**5. Conclusions**

Based on the above discussion of the research results, several conclusions can be drawn to summarize the research work:

**Author Contributions:** Conceptualization, M.Ł. and A.B.; methodology, M.Ł. and A.B.; investigation, A.B., K.P. and P.B.; resources, A.B.; writing—original draft preparation, A.B.; writing—review and editing, A.B., K.P., P.B. and M.Ł.; supervision, A.B. and M.Ł.; funding acquisition, M.Ł. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was funded by the National Center for Research and Development in Poland under grant: M-ERA.NET3/2021/70/GEOSUMAT/2022 "Materials for Circular Economy—Industrial Waste Based Geopolymers Composites with Hybrid Reinforcement" under M-ERA.NET 3 Call 2021.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Special thanks go to Joanna Marczyk, who performed the phase analysis studies of the base material. Publication cost of this paper was covered with funds from the Polish National Agency for Academic Exchange (NAWA): "MATBUD'2023—Developing international scientific cooperation in the field of building materials engineering" BPI/WTP/2021/1/00002, MATBUD'2023.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

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**Seasonal Variability of Vegetation and Its Relationship to Rainfall and Fire in the Brazilian Tropical Savanna**

Jorge Alberto Bustamante, Regina Alvalá and Celso von Randow *Brazilian National Institute for Space Researches Brazil*

#### **1. Introduction**

76 Remote Sensing – Applications

Zhang, M. H., Qin, Z. H., Liu, X., & Ustin, S. L. (2003). Detection of stress in tomatoes

Zhang, J. C., Huang, W. J., Li, J. Y., Yang, G. J., Luo, J. H., Gu, X. H., & Wang, J. H. (2011).

yellow rust in winter wheat. *Precision Agriculture,* 12, 716-731.

310.

induced by late blight disease in California, USA, using hyperspectral remote sensing. *International Journal of Applied Earth observation and Geo information*, 4, 295–

Development, evaluation and application of a spectral knowledge base to detect

The Brazilian savanna, named locally Cerrado, is the second largest Brazilian biome, covering approximately two million km2, especially in the Central Highlands (Ratter *et al*., 1997). This biome is composed predominantly of tropical savanna vegetation and is considered as one of the world's biodiversity hotspots, a priority area for biodiversity conservation in the world (Myers et al., 2000). The Cerrado region is considered the last agricultural frontier in the world (Borlaug, 2002), which has been converted in the last 50 years especially for agriculture and pasture purposes, where natural and mainly anthropogenic annual burning is a common practice. Currently, around 50% of natural vegetation in the Cerrado region has been converted to pastures and crops (PROBIO-MMA, 2007).This conversion has impacted the biological diversity, the hydrological cycle, the energy balance, the climate and the carbon dynamics at local and regional scales due to habitat fragmentation, invasive alien species, soil erosion, pollution of aquifers, degradation of ecosystems and changes in fire regimes (Klink & Machado, 2005; Aquino & Miranda, 2008). The knowledge of spatial distribution, temporal dynamics and biophysical characteristics of the vegetation types, are important elements to improve the understanding of what is the interaction like between vegetation, precipitation and fire.

The objective of this study is to determine the relationship of environmental variables, such as precipitation and fire, with spatial and temporal distribution patterns of main vegetation type of the Brazilian tropical savanna. Thus, we seek to answer the question: how environmental variables, like rain and fire, influence the main vegetation types, like herbaceous, shrubs, deciduous trees and evergreen trees, in the Cerrado biome taking in account the seasonal patterns of the variables involved?

In this study, the potential of multi-temporal satellite data, like TRMM data for precipitation, MODIS vegetation indices products for land cover mapping, and others sensors like GOES and MODIS for fire detection is explored by the use of remote sensing and geographic information systems (GIS) techniques.

#### **1.1 Seasonality of Cerrado vegetation**

Phenological parameters of vegetation, such as start and end of the growing season, are strongly influenced by atmospheric conditions (like precipitation, temperature and humidity)

Seasonal Variability of Vegetation and Its

**2. Methodology 2.1 Study area**

**2.2 Methodology**

and vegetation (NDVI).

considered ecotones of the two extremes.

Relationship to Rainfall and Fire in the Brazilian Tropical Savanna 79

patterns of fire occurrences, frequency, size, severity, and sometimes vegetation and fire effects as well. For example, savanna fires are often of low intensity and high frequency (often annual), while forest fires are often of low frequency (once every few centuries) and very high intensity (Bowman & Murphy, 2010). Most of the wildland fires occur by the combination of edaphic, climatic and human activities (Roy, 2004). Natural fires are generally started by lightning, with a very small percentage started by spontaneous combustion of dry fuel such as sawdust and leaves. This kind of fire is insignificants in comparison to number of fires started by humans (Roy, 2004). Most tropical fires are set intentionally by humans (Bartlett 1955, 1957, 1961) and are related to several main causative agents (Goldammer, 1988): deforestation activities (conversion of natural vegetation to other land uses, e.g. agricultural lands pastures, exploitation of other natural resources); traditional, but expanding slash-and-burn agriculture; grazing land management (fires set by graziers, mainly in savannas and open forests with distinct grass strata); use of non-wood forest products (use of fire to facilitate harvest or improve yield of plants, fruits, and other forest products, predominantly in deciduous and semi-deciduous forests); wildland/residential interface fires (fires from settlements, e.g. from cooking, torches, camp fires etc.); other traditional fire uses (in the wake of religious, ethnic and folk traditions; tribal warfare) and socio-economic and political conflicts over questions of land property and land use rights.

Satellite-borne sensors can detect fires in the visible, thermal and mid-infrared bands.  Justice et al. (2003) analyzed global remote sensing data and showed that occurrence of landscape fire is not random across the world, which is strongly influenced by climatic variables, like

The study area represents almost all (more than 90%) of the Brazilian savanna (Cerrado) biome, excluding only the southern region, which is characterized by few small isolated patches of savannas with intense anthropic activities like agriculture and ranching. The Cerrado vegetation exhibits a wide range of physiognomies. Following the "forest-ecotonegrassland" concept (Coutinho, 1978), the Cerrado ranges from *campo limpo*, a grassland, to *cerradão*, a tall woodland. The intermediate physiognomies (*campo sujo* - a shrub savanna, *campo Cerrado* - a savanna woodland, and *Cerrado sensu stricto* - a woodland) are

The soil surface dries out during the dry season, leading the herbaceous and sub shrub plants suffering water stress. Thus, leaves dry out and die, while the underground plant structures are kept alive. The presence of dead leaves by water stress and also by frost greatly increases

The methodology involves the use of two spatial approaches, regional and local, to analyze the spatio-temporal relationships between environmental variables (precipitation and fire)

the litterfall and, consequently, the risk of fire (Nimer, 1977; Coutinho, 2000).

moisture deficit, wind speed, relative humidity and air temperature.

at different time scales (intrannual, inter-annual, interdecadal, and so on). Atmospheric conditions at intrannual scale influence the main phenological events that the plant experiences during the annual cycle of growth (Reed et al. 1994). At greater time scales, climate influence on the spatial and temporal distribution of vegetation (Schwartz, 1994). On the other hand, the vegetation influence atmosphere while maintaining or modifying the flows of matter and energy, albedo, roughness, CO2, which in turn affect the regional and/or global climate.

Savanna ecosystems that cover approximately 20% of the global land surface have mechanisms that control the flow of matter and energy in tropical savannas. These ecosystems are not well understood, which has hindered the inclusion of this biome in studies of regional and global modeling (Law et al., 2006).

#### **1.2 Climate and precipitation regime**

Climate patterns from intra-seasonal to decadal and century scales directly influence the timing, magnitude (productivity), and spatial patterns of vegetation growth cycles, or phenology (Reed et al., 1994; Schwartz, 1994).

The Savanna biome has a wet/dry climate. Its Köppen climate group is **Aw**. The *A* stands for a tropical climate, and the *w* for a dry season in the winter and the rainy season in the summer. During the dry season of a savanna, most of the plants shrivel up and die. Some rivers and streams dry up (Parker, 2000; Ritter, 2006). In the wet season all of the plants are lush and the rivers flow freely. The temperature of the savanna climate ranges from 20° to 30° C. In the winter, it is usually about 20° to 25° C. In summer the temperature ranges from 25° to 30° C. The savanna temperature does not change a lot, although when it does, it is very gradual and not drastic.

Because of its latitudinal position, the Brazilian savanna region is characterized by the transition between the warm climates of low latitudes and mesothermal climates of middle latitudes (Nimer, 1989). This region is considered almost homogeneous on the length and location of the dry and rainy periods (Rao & Hada, 1990). However, Castro et al. (1994) show that this region has a certain degree of heterogeneity due to the variation of length in the dry and rainy periods. This heterogeneity is determined by the interaction of atmospheric circulation systems in the lower and upper troposphere over the region. Some of these systems are: The South Atlantic anticyclone also known as South Atlantic Convergence Zone (SACZ), Polar anticyclone and Chaco low. SACZ is one of the main phenomena that determine the rainfall across the region (Satyamurty et al., 1998). In general, rainfall in the region ranges from 1000 to 1500 mm.

The climate of the Cerrado is tropical warm and semi-humid, with just two seasons, a dry one from May to September and a rainy one from October to April. Monthly rainfall in dry season (that include fall and winter) reduces considerably, reaching zero, resulting in a dry period that varies from three to five months duration (Coutinho, 2000). The rainy season (spring and summer) sometimes has short dry periods named locally "*veranicos*". The mean annual temperatures vary between 22 and 27°C and the mean annual precipitations between 600 and 2.200 mm.

#### **1.3 Fire regime and detection**

Fire is one of the most important drivers that influence vegetation function and structure. Fire incidence, in a given area or ecosystem, is part of a fire regime which has specific patterns of fire occurrences, frequency, size, severity, and sometimes vegetation and fire effects as well. For example, savanna fires are often of low intensity and high frequency (often annual), while forest fires are often of low frequency (once every few centuries) and very high intensity (Bowman & Murphy, 2010). Most of the wildland fires occur by the combination of edaphic, climatic and human activities (Roy, 2004). Natural fires are generally started by lightning, with a very small percentage started by spontaneous combustion of dry fuel such as sawdust and leaves. This kind of fire is insignificants in comparison to number of fires started by humans (Roy, 2004). Most tropical fires are set intentionally by humans (Bartlett 1955, 1957, 1961) and are related to several main causative agents (Goldammer, 1988): deforestation activities (conversion of natural vegetation to other land uses, e.g. agricultural lands pastures, exploitation of other natural resources); traditional, but expanding slash-and-burn agriculture; grazing land management (fires set by graziers, mainly in savannas and open forests with distinct grass strata); use of non-wood forest products (use of fire to facilitate harvest or improve yield of plants, fruits, and other forest products, predominantly in deciduous and semi-deciduous forests); wildland/residential interface fires (fires from settlements, e.g. from cooking, torches, camp fires etc.); other traditional fire uses (in the wake of religious, ethnic and folk traditions; tribal warfare) and socio-economic and political conflicts over questions of land property and land use rights.

Satellite-borne sensors can detect fires in the visible, thermal and mid-infrared bands.  Justice et al. (2003) analyzed global remote sensing data and showed that occurrence of landscape fire is not random across the world, which is strongly influenced by climatic variables, like moisture deficit, wind speed, relative humidity and air temperature.


*2.1.5. Cyclopia*

**Definition:** Cyclopia is another type of anomaly, in which the fetus has only one single orbital fossa, with bulbs, eyelids and lacrimal apparatus fused to a variable degree. In many cases, there is one single eye or one partially divided eye, in a single orbit and arhinia with proboscis (**Figure 12**), [14, 15].

**Incidence:** Cyclopia results from the incomplete cleavage of the prosencephalon into right and left hemispheres, a process which should be occurring between the 18th and the 28th day of pregnancy, and it is a lethal human malformation, relatively complex, but also quite rare. Moreover, holoprosencephaly occurs in 1/16,000 live births [16].

**Figure 12.** A. Axial and sagittal scans of a fetus at 15 weeks of gestation show cyclopia and proboscis. B. Ethmocephaly— Postmortem demonstrating hypotelorism and proboscis.

**The etiology** of this rare syndrome incompatible with life is still not known in detail, because most cases are sporadic, even if the implication of heterogeneous risk factors has been proven. Among risk factors, we include maternal diabetes (the only formally recognized environmental factor, with a 1% risk and a 200-fold increase in fetal holoprosencephaly), infections during pregnancy (TORCHs), active drugs during pregnancy physical agents (ultraviolet light), and chromosomal (mostly trisomy 13) and genetic causes (familial occurrences in twins and in consanguineous marriages [17].

In order to get **the differential diagnosis** of these cases, we must distinguish between ethmocephaly and cebocephaly. In other words, we must be able to trace extreme hypotelorism, arhinia and blinded proboscis located between the eyes as opposed to hypotelorism and a single nostril nose without midline cleft. In case the image shows united palatine and lacrimal bones, as well as no sign of nasal bones, maxilla and nasal septum, then the diagnosis is ethmocephaly [15].


Preface

Epilepsy is the most frequent neurological pathology in the world and affects individuals of all ages with important effects on neurodevelopment and cognitive and behavioral skills. Despite increasing prevalence and knowledge of the disease, epilepsy is still strongly burdened by social stigma. Treating epileptic pathology is possible thanks to the numerous and various therapeutic techniques now available, many of which do not involve the use of drugs.

The disease affects about 65 million people globally, 80% of whom live in developing countries. The management of epilepsy generally involves antiseizure drugs or surgery for those patients who are candidates. There are also numerous other therapeutic options to support drug therapy such as a ketogenic diet or stimulation of the vagus nerve, in addition to neuromodulation techniques.

Epilepsy has always been cloaked by a mysterious aura. Since ancient times, cultures and civilizations have attempted to understand why a person may appear normal and then suddenly present with intense and uncontrollable convulsions. The first hypothesis indicated a spiritual cause.

Among the oldest texts is one dating back to about 2000 BC written by a Mesopotamian. It describes a patient who has wide eyes and hands, tense feet, and a lack of consciousness. The text attributes the symptoms to the "hand of sin." In 1790 BC, the Hammurabi code listed a "return clause" for slaves who were purchased and subsequently found to experience epileptic crises. The clause was presumably written due to the fear about the cause of the disease. About 700 years after the Hammurabi code was decreed, a Babylonian text outlined a spiritual solution for epileptic patients said to be possessed by evil spirits.

The modern opinion that the etiology of epilepsy is physiological and not divine has its roots largely in the studies of Hippocrates of Kos of 400 BC, who argues that the etiology of epilepsy is precisely similar to that of any other disease, that is, based on physiological principles. This debate between divine and physiological causes would continue for another 2000 years.

Epilepsy is not curable but can be commonly controlled with modern anticonvulsants that prevent convulsions or reduce their intensity. However, more than 30% of people with epilepsy have uncontrolled convulsions even with the best drugs available. Throughout history, epilepsy was viewed with amazement, and uncontrollable convulsions were often attributed to the influence of spirits. It is thought that people in the Stone Age drilled into the skull to dissipate demons. In ancient Greek Hippocratic thought, convulsions were sometimes interpreted as a sign of a person possessing prophetic skills. Early Christian and medieval belief was that epilepsy was a punishment from God, and in early modern times epilepsy was viewed according to the concepts

of humoral pathology as a sort of imbalance among the four bodily fluids or humors: blood, phlegm, black bile, and yellow bile.

The first synthetic anticonvulsant, paraldehyde, was introduced in 1882. Subsequently, phenobarbital became the main drug prescribed for epilepsy, followed in 1938 by Difenilhidantoina (Dilantin, phenytoin). Before these drugs, people worldwide depended mainly on plants to treat epileptic convulsions.

Since epilepsy is a disease characterized by sudden and mysterious symptoms, it is not surprising that people have been looking to understand its cause since it was first encountered. The initial attempts to surgically treat epilepsy trace their roots back to Greek, Roman, Egyptian, and South American cultures. Despite the lack of scientific etiology for epilepsy, it was thought that these primitive procedures, which more commonly consisted of drilling, freed the demons that caused the disease. These first surgical concepts are captured in various works of art that span many centuries; the oldest of these artistic references to epilepsy surgery dates back to the late twelfth century.

Recent history shows that public opinion on epilepsy continues to evolve. The second half of the twentieth century, in particular, saw many changes in the treatment by the public of those who have epilepsy. Starting in the 1970s, the United Kingdom began to allow epileptic patients to marry and the United States began to allow them to go to public places such as restaurants and movie theaters. In addition, according to a Gallup survey of Americans from 1949 to 1979, the percentage of the public who said no to the belief that epilepsy is madness has increased from 59% to 92%. The percentage of the public that would allow their children to play with other children with epilepsy had increased from 57% to 89%.

Section 1

New and Old Perspectives

on Epilepsy

Modern medicine fixes a more physiological logic at the origin of the disease, but there remain critical questions about its mechanism and the most appropriate treatment strategies. This text was born with the idea of providing innovative contributions to epilepsy. Written by scholars on epilepsy worldwide, this book will help readers find new answers to ancient doubts about epilepsy.

> **Marco Carotenuto** Department of Mental and Physical Health and Preventive Medicine, University of Campania "Luigi Vanvitelli", Caserta, Italy

Section 1


**4. Discussion**

Body and facial defect, injury, congenital deformities, status post-tumor excisions, secondary contractures are common encounters in daily practice of plastic surgeons.

In this chapter, we present miscellaneous kinds of measures employed for resurfacing, contour restoration and functional rehabilitation of the face, extremity, and body parts. Tissue expansion, tumor excision, skin grafting (split thickness/ full thickness), composite graft, cartilage graft, hair composite graft are commonly used measures. Flap surgery should be elaborated in proper situations. Selection of proper tissue for reconstruction is of utmost importance.

The ailments that were cited and treated are as the followings:

The flaps that we described and introduced in this chapter were as the followings: forehead flap, Abbe flap for upper lip reconstruction, cheek flap, nasolabial flap for nasal defects, greater palatine artery flap and pharyngeal flap for complex palatal defect, dorsalis pedis free flap for penile and urethal reconstruction, free

**131**

**Author details**

Shihheng Chen1

, Hung-Chi Chen2

2 China Medical University Hospital, Taichung, Taiwan

4 National Taiwan University Hospital, Taipei, Taiwan

\*Address all correspondence to<EMAIL_ADDRESS>
© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

5 Plastic Surgery Society of Taiwan, Taiwan

provided the original work is properly cited.

6 Aesthetic Surgery Society of Taiwan, Taiwan

1 Chang-Gung Memorial Hospital at Linko, New Taipei City, Taiwan

3 Far-Eastern Memorial Hospital at Banqiao, New Taipei City, Taiwan

and Yueh-Bih Tang3,4,5,6\*

*Finesse in Damage Control Reconstruction for Trauma in Plastic Surgery*

bringing excellence to life has always been our ultimate goals.

fibular osteoseptocutaneous flap for reconstruction of composite bone and skin

Difficult reconstructions are not only challenging, but also formidable tasks in terms of anatomical restoration, functional rehabilitation and esthetic refinements. With deliberate planning, selection of proper measure and tissue for reconstruction, optimal results can always be achieved. Pursuing excellence in plastic surgery,

The authors are indebted to Mr. Ming-Chieh Li for his help in editing this

*DOI: http://dx.doi.org/10.5772/intechopen.92975*

defect of the lower extremity.

**5. Conclusion**

**Acknowledgements**

manuscript.

fibular osteoseptocutaneous flap for reconstruction of composite bone and skin defect of the lower extremity.


**Abstract**

The chapter exposes how a sound methodology can be instrumented to both, biogeochemically speciate heavy metal (HM) polluted mine wastes and soils, and to develop solid strategies to agriculturally stabilize and remediate HM-polluted terrestrial environments. Using single- and sequential extraction procedures, polluted environments can be chemically speciated to successfully remediate impacted sites. Once metal(loid) toxic levels are determined, common amendments (compost, P-fertilizers, lime, gypsum) can be added to abate HM levels, and to re-sustain vegetation, based on bioassay results of HM-sensitive plants. The approach addresses first: a) a discussion of concepts and relevant chemistry that apply to study mine tailing materials and soils, via single or multiple HM-fractionation schemes; b) characterizing chemically mine tailings and soils, in terms of the metal(loid)-sorptioncomplexing affinities, and c) creating a "fertile environment" by agriculturally reconditioning the HM-polluted acidic mine waste to allow the vegetation regrowth, based on bioassay test performance. Results of two successful cases of study are included; one showing the use of single extraction procedures to evaluate phytoavailable/toxic HM levels to agriculturally remediate polluted sites, and another showing the role of sequential extraction procedures to discriminate heavy metal (loid)s of a spill from other metal deposits of the same ore.

**Keywords:** soil pollution, metal mine tailings, chemical speciation, heavy metalloid bioremediation, heavy metal bioassays


**3.3 Parameters affecting electrocoagulation/electroflotation**

Several parameters can affect the efficiency of removal by electrocoagulation/ electroflotation. The most important parameters are described bellow.

### **3.3.1 Current density or charge loading**

The current density (j) is expected to exhibit a strong effect on EC: higher the current, shorter the treatment. This is ascribed to the fact that at high current density, the extent of anodic dissolution of aluminum (iron) increases, resulting in a greater amount of precipitate for the removal of pollutants. Moreover, bubble generation rate increases and the bubble size decreases with increasing current density. These effects are both beneficial for high pollutant removal by H2 flotation. As a first approximation, the amount of Al released is proportional to the product φAlIt. The values of the Faradic yield *φ*Al are between 100% and 160%; they decrease with increasing time in the first minutes of the run, but also with higher current density. These trends have already been reported in the literature (Essadki et al., 2009). This mass overconsumption of aluminum electrodes may be due to the chemical hydrolysis of the cathode but it also often explained by the "corrosion pitting" phenomenon which causes holes on the electrode surface. The mechanism suggested for "corrosion pitting" involves chloride anions and can be summarized as follows:

$$\text{2Al} + 6\text{HCl} \rightarrow 2\text{AlCl}\_3 + 3\text{H}\_2$$

$$\text{AlCl}\_3 + 3\text{H}\_2\text{O} \rightarrow \text{Al(OH)}\_3 + 3\text{HCl}$$

This mechanism can therefore produce more aluminum hydroxide flocs and H2 bubbles than the equivalent current supplied should. Conversely, high current density allows the passivation of the cathode to be reduced but an increase in energy consumption that induces heating by Joule effect. As a result, too high current densities have generally to be avoided.

The specific electrode consumption per kg pollutant (*Al*) is determined by the following expression:

$$\mu\_{Al} \text{(kgAl / kgpollutant)} = \frac{3600. M\_{Al}.It.\rho\_{Al}}{3F} \frac{1}{V \left(\mathbb{C}\_{0}Y\right)}\tag{11}$$

using initial pollutant concentration *C0* (kg/m3), current intensity *I* (A), cell voltage *U* (V), electrolysis time *t* (h), liquid volume *V* (m3), molar weight of aluminum *MAl* = 0.02698 constant *F* (96,487 C/mol*e*–) and the faradic yield *Al* of Al dissolution, Y is the removal efficiency. *Al*is estimated as the ratio of the weight loss of the aluminum electrodes during the experiments *mexp* and the amount of aluminum consumed theoretically at the anode *mth*:

$$
\phi\_{Al} = \frac{\Im F}{3600. M\_{Al^\*} I.t} \Delta m\_{\exp} \tag{12}
$$

Electrochemical Probe for Frictional Force and Bubble Measurements

at high pH values:

**3.3.4 Power supply**

**3.3.5 Temperature**

aluminum oxide film on the electrode surface.

(2004) is often recommended for non-passivated electrodes:

**3.4 Design of electrocoagulation cell**

Al(OH)4−

as follows:

and Innovative Electrochemical Reactors for Electrocoagulation/Electroflotation 59

At low pH, such as 2–3, cationic monomeric species Al3+ and Al(OH)2+ predominate. When pH is between 4–9, the Al3+ and OH− ions generated by the electrodes react to form various monomeric species such as Al(OH)2+, Al2(OH)22+, and polymeric species such as Al6(OH)153+, Al7(OH)174+, Al13(OH)345+ that finally transform into insoluble amorphous Al(OH)3(s) through complex polymerization/precipitation kinetics. When pH is higher than 10, the monomeric Al(OH)4− anion concentration increases at the expense of Al(OH)3(s). In addition, the cathode may be chemically attacked by OH− ions generated together with H2

2Al + 6H2O + 2OH−→ 2Al(OH)4− +3H2 (10) Two main mechanisms are generally considered: *precipitation* for pH lower than 4 and adsorption for higher pH. Adsorption may proceed on Al(OH)3 or on the monomeric

From an energetic point of view, energy consumption during EC is known to vary as the product *UIt*. Energy requirements per kg of pollutant removed (*E*pollutant) to achieve a certain

The specific electrical energy consumption per kg pollutant removed (*E*pollutant) is calculated

... ( / pollutant) 1000. .

Few papers were investigated to show the effect of temperature on EC efficiency. The current efficiency of aluminum was found to be increased with temperature until about 60°C (Chen, 2004) where a maximum was found. Further increase in temperature results in a decrease of EC efficiency. The increase of temperature allows to a destruction of the

The position of the electrodes in the reactor can be optimized as a function of hydrodynamic parameters and current density (j). Complementary rules should include the influences of electrode gap (e) and operating conditions on voltage U (and consequently on energy consumption). The measured potential is the sum of three contributions, namely the kinetic overpotential, the mass transfer overpotential and the overpotential caused by solution ohmic resistance. Kinetic and mass transfer overpotentials increase with current density, but mass transfer is mainly related to mixing conditions: if mixing is rapid enough, mass transfer overpotential should be negligible. In this case, the model described by Chen et al.,

0.76 0.20 ln ( ) *e U jj k*

0

(14)

*UIt E kWh kg V Y C* (13)

percentage of efficiency (Y) shows a continuous increase of *E*pollutant with *j*.

This parameter depends upon the pH and the amount of other species present in solution, for example co-existing anions.

#### **3.3.2 Conductivity**

The increase of the conductivity () by the addition of sodium chloride is known to reduce the cell voltage U at constant current density due to the decrease of the ohmic resistance of wastewater. Energy consumption, which is proportional to *U.I*, will therefore decrease. Chloride ions could significantly reduce the adverse effects of other anions, such as HCO3 – and SO4 2−, for instance by avoiding the precipitation of calcium carbonate in hard water that could form an insulating layer on the surface of the electrodes and increase the ohmic resistance of the electrochemical cell (Chen et al., 2004). Chloride anions can also be oxidized and give active chlorine forms, such as hypochlorite anions, that can oxidize pollutants. The main mechanism is as follows:

$$\rm Cl\_2 + 2e^- \to 2Cl^-$$

$$\rm Cl\_2 + H\_2O \to Cl^- + ClO^- + 2H^+$$

However, an excessive amount of NaCl (higher than 3 g/L) induces overconsumption of the aluminum electrodes due to "corrosion pitting" described above; Al dissolution may become irregular.

#### **3.3.3 pH effect**

pH is known to play a key role on the performance of EC. An optimum has to be found for the initial pH, in order to optimize the EC process. However, the pH changed during batch EC. Its evolution depended on the initial pH. EC process exhibits some buffering capacity because of the balance between the production and the consumption of OH- (Chen et al., 2004), which prevents high change in pH. The buffering pH seems just above 7: when the initial pH is above this value, pH decreases during EC; otherwise, the opposite behavior is observed.

The effect of pH can be explained as follows. The main reactions during EC are:

Anode: Al0(s)→ Al3+ + 3e− Cathode: 2H2O + 2e- → H2(g) +2OH- At low pH, such as 2–3, cationic monomeric species Al3+ and Al(OH)2+ predominate. When pH is between 4–9, the Al3+ and OH− ions generated by the electrodes react to form various monomeric species such as Al(OH)2+, Al2(OH)2 2+, and polymeric species such as Al6(OH)153+, Al7(OH)174+, Al13(OH)345+ that finally transform into insoluble amorphous Al(OH)3(s) through complex polymerization/precipitation kinetics. When pH is higher than 10, the monomeric Al(OH)4− anion concentration increases at the expense of Al(OH)3(s). In addition, the cathode may be chemically attacked by OH− ions generated together with H2 at high pH values:

$$\text{2Al} + 6\text{H}\_2\text{O} + 2\text{OH}^- \rightarrow 2\text{Al(OH)}^{4-} + 3\text{H}\_2 \text{(10)}$$

Two main mechanisms are generally considered: *precipitation* for pH lower than 4 and adsorption for higher pH. Adsorption may proceed on Al(OH)3 or on the monomeric Al(OH)4−


**1. Introduction**

Agricultural and food products most often have a complex structure and exhibit complex behavior during processing. These determine the suitability for processing and constitute the basic quality criterion for acceptance by contractors and customers. This also applies to wheat grains. Wheat grain has a multi-layered and complex structure that depends on the genetic characteristics, the environment, and the specific cultivation conditions. The individual components of its structure exhibit different mechanical properties, including resistance to external loads and resistance to the formation and propagation of cracks. It results, on the one hand, in different susceptibilities of the varieties to the production of flour and, consequently of bread, biscuits, or pasta, and on the other hand, in the complexity and diversity of numerous grain processing technologies. Hence, the optimization of wheat breeding programs as well as the appropriate wheat selection for technology for many years required proper assessment of the technological quality of grain. However, the quality of wheat cannot be simply defined, since it changes depending on the farmer, grain dealer, seed company, milling industry,

pasta industry, and consumer. For the farmers it can be yield or resistance to disease, for miller's protein content, hardness, or many others. By knowing the quality, processors can avoid purchasing grain that does not meet their needs. There are a lot of methods used to determine the technological quality of kernels. A number of these are time consuming and expensive (farinograph, alveograph), and hence are often impractical to use as the way to pay producers premiums based on kernel quality expectations. Finding measures of wheat which can be conducted quickly and less expensively gives the opportunity to develop new approaches to predict kernel or dough behavior. Between them, the research on mechanical properties of kernels continues to expand.

Mechanical properties of wheat kernels are naturally associated with the conditions of mechanical separation of the endosperm and the outer bran layer, the breaking resistance of the bran or the breaking susceptibility of the endosperm itself, or starch and proteins. Khazaei and Mann [1] stated that the relaxation data could be useful in estimating the susceptibility of materials to damage. Ponce-García et al. [2] successfully applied rheological measurements to distinguish among wheat classes, varieties, and different moisture contents. The test on intact kernels was used by Figueroa et al. [3,4] to establish relationships among protein composition, viscoelasticity of dough, and baking outcomes. However, there are many different conclusions and opinions regarding the scope of application of many studies. The reason for this is undoubtedly the differences arising from the use of different materials, equipment, procedures, detailed measurement conditions, and different methods of interpretation of results.

The interpretation of stress relaxation test results is most often based on rheological models (mechanical analogs) known from the rheology theory. According to these, food materials can be assumed to be composed of springs (representing ideal solids) and dashpots (considered ideal fluids) arranged in different ways. The most frequently used mechanical analogs are the Maxwell model, the Kelvin–Voigt model, and the standard linear model, SLM [5]. The spring constants in the Maxwell model give information on the stiffness, that is, the solid nature of the sample. The dashpots constants represent the relaxation times of individual Newtonian elements. The Maxwell model is suitable for representing stress relaxation data, but it does not consider the equilibrium stress. For this reason, a generalized Maxwell–Wiechert model consisting of a few elements in parallel with a spring is better for describing the viscoelastic behavior of food [5]. It was reported by Sozer and Dalgic [6] that most viscoelastic foods do not follow the simple Maxwell model; therefore, it is necessary to use more complex models to describe their stress relaxation behavior.

The Peleg and Normand model has fewer constants than the Maxwell model, however it is a simple, quick, and effective method to handle stress relaxation data [7]. According to Sozer et al. [8], the Peleg and Normand equation can be a good alternative to the Maxwell model. The authors observed that at large deformations, the Maxwell model did not fit their data very well, and the Peleg and Normand model helped overcome this problem. Apart from its debated mathematical convenience, the Peleg and Normand simulation are easy to perform and analyze the results. Stress relaxation parameters were sensitive enough to account for the textural characteristics of raw, dried, and cooked noodles [9–11]. Lewicki and Łukaszuk [12] concluded that the rheological properties, based on the Peleg model, strongly correlated with the moisture content and reflected the changes in the distribution of components and structure of apple tissue during convective drying. On the other hand, Bu ˇnka et al. [13] stated that only the parameter expressing the extent of material relaxation provided an adequate description of the actual changes occurring during the ripening of Edam cheese. The initial rate at which the stress relaxed provided no relevant description. Similarly, Singh et al. [14] and Filipˇcev [15] support the opinion of the inadequacy of parameter *k*1 to follow textural changes or in differentiating various food products. Other authors found, however, that this parameter differed between various samples, such as cooked spaghetti [8], cooked Asian noodles [9], or wheat breads [16].

More studies have reported rheological properties evaluated under large strains (e.g., 5% strain or greater) [17]. This results from the fact that during real processing conditions, large deformations occur, resulting in the expected irreversible deformation, flow, or fracture of the material [18]. Creep and relaxation tests for large deformations were carried out for various food products, but their results indicate different opinions on the adequacy and variability of parameters of various rheological models. Higher applied stress can result in faster relaxation, according to Guo et al. [19], and larger proportion of the unrelaxed stress, according to [20]. The constants of the Peleg and Normand model for steamed bread [7] and bulk relaxation test of Jatropha curcas seeds [21] significantly decreased with larger strains. Karaman et al. [22] indicated a slightly more elastic behavior of cheese at higher compression levels. According to Khazaei and Mann [1], the deformation level had a definite effect on the decay forces for Maxwell elements for buckthorn berries, whereas the effect on relaxation times was not always ambiguous and different for each Maxwell element. Also, Bargale and Irudayaraj [23] stated that the effect of deformation level on relaxation time was stable and did not show a clear tendency. Moreover, Faridi and Faubion [24], following Shelef and Bousso [25], pointed to the independence of the relaxation parameters on the initial stress.

In summary, there are still different opinions on specific rheological measurements, and many particular cases must be treated individually. The goal of this study is the determination of the influence of the initial loading level on relaxation characteristics described by the model of Peleg and Normand. The effect was examined for different wheat types and varying moisture content.

#### **2. Materials and Methods**

#### *2.1. Material Selection*

Four wheat varieties were used in the experiments, two soft-type endosperms and two hard-type endosperms. Of these, two soft Polish cultivars (Zawisza and Wydma) and one hard SMH87 originated from the plant breeding station HR Smolice (harvested in 2015). The second hard variety was obtained from the pasta production company, Lubella. The names of the wheat varieties are denoted here as: Zawisza—SOFT1, Wydma—SOFT2, SMH87—HARD1, and wheat from Lubella—HARD2. The selection of both the soft-type and hard-type endosperm varieties was motivated by their confirmed significant differences in stress–strain behavior and fragmentation susceptibility.

### *2.2. Chemical Analyses*

Chemical analyses were performed in the Central Agroecological Laboratory of the University of Life Sciences in Lublin, Poland. The Infratec™ 1241 (Foss, Denmark), a whole grain analyzer that uses near-infrared transmittance technology, was used to estimate the amounts of protein, wet gluten, starch, and Zeleny index. The apparatus uses a wavelength in the range of 570–1100 nm. From the batch sample about 200 g, 10 subsamples were scanned. The result of the analysis was the average of 10 measurements.

### *2.3. Moisture Determination*

The initial moisture content of a batch sample of wheat kernels was determined by applying the air oven method. Three 5 g samples of grains were dried for over 2 h at a temperature of 130 ◦C, in accordance with the Polish Standard, PN-EN ISO 712:2012. The initial moisture content (*mi*) was calculated according to the formula (wet basis):

$$m\_{\rm i} = \frac{M\_{\rm i\nu} - M\_{\rm d}}{M\_{\rm i\nu}} \cdot 100\%\_{\rm s} \tag{1}$$

where *Mw* is mass of the sample before drying and, *Md* is mass of the sample after drying.

Different moisture levels, i.e., 8%, 10%, 12%, 14%, 16%, 18%, and 20% (wet basis), were assigned in the experimental plan. To obtain the respective levels, each batch sample with known weight and moisture was dried at 40 ◦C until its weight corresponding to the driest sample (8%) was obtained. The batch sample was then divided into seven smaller samples, and to each of them, the required amount of water was added. The amount of water was calculated using simple mass balance equations.

#### *2.4. Stress Relaxation Procedures*

The measurements were performed in the laboratory of the Department of Food Engineering and Machines. A Zwick Z020 universal testing machine was used for relaxation measurements. It was equipped with 0.1 kN capture. Application of the 0.1 N capture with an accuracy grade of 0.5% resulted in a 0.5 N accuracy of force measurement. The machine was operated using the *testXpert* firmware supplied by Zwick (version 7.1). Measurements were carried out for all four wheat varieties with the seven specified levels of moisture content. A single kernel of wheat was placed with its ventral side on the bottom of the machine's plate and then loaded axially until a required load level was obtained. The constant compression rate during the loading at 10 mm/min was adjusted. The strain (deformation) of the kernel was kept constant during next 300 s. The decreasing value of the loading force as a function of time was measured with the help of the *testXpert* software at a frequency of 100 Hz. Twenty replications were performed.

The experiments were conducted for four distinct initial load levels, i.e., 20 N, 30 N, 40 N, and 50 N. The maximum applied value (50 N) corresponds to about half of the value of the force causing kernel rupture (at ambient conditions) (Figure 1).

**Figure 1.** An example of relaxation characteristics of wheat kernel.

#### *2.5. Calculations and Statistical Analysis*

Peleg and Normand [26,27] suggested that stress relaxation data could be calculated as normalized stress and fitted to formula (2). The formula has been used by many authors, and depending on the assumptions made, it is based on decay in stress, modulus, or force. Force, which was used in the study, is also a valid criterion [28]. Hence, the relaxation of wheat kernel can be expressed in terms of force decay, as:

$$\frac{F\_o \ t}{F\_o - F\_t} = k\_1 + k\_2 t\_\prime \tag{2}$$

where *F*o is the initial loading force in Newtons at time *t* = 0; *Ft* is the force in Newtons at relaxation time *t*; *t* is the time of relaxation in seconds; *k*1 and *k*2 are constants; the reciprocal of *k*1 determines the initial decay rate; and *k*2 is the hypothetical value of the asymptotic normalized force.

The decay parameter *Y(t)* was calculated according to:

$$Y(t) = \frac{F\_o}{F\_o - F\_{\text{(300)}}},\tag{3}$$

where *F(300)* – is the force established at 300 s.

*k*1 indicates how fast the material in question is relaxing in energy (at least initially). If the reciprocal of the *k*1 represents the initial decay rate, then its value can be associated with a low rate of decay, indicating a pronounced elastic behavior. A higher value of *k*1 suggests a harder, more solid-like material that dissipates less energy, thus needing more force to be compressed [29]. Parameter *k*2 represents the degree of solidity, and it varies between 1, for a material that is an ideal liquid, to infinity, for an ideal elastic solid where the stress does not relax at all [27].

The constants of the Peleg and Normand model were estimated by fitting the experimental data to the above formulas using Excel software. Experimental data were analyzed using Statistica, Dell Inc. [30] version 13. In all the analyses, a significance level at 0.05 was acquired.

#### **3. Results**

The chemical composition of wheat varieties is presented in Table 1. Hard varieties had higher gluten and protein content. The amount of wet gluten was close to 34–35% for hard wheats and 25–26% for soft types. The protein amounts were close to 15% and 12%, respectively. Higher starch content was characteristic for soft wheats (near 71%) in comparison to hard ones (about 64%). Finally, the ability of the flour proteins to swell in an acid medium expressed by Zeleny index was about two times higher for hard wheats.

The amount of protein in soft, hard, and durum can vary according to the differences in grading systems in different countries. However, within a species, wheat cultivars are further classified in terms of protein content as soft wheats (protein content about 10%), hard (protein content higher than in soft), and durum (are generally high in gluten-producing proteins, 15%). Generally, soft wheat has been bred to yield flour containing less protein than hard wheats, about 8% to 11% versus 10% to 14% protein, respectively [31]. Although the hardness is postulated to be strongly correlated with protein content [32], soft wheats can have a higher protein content than the hard cultivars, or wheat lines with different protein content can have very close hardness scores [33]. This all means that the cause of starch–protein adhesion is not straightforward, and is still being discussed [32–35].

d.b.—dry basis.

#### *3.1. The Force Decay*

Figure 2 shows the effect of wheat variety and moisture on the force decay rate. The decay *Y(t)* represents a rate of decline in the force as a result of relaxation. The Tukey test was used for the testing of hypotheses of means equality. Distinct letters denote groups for which the means differed

significantly at *p* < 0.05. Four groups were distinguished for studied cultivars. The highest values of the force decay rate were characteristic of hard wheats. The differences between the average values were particularly noticeable for lower grain moistures (up to 14%). For higher water amounts, the average values for hard and soft varieties were close. However, significant differences between soft and hard kernels were confirmed.

A considerable decline in the force decay was caused by increasing the moisture of kernels. Only for 18% and 20% moisture levels were the means were not statistically different, though the slower decline rate was observed already above 16%. The interactive effect between the two factors was also significant and confirmed by ANOVA analysis.

**Figure 2.** Influence of moisture content and wheat type on the force decay based on the Peleg and Normand equation: *a–d* and *a–f*—homogeneous groups; whiskers denote the standard error of the mean.

Figure 3 shows the effect of initial load level on the decay parameter for varied moisture and wheat type. For individual samples differing in moisture content, the effect of loading was not confirmed, although the significant effects of moisture, initial loads, as well as interaction of the two factors (moisture content × initial load) on the parameter were statistically evidenced by variance analysis. However, the effect of load was unclear both for hard and soft-type endosperm. In this case, two-way ANOVA proved no influence of initial load nor wheat type versus initial load interaction on the force decay rate *Y(t).*

**Figure 3.** Plot of interactions: (**a**) moisture–initial load and (**b**) wheat type–initial load on the force decay based on the Peleg and Normand equation; whiskers denote the standard error of the mean.

Next, Figure 4 shows the general effect of the initial loading level on the average values and the distribution of the decay parameter. Only slight variations of the parameter were observed for the initial loads applied. The averages ranged from 2.0 to 2.1, although some means were statistically different. The observed small decrease in decay for 50 N loading might be caused by higher plastic deformation for moist kernels. Apart from the averages, the variability of individual results represented by their histograms (Figure 4b) was also similar. The scale parameter of the distributions was found to be also similar and not statistically dependent on the initial loading. The two-fold higher number of observations in the range of 1.0–1.5 was a reason for wheat softness at higher moistures (at 18–20%) and fact that the decay tended to unity for softer (liquid) materials.

**Figure 4.** Influence of the initial loading level of wheat kernels on averages (**a**) and histograms (**b**) of *Y(t)*;/ *a–c*/—homogeneous groups; whiskers denote the standard error of the mean.

The results of three-way ANOVA analysis are presented in Table 2. There were seven null hypotheses with the analysis: (1–3) the population means of each of the three independent factors are equal; (4–6) there is no interaction between all possible combinations of two factors; and (7) there is no interaction between all three factors. The F-statistic is the mean square for each main effect, and the interaction effect divided by the within variance is simply a ratio of two variances. A large F ratio means that the null hypothesis is wrong (the data are not sampled from populations with the same mean)—variation among group means is more than you would expect to see by chance. In our case, all seven hypotheses were wrong. From the three analyzed main effects, the initial load was less significant (lowest F = 22.4). The highest effect was evidenced for the kernel moisture contents (largest F = 4783.6). It is necessary to remark that although there was very little or limited influence of loading level on the decay parameter, the latter was interactively dependent on all the two-way and all the three-way interactions, i.e., moisture, wheat variety, and the initial load (Table 2). However, the interaction of the studied initial load with the two other factors was the lowest. The "*p*" column presents the statistical significance level (i.e., *p*-value) of the three-way ANOVA. It can be seen that the statistical significance level of all seven hypotheses was close to zero (*p* = 0.0000). This value is <0.05 (i.e., it satisfies *p* < 0.05), which means that there was a statistically significant effect.

**Table 2.** Analysis of variance of the force decay parameter *Y(t)*.

SS—sum of squares, DF—degrees of freedom, MS—mean square, F—F ratio, *p*—*p*-value

#### *3.2. Coe*ffi*cients k1 and k2*

Figures 5 and 6 show the effect of moisture content on the coefficients *k*1 and *k*2 of the model presented by Peleg and Normand. Values of *k*1 decreased with an increase in the kernel moisture content from about 17.0 to 5.0. The means of *k*1 were significantly different for each moisture level, although the relationship was not proportional. A relatively fast decrease in the parameter with moisture rising up to 14% was noticeably slower for higher water levels, with practically no difference at 18% and 20%. This was observed both for soft and hard type wheats. On the contrary, for low moisture levels, in the 8–12% range, the differences between the kernel types were very clear. The values of the *k*1 parameter were evidently higher for the two hard varieties. In turn, for high moisture contents, these differences vanished completely. This is an interesting and noteworthy observation regarding the possibility of using the relaxation test to distinguish varietal differences. According to this, a too high moisture content may be the reason for disappearing differences in the values of Peleg and Normand parameters.

The values of parameter *k*2 decreased from about 3.3 to 1.4 together with the rise in the moisture contents used. The impact of both moisture and wheat type was very similar to the parameter *k*1 described above.

**Figure 5.** Influence of moisture and wheat type on the coefficient *k*1 of the Peleg and Normand model; *a–d* and *a–g*—homogeneous groups, whiskers denote the standard error of the mean.

**Figure 6.** Influence of moisture and wheat type on the coefficient *k*2 of the Peleg and Normand model; *a–c* and *a–g*—homogeneous groups, whiskers denote the standard error of the mean.

The effect of the loading level on the coefficients *k*1 and *k*2 is presented in Figures 7 and 8.

**Figure 7.** Influence of initial load on the coefficient *k*1 of the Peleg and Normand model for different moisture (**a**) and wheat type (**b**), whiskers denote the standard error of the mean.

The analysis for the individual moisture levels showed that the mean values of *k*1 did not differ significantly. Some differences were noted for 14% and 20% moisture levels. One-way ANOVA, in which the data were completely randomized, did not confirm any significant effect of the initial load on *k*1. However, two-way ANOVA, including both effects of the loading level and moisture level, confirmed their interaction on the values of the coefficient. The interactive effect was also confirmed for load and wheat type interaction. Similarly, three-way ANOVA analysis, which was the most appropriate for our three-factorial experiment, proved the interactive influence of the three analyzed factors, i.e., moisture, wheat variety, and load, on *k*1 (Table 3). Similarly, to the above analyzed decay parameter, from three factors, the initial load was less significant (F = 16.66). The highest effect was confirmed for wheat moisture (largest F = 2340.4). As for the previous analyses, the influence of wheat type was better evidenced than that of initial loading. K1 was interactively influenced by all the two-way and all the three-way interactions, i.e., moisture, wheat variety, and the initial load (Table 2). However, the interaction of the studied initial load with the two other factors was also the lowest.

SS—sum of squares, DF—degrees of freedom, MS—mean square, F—F ratio, *p*—*p*-value.

In the case of *k*2, the changing initial load had a more significant impact. We were able to distinguish two, three, and even four homogeneous groups, depending on the moisture level; however, no straightforward relationship was found. For *k*2, main effects, like wheat type and load, were significant, but their interaction was not. Neither the load nor the interactive effect was significant (*p* > 0.05). Finally, no effect of load was observed on the relaxation coefficients for either soft or hard endosperms. Like for *k*1, the three-way ANOVA analysis proved the interactive influence of the three analyzed factors on *k*2 (Table 4). All the individual factors and interactive effects were confirmed to be statistically significant at very low *p*-values (*p* = 0.0000). All the observations for *k*1 were similar, with two exceptions. The F-statistics for *k*2 were higher (stronger effect), and the effect of load in this case was slightly higher (F = 70.6) than that of wheat type (67.8).

**Figure 8.** Influence of initial load on the coefficient *k*2 of the Peleg and Normand model for different moisture (**a**) and wheat type (**b**), whiskers denote the standard error of the mean.

**Table 4.** Analysis of variance of the force decay parameter *k*2.

SS—sum of squares, DF—degrees of freedom, MS—mean square, F—F ratio, *p*—*p*-value.

The overall effect of the initial load no the coefficients *k*1 and *k*2 is presented in Figure 9. The values of the two constants changed only very little. *k*1 ranged from 10.04 to 10.73 and *k*2 from 2.14 to 2.27. The lowest and most statistically different value of *k*1 was noted at 50 N, whereas in the range from 20 to 30 N, the means were very close. In turn, the lowest values of *k*2 were obtained for the 20 N load level. The homogeneous groups marked in the figure were established by the tree-way ANOVA analysis.

**Figure 9.** Influence of initial load on the coefficients *k*1 (**a**) and *k*2 (**b**) of the Peleg and Normand model; *a–c*—homogeneous groups; whiskers denote the standard error of the mean.

Apart from the averages, we analyzed the variability of the individual model coefficients. Their distributions are presented in Figure 10. In both cases the distributions were similar and not statistically dependent upon the initial loading. For example, the scale parameter of the exponential distributions was 0.093–0.099 for *k*1 and 0.44–0.47 for *k*2. Independent of the distribution fitting model applied (normal, lognormal, Weibull, or others) their parameters were very close and independent of the loading level.

**Figure 10.** Histograms of the coefficients *k*1 and *k*2 of the Peleg and Normand model for different initial loading levels.

Finally, the analyzed impact of the initial load on the model parameters can be compared with the wheat type, which, as observed, was very small or insignificant for higher moisture contents. The histograms of the parameters *k*1 and *k*2 are shown in Figure 11. In the case of *k*1, the distributions (medians) were clearly shifted towards higher values for hard wheats, with a slightly larger spreading (scale factor). However, in the case of *k*2, the median values for the examined wheats were similar, with a slightly different scale factor.

**Figure 11.** Histograms of the coefficients *k*1 and *k*2 of the Peleg and Normand model for different wheat hardness.

#### **4. Discussion**

The observed decrease in *Y*(t) with moisture means that for dry kernels, only a small amount of the initial force was relaxed, and the residual force established at 300 s was relatively high. In fact, as moisture content increases, the kernel becomes softer, and consequently, a lower force is required to maintain a certain deformation level [1]. Water influences the rheological properties of both liquid and solid foods. It affects the response of solid foods to force [36]. Ozturk and Takhar [37] explained a decreasing trend in the values of relaxation constants as moisture level increased, with higher resistance to the relaxation of stresses at lower moisture levels. Samples with higher moisture content relaxed faster than the ones with lower moisture, as a result of the softening process. In the study, it was observed that an increase in the moisture content of kernels caused the force to decrease more quickly. This is due to the increased plasticity of the kernel and energy losses in non-recoverable deformations [28,38]. In the model of Peleg and Normand, the coefficient *k*1 represents the reciprocal of the initial decay rate. This means that a high value of *k*1 corresponds to a slower rate of force relaxation in the first few seconds. The coefficient *k*2 represents the hypothetical value of the asymptotic normalized force (not relaxed). With an increase in moisture content, the residual force at the end of relaxation decreased, as did the values of the coefficient. This demonstrates that for moist samples, much more energy was relaxed and less energy was necessary to keep the deformation at a constant level. Similar results have also been reported in other research works [28,37].

Generally, well noticeable differences between hard and soft cultivars were observed and statistically confirmed. All three parameters, Y(t), *k*1, and *k*2, were statistically higher for the hard type endosperm, but the differences could be confirmed only at lower moisture levels and they diminished with increasing water content in kernels (especially above 16%). The changes in all obtained coefficients of *Y(t)*, *k*1, and *k*2, for the studied wheat variety were very slight, and no clear tendency was evidenced. According to Edwards [39], the moisture sensitivity of starch strength, storage protein strength, and/or the strength of starch/protein adhesion may be responsible for differences in the strength of wheats with varying moisture contents. Such responses to water may be due to the structure of the endosperm and strength of the cell wall architecture. Soft and hard wheats exhibit the same trend with moisture content; however, they may do so at different response rates. A similar trend for soft and hard wheats was observed in our study, with little difference in rates, mainly for lower moisture contents. Glenn and Johnston [35] pointed to no significant differences in any of the mechanical properties of the starch and protein components, either within a variety or among soft, hard, and durum wheat types, though mean values for each parameter were highest for durum and the lowest soft caryopses. Many studies have underlined the meaning of the overall porosity of the endosperm structure and indicated that endosperm strength is unrelated to the protein content [40,41]. Accordingly, endosperm can be very resistant, either at low protein or high protein content, depending on the adherence between the protein and starch granules. Also, Haddad et al. [42] stated that relaxation tests make it possible to relate endosperm behavior to different types listed in materials science. However, their proposal for wheat classification was based only on the relationship between the rupture energy and the Young modulus. Following Ponce-Garcia [43] the elastic, viscoelastic, and flow properties allow the indirect measurement of wheat characteristics related to the chemical composition, including nongluten components. The elastic behavior of intact kernels was related to the sedimentation volume and composition of glutenin. He demonstrated that hard wheats showed higher plastic deformation work than durum wheat kernels, and therefore had higher elastic properties [44]. Some relationships between wheat hardness and Zeleny index and protein quality were observed in [45]. In our study, we determined the basic chemical composition of wheats, however any conclusions on their relationship to measured rheological parameters were not direct or simply not justified. Some generalization based on wheat hardness may be briefly discussed here; nevertheless, the link between the biochemical studies and mechanical studies is not established.

Irrespective of the wheat softness, no obvious or very little effect (some interactions were observed) of the initial load on the model of Peleg and Normand was demonstrated. Strain level is postulated to be meaningful in relaxation experiments; however, no unique opinion in research literature exists. Some reports showed that the initial stress can influence the amount of unrelaxed energy [20] and the relaxation speed [19], which directly relates to the constants of the relaxation models [21–23]. Lewicki and Spiess [20] showed that the proportion of the unrelaxed stress is larger if the developed stress is higher. Guo et al. [19] stated that higher applied stress can result in higher relaxation speed. The obtained results confirm those of Bargale and Irudayaraj [23] on the uni-axial compression of barley kernels, in which the authors reported that the effect of the deformation level on relaxation time was consistent and did not show a clear trend. Also, according to Faridi and Faubion [24] and Shelef and Bousso [25], stress relaxation can be fairly independent of the initial stress. An analysis of the available research results allows one to state that many conclusions are true for specific circumstances only. These results demonstrate that wheat properties, which may differ very slightly, influence the relaxation characteristics more significantly than the level of loading applied. This allows a statement on its little meaning for wheat compression in the range of loads applied in the study. The results showed that this method could be a useful tool to distinguish among wheat classes, cultivars, and different moisture levels in the kernels.

To the above-mentioned different conclusions, the general opinion about the limited applicability of the Peleg and Normand model must be emphasized. To date, relaxation tests have had few practical applications, however the reasons for this seem to be not in the method itself but in the dependence of the outcomes on a number of experimental conditions (material properties, load, speed, rate of deformation, time) and research works, which cannot be easily compared. Moreover, the presented results underline the meaning of the interactive effect between various measurement conditions. Hence, the obtained parameters of the Peleg and Normand model must be examined or

interpreted together with other, often very important, factors. One of these is the time of relaxation used for the model development. Only a few studies have reported the effect of relaxation time on the model parameters [13,46]. The dependence of the model parameters on the relaxation time and varied applicability to differentiate cheese samples was also reported in studies of Bunka et al. [13]. Morales et al. [46] observed that both *k*1 and *k*2 changed with relaxation time. The time significantly influenced the accuracy of both the rate the force decreased at the initial relaxation phase and the value of residual force. Depending on the relaxation time, the parameters were over or underestimated. Similar conclusions can be derived from the work of Al Aridhee [47]. He demonstrated very strong dependences of the both model parameters on the relaxation holding time—*k*1 significantly and linearly increased with time, whereas for longer relaxation periods *k*2 decreased exponentially. For times approaching zero, *k*2 increased to infinity. It must be added that the effect of time was quantitatively and qualitatively comparable to that of moisture contents.

### **5. Conclusions**

The study led to a clear identification of the effect of wheat moisture and confirmed the influence of the wheat type on the Peleg and Normand model coefficients. The parameters *k*1 and *k*2, and the force decay *Y(t),* decreased with the increase in water content in the kernels. The relatively fast decrease in the parameter with moisture rising up to 14% was noticeably slower for higher water levels, practically no difference at 18% and 20%. This was observed both for soft and hard type wheats.

The constants differed depending on the wheat hardness. The highest values of the force decay rate were characteristic of hard cultivars. For higher water amounts, the average values for hard and soft varieties were similar. On the contrary, for low moisture levels, in the 8–12% range, the differences between the grain types were very clear. This is an interesting and important observation regarding the possibility of using the relaxation test to distinguish varietal differences. According to this, too high moisture levels may be the reason for disappearing differences in the values of Peleg and Normand parameters.

The initial loading level had no effect or only a slight effect on the parameters of the Peleg and Normand model. The values of all three constants changed only very little. The effect was similar for all parameters, and none of the three analyzed parameters were influenced more than others. Apart from the averages, the distributions were very close and independent of the loading level. It must be added, however, that an interactive influence of moisture, wheat variety, and load level on the Peleg and Normand constants was statistically confirmed.

The research showed that in comparison to wheat moisture and wheat type, the influence of initial loading was very weak, ambiguous, or simply not present. Very small differences between the parameters of the Peleg and Norman model show the very weak influence of the initial load on wheat relaxation experiments, and the practical application of different loads will probably lead to similar results. It is reasonable to state that experiments on larger loads than those applied here may lead to the development and propagation of cracks, making the relaxation tests more difficult to control and interpret.

The grain protein content and protein quality aspects together with grain hardness explain most of the variation in wheat behavior. However, endosperm can be very resistant, either at low or high protein content. Thus, any relationships between wheat chemical composition and measured rheological parameters are not straightforward, and the link between the biochemical and mechanical studies has not been established yet.

Attempts to apply the Peleg and Normand model into practice need further study, including the influence of all relevant factors.

**Author Contributions:** Conceptualization: J.K.A.A., G.Ł.; methodology: J.K.A.A., G.Ł.; software: J.K.A.A.; validation: J.K.A.A., G.Ł.; formal analysis: G.Ł.; investigation: J.K.A.A.; resources: J.K.A.A., G.Ł., M.W., M.P.; data curation: J.K.A.A., G.Ł.; writing—original draft preparation: J.K.A.A.; writing:— J.K.A.A., Gr.Ł.; writing - review & editing: G.Ł., M.W., M.P.; visualization: J.K.A.A.; supervision: G.Ł., R.K.; project administration: J.K.A.A., G.Ł., R.K.; funding acquisition: J.K.A.A., G.Ł., M.W., M.P.

**Acknowledgments:** I deeply appreciate the scholarship from the Iraqi Ministry of the Higher Education and Scientific Research.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

### **References**

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http://dx.doi.org/10.5772/59954


Author details

Rocio Maricela Ochoa Palacios1 \*, Citlaly Castillo Rodriguez1 , Jesus Torres Torres2 , Perla Janet Resendiz Hernandez1 and Alfredo Flores Valdes2

1 Tecnologico Nacional de México/IT de Saltillo, Saltillo, Coahuila, México

2 Center for Research and Advanced Studies of the National Polytechnic Institute Metallurgical Engineering, Gustavo A. Madero, Mexico

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© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.