted by all age groups. Due to the production of a large variety of secondary metabolites with exceptional chemical structures and interesting biological actions they are a reservoir of valuable chemical resources. However, there is very little awareness of mushrooms as healthy food and as an important source of biologically active substances with medicinal value [16]. Mushrooms are highly nutritive, low-calorie food with good quality proteins, vitamins, and minerals. Mushrooms are an important natural source of foods and medicines. By virtue of having high fiber, low fat, and low starch, edible and medicinal mushrooms have been considered to be an ideal food for obese persons and for diabetics to prevent hyperglycemia. They are also known to possess promising antioxidative, cardiovascular, hypercholesterolemia, antimicrobial, hepatoprotective, and anticancer effects. More than 3000 mushrooms are mainly edible species but, only 100 species are cultivated commercially, and only 10 species are used at industrial scale and their global and economic value is now increasing slowly due to an increase in their value as a food as well as their medicinal and nutritional values [17].

1.5 Perspectives of foods with added value

Cereal products (bread, pastries, pasta, cookies, bars, etc.) are an important source of macro and micronutrients that are essential for human health and are widely consumed by all classes of society. Their high consumption, as a snack has earned it the matrix for food fortification. Research has been done on the enrichment of these products with legumes, oil crops, nuts, medicinal herbs as well as new alternative food sources (edible insects, flowers, algae, and mushrooms). Studies have shown that enrichment can influence the physicochemical and nutritional properties of cereals products. The regular consumption of enriched cereal foods can reduce the risk of several forms of cancer, coronary heart diseases and helps to regulate blood glucose levels and cholesterol, chemical and nutritional properties of cereals products. Mechanisms of the effect of phytochemicals from alternative food sources—edible insects, flowers, algae, and medicinal mushrooms are the subject of many planned studies with a link to practical use. These perspective sources can be attractive for enriched innovative cereal-based products with added values (Figure 1).

Figure 1.

Algae, edible flowers, and medicinal mushrooms as a perspective source for innovative cereal-based products with added value (Ivanišová).

2. Mechanical properties of candies

The quality of the confectionery represents the main aim of scientific studies. Authors [18] are interested in the study of the influence of process operating conditions and candy composition on the unsteady behavior of the cooling process of hard candies to improve final product quality. Hard candy is often sold as drops, lozenges, barrels, rods, and sticks. Hard candy comes in the form of ribbons and disks of cut rock, with colorful and unique shapes worked into the interior of each piece. Commercial hard candies, with water content in the range from 2–4%, typically have glass transition temperature values about 25 to 45°, depending on the sugar composition and moisture [19]. The sensory analysis is often applied to the determination of the firmness of the candies. The firmness can be slightly affected by the presence of watermelon flours that conferred a certain fibrous and graininess to the candy, even if most of the sensory scores were in the category of like very much (score 8) [20].

The aim of the study is the determination of the mechanical properties of the honey candies by compression method and discussion of the firmness and brittleness of the material during storage in dependence on the variation of the water content.

2.1 Material and methods

The mechanical properties of eight sorts of honey candies prepared by a selected company were studied by methods of compression between two parallel metal plates. Four sorts of honey candies were made from cane sugar and the next four sorts of honey candies were made from coconut sugar. Each sort of honey candies was coated in a different sort of starch or powdered sugar. There were four possibilities, coating with the powdered sugar (saccharose), corn starch, rice starch, and potato starch. We obtained eight combinations of the samples coated with different sugar. Samples of candies were then stored for 2 months at room temperature in paper wrappers and a test measurement was realized every 30 days. There were performed three measurements, at the beginning of the storage, after 30 days, and after 60 days. Five samples of each sort of coated candies were measured by the testing compression equipment for each of eight combinations of the candies coating.

Experimental compression equipment was developed at the Institute of electrical engineering, automation, informatics, and physics of the Slovak University of Agriculture in Nitra, Slovak Republic. The equipment includes the stepped motor that serves on the control of the screw shifting, power source, screw, PLC modules, and PLC software. Equipment was composed of the PLC X20CP3583 with processor Intel Atom. The universal mixing external module X20DM9324 with eight digital inputs and four digital outputs was used with a module of bus X20BM11 and terminal board X20T12. Digital servo amplifier Acopos 8V1016 was used for the control of the stepped motor.

The force sensor LT Lutron FG 6100SD was used for the measurement of the loading force. The sensor worked up to maximal load 1000 N with uncertainty ±0.1 N. Sensor of displacement WDS-500-P60-SR-U (Micro-Epsilon, Czech Republic) with 14-bit A/D converter, up to 500 mm with uncertainty ±0.5 mm and with the software uncertainty ±0.001 mm, was used.

2.2 Results of the compress properties

The firmness of the confectionery can determine as the force needed for the compression between teeth or between tongue and palate, from the sensoric point of view. Force (N) and compression (mm) of the candies were measured by test compression equipment. Dependencies of the force on the compression were determined by the compression of the candies between two parallel metal plates of the compression equipment. The measurement was performed at the speed of compression of 10 mm.min⁻¹. Failure strength was obtained from the maximum of the loading curves as the index of the confectionery quality. The compression diagrams of coconut sugar coated with powdered sugar for the five samples immediately after manufacturing are presented in Figure 2 as an example of the 40 measurements, which were realized.

Figure 2.

Compression diagram of five honey candy samples with coconut sugar coated with powdered sugar.

The regression equations of the curves from Figure 2 are shown in Table 1. Failure strength was obtained from the peaks of the curves. The peaks represent the firmness of the candies. The samples were measured immediately after manufacturing and they had low moisture, therefore they were brittle, and they cracked at the low loading up to 300 N. Crack was observed on the samples with the peaks. The sample without a peak was not cracked. Failure strength of the coconut candies coated with the powdered sugar was in the level 105–355 N and the compressions were about 1.4 mm.

Candies	Coating	n	Regression equations	Coefficient of determination
Coconut sugar	Powdered sugar	1	y = 33.4x^2 + 139.24x – 4.5261	R^2 = 0.9941
		2	y = 121.85x^2 + 37.017x – 45.107	R^2 = 0.9995
		3	y = 158.54x^2–1.5876x – 2.,776	R^2 = 0.9954
		4	y = 39.189x^2 + 130.99x – 84.432	R^2 = 0.985
		5	y = 104.34x^2–0.4932x – 4.6281	R^2 = 0.9991

Table 1.

Regression equations of the honey candies with coconut sugar coated by with powdered sugar.

The mechanical properties of all combination of coconut coating candies are presented in Table 2.

	Coconut sugar—powdered sugar		Coconut sugar—corn starch		Coconut sugar—rice starch		Coconut sugar—potato starch
n	Force	C	Force	C	Force	C	Force	C
	(N)	(mm)	(N)	(mm)	(N)	(mm)	(N)	(mm)
1	103.6	0.690	128.9	0.659	207.2	1.530	205.6	1.110
2	349.7	1.659	201.4	1.029	368.9	2.009	120.2	1.019
3	166.0	1.110	558.8	2.029	111.5	0.840	508.1	1.620
4	147.2	1.279	352.2	1.579	322.8	1.179	529.0	1.770
5	540.6	2.379	387.7	1.319	356.4	1.439	248.1	1.269
Average	261.413	1.423	325.783	1.323	273.356	1.399	322.194	1.357
SD	182.187	0.637	168.122	0.522	110.752	0.433	185.193	0.325
VC (%)	69.693	44.787	51.605	39.480	40.515	30.978	57.478	23.945

Table 2.

Mechanical properties of honey candies made from coconut sugar after the first measurement at the beginning of storage.

C, compression; SD, standard deviation; VC, coefficient of variation.

The compression diagrams of cane sugar coated with powdered sugar for the five samples immediately after manufacturing are presented in Figure 3 as an example of the 40 measurements, which were realized. The regression equations of the curves from Figure 3 are shown in Table 3. Dependencies are realized without peaks. The candies did not crack at the compression up to 500 N. The compression reached values of about 1.6 mm. The candies were brittle but simultaneously harder than coconut sugar candies.

Figure 3.

Compression diagram of five honey candy samples with cane sugar coated with powdered sugar.

Candies	Coating	n	Regression equations	Coefficient of determination
Cane sugar	Powdered sugar	1	y = 74.653x^2 + 270.29x - 34.46	R^2 = 0.9964
		2	y = 118.84x^2 + 198.26x - 41.114	R^2 = 0.9993
		3	y = 93.38x^2 + 278.75x - 28.937	R^2 = 0.9997
		4	y = 113.05x^2 + 202.94x - 59.155	R^2 = 0.9996
		5	y = 90.033x^2 + 255.38x - 118.71	R^2 = 0.9997

Table 3.

Regression equations of the honey candies with cane sugar coated with powdered sugar.

Measurement of the honey candies realized at the beginning of storage was characterized by the cracking of the coconut candies of all coatings at the low forces. Cane candies did not crack even if had low moisture. The phenomena could be the cause of the bigger hardness. The structure and the size of the starch molecules affect the mechanical properties of the starch coating. The amount of amylose influence the firmness of the starch coatings and gels and the molecular profile of amylopectin participates in the properties of the films [21].

The mechanical properties of all combination of cane coating candies are presented in Table 4.

	Cane sugar—powdered sugar		Cane sugar—corn starch		Cane sugar—rice starch		Cane sugar—potato starch
n	Force	C	Force	C	Force	C	Force	C
	(N)	(mm)	(N)	(mm)	(N)	(mm)	(N)	(mm)
1	556.5	1.620	524.8	1.370	533.2	1.579	530.0	1.620
2	556.7	1.589	534.2	1.579	514.2	1.659	557.8	1.800
3	542.4	1.409	510.2	1.719	526.0	1.569	521.0	1.569
4	547.4	1.599	497.3	1.740	528.9	1.679	537.3	1.610
5	557.2	1.679	507.1	1.279	520.3	1.630	557.9	1.780
Average	552.016	1.579	514.711	1.537	524.522	1.623	540.793	1.676
SD	6.737	0.101	14.654	0.206	7.419	0.048	16.616	0.106
VC (%)	1.221	6.417	2.847	13.430	1.414	2.976	3.072	6.339

Table 4.

Mechanical properties of honey candies made from cane sugar after the first measurement on the beginning of storage.

C, compression; SD, standard deviation; VC, coefficient of variation.

2.2.1 Results after 30 days of storage

Mechanical properties of the honey candies were measured after 30 days of storage at room temperature in paper wrappers. Again, were realized measurements of the coconut and cane candies of the coating by the powdered sugar (saccharose), corn starch, rice starch, and potato starch. There were realized 40 compressions of all combinations of the candies. Failure strength was obtained from the peaks of the curves. The candies had bigger moisture and there was more plastic because the samples did not crack. The mechanical properties of all combination of coconut coating candies are presented in Table 5. The mechanical properties of all combination of cane coating candies are presented in Table 6.

The samples after 30 days of storage were not so brittle but were more plastic, accordingly, the bigger forces were needed for deformation. The partial sticking occurred, but the samples were able to separate. Cane candies obtained after 30 days of storage had higher failure strength than at the beginning of storage. The cracking of the samples was observed at a force of 600 N. The coconut candies were more brittle than cane candies. The moisture of the candies was augmented in the direction from the border to the center of the confectionery.

2.2.2 Results after 60 days of storage

Mechanical properties of the honey candies measured after 60 days of the storage at room temperature in paper wrappers were realized also as 40 compressions of all combinations of the candies. The candies had a high amount of moisture at the time and they were plastic. The moisture content of the candies was from 4.5 to 5.5%, which was the reason for the gluing of the candies. It was not possible to separate them. It was not possible to measure coconut candies. The cracking was not observed. The candies had a texture like krowka candy. Only cane candies were measured. The mechanical properties of all combination of cane coating candies are presented in Table 7. The moisture of the candies significantly increased after 60 days of storage. The gluing of the cane candies was smaller than the coconut candies. The moisture of the cane candies was at the level of 4.5–5.5%. The cane glued candies were possible to separate and measure. The coconut glued candies had moisture at the level of 7–6%. The glued coconut candies changed their shape and the measurement was impossible. The brittleness of the hard candies was decreased in consequence of the increased moisture. The amount of moisture content of the hard confectionery is from 0.8 to 1.2%. Hard candies with a bigger amount of moisture than 2.5% are displayed by the deformations without cracks [22].

The confectionaries were only compressed, but not cracked after compression. Hard confectionaries made from cane sugar save relatively high hardness, even if condensed at relatively high temperatures.

Results obtained at the measurement of the firmness and hardness of the confectionaries demonstrate that the important parameter indicating the firmness or brittleness of the material is the measure of the saturation of the material by the wet air. Different proportion of moisture affects the plasticity of the hard candies. An increasing of the moisture inflicts a decrease of the hardness and the increasing of the plasticity of the material. Increased moisture during storage contributes to the gluing of the hard candies.

3. Rheologic properties

Rheological parameters could be used in many areas, such as product quality evaluation, engineering calculations, and process design. Understanding the flow behavior is important, for example, in the determination of the size of the pump and pipe and energy requirements. For designing food engineering processes in connection with energy and mass balances, rheological models from experimental measurements can be used. Influence of processing on rheological properties must be known for process control [23, 24].

Fluids could be divided by Newton’s law of viscosity

$$\tau =\eta gradv$$E1

where τ is shear stress; grad v is the gradient of velocity (in many cases called as shear rate).

Newtonian fluids are following this law. Dynamic viscosityη, as the slope in the shear stress shear rate dependency, is constant and independent of the shear rate for these liquids [25].

The viscosity of Newtonian materials is influenced only by temperature and material composition, and it is independent of the shear rate and previous shear history. Examples of Newtonian materials are oils, water, beer, wine, milk, clear fruit juices, sucrose solutions, most honey, cream (till fat content of 40%), butter (above the melting point), etc. [24, 26, 27].

For the characterization of Newtonian fluids is also often used kinematic viscosityν, which is defined as a ratio between dynamic viscosity and densityρ of fluid at the same temperature

$$\nu =\frac{\eta }{\rho }$$E2

The physical unit of kinematic viscosity is m²·s⁻¹, but for liquids is more often used unit mm²·s⁻¹. An important role is also played by the fluidity φ of materials, which can be expressed as a reciprocal value of dynamic viscosity and that is why its unit is Pa⁻¹·s⁻¹ [28].

$$\phi =\frac{1}{\eta }$$E3

Values of fluids' dynamic viscosity are changing from 10⁻⁵ Pa·s for some gases to values of the order of Pa·s for some oils.

Oppositely there are non-Newtonian fluids that are affected by the shear rate. For this type of liquid, apparent viscosity η is applied, which is expressed in the same units as dynamic viscosity (Pa·s or in mPa·s) [26].

On the base of the shearing time, non-Newtonian foods could be separated into two groups—time-independent and time dependent. Apparent viscosity of time-independent fluids is affected only by the shear rate, but in the case of time-dependent fluids, it is also the time duration of the shearing. In time-independent flow, shear-thinning (pseudoplastic) and shear-thickening (dilatant) behavior could be observed, which depends upon whether the viscosity decreases or increases with an increase in shear rate [26]. Shear-thinning and shear-thickening fluids could be described by the power law model (Ostwald—de Waele equation)

$$\tau =K{\left(gradv\right)}^n$$E4

where K is coefficient of consistence and n is flow behavior index [25]. For the shear-thinning (pseudoplastic) fluids is index n lower than 1 and for the shear-thickening fluids is this index higher than 1. Newtonian liquids can be considered as a special case of this model, where n is 1 and K corresponds to dynamic viscosity. The slope in stress—rate dependency is not constant for non-Newtonian fluids. In pseudoplastic flow behavior, it could be observed that shear stress is increasing while viscosity (or friction between layers) is decreasing with increasing shear rate, therefore it is called shear-thinning behavior [24, 25]. Due to the shearing, entangled, long-chain molecules are straightening out and become aligned with the flow, therefore it reduces the viscosity. Different shear rates mean different viscosities and for non-Newtonian fluids apparent viscosity η_a is applied, which can be defined as a ratio of shear stress and corresponding shear rate

$${\eta }_a=\frac{\tau }{\mathit{grad}v}$$E5

When we apply Eq. (4) together with previous Eq. (5) then apparent viscosity could be derived

$${\eta }_a=K{\left(\mathit{grad}v\right)}^{n-1}$$E6

Into shear-thinning materials could be included concentrated fruit juices, melted chocolate, French mustard, mayonnaise, dough, butter, honey, dairy cream, fruit and vegetable purees, etc. [23, 24, 26, 29].

Fluids, for which increasing in shear rate results in an increase in internal friction and apparent viscosity, are called shear-thickening fluids. When the increase in viscosity is connected with volume expansion, these fluids are called dilatant fluids. Dilatant flow behavior could be observed when the shear stress is increasing with the shear rate increase. Viscosity is also increasing with increasing shear rate and that is why this case is referred to as shear-thickening behavior [24, 25].

Shear-thickening materials are not very common among the food materials, but this behavior can be observed in some honey, corn starch suspension, etc. [26, 24].

Bingham plastic fluids are fluids that remain rigid when the value of shear stress is smaller than the yield stress τ_o, but when it overcomes this value they flow like a Newtonian fluid. That is why these liquids could be used in this equation

$$\tau ={\tau }_0+K\mathit{grad}v$$E7

For example, mayonnaise, tomato paste, ketchup, concentrated pulpy and granular suspensions, chocolate can be included into Bingham plastic fluids [29, 24].

When the stress is lower than the yield stress then the material behaves like a solid, its energy is stored in small strains and a flat surface is not obtained under the influence of gravity. This behavior is necessary for designing processes and assessing the quality of materials, such as butter, yogurt, and cheese spreads [29]. Sometimes also in non-Bingham plastic fluids, yield stress must be applied and only after it exceeds its value, the flow is enabled. In this case, the dependency of shear stress on the shear rate is not linear like in Bingham plastic fluids [24, 25].

There are other models for describing the non-Newtonian fluid behavior, for example, Herschel—Bulkley model valid for minced fish paste, raisin paste, and rice flour-based batter, Casson model which was used for molten milk chocolate [23, 24, 25].

Non-Newtonian foods with time-dependent flow properties can be subdivided into thixotropic and rheopectic fluids. In thixotropic fluids, at a fixed shear rate, the viscosity decreases with time, whereas the viscosity of a rheopectic fluid increases with time [26].

Fluids for which shear stress and viscosity are decreasing with time at a fixed shear rate are called thixotropic fluids (shear-thinning fluids with time). Shear thinning with time is caused by the breakdown of the material structure during the continuous shearing. The reason for decreasing viscosity is assumed to be caused by a decrease in the intermolecular interactions within the molecular structure of the material [25, 24]. Thixotropic behavior has been observed for various materials, such as gelatine, condensed milk, mayonnaise, butter, honey, dough, and egg white. [26, 23, 24].

Another type of fluid is rheopectic fluid (shear-thickening fluids with time), where shear stress and viscosity increase with time at a fixed shear rate, and the material structure builds up during the continuous shearing. Viscosity (or shear rate) increase arises from the intermolecular interactions that cause the friction to increase with time at a constant shear rate within the molecular structure of the material [25, 24]. Rheopectic behavior is also referred to as antithixotropic behavior. Antithixotropic behavior in food systems is very rare [23].

Fluid foods go through different processes, such as processing, storage transportation, marketing, and consumption, where the temperature is changing. That is why the rheological properties are examined in the temperature dependencies. Arrhenius equation could be used for expressing the influence of temperature on the viscosity, which is set at a certain shear rate [26].

For overcoming energy barriers thus enabling the flow of the fluid food material, the energy is needed, and it is called activation energy E_A. This energy is more readable at higher temperatures and the flowing of the fluid is easier. The dependence of viscosity on the temperature could be modeled by an Arrhenius type equation

$$\eta ={\eta }_0{e}^{-\frac{E_A}{RT}}$$E8

where η₀ is the initial value of dynamic viscosity; R is gas constant; and T is absolute temperature [25, 24].

3.1 Rheologic properties utilization

The piezoelectric-excited membrane device was used for rapid measurement of liquids viscosity and density, the principle is based on the membrane’s resonant frequency and Q factor responses to the damping effects of a surrounding liquid. The authors performed measurements on five samples of oils, which had viscosity in the interval (19.88–1733) mPa·s and density in the range (829–886) kg·m⁻³. Authors stated that viscosity and density measurement could be used at monitoring the oil quality and the determination of the pipeline elements' design; the texture of liquid foods could be achieved by maintaining the viscosity and density control during the production, which will cause high production efficiency and cost-effectiveness; for the treatment of certain vascular diseases, the blood fluidity control is required, so the blood viscosity must be monitored [30]. The density of food materials is needed in many areas, for example in separation processes, pneumatic and hydraulic transports, determination of the power required for pumping, etc. [23].

The food spoilage extent by using an activatable molecular rotor was also examined. The authors measured the viscosities of various liquids (white spirit, pure milk, green tea, edible vinegar, fresh broth, and lemon juice). For fresh broth and lemon juice, the authors examined the influence of different storing conditions on the viscosity values. The viscosity of the fresh broth and lemon juice increased more rapidly at an ambient temperature of 25°C within 9 days and on the other hand, a slower increase was observed in samples stored at a fresh-keeping temperature of 4°C [31].

Texture properties of yogurts (with wheat bran; with different amounts of date fiber; with date fiber and vanilla flavor) were compared [32]. The author's study has shown that fortifying yogurt with 3% of date fiber is an acceptable product with potential beneficial health effects.

Rheological behavior of Galician’s honey was also examined [33]. The authors performed measurements on 11 samples of honey. According to the dependency of apparent viscosity on the shear rate, the authors had concluded that measured honey can be classified as non-Newtonian fluids but indicated that most honey can be included in Newtonian fluids. Non-Newtonian rheological behavior has been detected when low values of shear rate have been applied, whereas, for high values of shear rate, honey tends to have a Newtonian fluid behavior. The authors observed pseudoplastic behavior and fitted it with the Ostwald model. They also proved that honey with higher water content has the lowest viscosity values, whereas honey with high sugar contents shows elevated values of apparent viscosity [33].

The dynamic oscillatory shear rheological characteristics of honey samples (pine, citrus, and flower) from different floral sources were evaluated at three different temperatures (10°C, 15°C, and 20°C). The authors found that all honey samples showed liquid-like behavior (Newtonian flow behavior) [34]. The shear stress of Turkish honey samples increased, while the apparent viscosity remained constant with increasing shear rate. The authors obtained highest complex viscosity for pine honey at 10°C, while the lowest value was found for citrus honey at the same temperature. Temperature dependency of complex viscosity of honey samples was modeled by the Arrhenius model [34].

Rheologic properties of natural and reduced-calorie Israeli honey were also analyzed. Authors found that the viscosity of honey was Newtonian, even in reduced-calorie varieties, and adhered to the Arrhenius equation, where viscosity exponentially decreases with temperature [35].

The temperature and time of constant temperature heating on the rheological properties of light (apple, Ziziphus, and citrus) and dark (black horehound, globe thistle, and squill) types of honey were examined. The authors in their research found that both honey types behave like a Newtonian fluid regardless of the conditions of heating. Another finding of the authors was that light-colored honey, which had lower water contents than dark-colored ones, after heat treatment showed a change in viscosity only at higher heating temperatures in comparison with the fresh untreated control sample. On the other hand, dark-colored, heat-treated honey showed a change in viscosity at all levels of heating temperature [36].

The influence of temperature on the rheological behavior of Portuguese honey was also investigated. The authors found that all honey showed flow independence over time and behaved as Newtonian fluids at the studied temperature and shear rate ranges. For all honey, the authors observed that the viscosity decreased with temperature and good regression coefficients were obtained by fitting the experimental data with the Arrhenius model [37].

At the determination of the temperature dependency of viscosity was observed that viscosity of creamed honey decreased with increase in temperature level for which they applied the Arrhenius equation. The authors had explained that when the thermal expansion occurs as a result of an increase in temperature, less energy is required to breakdown the structure at higher temperature levels due to the reduction of intermolecular forces, increasing intermolecular distance between the molecules, thus causing the reduction of intermolecular forces, finally the decrease in viscosity [38].

The jams typically display a shear-thinning behavior associated with a tendency to reach a yield stress value. This flow behavior has been frequently expressed in terms of the Herschel-Bulkley model. Authors [39] found that viscosity values increased with pectin concentration and decreased with increasing temperature. Arrhenius-type relationships were used to quantify the influence of temperature on the storage modulus, yield stress, and consistency index. The authors also found higher temperature dependence of linear viscoelastic functions for model borojó jam formulations in comparison with the peach jam sample, but it was still lower than borojó commercial jam. On the contrary, the influence of temperature on the yield stress was lower for both commercial jams, whereas the effect on the consistency index was very similar for all examined jam samples [39].

The structural parameters (particle content, particle size, and serum viscosity) influence the rheological properties of Golden Delicious apple purees. It was observed that all the purees presented a shear-thinning behavior, yield stress, and they were not time dependant. The apparent viscosity, yield stress, and elastic modulus decreased as particle size decreased and they increased as insoluble solids content increased. Herschel Bulkley's model was used for the estimation of the yield stress [40].

The flow behavior of two commercial date pastes was of interest. The shear stress–shear rate data were fitted by using six famous models known as Power-law, Bingham plastic, Herschel–Bulkley, Casson, Sisko, and Vocadlo. It was observed that date pastes exhibited the shear-thinning pseudoplastic behavior and that the Casson model was best for describing the experimental data at all temperatures. For the temperature dependence of apparent viscosity, authors used the Arrhenius model with a very high coefficient of determination [41].

Influence of composition on vegetable oils oxidation using differential scanning calorimetry was studied under non-isothermal conditions at five different heating rates, in a temperature range of 100–400 °C [42]. Eight vegetable oils were examined—refined palm oil, olive oil, grapeseed oil, sunflower oil, corn oil, soybean oil, safflower oil, and sesame oil. Activation energy of measured oils was also determined, and the authors found that refined palm oil exhibited the highest values of activation energy. Authors declared that vegetable oils play an important role in the human diet due to their nutritional and sensory properties. Authors also pointed out that oils are susceptible to oxidation reactions during food processing and subsequent storage of food products [42].

The rheological behavior of the pure BIO chocolate at different temperatures was also studied. Authors had observed that the chocolate unambiguously demonstrated plastic behavior and flow curves were fitted by the power-law model (Herschel–Bulkley model), Bingham model, and Casson model, with the best coefficient of determination for the Casson model, but the Herschel-Bulkley model also gave very accurate results of pure chocolate flow curve. It also examined the temperature dependence of apparent viscosity of chocolate in the range from 35°C up to 62°C. It was found that the apparent viscosity decreases in the studied temperature range and this decrease could be fitted by using the power law equation. Authors found from flow curves of pure chocolate that temperature increase had caused shear stress decrease [34].

Changes in tomato powder during storage at different storage temperatures were evaluated for 5 months. Higher storing temperatures had a significant effect on tomato powder qualities, while lower temperature had less effect. Tomato powders have many advantages, including ease of packing, transportation, and mixing. In addition, tomato powder can be used as an ingredient in many food products, mainly soups, sauces, and ketchup. The consumption of tomato and tomato-based products has been associated with a lower risk of developing certain types of cancers [44].

The nutritional, rheological, and sensory properties of tomato ketchup with increased content of natural fibers made from fresh tomato pomace were evaluated, and the results were compared with five commercial products. The rheological properties of the ketchup with increased fiber content depend mostly on total solids and insoluble particle content, but properties remained within the limits for standard tomato products. Rheological properties of tomato products are considered as one of the most important quality attributes since they influence product processing parameters, especially flow properties during transport, as well as consumers’ acceptability [45].

It was stated that ultrasonic pulses could be combined with rheological measurements at the determination of solids concentration in different highly concentrated and industrial food suspensions, such as tomato, vegetable and pasta sauces, seafood powder, strawberry yogurt, and cheese sauce with vegetables. Low-intensity ultrasound-based techniques could be applied to the detection and identification of foreign bodies in food products [46].

3.2 Measurement results

In the following part, examples of rheologic properties measurements are presented. Majority of analyzed materials were Newtonian materials and therefore these properties were studied—dynamic viscosity, kinematic viscosity, and fluidity. In the case of non-Newtonian material, apparent viscosity was determined. Effect of various factors on these properties was investigated. Relations of dynamic (or apparent), and kinematic viscosity to the temperature can be described by decreasing exponential functions Eqs. (9) and (10), and in the case of relations of fluidity to the temperature can be used increasing exponential function Eq. (11).

$$\eta =C{e}^{-D\left(\frac{t}{t_0}\right)}$$E9

$$\nu =E{e}^{-F\left(\frac{t}{t_0}\right)}$$E10

$$\phi =G{e}^{H\left(\frac{t}{t_0}\right)}$$E11

where t is temperature; $t_0$ = 1°C, C, D, E, F, G, H−constants dependent on the kind of material, and on ways of processing and storing. A similar model was used by the author [47].

Temperature dependencies of dynamic viscosity for milk different fat content are presented in Figure 4. From Figure 4 it is clear that the highest dynamic viscosity values were observed for the sample milk with the highest fat content (3.5%) and the lowest dynamic viscosity values were obtained for the sample milk with the lowest fat content (0.5%). The highest viscosity value 1.99 mPa·s was obtained at the lowest measured temperature for milk with the highest fat content and oppositely the lowest dynamic viscosity 1.30 mPa·s was obtained at the highest temperature for milk with the lowest fat content. Decreasing exponential function can be used for the description of presented dependencies and that is in accordance with Arrhenius Equation [23, 25, 43, 48, 49].

Figure 4.

Temperature dependencies of milk dynamic viscosity: Fat content 0.5% (); fat content 1.5% (); fat content 3.5% ().

Figure 5 depicts temperature dependencies of kinematic viscosity observed in analyzed samples of vegetable oils. For both samples, decreasing function describes the dependencies of kinematic viscosity on temperature. In contrast to extra virgin olive oil, sunflower oil showed higher values of kinematic viscosity. This is most likely due to different compositions of oils.

Figure 5.

Temperature dependencies of vegetable oils kinematic viscosity, sunflower oil (), extra virgin olive oil (+).

Fluidity increases with temperature for all beer samples (Figure 6). The proportion of the curves could be caused by the alcohol content because the highest alcohol content 5.4% had beer sample Topvar 12%, and it had the lowest fluidity, and on the contrary the lowest alcohol content 4.3% had beer sample Zlatý Bažant 10%, which had the highest fluidity.

Figure 6.

Temperature dependencies of beer fluidity: Topvar 10% (), Topvar 12% (), Zlatý Bažant 10% (), Zlatý Bažant 12% ().

In Figure 7, temperature dependencies of ketchup's apparent viscosity are presented. It is possible to observe that the apparent viscosity of the ketchup is decreasing with increasing temperature. The progress can be described by decreasing exponential function, which is in accordance with Arrhenius Eq. (8).

Figure 7.

Temperature dependencies of ketchup apparent viscosity−Snico (), Hellmanns (), Heinz (), Hamé ().

Comparable rheological results for ketchup were reported [50]. Approximately similar values of the parameter in the regression equation contributing to apparent viscosity can be found in the paper [51]. It is also visible in Figure 7 that the highest apparent viscosities were obtained for a sample of Ketchup Snico, and the lowest values for Ketchup Hamé, which could be caused by different compositions of ketchup.

It could be observed that density and rheologic properties are influenced by several factors, including fat content, alcohol content, time of storing, composition, concentration, and even added ingredients.

4. Thermophysical properties

4.1 Calorimetric methods

The thermal analysis includes techniques in which a physical property of a substance is measured as a function of temperature [52]. The chosen calorimetric analysis depends on the type of research. Different calorimetric methods can be used for quantitative and qualitative evaluation of biomass, polymeric materials, and various types of foods produced industrially or even fresh fruits and vegetables.

A better understanding of the temperature influence on the food properties allows food manufacturers to improve the processing and reforming quality of products. It is, therefore, important for food science to have analytical techniques to monitor the temperature changes that occur in foods. These techniques are often grouped under the general heading of thermal analysis. Most foods are subjected to variations in their temperature during production, transport, storage, preparation, and consumption. It is possible to use thermal analysis for tracking pasteurization, sterilization, evaporation, cooking, and freezing.

4.2 Thermogravimetric analysis (TGA)

The main applications of TGA are processes, such as evaporation, desorption and vaporization behavior, thermal stability, kinetics of decomposition, and compositional analysis. Thermogravimetric techniques connect the measurement of sample mass during the heating or cooling at a controlled rate (heating rate) or at a set isothermal part for a time. Thermogravimetry is useful for monitoring processes that involve a change in the mass of a food or its individual parts. Thermogravimetric instruments have been specially designed to allow measurements to be carried out under specific environments, controlled pressures, or atmospheres. The mass of a sample may change with temperature or time depending on the occurring specific physicochemical processes. Heating often leads to a reduction in mass because of the evaporation of water or other volatile parts. On the other hand, the mass of food may increase due to the moisture absorption from the atmosphere at specific conditions of storage. The ability to carefully control the temperature, pressure, and composition of the gasses surrounding a sample is extremely valuable for food sciences because it could be used for modeling processes, such as drying, cooking, and absorption of moisture during the storage. Determination of moisture and water distribution in food could be done by thermogravimetric analysis. It is possible to set the final drying temperature as well as the heating rate and a certain temperature to end the drying [53, 54]. The drying process is recorded at each performed analysis. In the process of heating to temperatures above 300°C, it is possible to diagnose the quantity and quality of food composition. The TGA plot is an overlay of thermograms of the same type of food from different manufactured batches showing variations. It demonstrates that TGA is a valuable tool to check if materials conform to set decomposition requirements. The result of TGA measurements is usually displayed as a TGA curve (thermogram), temperature or time dependence of mass. Another important information is the use of the first derivative of the TGA curve with respect to temperature or time (usually linear dependence). This shows the rate at which the mass changes and is known as the differential thermogravimetric (DTG) curve.

Mass changes occur when the sample loses material in one of several different ways or reacts with the surrounding atmosphere. This leads to the production of steps in the TGA curve or peaks in the DTG curve. A number of different effects can cause loss, or even gain of mass in the sample and produce steps in the TGA curves. TGA thermal decomposition thermogram of biological materials generally follows similar patterns in 2 to 4 steps. Foods are multi-ingredient materials therefore more steps are expected [55]. Another important factor in TGA or other thermal analysis is the heating rate. The systematic deviation exists between the true sample temperature and the measured temperature, and it is heating-rate dependent. Real samples (mixtures) can of course exhibit quite different thermal conductivity behavior. If the sample undergoes chemical reactions, the temperature region in which the reaction occurs is very much dependent on the heating rate. In general, higher heating rate causes reactions to shift to higher temperatures. If unsuitable heating rates are used, the reactions may overlap and remain undetected. A quite different approach for separating overlapping reactions is based on the use of DTG.

4.2.1 Measurement results

Measured materials in the thermogravimetric analysis were gluten-free biscuits with dried and crushed additions of agrimony (Agrimonia eupatoria L.), littleleaf linden (Tilia parvifolia L.), breckland thyme (Thymus serpyllum L.), and oregano (Origanum vulgare L.), respectively. TG analysis was performed in the nitrogen atmosphere from 25–700°C by a heating rate of 10°C.min⁻¹. The analyzer TGA/DSC 1 from Mettler Toledo (Switzerland) was used at measurement. Flow of carrying gas was 50 ml.min⁻¹. Biscuits were weighted in alumina crucibles with lids. Whole volume of the crucible is 70 μl, it has a cylindrical shape and the circle lid is pierced. Mass of all materials was from 13.9 mg to 22.8 mg. On the thermogram (Figure 8) is visible 4 decreases that are common to all samples. First decline corresponds with the evaporation of water in biscuits.

Figure 8.

TGA of gluten-free biscuits ( Agrimony; Breckland thyme; Control sample; Littleleaf linden; Oregano).

These steps are most affected by storage and are observed at 150°C. Around 250°C, it is the decomposition of sucrose; the average decrease corresponds to 9.6% of mass loss. The second and third decreases from 300–450°C correspond to the decomposition of the starch structures of the biscuits. These are the largest components of the food, as the average decrease is from 19–30% for the temperature range (300–380) °C and 16–30% for temperatures above 390°C. Percentage decrease corresponds to mass loss and the amount of the components in samples. The second, third, and fourth mass loss are similar and therefore no major change in the quantity of composition is expected. Obvious change in decline is in steps of evaporation, where biscuits with agrimony and Breckland thyme had the greatest decline.

4.3 Differential scanning calorimetry (DSC)

A differential scanning calorimeter measures the heat flow that occurs in a sample when it is heated, cooled, or held isothermally (constant temperature). The technique is also called differential scanning calorimetry (DSC). It allows to detect endothermic and exothermic effects, measure peak areas (reaction enthalpies), determine temperatures that characterize the peak or other effects, and specific heat capacity c. Since this amount of supplied heat causes a corresponding increase in the enthalpy (H) of the sample, we can write Eq. (12) (thermodynamic definition).

$$C_p=\frac{\mathit{dH}}{\mathit{dT}}$$E12

where C_p is heat capacity at constant pressure; $\frac{\mathit{dH}}{\mathit{dT}}$ is the heat capacity and thus the slope of the enthalpy—temperature function.

The most important effects that can be analyzed by DSC are the melting point, melting range, and melting behavior. DSC is used to determine the heat of fusion, purity, polymorphism, glass transition, and oxidation stability [56]. Food researchers and food industry have shown an increased interest in techniques that can predict modifications in quality and thermophysical properties of food products during processing and storage. Differential scanning calorimetry (DSC) has attracted the interest of food scientists because only a small amount of sample is needed for analysis and to give exact results [57]. The DSC thermal method is used to describe and determine thermal processes in food. It is a differential scanning calorimetry that can be used to determine the process temperature and temperature requirement for an ongoing process in food. It is possible to determine the purity and authenticity of oils. For ingredient foods, it is possible to determine the decomposition temperature of the food ingredient and to describe the phase events in the food [58, 59]. The DSC method can be implemented alone or as an associated DTA to TGA method. DSC is rapid, facile, and capable of supplying both thermodynamic (heat capacity, enthalpy, and entropy) and kinetic data (reaction rate and activation energy) on protein denaturation. For independent implementation of DSC, it is possible to examine food even at sub-zero temperatures and to monitor events, such as crystallization or solidification of individual food components. At higher temperatures, it is possible to monitor the breakdown of proteins or the melting of individual mixtures of food ingredients. Differential scanning calorimetry can be used to characterize mixtures of polymorphic forms of fats as well as to evaluate moisture and various thermal load for their effectiveness in bringing about desired polymorphic changes. DSC has also been employed for the investigation of the physical state and water content in foodstuffs [60].

In special DSC device settings, it is also possible to determine specific heat capacity, where the specific heat capacity for DSC is determined from Eq. (13).

$$Q=m{c}_p\frac{\mathit{dT}}{\mathit{dt}}$$E13

where Q is heat flow per time unit; m is sample mass; c_p is specific heat capacity; and $\frac{\mathit{dT}}{\mathit{dt}}$is the rate of external temperature change.

The specific heat capacity (c) is significant information in the food industry and it means the amount of energy that must be supplied or taken from material to change its temperature by a given amount [58]. The specific heat capacity of a material determination is therefore important in the design of processes, such as storage at low or high temperature and treatment of foods.

Dynamic DSC measurements are usually plotted as dependencies on temperature rather than on time. If the reference temperature is chosen as the main axis, the curve remains linear with time and does not change in appearance. DSC curves are distorted if they are plotted with respect to sample temperature.

4.3.1 Measurement results

The tested samples were value-added biscuits with the addition of dried and crushed orange, cole, and raspberry. Temperature was elected for the determination of melting of cocoa butter. According to the literature, cocoa butter melts in the temperature range (32–36°C) [59, 61].

DSC measurement was performed in the nitrogen atmosphere from 0–70°C by heating rate of 10°C.min⁻¹. The analyzer DSC 1 from Mettler Toledo (Switzerland) was used at measurement. Flow of carrying gas was 50 ml.min⁻¹. Biscuits were weighted on aluminum crucibles with lids. Whole volume of crucible is 100 μl. Mass of all materials was from 13.6 mg to 14.4 mg. On the DSC (Figure 9) is visible one peak, which is common to all samples. An endothermic peak at a temperature of about 36°C can be seen for all biscuit samples with various compositions.

Figure 9.

DSC curves of biscuits ( Raspberry; Cole; Only cole powder, Orange).

This peak represents the melting of the cocoa butter contained in the biscuits. According to the shape and size of the peak, this is not a significant component of the investigated food. The start of the process can be set at 29.93°C. The addition of milk fat to the chocolate lowers its melting point, and palms fat can increase the melting point of the chocolate. However, a large temperature difference in the melting process is not seen, indicating a minimum content of fats in the sample. Total heat that is needed for the process of melting cocoa butter as is show in Table 8 is from 2.5 J·g⁻¹ to 4.6 J·g⁻¹.

	Coconut sugar—powdered sugar		Coconut sugar—corn starch		Coconut sugar—rice starch		Coconut sugar—potato starch
n	Force	C	Force	C	Force	C	Force	C
	(N)	(mm)	(N)	(mm)	(N)	(mm)	(N)	(mm)
1	806.8	2.270	830.6	2.346	821.2	3.9	819.6	2.750
2	285.0	1.840	710.0	5.426	703.4	4.0	671.4	1.640
3	779.2	3.020	490.6	0.680	426.8	3.2	804.4	9.170
4	530.0	3.230	622.2	2.140	809.8	2.6	814.2	3.080
5	218.4	3.030	815.4	3.020	832.6	3.0	723.4	7.190
Average	523.880	2.678	693.760	2.722	718.760	3.324	766.600	4.766
SD	243.167	0.532	126.633	1.552	153.113	0.521	59.047	2.896
VC (%)	46.417	19.860	18.253	57.024	21.302	15.665	7.702	60.775

Table 5.

Mechanical properties of honey candies with coconut sugar after 3 days of storage.

C, compression; SD, standard deviation; VC, coefficient of variation.

	Cane sugar—powdered sugar		Cane sugar—corn starch		Cane sugar—rice starch		Cane sugar—potato starch
n	Force	C	Force	C	Force	C	Force	C
	(N)	(mm)	(N)	(mm)	(N)	(mm)	(N)	(mm)
1	703.4	2.030	837.6	2.770	811.4	2.430	837.8	1.217
2	816.8	2.040	801.2	1.220	805.6	2.380	810.0	2.260
3	802.6	2.340	656.8	2.280	804.2	2.200	634.2	1.640
4	802.0	1.740	418.8	1.640	644.0	1.320	826.2	2.540
5	851.2	2.860	798.2	2.850	834.2	3.150	827.2	2.160
Average	795.200	2.202	702.520	2.152	779.880	2.296	787.080	1.9634
SD	49.256	0.380	154.743	0.635	68.790	0.586	76.955	0.473
VC (%)	6.194	17.248	22.027	29.509	8.821	25.516	9.777	24.114

Table 6.

Mechanical properties of honey candies with cane sugar measurement after 30 days of storage.

C, compression; SD, standard deviation; VC, coefficient of variation.

	Cane sugar—powdered sugar		Cane sugar—corn starch		Cane sugar—rice starch		Cane sugar—potato starch
n	Force	C	Force	C	Force	C	Force	C
	(N)	(mm)	(N)	(mm)	(N)	(mm)	(N)	(mm)
1	828.2	2.540	816.8	1.570	804.0	1.200	825.000	2.14
2	804.2	1.540	807.2	2.310	852.4	1.730	829.000	1.93
3	825.8	1.970	821.6	1.900	810.0	2.890	716.000	2.13
4	852.0	2.430	816.0	1.670	805.4	1.550	803.200	2.27
5	814.4	2.170	845.0	3.350	814.4	2.380	215.200	1.88
Average	824.920	2.130	821.320	2.160	817.240	1.950	677.680	2.07
SD	17.922	0.398	14.221	0.724	20.076	0.678	262.541	0.161
VC (%)	2.173	18.682	1.731	33.500	2.456	34.775	38.741	7.797

Table 7.

Mechanical properties of honey candies with cane sugar after 60 days of storage.

C, compression; SD, standard deviation; VC, coefficient of variation.

Sample	Normalized enthalpy (J·g^−1)	Onset temperature (°C)	Peak temperature (°C)
Cole powder biscuit	2.48	30.11	34.47
Cole biscuit	3.70	29.93	36.11
Orange biscuit	4.60	30.60	36.45
Raspberry biscuit	2.50	30.06	35.56

Table 8.

Results of biscuit DSC measurement.

5. Electrical properties

Electrical properties are utilized in many areas of human activities. They have the biggest application in moisture content measurements. The research on moist material is very important. The wide spectrum of electromagnetic waves can be used for material quality control and moisture content determination in many industries, for example, woodworking, civil engineering, also in agriculture, commerce, food quality investigation, and so on (e.g., [62]).

The moisture content is a very important property of materials. It is determined by various types of material, for example, soil, fruits, vegetables, cereals, foods, timber, and so on. Moisture content profiles in the material are also of interest. The distribution of moisture affects physical and biological processes, such as drying; moisture and heat transport, solute movement; uniformity of dye absorption in textiles; and the quality of many food products (e.g., [63]). The application of a method for noninvasive, nondestructive moisture profile measurement is very wide [63].

Electrical properties characterize the transport of charge carriers in materials or the propagation of electromagnetic waves in materials. In the group of electrical properties can be included—conductance G (S), which is an ability to conduct the electrical current, and the conductivity σ (S.m⁻¹), it is the conductance in relation to the proportions of the material sample. Reciprocal value of conductance is the resistance R (Ω), and reciprocal value of conductivity is resistivityρ (Ω.m).

Electrical conduction of the material is influenced by the chemical composition of material. Various types exist in the materials. Electronic conductance is characteristic of metals, charge carriers are electrons, and it occurs in conductors. Ionic type of conductivity is characterized by the fact that the charge carriers are ions and it occurs mainly in electrolytes. This type of conductivity also applies to food materials if they have sufficient moisture. Another type—hole conductivity or P-type conductivity exists in semiconductors; charge carriers are positively charged particles. There is also electrophoretic conduction; charge carriers are macromolecules or particles aggregation, which can be used in biological materials. The material resistance in case of alternate current is impedance Z (Ω). Alternate conductance is admittance Y (S) (e.g., [63]).

When the electric current travels through the biological material the electric conductivity type changes. The ionic conductivity occurs inside the cell. In cellular membrane exists displacement current. The definition of the electric current density i is

$$i=\frac{1}{S}\frac{dQ}{d\tau }$$E14

where Q is charge (C); τ is time (s); S is surface (m²) (e.g., [63]).

The current density is the function of electric field intensity

$$\overrightarrow{i}=\sigma \overrightarrow{E}=-\sigma \mathit{grad}U$$E15

where σ is electric conductivity (S·m⁻¹); grad U is gradient of the electric voltage (V·m⁻¹); E is intensity of the electric field (V·m⁻¹).

This equation is also valid for electrolytes at low values of electric field intensity.

If the current passing through the material is unsteady, for density of electric current is valid

$$i=\sigma E+\frac{d\mathit{\epsilon E}}{d\tau }$$E16

where ε is the permittivity of material (F·m⁻¹).

Material permittivity characterizes the ability of matter to polarize (form dipoles). Vacuum has the lowest permittivity because it contains no particles and no polarization occurs. Its permittivity is denoted by ε_o and has a value of 8854.10⁻¹² F·m⁻¹, which is one of the basic physical constants. To find out how many times the permittivity of a given medium is greater than the permittivity of the vacuum, the relative permittivity ε_r (also called dielectric constant) is used, which is defined as the ratio of the permittivity of the medium and the permittivity of the vacuum (e.g., [64]).

The relative permittivity of food materials depends mainly on the presence of water. Water is a polar liquid and simply polarizes, it has a high relative permittivity of up to 81 at low frequencies. Relative permittivity of food mineral components is from 3 to 7; for organic material 2 to 5; for ice 3 (e.g., [28]).

In materials of biological origin, conductive parts (cell interiors) alternate with non-conductive parts (cell membranes). This alternation creates biological capacitors whose surface capacity reaches about 1 μF.cm⁻². There is only a negligible amount of free charge carriers in the cell membranes. When they are placed in an electric field, polarization occurs and dipoles form. Dipoles can move in the direction of an external electric field−this mfovement is called a displacement current.

If the material is situated in an alternating electric field, we have to use the complex value of current density in the shape

$$\widehat{i}=\left(\sigma +j\omega \epsilon \right)\widehat{E}$$E17

where $\widehat{E}$ is complex value of electric field intensity; j is imaginary unit (1); ω is angular frequency (s⁻¹).

In the case of moist material, permittivity is a complex value. It consists of a real part ε´ and an imaginary part ε″

$$\widehat{\epsilon }={\epsilon }^{\prime }-j{\epsilon }^{″}=\epsilon -j\frac{\sigma }{\omega }=\epsilon \left(1-\mathit{tg\delta }\right)$$E18

where δ is the loss angle (1) of the dielectric and

$$\mathit{tg\delta }=\frac{\sigma }{\omega \epsilon }$$E19

where tgδ is tangent of loss angle and also loss factor (1) (e.g., [63]).

ε_r˝ is interpreted to include the energy losses in the dielectric arising from all-dielectric relaxation mechanisms and ionic conduction. The imaginary part characterizes the dielectric losses occurring in the material and represents the ability of energy dissipation in the dielectric, by which the energy of the electric field is converted into thermal energy in the dielectric [62, 63, 64].

The penetration depth d_p is the depth at which the waves power decreases to 1/e (about 37%) of the surface power of the material. In materials where dielectric losses exist, it is possible to calculate this depth according to the relationship [65].

$$d_p=\frac{c}{2\pi f\sqrt{2{\epsilon }^{\prime }\left(\sqrt{1+{\left(\frac{{\epsilon }^{″}}{{\epsilon }^{\prime }}\right)}^2}-1\right)}}$$E20

where c is light velocity in a vacuum; f is frequency.

5.1 Electrical properties utilization

The electrical impedance measurement was used in the detection of chemical substitutes in liquid food products [66]. Impedance spectroscopy was used [67] on tea quality control. In the paper [68], a method for quality control and detection of oil adulteration that used microwave electrical properties and Cole-Cole dielectric parameters was described. An open-ended coaxial sensor was developed and used [69] by Abbas et al. [69] to determine the oil and water content of olive trees as well as their ripeness. Other authors [70] determined the activation energy of fatty acids obtained from olives in the microwave region. Many authors have also dealt with milk properties [71, 72]. In another paper [73], the deactivation of Escherichia coli bacteria in milk at high pulsed electric field intensities was described. New method for determining the water content of milk using measurements of dielectric properties in the radio and microwave range was developed [74].

The electrical properties of fruits and vegetables have also been investigated in many works. Correlations between electrical properties and quality indicators of different types of fruits and vegetables were sought. The greatest growth has been recorded in non-destructive methods for evaluating or controlling fruit quality parameters, including internal errors, but also taste, sugar content, etc. [26, 75, 76]. Further studies have examined the usefulness of using the dielectric properties of apples to detect changes in their quality during storage [77]. The quality of the apple pulp during storage was assessed based on electrical impedance and Cole-Cole diagrams [78].

The electrical properties of bulk materials, especially grains and seeds used in the food industry, have been in the area of interest for many years. They were mainly used to determine their moisture content (e.g., [63, 64]), moisture content in chickpea flour [65], and it was found [79] a relationship between dielectric properties and the oatmeal admixture in wheat flour.

5.2 Electrical properties measurement results

The samples of value-added foods were prepared by the Faculty of Biotechnology and Food Sciences (Figure 10).

Figure 10.

Conductivity of pasta samples versus frequency (−control, −pasta with nettle, −pasta with carrot, −pasta with elderberry).

The pasta control sample had the following recipe—wheat flour, salt, water, and egg. Other samples contained a 3% mixture of dried and crushed nettle (Urtica dioica L.), carrot (Daucus carota L.), or elderberry (Sambucus nigra L.), respectively. The highest values of conductivity were achieved by the pasta with elderberry, lower pasta with carrots, and the lowest by pasta with nettle and a control sample. Up to a frequency of 20 kHz, the conductivity of the samples is difficult to distinguish. At higher frequencies, it would be possible to use measurements of electrical properties to assess admixture in the basic type of control sample. The nettle and control pasta samples have relatively the same and low values compared to the others. The dry matter content of the individual pasta was almost the same. Thus, the displacement of dependencies was caused by the added materials.

We also measured the electrical properties of six samples of value-added chocolates. One was a control without an addition. Other samples included admixture as candied pulp of butternut pumpkin (Cucurbita moschata L.), dried capuchin (Tropaeolum majus L.) leaves, candied young spruce shoots (Picea abies L.), dried peppermint (Mentha piperita L.) leaves, candied josta fruits (Ribes nidigrolaria L.), and dried rosehip leaves. The preparation of the samples was as follows—cocoa beans from Madagascar first underwent fermentation, followed by drying, roasting, peeling, grinding, and conching, which lasts 6 hours. The first layer of chocolate is poured into a mold, allowed to partially solidify, a layer of added materials (thickness about 2 mm) is applied, then another layer of chocolate and the whole is allowed to solidify.

In Figure 11, we can see that the capacity decreases with increasing frequency for all samples. At all frequencies, the highest capacity values were measured on a sample of chocolate with a pumpkin. In contrast, the lowest values were measured on a sample with added josta and rose. Dependencies on Figure 11 can be modeled using a regression function that has the form

Figure 11.

Frequency dependencies of various chocolate capacity (—Candied pulp of butternut pumpkin, —Control, —Dried capuchin leaves, —Candied young spruce shoots, —Dried peppermint leaves, —Candied josta fruits and dried rosehip leaves).

$$C=C_0{\left(\frac{f}{f_0}\right)}^{-k}$$E21

where C₀ is reference value of capacity; k is constant, f₀ = 1 kHz.

The regression equation has very high coefficients of determination for all samples of value-added chocolates. Similar dependencies were also obtained for relative permittivity. In this case, the displacement of curves is caused also by ingredients added to chocolate. It means that the electrical properties could be used in the recognition of material added to the chocolate.

The process of pasta and chocolate enriching was effective because it can increase antioxidant activity as well as the content of biologically active compounds.

The measurements of electrical properties in most cases have no effect on foods, it is a non-destructive measurement. Measuring with electrical sensors is quick, easy, and reliable and no operator is required, while the resulting data can be directly processed into the required electronic form. Dielectric spectroscopy techniques enable nondestructive and noninvasive measurements of the agricultural materials and foods, therefore providing tools for rapid evaluation of their water content and quality [65, 80, 81].

6. Conclusions

Innovative foods with added value, being one of the major food categories of the global health and wellness market, are becoming a major focus of new product development in the food industry. The development of these kinds of foods is more complex than traditional food, calling for a concerted effort from researchers and experts to explore and understand the process in more detail (technology, nutritional properties, hygienic, sensory, and physical properties).

Results obtained at the measurement of the firmness and hardness of the confectionaries demonstrate that the important parameter indicating the firmness or brittleness of the material is the measure of the saturation of the wet air by the material. Different proportion of moisture affects the plasticity of the hard candies. Increasing the moisture inflicts a decrease in the hardness and an increase in the plasticity of the material. Increased moisture during storage contributes to the gluing of the hard candies.

It could be observed that density and rheologic properties are influenced by several factors, including temperature, fat content, alcohol content, time of storing, composition, concentration, and even added ingredients.

Most foods are subjected to variations in their temperature during production, transport, storage, preparation, and consumption. It is possible to use thermal analysis for tracking pasteurization, sterilization, evaporation, cooking, and freezing.

Electrical properties measurement enables nondestructive and noninvasive measurements of the agricultural materials and foods, therefore providing tools for rapid evaluation of their quality and also distinguishing the added material. We found out that a correlation exists between electrical properties and the type of admixture added to the foods.

Further research in the area of physical properties is needed to clarify the impacts of value-added components on food quality.

Acknowledgments

This research has been co-funded by the European Community under the project No 26220220180: Building the Research Centre AgroBioTech and was supported by the Operational Program Integrated Infrastructure within the project: Demand-driven research for the sustainable and innovative food, Drive4SIFood 313011 V336, co-financed by the European Regional Development Fund.