The final kefir grain mass concentration in the culture medium, ycc/f, daily kefir grain increase mass, mKG, di, and daily kefir grain increase mass fraction, wKG, di, experimentally determined under different conditions proposed by the DoE (Table 2), are presented in Table 3. Daily kefir grain increase mass fraction, wKG/i is the quotient between the kefir grain increase mass concentration (yG, f — 40 g/L) and the initial kefir grain mass concentration (ycG = 40 g/L).

Table 3 shows that the highest daily kefir grain increase mass fraction (wKG, i = 51.5 %) was found at the rotational frequency of the stirrer, fm = 90 (1/min), at culture medium temperature, 3 = 24 °C, with a glucose mass concentration, yG = 20 g/L, and without baker’s yeast (yy = 0 g/L).

Moreover, the average impacts of the bioprocess parameters along with interactions at the assigned levels on the daily kefir grain increase mass are shown on Fig. 1. The difference between levels of each bioprocess parameters indicates their relative impact (Prasad et al., 2005). The larger the difference, the stronger is the influence.

It can be observed from Fig.1 that among bioprocess parameters studied rotational frequency of stirrer showed the strongest influence and followed by glucose mass concentration, culture medium temperature and baker’s yeast mass concentration. However, the relative impact of the proposed influencing bioprocess parameters on daily kefir grain increase mass were estimated by ANOVA. The sum of squares or deviation, Sj, and the variance of individual bioprocess parameters, Vj, were calculated by equations (2) and (4), and the error value by equations (3) and (6), respectively. The variance ratio, Fj, is the ratio of variance due to the effect of an individual bioprocess parameter and variance due to the error term. It was calculated by equation (9). The results of ANOVA are shown in Table 4.

The degrees of freedom of bioprocess parameter j and error variance equaled (f = fe = 3) in all cases. At 90 % confidence (level of importance 0.1), the value F3/3 = 5.3908 was determined through standardized tables of F-statistics. Table 5 shows that the variance ratio of all bioprocess parameters fell below F3,3. In accordance with the Taguchi’s method algorithm, we pooled baker’s yeast mass concentration from further statistical consideration as the least important bioprocess parameter, i. e., with the lowest variance ratio compared to F3/3.

Pooling of the baker’s yeast as an insignificant bioprocess parameter requires a repeated variance analysis, whereby the sum of squares and the degree of freedom of the pooled bioprocess parameter are added to the error sum of squares and the degree of freedom of error variance, respectively. The results in Table 5 show that, consequently, the variance ratios of the remaining bioprocess parameters increase. In spite of this, a repeated comparison of variance ratio of each bioprocess parameter indicated in Table 5 with the F-statistics value, F3/6 = 3.2888, shows that culture media temperature does not meets the Fj > F3,8 condition. Nevertheless, regarding significant test criterion (Fj > Fm, n) and especially Taguchi’s recommendation, we pooled only baker’s yeast mass concentration as insignificant bioprocess parameter on daily kefir grain increase mass. The final results of ANOVA terms, which were modified after pooling baker’s yeast mass concentration, are shown in Table 5. The relative influences of the bioprocess parameter j and error on the daily kefir grain increase mass were calculated using equations (10) and (11), respectively.

The results, shown in Table 5, assign the highest relative influence on the daily kefir grain increase mass (37.3 %) during 24 h incubation to the rotational frequency of the stirrer. The impact of glucose mass contraction and culture medium temperature within the observed ranges (yG = (0-30) g/L and 3 = (20-26) °C) show the lower ones, 18.8 % and 9.9 %, respectively. The remaining fraction represents error influence.

It is well known that kefir grains are bulky and awkward to handle (Bylund, 1994). Despite extensive and careful kefir grain biomass activation, their variegated symbiotic microbial community makes it impossible to retain the constant viability over a long time period. This fact, together with neglecting of possible secondary interactions between bioprocess parameters, mainly explains the relatively high error influence on daily kefir grain increase mass (34.0 %).

2. Conclusion

Using the Taguchi’s fractional factorial design approach we analyzed the bioprocess parameters impacts on daily kefir grain increase mass during 24 h incubation in fresh high temperature pasteurized whole fat cow milk. Experiments proposed by the design of experiments (OA L16) were performed in an RC1 reactor system. We determined those conditions which assure the highest kefir grain increase mass fraction and, using analysis of variance, estimated the relative impact of the proposed bioprocess parameters on daily kefir grain increase mass. In the observed bioprocess parameters ranges, we established that the yeast mass concentration was insignificant compared to the other bioprocess parameters. The most influential bioprocess parameter is found to be the rotational frequency of the stirrer (37.3 %), followed by the glucose mass concentration (18.8 %), and the medium temperature (9.9 %), while the remaining share represents an error.

Summarily, this chapter deals with the experimental determination of the relative impacts of various significant bioprocess parameters, that influence one of the most difficult bioprocesses in the dairy industry. The presented results confirm and, even more importantly, upgrade well-known findings about influence of various bioprocess parameters on kefir grain increase mass. On the other side, the presented results also confirm the tremendous importance of optimal kefir grain biomass managements. In addition, the results also clearly verify the fact, that inadequate combination of different significant critical bioprocess parameters has a strong negative influence on daily kefir grain increase mass. For instance, in the worst case the kefir grains growth is almost totally stopped. Last but not least, the presented chapter presents important cutting-edge and, in scientific and commercial society, shortfall basic knowledge needed either for kefir grains mass growth kinetic studies or designing, optimization and commercialization of modern batch or continuous industrial kefir grains production processes.

3. Nomenclature

ALR Automatic Lab Reactor ANOVA ANalysis Of VAriance DoE Design of Experiments f degree of freedom of error variance (1)

Fj variance ratio of bioprocess parameter j (1)

fj degree of freedom of bioprocess parameter j (1)

fm rotational frequency of the stirrer (1/min)

Fm, n standardized value from the F tables at defined level of significance (1)

fT total degree of freedom of result (1)

HTP High Temperature Pasteurized

L number of levels (1)

M number of bioprocess parameters (1)

mKG, di daily kefir grain increase mass (g)

N total number of experiments (1)

Nk number of experiments on k level (1)

OA Orthogonal Array

Se error sum of squares (/)

Sj sum of squares of bioprocess parameter j (/)

ST total sum of squares (/)

Ve variance error (/)

Vj mean square (variance) of bioprocess parameter j (/)

wKG, di daily kefir grain increase mass fraction (%/d)

Xe relative impact of error on optimization criterion (%)

Xj relative impact of bioprocess parameter j on optimization criterion (%)

Yi i value of optimization criterion (/)

yG glucose mass concentration (g/L)

yKG kefir grain mass concentration (g/L)

ycG/f final kefir grain mass concentration in culture medium (g/L)

yY baker’s yeast mass concentration (g/L)

& temperature (°C)

SRT contributes to a distinct treatment performance and membrane filtration, and therefore, to system economics. Specifically, these parameters act on biomass concentration (MLSS), generation of soluble microbial products (SMP) and oxygen transfer efficiency.

Increasing the SRT increases the sludge solids concentration and therefore, reduces bioreactor volume required. Furthermore, because of the low growth rates of some microorganisms (specifically nitrifying bacteria), a longer SRT will achieve a better treatment performance, as well as generating less sludge. In addition, it has been reported that high values of SRT can increase membrane permeability by decreasing SMP production (Trussel et al., 2006). Conversely, high solids concentration results in a higher viscosity of the microbial suspension (Rosenberger et al., 2002b), as a consequence, higher concentrations decrease air sparging efficiency and oxygen transfer rate to the microorganisms, resulting in a higher energy demand as well as increasing membrane fouling and the risk of membrane clogging. Given all of these factors, for economical reasons, most full-scale facilities are designed for MLSS range of 8-12 g/l and SRT range of 10-20 d (Asano et al., 2006; Judd, 2010).

Values of determined qualitative parameters of food wheat grains (bulk density, falling number, protein content, gluten content and sedimentation index) were different according to the cultivation years; statistically significant difference has been proved between years. No statistically significant difference was observed between values of the grain qualitative parameters of plants treated with brassinosteroids or untreated. Average values of qualitative parameters of wheat grains in individual years are reported in Table 8.

1.4 Yield of wheat grain after BRs treatment (field experiment)

Yield of grain and values of thousand-grain weight (TGW) were different during the investigated period; a statistically significant difference between years was demonstrated. In 2005, an increase of TGW was determined in all treated variants. The yield per hectare decreased by 6.9% in the variant treated with 24-epiBL. In 2006 and 2007, no difference between grain yields of control and treated plants was observed. No difference was found also between TGW values. Average grain yields and TGW values are given in Table 9.

Cadmium (atomic weight 112.41) is a silver white metal (density 8.65 g/mL). The oxidation states are 0, +2. The main uses of cadmium were steel production, non-ferrous metal production, refining, cement manufacture, cadmium plating, battery manufacture, waste and combustion, and phosphate fertilizers. Nowadays, because of concerns about its environmental toxicity, the use of cadmium has drastically decreased. About two thirds of the cadmium in use today come from nickel-cadmium batteries, the rest from pigments, metal plating and the plastic industry. It is a lot like lead and mercury, in that it accumulates both in the environment and in the body, causing long-term damage to life.

Cadmuim toxicity can manifest in a variety of syndromes, as hypertension, renal dysfunction, bone defects, hepatic injuries, lung damage, and reproductive effects. The maximum acceptable cadmium in drinking water is 0.003 mg/L (WHO, 2008).

1.1.1 Chromium

Chromium (atomic weight 51.99) is a lustrous, brittle, hard silver-gray metal (density 7.14 g/ mL). It exists in different oxidation states: -2, 0, +2, +3, +6. Chromium is mainly used in steel production and in chrome plating. Its products are also used in leather tanning, printing, dye production, pigments, wood preservatives, and many others.

The respiratory and dermal toxicity of chromium are well-documented. Workers exposed to chromium have developed nasal irritation (at <0.01 mg/ m3, acute exposure), nasal ulcers, perforation of the nasal septum (at ~2 pg/ m3, subchronic or chronic exposure) and hypersensitivity reactions and "chrome holes" of the skin. Among the general population, contact dermatitis has been associated with the use of bleaches and detergents. Compounds of both Cr(VI) and Cr(III) have induced developmental effects in experimental animals that include neural tube defects, malformations, and fetal deaths. The speciation of chromium has become of relevant interest because of the association Cr(VI)-cancer. The different toxicity of the two forms Cr(VI) and Cr(III) are now under examination, even if at the moment the WHO Guidelines report the provisional value 0.05 mg/L referred to total chromium (WHO, 2008).

Elisa Helena Giglio Ponsano1, Leandro Kanamaru Franco de Lima2

and Ane Pamela Capucci Torres1

1Unesp Univ Estadual Paulista, Faculty of Veterinary Medicine, Aragatuba,

2Brazilian Agricultural Research Corporation, Embrapa Fisheries and Aquaculture, Palmas,

Brazil

1. Introduction

Industrial activities have always been associated to the economic development of nations and their population. Nevertheless, they are also associated to the generation of industrial byproducts, generally considered undesirable due to the environmental damage they impose to society (Pipatti et al., 2009). Industrial byproducts have variable characteristics and compositions, since they are directly dependent on crude matter essence, kind of processing, facilities characteristics and volume of output, among so many other factors. Nowadays, the broad range of industries spread all over the world in an effort to supply the necessity of global population makes evident the need for the adoption of strategies capable of equilibrating economic development and environmental preservation as a way of reaching a sustainable industrial production (Parente & Silva, 2002).

In that way, transformation industries are currently searching for productive technologies of low environmental impact, which include practices like minimization of byproducts generation and/or recuperation and recycling of these residues, so aiming at the optimization of industrial processes (Juskaite-Norbutiene et al., 2007; Leite & Pawlowsky, 2005; Souza & Silva, 2009). The adoption of such technologies is a differential for the establishment and maintenance of industries in the current social and economic world scenery (Leite & Pawlowsky, 2005).

The management of industrial byproducts generally combines techniques as recuperation, treatment and safe disposal. Regarding to liquid waste, also called wastewater or effluent, treatments performed in the food industry generally consist of physical, chemical and biological operations. Physical treatments provide the removal of suspended solids and the separation of oils and fats by means of filtration, grading, sedimentation or floating techniques, while chemical treatments provide the removal of dissolved matter and even of microorganisms by using different chemicals (Giordano, 2006). The biological treatments, in turn, count on the ability of bacteria, fungi, micro algae and protozoa in transforming organic matter into new cells, called biomass, and gases (Arvanitoyannis & Tserkezou, 2009; Giordano, 2006). This kind of treatment simulates the natural remediation processes that occur in nature and brings as an advantage the production of compounds with particular applications, which may be appropriately separated and used for distinct purposes (Liu, 2007). Microbial biomass, for instance, has been considered as an alternative source of

proteins for foods and feeds and may be produced in different substrates, including effluents from industries and farms (Nasseri et al., 2011).

Some organisms may be used for the removal of organic matter from agro industrial residues yielding a biomass with potential for use in animal feeding, such as the phototrophic bacteria (Azad et al., 2003; Izu et al., 2001; Ponsano et al., 2008). Purple Non Sulfur Bacteria (PNSB), for example, are phototrophic bacteria commonly found in rivers, ponds, lakes and wastewater treatment systems, that can grow both as photoautotroph and photoheterotroph under anaerobic-light or microaerobic-light conditions (Choorit et al., 2002; Kantachote et al., 2005). Some PNSB also can grow in the dark using fermentation when they are in anaerobic environments or respiration when in aerobiosis (Devi et al., 2008; Kantachote et al., 2005; Kim et al., 2004; Ponsano et al., 2002a). Due to the ability of phototrophic bacteria to utilize diverse metabolic activities in different substrates and growth conditions, they find a role in the depollution of wastewaters from food industries, still producing a biomass rich in proteins, vitamins and carotenoids that may be used in the supplementation of animal feed (Carlozzi & Sacchi, 2001; Izu et al., 2001; Kantachote et al., 2005; Ponsano et al., 2002a, 2003a, 2004a, b; Zheng et al., 2005 a, b).

Rubrivivax gelatinosus, formerly named Rhodocyclus gelatinosus is a PNSB commonly found in many wastewaters in which it grows as an autotrophic or a heterotrophic, depending on light and oxygen conditions (Ponsano et al., 2003a, 2008). As the bacterium produces oxycarotenoids as photosynthetic pigments, its biomass can find use as a pigmenting additive in animal production, as previously suggested and tested by Ponsano et al. (2002b, 2003b, 2004a, b) and Polonio et al. (2010).

The use of pigmenting additives in animal production is justified by the fact that animals are unable to synthesize their own carotenoids and therefore, rely on dietary supply to achieve their natural pigmentation (Gouveia et al., 2003). The effectiveness of oxycarotenoids or xanthophylls in providing pigmentation to animals is possible because these carotenoids have the ability to deposit on different parts in animal bodies, such as muscles, fat, skin, feather, legs, ovaries and eggs (Ponsano et al., 2002b, 2004b).

Primarily, pigmenting additives were added into food formulations in order to replace color lost during the industrialization processes but, when the remarkable acceptance of consumers for well colored products was identified, industries started coloring a broad range of food items, reaching consumers desire and so improving its sales (Calil & Aguiar,

1999) . In case of poultry and fish production, for instance, either natural or synthetic additives are used when intensive rearing is adopted and/or when feed ingredients are poor in xanthophylls, so lacking in color in the final products. The most used synthetic additives for this purpose are apocarotenoic acid ethyl ester, canthaxanthin and astaxanthin, which show good stability and deposition rates on animal tissues. Nevertheless, more and more consumers around the world have been showing their preference for natural additives, what stimulates the search for natural sources of pigments, like those from biotechnological production. Among natural xanthophylls used in animal production, those from plants, algae, bacteria and yeasts have been previously described in literature (Akiba et al., 2000, 2001; Bosma et al., 2003; Gouveia et al., 1996; Liufa et al., 1997; Perez-Vendrell et al., 2001; Toyomizu et al. 2001).

The great acceptance that fish finds among consumers due to its nutritional and sensorial properties guarantees its market and yet claims for increases in production, which has been supplied by the aquaculture (Lem & Karunasagar, 2007). Nevertheless, fish is a perishable food and so requires the application of methods for its preservation, such as fermentation, refrigeration, freezing, canning, smoking, drying and others, that may be performed separately or in combinations. As it happens in any other food industry, fish processing generates great amounts of wastewaters with variable Chemical Oxygen Demand which depends on fish species, fish products and methods of processing, since water is involved in several stages of manufacturing, like butchering, evisceration, filleting, salting, cooking, canning, freezing, sterilization and cleaning operations (Arvanitoyannis & Kassaveti, 2008; Liu, 2007). The utilization of these effluents for the biomass production is an alternative for minimizing costs with treatment and environmental impacts. Moreover, in case the composition of the biomass finds an appropriate purpose, it can represent extra profits for the industry.

So, the hypothesis to be tested in this chapter is that an industrial byproduct may undergo a biological treatment yielding a product with application. The objective of this chapter was to describe a study on the transformation of a fish processing wastewater into a product with potential of use in animal rearing.

Each microcosm was made of commercially available high-density stable polyethylene container (14 cm length, 14 m width and 7 m depth) with 36 pores (1 mm diameter) of lid. Soils sampled in microcosms treated with three levels (12.5, 25 and 50 tons of dry matter ha-1 year-1) of MSS, ISS, LPS, AFPS and PMC for 4 consecutive years (twice annually) were sieved gently through a 2 mm mesh sieve. In a preliminary experiment, ~40% of water holding capacity was optimal for microcosm test. Amount of 300-g fresh soil was hydrated to ~40% of water holding capacity. Hydrated water required to achieve the desired hydration was calculated according to the method of Greene et al. (1988). Ten earthworms were placed into each microcosm. The microcosms were kept in the controlled chamber at 20°C and 60±5% relative humidity under a 16:8 h light:dark cycle. Mortalities were assessed by emptying the test soil onto a tray and sorting the worms from the soil. Earthworms were considered to be dead if their bodies and anterior did not move or respond when they prodded with fine wooden dowels. Live worms were placed back into their original microcosms. The numbers of live and dead worms in each microcosm were recorded every 2 weeks and the dead worms were discarded. A randomized complete block design with three replicates was used. Mortality percentages were transformed to arcsine square root values for analysis of variance. The Bonferroni multiple-comparison method was used to test for significant differences among the treatments (SAS Institute, 2004).

Toxic effects of MSS, ISS, LPS, AFPS and PMC treatments on E. fetida in microcosm tests were evaluated (Table 4). All treatments did not affect any adverse effects on the organisms 2 weeks after treatment. At 4 weeks after treatment, effect of test waste material (F = 3.73; df = 4,44; P = 0.0141) on the mortality was significant but that of treatment level (F = 1.83; df = 2,44; P = 0.1785) was not significant. The material by level interaction was also significant (F = 2.34; df = 8,44; P = 0.0436). At 8 weeks after treatment, effect of test waste material (F = 200.90; df = 4,44; P < 0.0001) and treatment level (F = 5.37; df = 2,44; P = 0.0101) on the the mortality was significant. The material by level interaction was also significant (F = 9.49; df = 8,44; P < 0.0001). After 16 weaks after treatment, effect of test waste material (F = 124.11; df = 4,44; P < 0.0001) and treatment level (F = 9.73; df = 2,44; P = 0.0006) on the mortality was significant. The material by level interaction was also significant (F = 63.42; df = 8,44; P < 0.0001).

Heimbach et al. (1992) demonstrated that there is a good correlation (r = 0.86) between LC50 values of pesticides from an artificial soil test and the number of earthworms collected from a standardized field test. Our present and previous studies indicate that microcosm soil test using earthworms can predict results from a field test for assessing side effects occurred by long-term exposure of soil contaminants. Burrows & Edwards (2002) have been tried to use integrated soil microcosm based upon earthworms to predict effects of pollutants on soil ecosystems.

Range management problems in Balochistan are diverse and complex. The ranges of Balochistan are open and no one is responsible for management. Rangeland ownership is not clear or very poorly defined ownership. There are four major land ownership systems (Individual ownership, Tribal claims, Community ownership, State Ownership). Approximately 4% rangelands are under the Forest Department and the rest belongs to different groups. As a result of open grazing system the ranges are degrading very rapidly. The major range degradation factors are forage shortage, elimination of desirable range species, dominance of less preferred species, desertification, soil erosion, increased runoff and reduced infiltration (Fig. 3). Perennial grasses like Chrysopogon aucheri and Cymbopogon jwarancusa have completely eliminated in many ranges and are only found in some protected range areas. Similarly, many desirable shrub species like Caragana ambigua, Stocksia brahvica, Berberis Balochistanica, Prunus eburnea etc. have been replaced by Haloxylon grifithii and other unpalatable species. Limited information is available on rangeland resources, potential, and management options. Most of the Pastoral communities are in isolation especially in the mountain areas of Balochistan. Moreover, there is a transformation of these communities due to rapid extension in irrigated agriculture and changes in traditional migratory routes. From the last few years it has also been observed that to crop production on marginal lands is also increasing and resulting in conversion of rangelands into agricultural activities. Early spring migration of nomads from lowlands to highlands did not allow range plants for growth and seed production.

Generally, range management is a low priority area and lack of integrated range management approach and non-involvement of range management activities in other Natural Resource Management Projects is a common practice. Many Range Management Projects in Balochistan have adapted only technical range management approach ignoring the traditional customs, rights and local arrangements. Generally, most of the range management programs last two to three years. This duration is not sufficient to show any positive impact to communities on range management/improvement and livestock production. Removal of range vegetation for fuel wood is a major concern all over the Province and no alternate energy sources like solar cookers and other efficient cooking and heating devices are available. Recurrence of drought is a common phenomenon in Balochistan. However, no sound viable options are available to reduce the livestock mortality and rangeland degradation under drought conditions. Some productive ranges at present are under utilization due to non-availability of stock water. Community participation is one of the main factors for any successful range management Program. However, in Balochistan, very weak community participation in range management activities has been observed. Moreover, communities are in view that they there are no incentives for range management and they alone cannot bear the range management cost. Some other issues like limited research activities on all aspects of range management, lack of awareness, education and dissemination of knowledge, lack of trained manpower and reform in existing range management policies are also important for effective range management.

Single-celled organisms living freely in nature, such as yeasts, face large variations in their natural environment. Environmental conditions that threaten the survival of a cell, or at least prevent it from performing optimally, are commonly referred to as cell stress. These environmental changes may be of a physical or chemical nature: temperature, radiation, concentrations of solutes and water, presence of certain ions, toxic chemical agents, pH and nutrient availability. In nature, yeast cells often have to cope with fluctuations in more than one such growth parameter simultaneously (Hohman & Mager, 2003). In industry, yeast stress has several very important practical implications. In brewing, for example, if yeast is nutrient-starved during extended periods of storage, certain cell surface properties such as flocculation capability are deleteriously affected (Walker, 1998).

Carotenogenic yeasts are considered to be ubiquitous due to its world-wide distribution in terrestrial, freshwater and marine habitats, and to its ability to colonize a large variety of substrates. They can assimilate various carbon sources, including waste materials as cheap substrates. The red yeast is able to grow under a wide range of initial pH conditions from 2.5 to 9.5 and over a wide range of temperatures from 5 to 26°C (Libkind et al., 2008; Latha et al., 2005). The most important consenquence of environmental stress in red yeast is stimulation of carotenoid and other secondary (as well as primary) metabolite production. Changes of ergosterol production, lipid content, glycerol and trehalose as well as membrane remodeling are described as a response to stress (Hohman & Mader, 2003). Carotenoid pigments accumulation in most yeasts starts in the late logarithmic phase and continues in the stationary phase and is highly variable. Carotenoid production depends on differences between strains of the same species and is strongly influenced by the cultivation conditions. Addition of stress factors into cultivation medium led to different changes of growth according to the yeast species, type of stress factor or growth phase, in which stress factors were added (Marova et al., 2004).

Carotenogenesis in many organisms is regulated by light. However, the intensity and protocol of illumination varies with the microorganism. Temperature is another important factor affecting the performance of cells and product formation. The effect of temperature depends on the species specificity of the microorganism and often manifests itself in quantity variations of synthesized carotenoids. It was reported that lower temperatures (25°C) seemed to favor synthesis of P-carotene and torulene, whereas higher temperatures (35°C) positively influenced torularhodin synthesis by R. glutinis (Frengova & Beshkova,

2007) . The effect of aeration is dependent on the species of the microorganism. The aeration influenced not only the amount of carotenoids produced, but also the composition of individual pigments making up the total carotenoids (Simova et al., 2004). At higher aeration, the concentration of total carotenoids increased relative to the biomass and fatty acids in R. glutinis, but the composition of carotenoids (torulene > p-carotene > y-carotene > torularhodin) remained unaltered. In contrast, S. roseus responds to enhanced aeration by a shift from the predominant p-carotene to torulene and torularhodin (Davoli, 2004). Also other inducers of oxidative stress such as irradiation and free radical generators have a significant effect on the carotenoid production. By UV mutagenesis of the pink yeast R. glutinis the yellow colored mutant 32 was obtained which produced 24-fold more total carotenoids (2.9 mg/ g dry cells) and 120-fold more p-carotene than the wild-type in a much shorter time (Bhosale & Gadre, 2001). Production of carotenoids by Rhodotorula glutinis cells grown under oxidative stress was about 5-6 times higher than in wild-type (Marova et al., 2004; Marova et al., 2010).

Tolerance to deleterious factors (e. g., low pH) refers to a microorganism’s ability to survive a stress. This phenomenon is described as adaptive response, induced tolerance, habituation, acclimatization or stress hardening. Once cells have been challenged with a mild stress, they become more resistant to severe stress. Also exposure to one type of stress has been demonstrated to lead to tolerance to other types of stress as well (cross-protection) (Hohman & Mager, 2003). When cells are shifted to stress environments, they respond with changes in the expression of hundreds or thousands of genes, revealing the plasticity of genomic expression. Some of the expression changes are specific to each new environment, while others represent a common response to environmental stress. Comparative analysis of the genomic expression responses to diverse environmental changes revealed that the expression of roughly 900 genes (around 14% of the total number of yeast genes) is stereotypically altered following stressful environmental transitions. The functions of these gene products may protect critical aspects of the internal milieu, such as energy reserves, the balance of the internal osmolarity and oxidation-reduction potential, and the integrity of cellular structures. The protection of these features by the stress gene products likely contributes to the cross-resistance of yeast cells to multiple stresses, in which cells exposed to a mild dose of one stress become tolerant of an otherwise-lethal dose of a second stressful condition (Hohman & Mager, 2003; Gasch & Werner-Washburne, 2002; Gasch et al., 2000).

A critical component of cell survival is maintaining a viable energy source. Glucose is the preferred carbon source in yeast, and upon stress, the cell induces a variety of genes that affect glucose metabolism. This includes genes encoding glucose transporters that serve to import external glucose into the cell and glucose kinases that activate the sugar for subsequent catabolism. In response to stressful environments, the fate of glucose is divided between trehalose synthesis, glycogen storage, ATP synthesis through glycolysis, and NADPH regeneration by the pentose phosphate shuttle (Hohman & Mager, 2003).

XPS, introduced by the Nobel Prize winner Siegbahn in 1949, is the main technique used for qualitative and quantitative elemental analysis of surfaces. It provides significant information on the chemical bonding of atoms. The absorption of high-energy electromagnetic radiation (X-ray or UV) by surfaces leads to the emission of photoelectrons; those generated in the outermost layers emerge from the surface into the vacuum and can be detected. The measure of the kinetic energy of the emitted photoelectrons allows the determination of the binding energies of electrons and the intensity function (number of photoelectrons vs. kinetic energy), and quantitative results are obtained from the knowledge of the number of atoms involved in the emission process.

Ashkenazy et al., 1997, using X-Ray photoelectron spectroscopy (XPS) pointed out the involvement of nitrogen in lead sorption and the lead-oxygen interaction at the carboxyl group on the basis of the decrease in nitrogen concentration and of the shift of oxygen peak. The same technique confirmed that chromium was sorbed onto grape stalks in both its trivalent and hexavalent forms, and allowed the ascertainment of the oxidation state of chromium bound on pine needles. Furthermore it was used to explain the increase of cadmium and lead sorption onto baker’s yeast after modification of sorbent surface by cross linking cysteine.

Statistical analysis showed a significant effect of examined factors in experience on the starch yield of potato tubers (table 7). Intercrop fertilization caused a significant increase of starch yield in comparison with starch yield of potato tubers from the control object. The highest starch yield was obtained from the object fertilized with a mixture of white clover with Italian ryegrass, white clover, phacelia plowed down in autumn and left till spring in the form of mulch. Only after the application of Italian ryegrass the starch yield of potato tubers fertilized with Italian ryegrass was significantly lower than that recorded in the farmyard manure. Straw fertilization also modified the starch yield. At the sub-block with straw starch yield of potato tubers was significantly higher than at the sub-block without straw. An interaction has been shown that intercrop fertilization with straw fertilization, which shows that the highest yield of starch was obtained from the object fertilized with a mixture of white clover with Italian ryegrass and phacelia used in the form of mulch in combination with straw, and the smallest from control object, without intercrop fertilization.

potato tubers harvested from control object. The highest concentration of reducing sugars noted in potato tubers harvested from control object. Intercrop fertilization significantly decreased reducing sugars content in potato tubers in comparison with their concentrations recorded in potatoes tubers harvested from control object. Indeed, the lowest content of reducing sugars was noted in potato tubers fertilized with phacelia both plowed down in the autumn, and left till spring in the form of mulch. Straw fertilization also significantly differentiate the concentration of reducing sugars in potato tubers. Higher its content was noted in potato tubers in the sub-block without straw than on the sub-block with straw.