The system in Equation (17) is passive if *H*˙ (**x**˜) ≤ **y**˜ *T***u**˜ [33,34]. Therefore, to obtain a closed-loop dynamic based on Lyapunov theory, the energy function is shown as:

$$H(\bar{\mathbf{x}}) = \frac{1}{2} \bar{\mathbf{x}}^T \mathbf{P}^{-1} \mathbf{x},\tag{19}$$

where *H*(0) = 0 and *H*(**x**˜) > 0, ∀**x**˜ = 0, which satisfy the first and second conditions of the Lyapunov theorem. The time derivative of the proposed energy function is

$$\begin{split} \dot{H}(\ddot{\mathbf{x}}) &= -\ddot{\mathbf{x}}^T \mathbf{P}^{-1} \dot{\mathbf{x}} = -\ddot{\mathbf{x}}^T \mathbf{P}^{-1} \mathbf{R} \mathbf{P}^{-1} \ddot{\mathbf{x}} + \ddot{\mathbf{x}}^T \mathbf{P}^{-1} \mathbf{g} \ddot{\mathbf{u}}, \\ &= -\ddot{\mathbf{x}}^T \mathbf{P}^{-1} \mathbf{R} \mathbf{P}^{-1} \ddot{\mathbf{x}} + \ddot{\mathbf{y}}^T \ddot{\mathbf{u}} \le \ddot{\mathbf{y}}^T \ddot{\mathbf{u}}, \end{split} \tag{20}$$

which proves that the dynamics of the error are also passive.

The proposed controller can be written as follows:

$$
\dot{\mathbf{z}}\_{-} = -\ddot{\mathbf{y}}\_{/} \tag{21}
$$

$$
\ddot{\mathbf{u}}\_r = \begin{array}{c} -K\_P \ddot{\mathbf{y}} + K\_I \mathbf{z}\_r \\ \end{array} \tag{22}
$$

and it has a PI structure, where *KP* and *KI* are the proportional and integral gain matrices, respectively, which are diagonal and positive definite, and ˜**y** is

$$\mathbf{y} = \mathbf{g}^T \frac{\partial H(\tilde{\mathbf{x}})}{\partial \tilde{\mathbf{x}}} = \begin{bmatrix} v\_{dc} \begin{pmatrix} i\_d - i\_d^\* \\ v\_{dc} \end{pmatrix} \\ \upsilon\_{dc} \begin{pmatrix} i\_q - i\_q^\* \\ i\_q - i\_q^\* \end{pmatrix} \end{bmatrix}. \tag{23}$$

Note that **z** represents a set of auxiliary variables that allow the passive output feedback and the inclusion of an integral action to minimize steady-state errors on the control objective. For more details of the PI-PBC approach for non-affine dynamic systems, refer to [32].

Using Equation (11), the references of the *dq* axis currents and the modulation index can be obtained as follows:

$$\dot{\iota}\_d^\* = -\omega\_{dq} \mathbf{C} e\_q^\* + \dot{\iota}\_{\rm{Ld}} \tag{24}$$

$$\mathbf{i}\_q^\* = -\omega\_{d\eta} \mathbf{C} \mathbf{e}\_d^\* + \mathbf{i}\_{\mathbf{L}\eta} \tag{25}$$

$$m\_d^\* \quad = \frac{1}{v\_{dc}} \left( Li\_d^\* + Ri\_d^\* + \omega\_{d\eta} Li\_q^\* + e\_d^\* \right), \tag{26}$$

$$m\_q^\* \quad = \frac{1}{\upsilon\_{dc}} \left( \dot{L}\_q^\* + \dot{R}\_q^\* - \omega\_{d\eta} L \dot{i}\_d^\* + e\_q^\* \right), \tag{27}$$

where we have considered that *e*˙ ∗ *d* = *e*˙ ∗ *q* = 0 because *e*∗ *d* and *e*∗ *q* are constant values in the Park's reference frame.

Figure 3 shows the proposed control scheme. It can be observed that the modulation indexes are obtained using Equations (22) and (23) with Equation (27), and the references *e*∗ *d*, *e*∗ *q* , and *ωdq* are selected by the users. The angle *θdq* used in the Park's transformations is obtained from *dωdq dt* .

**Figure 3.** Block diagram of the proposed controller.

#### *3.2. Control Objective*

The main idea behind implementing VSCs for the integration of non-linear loads in the AC side using the energy provided by the DC network (see Figure 1) is to generate a three-phase sinusoidal signal with constant amplitude and frequency [19]. This implies that the references for the voltage signals in the capacitors in parallel to the load can be defined as follows:

$$\begin{aligned} \begin{aligned} \varepsilon\_a^\*(t) &= \sqrt{2}E\_{rms}\sin(\omega t),\\ \varepsilon\_b^\*(t) &= \sqrt{2}E\_{rms}\sin(\omega t - 2\frac{\pi}{3}),\\ \varepsilon\_c^\*(t) &= \sqrt{2}E\_{rms}\sin(\omega t + 2\frac{\pi}{3}), \end{aligned} \tag{28}$$

where *Erms* is the desired voltage magnitude and *ω* represents the angular frequency, i.e., for this study *ω* = 2*π f* , with *f* = 50 Hz.

From the desired three-phase voltage reference defined in Equation (28), we can observe that the electrical frequency is considered as an input for the Park's transformation (see Figure 3). This implies that if the controller makes its task, the voltage outputs in the load will be sinusoidal waves regardless of the load variations (linear or non-linear behavior) [50].

Note that if we send a constant frequency as input to the Parks' transformation, then the sinusoidal references in Equation (28) become constant values in the *dq* reference frame, i.e.,

$$\begin{aligned} e\_d^\*(t) &= \quad E\_{rms}, \\ e\_q^\*(t) &= \quad e\_0^\*(t) = 0, \end{aligned} \tag{29}$$

which implies that if the controller reaches these values in the *dq* reference frame, then the voltage output will be perfectly sinusoidal in the *abc* reference frame sinusoidal [19].

**Remark 2.** *Note that the voltage and frequency and regulation reported in this paper allows supporting a three-phase sinusoidal signal in load coupling based on the properties of the controller for following constant references in the dq frame [19]. This is different from classical approaches that regulate voltage and frequency by measuring active and reactive power by emulating synchronous machines using the virtual inertia concept [20,21].*

#### *3.3. Stability Analysis*

The stability analysis in modern control applications is an important aspect and should be demonstrated to show that they are suitable for implementation without fails [8,51]; one of the main attractive characteristics of the PBC design is that in a large class of linear and nonlinear system, stability can be guaranteed using Hamiltonian or Lagrangian representations [14,32]. Here, we present basic proof for analyzing the proposed PI-PBC approach, since it was extensively studied in [32,35].

We will enunciate the necessary conditions for guaranteeing stability of a nonlinear autonomous dynamical *x*˙ = *f*(*x*, *u*) system in the sense of Lyapunov around the equilibrium point *x* = *x*- as follows [52]:

Note that to prove these two necessary conditions for the proposed closed-loop dynamical system with state variables *x*˜ and *z*, we employed a quadratic function as recommended in [32] which fulfills the positive definiteness in all the solution space which is zero only in the equilibrium point, as follows:

$$V(\mathbf{\bar{x}}, \mathbf{z}) = H(\mathbf{\bar{x}}) + \frac{1}{2} \mathbf{z}^T K\_l \mathbf{z},\tag{30}$$

where *H*(**x**˜) is the desired Hamiltonian function. Note that the second term is a quadratic expression as function of the integral variables that deal with these new stable variables introduced by the PI-PBC method [33].

Now, if we take the time derivative of the candidate Lyapunov function in Equation (30), then the following result is yielded:

$$
\dot{V}(\ddot{\mathbf{x}}, \mathbf{z}) = \dot{H}(\ddot{\mathbf{x}}) + \mathbf{z}^T K\_I \dot{\mathbf{z}} \tag{31}
$$

where, if it substitutes the expression in Equation (20) by considering the PI-PBC design in Equation (22), then, the following result is reached:

$$\begin{split} \dot{V}(\ddot{\mathbf{x}}, \mathbf{z}) &= \begin{aligned} &-\mathbf{x}^{\mathrm{T}} \mathbf{P}^{-1} \mathbf{R} \mathbf{P}^{-1} \ddot{\mathbf{x}} + \mathbf{x}^{\mathrm{T}} \mathbf{P}^{-1} \mathbf{g} \ddot{\mathbf{u}} + \mathbf{z}^{\mathrm{T}} K\_{I}(-\ddot{\mathbf{y}}) \\ &\leq \ -\ddot{\mathbf{y}}^{\mathrm{T}} K\_{P} \ddot{\mathbf{y}} \leq 0, \end{aligned} \tag{32}$$

which clearly fulfills the second condition of the Lyapunov's theorem [34].

**Remark 3.** *As the derivative of the Lyapunov function with respect to time does not directly contain the state variables of interest, i.e., x*˜ *and z, based on the Lyapunov's stability theorem, we can affirm that the dynamical system* ˙*x*˜ *(see Equation (17)) is stable in a closed-ball that contain the equilibrium point x* = *x*˜*, as demonstrated in [35].*

Regarding the range of application of the aforementioned stability analysis of the real systems and its relation to the parameters of the system, it is important to highlight the following facts:

#### **4. Results**

The proposed PI-PBC controller was validated based on simulations and experimental results. The parameters used in the system in both the simulations and experimental tests are listed in Table 1.

**Table 1.** System parameters.

#### *4.1. Simulation Results*

The simulation results were obtained using a realistic model of the system, including the switching effects, losses, and a detailed transistor model. The simulations were implemented using the SimPowerSystem in MATLAB.

To validate the performance of the proposed controller, a test was performed by changing the load (linear and nonlinear). Figure 4a shows the *dq* axis output voltage and its reference, and Figure 4b shows the output voltage and inductor current for phase *a*. Initially, the system operated with a resistive load of 10 Ω, and the reference of the output voltage was *e*∗ *d* = 100 V and *e*∗ *q* = 0 V. When the load was changed from 10 Ω to 5 Ω (at 0.1 s), the *d*-axis voltage exhibited a transient variation; this also occurred for the voltage on the *q*-axis, but within a short time (approximately one cycle of the voltage), the controller was able to regulate the amplitude and frequency of the output voltage in the expected time and with the desired dynamic behavior. At 0.2 s, the nonlinear load was connected to the system. Again, the controller regulated the *dq* axis output voltages in an acceptable time and with suitable behavior. In addition, as shown in Figure 4b, the waveform of the voltage was sinusoidal (free of significant harmonics) even under a nonlinear load.

**Figure 4.** Voltage behavior in the Park's reference frame, and voltage and load current per-phase. (**a**) *edq* profile. (**b**) Voltage and load current in phase *a*.

Figure 5a shows the three-phase output voltage and Figure 5b depicts the currents in the filter inductors. The amplitude of the three-phase output voltage was regulated in advance of the load changes as expected.

**Figure 5.** Dynamical performance of the voltage and currents at the point of load connection. (**a**) Three-phase voltage profile. (**b**) Current profile for linear and nonlinear consumption.

With the objective to show the performance of the proposed controller in the presence of parametric variations, a test with varying inductance and resistance values of the LC filter was performed. Figure 6 shows the *d*-axis output voltage for the same test of Figure 4 when the value of the inductance and resistance of the filter are different and the same are then considered in the controller. The parameter variation consists of a change of 50% in the inductance and resistance of the filter. It can be observed that the *d*-axis voltage exhibited transient differences between the situations with and without parameter variation, while in regimen, the voltage is regulated in the reference value as the integral action of the PI eliminates possible steady-state errors (see [54]).

**Figure 6.** Behavior of *d*-axis voltage for parametric variation.

For comparison purposes with classic approaches (PI controllers), Figure 7 shows the output voltage behavior in *dq*-coordinates for the same test of Figure 4, when a PI controller (in blue) and the proposed PI-PBC controller (in red) are working. It can be observed that the classic PI controller performs similar to PI-PBC when a linear load is considered, but when a non-linear load is connected, the system with the PI controller presents high harmonic content, and therefore the performance is reduced. This test shows the superiority of the proposed strategy to mitigate the harmonics due to non-linear loads.

**Figure 7.** Voltage behavior in the Park's reference frame, comparison between the proposed controller (in red) and the classic PI approach (in blue).

With the aim to demonstrate the performance of the proposed controller under highly demanding conditions, Figure 8 presents a test with a change of the amplitude reference of the output voltage and a change in load when the converter supplies the nonlinear load, as considered in Figure 4. In Figure 8a, the dq axis output voltage and its reference is illustrated and in Figure 8b, the output voltage and inductor current for phase *a* is shown. It can be seen that the proposed PI-PBC controller (in red) regulates the amplitude of the output voltage in the presence of changes of the amplitude reference (0.1 s) and load changes (0.2 s) even when the power of the nonlinear load is increased. Furthermore, in this test, a comparison between the proposed controller (in red) and the classic PI (in yellow) approach was performed. The performance of the classic PI presents high harmonic content as the value of the power of the nonlinear charge increases while the oscillations presented by the PI-PBC are small. It should be noted that the elimination of these oscillations can be accomplished using a multiple reference controller but it will increase the complexity of the control algorithm [24].

**Figure 8.** Test system behavior under very demanding conditions for the proposed controller (in red) and the classic PI approach (in yellow). (**a**) Voltage behavior in the Park's reference frame. (**b**) Phase *a* voltage profile.

Figure 9 depicts the harmonic spectrum of the output voltage using the proposed PI-PBC controller. It can be seen that the amplitude of individual harmonics remains below the required limits by the standards and the total harmonic distortion (THD) is 2.17%. This value is lower than the values established by IEC 62040-3 (less than 8%) [16]. Figure 10 shows the harmonic spectrum of the output voltage for the classic PI controller, the THD is 7.37%, which is very close to the allowed limits. The harmonic spectra were obtained for the system feeding the nonlinear load corresponding to Figure 8 between 0.2 s to 0.3 s.

**Figure 9.** Voltage harmonic spectrums of proportional-integral passivity-based controller (PI-PBC).

**Figure 10.** Voltage harmonic spectrums of Classical PI controller.

Finally, Figure 11 shows a test for an unbalanced load. It can be seen that the three-phase voltage is maintained with constant amplitude and frequency, and without significative harmonics when considering these types of loads.

**Figure 11.** Test system behavior under very demanding conditions for the proposed controller (in red) and the classic PI approach (in yellow). (**a**) Voltage behavior in the Park's reference frame. (**b**) Phase *a* voltage profile.

#### *4.2. Experimental Results*

The experimental results were obtained using a laboratory prototype with the same parameters employed in the simulation tests (see Table 1). The DC-AC converter was constructed using an IGBT module (SEMiX101GD12E4s), and the controller was implemented in a TMS320F28335 floating-point DSP (Texas Instruments).

Figure 12 shows the three-phase voltage generated by the system under a resistive load change (50 Ω to 25 Ω). The controller regulated the amplitude and frequency of the output voltage, and the time response required for the load change was approximately one cycle, thereby agreeing with the previously described simulation results, and this time is acceptable in practical applications as well.

**Figure 12.** Experimental results: three-phase generated voltage for a resistive load change.

The output voltage and inductor current for phase *a* are shown in Figure 13 for the resistive load change and in Figure 14 for a nonlinear load. The results show that dynamic behavior was acceptable in both cases. In addition, when a nonlinear load was considered (Figure 14), the controller generated an output voltage that was free of significant harmonics. The voltage total harmonic distortion is 3.6%. This value is lower than the values established by IEC 62040-3 (less than 8%) [16].

**Figure 13.** Experimental results: output voltage and inductor current for phase *a* with a resistive load change.

**Figure 14.** Experimental results: output voltage and inductor current for phase *a* under a nonlinear load.

#### **5. Conclusions**

In this article, we present a PI-PBC controller to regulate the amplitude and frequency of a three-phase output voltage in a DC-AC converter with an LC filter. Simulations and experimental results show that the proposed controller allowed regulating the amplitude and frequency of the output voltage of the converter under both linear and nonlinear load changes. Additionally, the PI-PBC allowed generating well-defined sinusoidal signals to linear and nonlinear loads. The results of the performance analysis showed that the proposed design combines the simplicity and robustness of PI-based controllers with the stability and performance characteristics of PBC as a powerful tool for the design of power converter controllers used in hybrid power systems.

Derived from this research, some future recommendations have been made as follows: (1) to develop a direct power control approach to operate DC-AC converters in constant power load applications; (2) to combine the advantages of the passivity-based control designs with virtual inertia emulators to develop robust controllers for weak three-phase distribution networks with unbalanced and nonlinear loads; and (3) to employ advanced methodologies for optimal adjustment of PI control gains using active and passive strategies in order to improve the dynamical performance of the proposed PI-PBC method in applications with large variations in the load terminals.

**Author Contributions:** Conceptualization and writing—review and editing, F.M.S., L.M.F., O.D.M., W.G.-G., and J.C.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was partially supported by the Universidad Nacional de San Luis under project PROICO: 142318 "Control de convertidores de potencia para la integración de fuentes de energía renovables y vehículos eléctricos a la red"and by Fondo para la Investigación Científica y Tecnológica (FONCyT) under project PICT-2017-0794 "Cargadores de baterías para vehículos eléctricos: integración con la red y fuentes de energía renovables" and in part by the Universidad Tecnológica de Bolívar under grant CP2019P011 associated with the project: "Operación eficiente de redes eléctricas con alta penetración de recursos energéticos distribuidos considerando variaciones en el recurso energético primario".

**Acknowledgments:** The authors want to express to thanks to Universidad Nacional de San Luis and Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) in Argentina, Universidad Distrital Francisco José de Caldas, Universidad Tecbológica de Bolivar in Colombia and University of Jaén in Spain.

**Conflicts of Interest:** The authors declare no conflicts of interest.

#### **Abbreviations**

The following abbreviations and nomenclature are used in this manuscript:

*Acronyms*

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*


**3. Results**

The following section summarizes the results for all three clusters and identifies the main di fferences between them. The results are not necessarily presented according to the questionnaire's sequence. Instead, particularly interesting statements, points of clear consensus, or opposing positions are outlined.

#### *3.1. Scientific Background of Respondents*

The scientists were asked about which field of activity their project could be assigned to. Multiple answers were possible. According to the responses, the majority (53%) related their projects to biomass production, conversion, and product development. The remaining 47% were assigned to evaluation, systems analysis, and societal framework conditions (Figure 1). Some respondents gave multiple answers, which indicated that their projects deal with di fferent technical or di fferent assessment aspects at the same time or even combine technical and nontechnical research areas such as sustainability assessment. In the biogas cluster, the share of assessment-related research activities was highest, with 58% of the responses. In the microalgae cluster, it was lowest, with 30%. This can be explained by the di fferent development stages of the fields. While the biogas sector is well elaborated from a technical point of view and challenges arise from the lack of competitiveness and acceptance by local residents, microalgae production and conversion is a rather new approach that requires further technical research, with the consequence that an assessment of environmental and societal issues fades into the background.

**Figure 1.** Respondents' assignment of projects to fields of activity (*n* = 79, n = number of answers, multiple answers possible).

Thirty-one percent of the respondents assigned their project to basic research and 69% to applied research (Figure 2). This relation was quite di fferent for each cluster. In biogas research, 96% of the responses referred to applied research, while this share was only 55% in the lignocellulose cluster and 62% in the microalgae cluster. In all three clusters, a limited number of multiple answers were given, i.e., the respective research projects were assigned to both basic and applied research. The number of multiple entries was highest in the lignocellulose cluster, which could indicate that some research projects are in a transition process from basic to applied research or that the nature of the research is both basic and applied due to the interdisciplinary approach of most projects in this cluster. Moreover, the responses indicated that accompanying research projects in the fields of systems analysis, assessment, and societal framework conditions are classified as either basic or applied research.

In answering the question of how long the scientists have been active in the field of bioeconomy, 43% of the respondents stated that they had already done research on bioeconomy before starting the research project with the Bioeconomy Research Program Baden-Württemberg. Fifty-seven percent entered the field at the start of the project (Figure 3). In the biogas network, more than half of the researchers had previous experience in bioeconomy research, and the majority of respondents in the lignocellulose group (59%) first came into contact with the topic through the current project. In the microalgae network, this share was even higher, at 67%. These di fferences can be explained by the fact that Baden-Württemberg has been seen as a pioneer in biogas research and implementation since

the 1980s, while biotechnology-driven microalgae and lignocellulose research are quite new fields. Overall, the answers reflected the mixed composition of the project teams, which consist of professors or project managers with many years of experience in the field of bioeconomy and doctoral candidates who have recently entered the subject.

**Figure 2.** Respondents' assignment of projects to type of research (*n* = 80, multiple answers possible).

**Figure 3.** Respondents' familiarity with the bioeconomy topic (*n* = 80).

#### *3.2. Understanding of Bioeconomy*

In a first step, the scientists were asked to define bioeconomy in their own words. The aim of this open question was to get insight into the respondents' understanding of bioeconomy, regardless of already existing definitions.

The 56 definitions given were assigned to the following categories:

Almost all of the definitions (89%) saw the production and use of biogenic resources as core elements of the bioeconomy (resource-oriented definition, see Figure 4). Thirty-six percent of the definitions were related to the resource-oriented understanding, and 37% went beyond the pure resource orientation and outlined that biomass use should also meet environmental and social sustainability criteria (sustainability focus). Sixteen percent of the definitions emphasized the substitution of fossil resources with renewable feedstock (substitution focus). Only 7% of the definitions stressed the importance of science and technology in the context of a resource-oriented understanding (science and technology focus). Besides these resource-oriented understandings, 4% of the definitions concentrated on the use of innovative technologies for biomass conversion (technology-oriented definition). While the resource-oriented definition played an important role in all research clusters, the science and technology focus was most supported by respondents from the lignocellulose cluster, and the technology-oriented definition appeared only in the microalgae cluster. Table 1 provides some examples of definitions for the five categories.

**Table 1.** Examples of bioeconomy definitions formulated by the interviewed scientists.

In a second step, the respondents were asked to indicate to what extent they agree with the definitions formulated in official bioeconomy strategies. This question aimed to find out about support or skepticism toward official bioeconomy definitions by scientists working in the field of bioeconomy.

The OECD definition was selected as an example of a technology-oriented understanding. The European Commission's definition focuses on the sectors involved in the bioeconomy (sector-oriented definition). The German Federal Ministry of Education and Research based its definition on the resources used (resource-oriented definition), and the definition by the German Federal Ministry of Food and Agriculture can be seen as an example of a target-oriented definition. The wording in the questionnaire was as follows:

Overall, all types of definitions originating from official bioeconomy strategies were supported by the majority of respondents (Figure 5). However, the highest level of full agreement (over 60%) was recorded for the resource-oriented definition, followed by the target-oriented definition. This was in line with the results of the respondents' own definitions, where the majority of definitions were solely resource-oriented or had an additional focus on sustainability. The sector-oriented definition showed a lower level of full agreement. Nevertheless, summing up the answers "fully agree" and "rather agree", the sector-oriented definition received the highest agreement, together with the resource-oriented definition. The lowest level of full agreement was achieved for the technology-oriented definition. The proportion of people slightly disagreeing with this definition was highest, about 30%. The comparison to the researchers' own definitions showed that the given technology-oriented definition received more support than a similar understanding formulated by the respondents themselves.

**Figure 5.** Respondents' agreement with official bioeconomy definitions differing in their thematic focus (*n* = 66).

When looking at the di fferent research clusters, it became obvious that researchers from all clusters strongly agreed with the resource-oriented definition, which was in accordance with the researchers' own understanding. While the majority of the biogas cluster respondents rejected the technology-oriented definition, agreement was rather high among the respondents from the microalgae cluster, where biotechnology plays an important role.

#### *3.3. Visions and Objectives*

The bioeconomy strategies of di fferent countries and institutions include visions and far-reaching societal goals that should be achieved through the realization of a bio-based economy. The researchers were asked to rate the goals of the German Policy Strategy Bioeconomy according to their importance. The goals relate to sustainability issues, international competitiveness, specific fields of technology and research, as well as cooperation between science, business, and society [26] (pp. 20 ff.). The results showed that all objectives were predominantly considered essential or important (Figure 6). The majority of the respondents viewed the following two targets as essential: "production of raw materials in line with environmental and climate protection" and "conversion of the economic production base from fossil to biogenic sources in the long run". This result matched well with the respondents' predominant agreement with the resource-oriented definition of the bioeconomy (see Section 3.2). Furthermore, the goals "implementation of cascade use", "sustainable consumption", "networking between science and business", as well as "inter- and transdisciplinary research" were regarded as relevant. The leading role of Germany in solving global challenges was rated as the least important goal. Between 20% and 30% of respondents considered "economic growth", "job creation", "strengthening of industrial biotechnology as an economic field", and "international competitiveness" as less important.

**Figure 6.** Respondents' evaluations of the objectives of the German Policy Strategy Bioeconomy according to importance (*n* = 65).

The comparison between the clusters showed that the aim of Germany taking a leading role in solving global challenges was considered to be of little importance in the biogas and microalgae clusters, while the lignocellulose network considered this goal essential or important. The production of raw materials in line with the objectives of environmental and climate protection was regarded as less important in the microalgae group than it was in the biogas and lignocellulose consortium, for which the proportion of "essential" mentions was significantly higher. This may have been due to the fact that microalgae production is a technical process that is independent from agricultural production and does not compete for land resources with other biomass utilization pathways such as food production. Furthermore, it is striking that half of the respondents from the microalgae cluster considered a strengthening of industrial biotechnology essential or important, while the other half considered this less or not important. Thus, there were di fferent views on the economic importance of the broad industrial application of biotechnology in the algae network, which could be explained by the early development stage of algae technologies and the resulting uncertain applications. The majority of respondents from the biogas cluster attached importance to strengthening industrial biotechnology as an economic sector with great market and value-added potential, while the majority of this group refused to equate bioeconomy with modern biotechnology (see Section 3.2). This was in line with the prevailing understanding in Germany (and the EU) that biotechnology is a key technology, but also that relevant technology and innovation approaches to bioeconomy comprise more than biotechnology.

In addition, participants were asked to assess the expected contribution of their research to the objectives of the Baden-Württemberg Research Strategy Bioeconomy. The objectives were related to, e.g., the country's resource independence; competitiveness and innovation capacity; new conversion technologies and bio-based products; local value and job creation; and the information flow between business, research, and society [21] (p. 8 f.). Nearly 70% of respondents answered that their project results could contribute significantly or moderately to "using the country's diversity of plant biomass", followed by "developing new technologies for the conversion of biomass", "increasing international visibility and competitiveness of the country", and "networking between the country's research institutes" (Figure 7). The fact that the most significant contributions were expected for "developing new technologies for the conversion of biomass" and "developing new bio-based substances and materials" could be explained by the technical focus of many projects. The proportion of respondents saying that their research could only make a small or even no contribution was over 50% and 60%, respectively, for the objectives "developing cascade systems" and "safeguarding a high standard of nutrition".

The comparison between the clusters showed that the di fferences were quite small. One major di fference was that the respondents from the microalgae cluster indicated that their projects could contribute to ensuring a high standard of nutrition, while this was, of course, not the case in the biogas and lignocellulose teams. It should be noted that, logically, the specific projects cannot contribute to all objectives of the research strategy, but only a few. In particular, answers from the accompanying assessment projects showed that some of them can only make limited contributions to achieving the strategy's objectives, since critical reflection rather than fulfillment of objectives is in the foreground of this type of research.

#### *3.4. Alternative Implementation Pathways*

Both in scientific and societal debate on the concept of bioeconomy, di fferent implementation paths are discussed. An analysis of the European discourse, carried out by the authors of this article, revealed that the understandings can be assigned to two main strands of discussion: the technology-based and socio-ecological approaches [8]. Based on a selection of contrasting positions (Table 2), respondents were asked to indicate to what extent they agreed with individual elements of possible implementation pathways. The selected positions covered a wide spectrum of topics relevant to the bioeconomy, from resource use and consumer behavior to innovation focus and production approach to perspectives on nature and participation.

**Figure 7.** Contribution of the projects to realizing the objectives of the Baden-Württemberg Research Strategy Bioeconomy (*n* = 63).

Source: Excerpt from Reference [8] (p. 15).

The majority of participants agreed with all statements, except for two (Figure 8). The highest level of full agreement (more than 50% and 60% of the respondents, respectively) was with "improving resource efficiency" and "promoting sustainable consumption". Total agreement was very high (nearly 90% up to 100%) for the elements "improving resource efficiency", "adjusting industrial metabolisms to

natural cycles", "establishing close partnerships between companies, research, and politics in the fields of key technologies", "strengthening multifunctional, agroecological land cultivation", and "involving civil society in shaping and advancing the bioeconomy". Agreement with statements on shaping the future bioeconomy was in line with the respondents' evaluations of the objectives of the German National Policy Strategy Bioeconomy (see Figure 6). This was especially the case for improved resource efficiency for environmental and climate protection, sustainable consumption, and close cooperation between companies, research, politics, and social players.

**Figure 8.** Respondents' agreement with selected positions on shaping the future bioeconomy (*n* = 61).

All in all, the agreement levels were rather similar for all points, which means that the participants considered the positions complementary and not mutually exclusive. Disagreement was expressed by more than 50% and 70% of the respondents, respectively, for the elements "extending technological leadership" and "adjusting nature to industrial processes". The relatively low importance attached to the objective "strengthening the international competitiveness of Germany" corresponded with the limited support for the statement "extending technological leadership".

The research clusters showed some differences. While the biogas and microalgae networks strongly disagreed with "extending technological leadership, intellectual property (e.g., patents), and economic influence of multinational companies", the majority of respondents from the lignocellulose cluster agreed with this approach. One reason for this could be that there is considerable innovation potential in the field of lignocellulose utilization and biorefinery concepts, combined with the ambition to take a pioneering role. Another difference intrinsic to the topics of the three clusters was that full agreement with "promoting research and innovation in the field of life sciences" was highest in the microalgae and lignocellulose networks, while this played a less important role in the biogas network.

## *3.5. Existing Collaborations*

Another part of the questionnaire was about ongoing collaborations the scientists from the research program are involved in (Figure 9). The majority of respondents answered that they were engaged with scientific partners within the cluster and the overall research program. Moreover, collaborations with scientific partners all over Germany and Europe were quite common. A smaller proportion of respondents was involved in collaborations with industry, in particular with companies in the fields of renewable energies, biotechnology, chemistry, and the food industry. Only a small number of respondents cooperate with societal stakeholders and users, with cooperation with users and farmers or agricultural organizations being more common than with nongovernmental groups.

**Figure 9.** Respondents' collaborations with different types of partners (*n* = 48).

The comparison between the clusters showed a quite similar picture. One difference was that the biogas cluster had more collaboration with farmers and users compared to the other networks. This was due to the fact that manifold contacts have been established during the long history of biogas research in Baden-Württemberg, while the other two topics are quite new and cooperation still needs to be further developed.

Moreover, the scientists were asked to classify their networking activities according to forms of cooperation (Figure 10). The responses showed that traditional forms of scientific cooperation prevail, such as the exchange of information and results, joint definitions of research questions, agreement on methods, joint use of equipment, and agreement on raw materials. The majority of respondents indicated that their collaborations aim to sound out common interests, and only a marginal proportion is involved in collaborations aimed at discussing marketing strategies and developing new business models.

The clusters differed with regard to the discussion and exchange of results. This point was mentioned most frequently in the biogas group. This may have been due to the fact that, in the biogas network, there is a focus on applied research, which takes place in cooperation with partners from practice and involves more exchange, while the lignocellulose and microalgae networks conduct more basic research, which implies increased joint use of equipment and infrastructure and increased coordination of the raw materials to be used. In the microalgae cluster, the different forms of scientific communication play an even more important role than in the other clusters. A possible reason for this could be the specific structure of the research network, where the projects are arranged along the value-added chain from algae cultivation, extraction of ingredients, and conversion up to product design. The project members are therefore dependent on the results of the previous stages, which leads to closer coordination.

**Figure 10.** Respondents' forms of cooperation (*n* = 48).

#### *3.6. Obstacles to the Further Development of the Thematic Fields*

The participants were asked about possible obstacles to the realization of the biomass value chains they are working on. This included a discussion of competing uses, environmental impacts, and acceptance of the production process or end products by local residents and end users.

#### 3.6.1. Biogas Value Chain

Due to the limited potential of primary biomass and the discourse about adverse effects of energy crop cultivation, there have been attempts to put a stronger focus on the use of residual and waste materials for biogas production in Germany. In our survey, the researchers outlined some challenges for switching to these sources. First, they emphasized that, even with increased mobilization of residues, the demand for energy crops would probably remain the same. Arguments for this assessment were that, on the one hand, the available residual and waste materials would not be sufficient to cover the raw material requirements of the existing biogas plants. On the other hand, it was argued that such feedstocks were technically unsuitable for use in agricultural biogas plants previously operated with energy crops. Second, it was assumed that the expansion of waste fermentation plants would continue to increase. In addition, economic reasons (high transport costs) and legal obstacles (the need to reauthorize agricultural biogas plants) would militate against the widespread use of residual and waste materials. Moreover, it was expected that this could lead to competition between the biogas and lignocellulose paths with regard to certain feedstocks such as landscape conservation material. Third, for this kind of biogas production, the majority of respondents considered sites close to settlements to be useful or necessary, with increased use of alternative substrates such as food waste. This would be useful particularly from an ecological point of view and would also offer possibilities for heat utilization. The respondents mentioned positive examples of near-settlement facilities planned with the involvement of local residents, but also mentioned counterexamples. The main obstacles were

seen in the legal requirements for waste fermentation plants and in acceptance by local residents. In addition, respondents expressed doubts about the expected positive environmental impacts related to the use of perennial energy crops, waste, and residual materials.

#### 3.6.2. Lignocellulose Value Chain

The availability of woody biomass was mentioned as a central challenge to the implementation of novel lignocellulose-based value chains. Some respondents assumed that unused wood potential exists, in particular in private forests and in the form of alternative feedstocks such as landscape conservation material, agricultural residues (e.g., straw), and wood waste from the private and commercial sectors. In contrast, the majority of respondents argued that alternative feedstocks are limited and not su fficiently available. Some of the reasons mentioned were competing uses (e.g., the use of agricultural residues in biogas production), conflicts in objectives (e.g., nature conservation opposes the removal of dead wood and cortices from forests), and technical obstacles (e.g., the need for a uniform lignin quality in certain conversion processes, which cannot be ensured when using residues). The majority of respondents expected increasing competition for raw materials, especially in the energy wood market. It was assumed that the innovative value-added chains aspired to by the research cluster would rarely compete with the wood, paper, construction, and furniture industries, but that there might be synergies with these industries in using their residual materials or byproducts.

#### 3.6.3. Microalgae Value Chain

Numerous inhibiting factors were identified for the production and use of microalgae, in particular related to the consumption of resources, acceptance, and competitiveness of algae production. In algae production, there is a considerable need for nutrients, energy, and other inputs. Nitrogen, phosphate, and iron are the main nutrients. If large algae yields are to be achieved, the estimated nutrient requirements are high, but this depends on the type of algae and the cultivation method used. Theoretically, wastewater streams as well as sea and brackish water could be used as nutrient sources. In practice, there are legal barriers because the specifications for nutrient sources used in food production are very high. The use of synthetic fertilizers therefore seems likely. Moreover, further adjuvants and process substances are needed: CO2, ethanol as an extraction agent, flocculants, disinfectants for the sterilization of reactors, and herbicides. In particular, CO2 demand is a limiting factor for the cultivation of algae. To ensure the safe use of algae as food, exhaust gases can only be used if no contamination with pollutants can be guaranteed. Therefore, combustion gases cannot be used.

The energy balance of algae production was predominantly classified as negative, though optimization potential was expected from autotrophic cultivation, cultivation in the field, and process improvements. Moreover, industrial algae production has high space requirements. Integration into the city landscape is considered possible. Few respondents expected major impacts on the landscape. However, it was emphasized that there has been no experience with visual impacts and acceptance of larger systems until now. Consumer acceptance is a key factor for realizing the potential of microalgae in nutrition. A clear majority of respondents considered consumer acceptance to be high, especially with regard to use as food ingredients, e.g., in functional foods. Acceptance was expected to be particularly high among younger, health-conscious and environmentally conscious consumers. Acceptance of algae as a substitute for animal-derived food was assumed to be rather low. The following inhibiting factors were mentioned: highly technically processed products do not meet the demand for naturalness; quality losses due to bacterial contamination; di fficult imitation of the taste of meat in connection with cultural reservations about the color, taste, and smell of algae; and low popularity in general. Almost all respondents assessed the food industry's interest in algae-based products to be very high, especially with regard to algae-based ingredients and niche products (e.g., in the vegan food sector or fitness industry). However, there was consensus that the demand will hardly shift from animal-based food products to algae-based products. Besides the lack of consumer acceptance, the main reason is the lack of competitiveness of algae products.


**2.1 Anti-nutritional factors (ANFs) and nutritional quality**

The nutritional quality of soybean products for poultry and swine feeding is determined not only by the quantity of nutrients (protein, amino acids, fat and others), but mainly by nutrients availability for the animals. According Durigan (1989), anti-nutritional factor whole substance is synthesized by normal plant metabolism, which may result from different mechanisms to reduce the efficiency of utilization of the diet. The ANFs present in soybean can to affect the nutrients availability for poultry and swine.

Most of the ANFs present in the raw soybeans, as protease inhibitors and lectins is heatlabile, but others as phytic acid and polysaccharides non starch (PNS) only decrease with enzyme addition in diet, because poultry and swine has no ability to produce enzymes to degrade the PNS.

From Liener (1994), soybeans contain some heat-labile protease inhibitors and hemagglutinins. Soy also contains factors that are relatively heat-stable, though of lesser significance, such as: Goitrogens: substances that cause goiters, an enlargement of the thyroid gland; Tannins: complex plant compounds that are often bitter or astringent; Flatusproducing oligosaccharides: carbohydrates of small molecular weight that cause flatulence (gas); Phytates: which bind minerals preventing absorption; Saponins and Antivitamins.

This ANFs of soybean can cause inhibition of growth, decreased feed efficiency, goitrogenic responses, pancreatic hypertrophy, hypoglycemia, and liver damage in nonruminant animals depending on species, age, size, sex, state of health and plane of nutrition (Palacios et al., 2004)

#### **2.1.1 Proteases inhibitors**

Proteases inhibitors are substances that ability has to inhibit the activity of certain digestive enzymes (Durigan, 1989). They are polypeptides of 181 and 71 amino acid residues, respectively, which form well-characterized stable enzyme inhibitor complexes with pancreatic trypsin on a one-to-one molar ratio. The content of soya bean trypsin inhibitors varies in different varieties of soya bean and germination process (Bau et al., 1997). These are known as the Kunitz inhibitor and the Bowman-Birk inhibitor which are active against trypsin, while the latter is also active against chymotrypsin (Liener, 1994), because Bowman (1944) identified a protein in soy can inhibit trypsin and chymotrypsin and subsequently purified by Birk et al (1961), called Bowman-Birk inhibitor. It is a heat-stable protein, due to its large number of sulfur bridges. Later Kunitz (1945) identified and crystallized other protein, Kunitz trypsin inhibitor, which strongly inhibited the activity of digestive enzyme trypsin.

These protease inhibitors interfere with the digestion of proteins, resulting in decreased animal growth. Protease inhibitors stimulate protein synthesis and enzyme secretion from the pancreas. Inhibition of proteolysis, the presence of undigested protein in the intestinal tract, and a decreased release of amino acids in raw soy diets induce a compensatory reaction in the pancreas and a general stimulatory effect on other endogenous secretions (Rackis & Gumbmann, 1981). The compensatory effect of the pancreas is effective since that the urease activity of the soy is in up to 0,20 (Butolo, 2010). The effect 8of hypertrophy of pancreas followed by a stimulation of its secretory activity can also result in an endogenous loss of the pancreatic enzymes, trypsin and chymotrypsin which are rich in the sulphurcontaining amino acids, and thus accentuating the deficiency of methionine, being the first limiting amino acid in soybean (Johri, 2005)

Coca-Sinova et al. (2008) had evaluated the coefficient of apparent ileal digestibility (%) of DM, N, energy, and amino acids (AA) of the diet with different soybean meal (SBM) origin in broilers of 21 d of Age. They observed that digestibility coefficients were higher for SBM contained lower levels of TIA - trypsin inhibitor activity (1,8 mg/g), when compared with SBM with higher levels of TIA (4,8mg/g).

### **2.1.2 Lectins (haemagglutinins)**

Lectins are glycoproteins with the ability to bind carbohydrate-containing molecules on the epithelial cells of the intestinal mucosa, with the property of agglutinating the erythrocytes of higher animals (Liener, 2000).) The cells of the intestine in the presence of lectin, tend to collapse by reducing the absorption (Butolo, 2010). According Fasina et al. (2004), when lectins are ingested by animals, they can be degraded by intestinal digestive enzymes or survive intestinal digestion and bind to enterocytes on the brush border membrane (BBM). However if bind, lectins may cause antinutritional effects such as disruption of the intestinal microvilli, shortening or blunting of villi, impairment of nutrient digestion and absorption, increased endogenous nitrogen loss, bacterial proliferation, and increased intestinal weight and size (Pusztai, 1993 cited by Fasina et al., 2004).

#### **2.1.3 Goitrogens**

176 Recent Trends for Enhancing the Diversity and Quality of Soybean Products

enzyme trypsin. They also contain other antinutrients, including hemagglutinins or lectins, which contribute to reduce nutrient use. Nutritional composition of the soybeans and soybean products is affecting by percentage of anti-nutritional factors (ANFs), variety genetic, efficiency of the oil-extraction process and the amount of residual hulls present, the

The nutritional quality of soybean products for poultry and swine feeding is determined not only by the quantity of nutrients (protein, amino acids, fat and others), but mainly by nutrients availability for the animals. According Durigan (1989), anti-nutritional factor whole substance is synthesized by normal plant metabolism, which may result from different mechanisms to reduce the efficiency of utilization of the diet. The ANFs present in

Most of the ANFs present in the raw soybeans, as protease inhibitors and lectins is heatlabile, but others as phytic acid and polysaccharides non starch (PNS) only decrease with enzyme addition in diet, because poultry and swine has no ability to produce enzymes to

From Liener (1994), soybeans contain some heat-labile protease inhibitors and hemagglutinins. Soy also contains factors that are relatively heat-stable, though of lesser significance, such as: Goitrogens: substances that cause goiters, an enlargement of the thyroid gland; Tannins: complex plant compounds that are often bitter or astringent; Flatusproducing oligosaccharides: carbohydrates of small molecular weight that cause flatulence (gas); Phytates: which bind minerals preventing absorption; Saponins and Antivitamins. This ANFs of soybean can cause inhibition of growth, decreased feed efficiency, goitrogenic responses, pancreatic hypertrophy, hypoglycemia, and liver damage in nonruminant animals depending on species, age, size, sex, state of health and plane of nutrition (Palacios

Proteases inhibitors are substances that ability has to inhibit the activity of certain digestive enzymes (Durigan, 1989). They are polypeptides of 181 and 71 amino acid residues, respectively, which form well-characterized stable enzyme inhibitor complexes with pancreatic trypsin on a one-to-one molar ratio. The content of soya bean trypsin inhibitors varies in different varieties of soya bean and germination process (Bau et al., 1997). These are known as the Kunitz inhibitor and the Bowman-Birk inhibitor which are active against trypsin, while the latter is also active against chymotrypsin (Liener, 1994), because Bowman (1944) identified a protein in soy can inhibit trypsin and chymotrypsin and subsequently purified by Birk et al (1961), called Bowman-Birk inhibitor. It is a heat-stable protein, due to its large number of sulfur bridges. Later Kunitz (1945) identified and crystallized other protein, Kunitz trypsin inhibitor, which strongly inhibited the activity of digestive enzyme

These protease inhibitors interfere with the digestion of proteins, resulting in decreased animal growth. Protease inhibitors stimulate protein synthesis and enzyme secretion from the pancreas. Inhibition of proteolysis, the presence of undigested protein in the intestinal tract, and a decreased release of amino acids in raw soy diets induce a compensatory reaction in the pancreas and a general stimulatory effect on other endogenous secretions

heat processing and other factors.

degrade the PNS.

et al., 2004)

trypsin.

**2.1.1 Proteases inhibitors**

**2.1 Anti-nutritional factors (ANFs) and nutritional quality**

soybean can to affect the nutrients availability for poultry and swine.

The soybean and its products have been considered goitrogenic in humans and animals (Doerge et al., 2002)., because the acidic methanolic extract of soybeans contains compounds that inhibit thyroid peroxidase-(TPO) catalyzed reactions essential to thyroid hormone synthesis (Divi et al., 1997). Pigs feeding goitrogens (0.075% 1-methyl-2-mercaptoimidazole or .5% potassium thiocyanate) produced symptoms of hypothyroidism in a relative short period of time, usually 3 to 4 weeks with pronounced growth depression, but when the goitrogens were withdrawn from the diet, there was a marked increase in growth rate. The effects of goitrogens are more common in humans, mainly infants (Shepard et al., 1960), than in pigs, because the soybeans used in feed for pigs is generally thermally processed. the goitrogens of soybean can removed by heat treatment (Liener, 1970; Zhenyu et al., 2000).

#### **2.1.4 Tannins**

Tannins are complex plant compounds that are often bitter or astringent, they are naturallyoccurring plant polyphenols which combine with proteins and other polymers such as cellulose, hemicellulose and pectin, to form stable complexes (Mangan, 1988). Egounlety et al. (2003) observed that the hulls were much richer in tannins than the whole soybean (2. 31 x 1.52 mg catechin equivalent/g). Soaking soybean for 12–14 h reduced the tannin content by 54.6%. No tannin was detected in dehulled and cooked and in fermented Soybean.

In contrast to the position with ruminant animals where tannins in the diet may have considerable benefits, and in plants where tannins give partial protection against predators, in simple-stomached animals, including man, tannins in the diet are generally undesirable,

Soybeans (*Glycine max*) and Soybean Products in Poultry and Swine Nutrition 179

showed that the addition of 40 g/kg NSP to a commercial broiler diet decreased the weight gain, feed efficiency and apparent metabolizable energy (AME) by 28.6, 27.0 and 21.2%,

According Smits & Annison (1996) the physicochemical properties of non-starch polysaccharides (NSPs) are responsible for their antinutritive activities in the poultry and swine. In particular, soluble viscous NSPs depress the digestibilities of protein, starch and fat. It is suggested that the gut microflora can mediate the antinutritive effects of soluble and viscous NSP. On the other hand, insoluble and non-viscous NSPs may have a beneficial effect. Hetland et al. (2004) reported that digestibility of starch is higher and digesta passage rate faster when a moderate level of insoluble fibre is present in the diet. The effect of insoluble fibre on gut functions stems from its ability to accumulate in the gizzard, which seems to regulate digesta passage rate and nutrient digestion in the intestine. NSP content of soybean meal is approximately 61 and 103 g kg-1 (dry matter basis) for soluble NSP and

Choct et al. (2010) reported in review that non-digestible oligosaccharides can be fermented throughout all sections of the gastrointestinal tract including the large intestine, and the effects are most variation depends of specie. Soy oligosaccharides increased microbial activities as indicated by the increased volatile fat acids (VFA) contents and can cause

The –galactoside family of oligosaccharides cause a reduction in metabolizable energy with reduced fiber digestion and quicker digesta transit time. Birds do not have an -1:6 galactosidase enzyme in the intestinal mucosa (Leeson & Summers, 2008). Enzyme addition in diet for poultry and swine is most utilized for improve the nutritional value of soybean products. Zanella et al. (1999) found better body weight gain for broilers fed soybean meal (45% CP), extruded soybeans (38% CP) and roasted soybeans with addition of blend enzyme

Three terminologies, namely phytate, phytin and phytic acid, are used in the literature to describe the substrate for phytase enzymes. The most commonly used term, phytate, refers to the mixed salt of phytic acid (myo-inositol hexaphosphate; IP6). The term, phytin, specifically refers to the deposited complex of IP6 with potassium, magnesium and calcium as it occurs in plants, whereas phytic acid is the free form of IP6 (Selle & Ravindran, 2007). Historically, phytates have been considered solely as antinutrients because they are known as strong chelators of divalent minerals such as Ca2+, Mg2+, Zn2+ and Fe2+. Moreover, phytates are also capable of binding with starch and proteins while preventing their assimilation through the digestive system (Afinah et al., 2010). The soybean meal present 0.34% of phytate the soybean has 0.34% of phytate which represents approximately 60% of the amount of total phosphorus (Kornegay, 2000). To improve the utilization of phosphorus and other nutrients complexed to phytate has been used in the enzyme phytase in diets for poultry and pigs. In Brazil more than 50% of poultry diets

Soybean processing is useful and necessary to destroy or remove undesirable constituents, make nutrients more accessible or to improve palatability. However, processing toward

(xylanase, protease and amylase) ,when compared to without enzyme..

respectively.

intestinal disorder.

**2.1.8 Phytate**

were formulated using phytase.

**2.2 Soybean processing**

insoluble NSP, respectively (Bach Knudsen, 1997).

because they present effects as decrease of protein digestibility and reduction of the animal growth(Mangan, 1988).

#### **2.1.5 Saponins**

Saponins are steroid or triterpenoid glycosides, common in a large number of plants and plant products that are important in human and animal nutrition (Francis et al., 2002). Saponins have long been known to cause lysis of the erthrocytes when given in vitro. The hemolytic activity of saponins, coupled with this cholesterol inhibition effect, has been extensively used as a means of detecting and quantifying saponins in plant material. Such saponins activity results from their affinity for membrane sterols (Yoshiki et al., 1998).

Most listings of soybean antinutritional factors in the past included saponins, although with little or no justification. Toxicity was attributed to them simply by analogy with saponins from other sources that, in deed, are toxic (Anderson et al., 1995). Soyabean saponins did not impair growth of chicks when added at five times the concentration in a normal soyabeansupplemented diet (Ishaaya et al., 1969 as cited in Francis et al., 2002). Acording Yoshiki et al. (1998) no hemolytic activity in soybean saponin was observed, while 80% methanolic soybean extract had strong activity. As a result of detailed studies, the hemolytic compounds in soybean are indentified as linoleic acid and lipoxin, wich are secondary metabolitic products from lipoxigenase.

#### **2.1.6 Antivitamins**

There exists a fairly large category of natural substances that interfere with the utilization of certain minerals and vitamins. As examples, isolated soya-bean protein has been shown to interfere with the availability of such minerals as zinc, manganese, copper and iron as well as vitamin D (Liner, 1970) or other diverse but ill-defined factors appear to increase the requirements for vitamins A, B12, D, and E (Liner, 1994).

Raw soybean contains an enzyme lypoxygenase which catalyses oxidation of carotene, the precursor of vitamin A and can be destroyed by heating soybeans for 15 min at atmospheric pressure. Autoclaving of soybean protein or supplementation with vitamin D3 for about 8- 10 times can eliminate the rachitogenic activity (Johri, 2005). Fisher et al. (1969) reported anti-vitamin E activity of isolated soy protein for the chick.

#### **2.1.7 Olygosaccharides and polysaccharides non starch**

Soybean carbohydrates make up approximately 35% of soybean (SB) seed and 40% of soybean meal (SBM) dry matter (DM). Approximately half of these carbohydrates are nonstructural in nature, including low molecular weight sugars, oligosaccharides, and small amounts of starch, while the other half are structural polysaccharides, including a large amount of pectic polysaccharides (Karr-Lilienthal et al., 2005). The fibre component of the grain consists primarily of nonstarchpolysaccharides (NSP) which in cereals form part of the cell wall structure. In legumes, NSP also play a role as an energy storage material. The role of fibre in monogastric diets has attracted much attention in recent years, due to the facts that (a) the soluble NSP elicit anti-nutritive effects (Choct, 1997). The components of nondigestible carbohydrates of a feedstuff are NSP, consisting of water insoluble cellulose and water soluble gums, hemicelluloses, pectic substances and mucilages (Mekbungwan, 2007) . Polysaccharides are plymers of monosaccharides joined through glycosidic linkages and are defined and classified in terms of the following structural (Choct, 1997). Choct et al. (1995) showed that the addition of 40 g/kg NSP to a commercial broiler diet decreased the weight gain, feed efficiency and apparent metabolizable energy (AME) by 28.6, 27.0 and 21.2%, respectively.

According Smits & Annison (1996) the physicochemical properties of non-starch polysaccharides (NSPs) are responsible for their antinutritive activities in the poultry and swine. In particular, soluble viscous NSPs depress the digestibilities of protein, starch and fat. It is suggested that the gut microflora can mediate the antinutritive effects of soluble and viscous NSP. On the other hand, insoluble and non-viscous NSPs may have a beneficial effect. Hetland et al. (2004) reported that digestibility of starch is higher and digesta passage rate faster when a moderate level of insoluble fibre is present in the diet. The effect of insoluble fibre on gut functions stems from its ability to accumulate in the gizzard, which seems to regulate digesta passage rate and nutrient digestion in the intestine. NSP content of soybean meal is approximately 61 and 103 g kg-1 (dry matter basis) for soluble NSP and insoluble NSP, respectively (Bach Knudsen, 1997).

Choct et al. (2010) reported in review that non-digestible oligosaccharides can be fermented throughout all sections of the gastrointestinal tract including the large intestine, and the effects are most variation depends of specie. Soy oligosaccharides increased microbial activities as indicated by the increased volatile fat acids (VFA) contents and can cause intestinal disorder.

The –galactoside family of oligosaccharides cause a reduction in metabolizable energy with reduced fiber digestion and quicker digesta transit time. Birds do not have an -1:6 galactosidase enzyme in the intestinal mucosa (Leeson & Summers, 2008). Enzyme addition in diet for poultry and swine is most utilized for improve the nutritional value of soybean products. Zanella et al. (1999) found better body weight gain for broilers fed soybean meal (45% CP), extruded soybeans (38% CP) and roasted soybeans with addition of blend enzyme (xylanase, protease and amylase) ,when compared to without enzyme..

## **2.1.8 Phytate**

178 Recent Trends for Enhancing the Diversity and Quality of Soybean Products

because they present effects as decrease of protein digestibility and reduction of the animal

Saponins are steroid or triterpenoid glycosides, common in a large number of plants and plant products that are important in human and animal nutrition (Francis et al., 2002). Saponins have long been known to cause lysis of the erthrocytes when given in vitro. The hemolytic activity of saponins, coupled with this cholesterol inhibition effect, has been extensively used as a means of detecting and quantifying saponins in plant material. Such saponins activity results from their affinity for membrane sterols (Yoshiki et al., 1998). Most listings of soybean antinutritional factors in the past included saponins, although with little or no justification. Toxicity was attributed to them simply by analogy with saponins from other sources that, in deed, are toxic (Anderson et al., 1995). Soyabean saponins did not impair growth of chicks when added at five times the concentration in a normal soyabeansupplemented diet (Ishaaya et al., 1969 as cited in Francis et al., 2002). Acording Yoshiki et al. (1998) no hemolytic activity in soybean saponin was observed, while 80% methanolic soybean extract had strong activity. As a result of detailed studies, the hemolytic compounds in soybean are indentified as linoleic acid and lipoxin, wich are secondary

There exists a fairly large category of natural substances that interfere with the utilization of certain minerals and vitamins. As examples, isolated soya-bean protein has been shown to interfere with the availability of such minerals as zinc, manganese, copper and iron as well as vitamin D (Liner, 1970) or other diverse but ill-defined factors appear to increase the

Raw soybean contains an enzyme lypoxygenase which catalyses oxidation of carotene, the precursor of vitamin A and can be destroyed by heating soybeans for 15 min at atmospheric pressure. Autoclaving of soybean protein or supplementation with vitamin D3 for about 8- 10 times can eliminate the rachitogenic activity (Johri, 2005). Fisher et al. (1969) reported

Soybean carbohydrates make up approximately 35% of soybean (SB) seed and 40% of soybean meal (SBM) dry matter (DM). Approximately half of these carbohydrates are nonstructural in nature, including low molecular weight sugars, oligosaccharides, and small amounts of starch, while the other half are structural polysaccharides, including a large amount of pectic polysaccharides (Karr-Lilienthal et al., 2005). The fibre component of the grain consists primarily of nonstarchpolysaccharides (NSP) which in cereals form part of the cell wall structure. In legumes, NSP also play a role as an energy storage material. The role of fibre in monogastric diets has attracted much attention in recent years, due to the facts that (a) the soluble NSP elicit anti-nutritive effects (Choct, 1997). The components of nondigestible carbohydrates of a feedstuff are NSP, consisting of water insoluble cellulose and water soluble gums, hemicelluloses, pectic substances and mucilages (Mekbungwan, 2007) . Polysaccharides are plymers of monosaccharides joined through glycosidic linkages and are defined and classified in terms of the following structural (Choct, 1997). Choct et al. (1995)

growth(Mangan, 1988).

metabolitic products from lipoxigenase.

requirements for vitamins A, B12, D, and E (Liner, 1994).

anti-vitamin E activity of isolated soy protein for the chick.

**2.1.7 Olygosaccharides and polysaccharides non starch**

**2.1.6 Antivitamins**

**2.1.5 Saponins**

Three terminologies, namely phytate, phytin and phytic acid, are used in the literature to describe the substrate for phytase enzymes. The most commonly used term, phytate, refers to the mixed salt of phytic acid (myo-inositol hexaphosphate; IP6). The term, phytin, specifically refers to the deposited complex of IP6 with potassium, magnesium and calcium as it occurs in plants, whereas phytic acid is the free form of IP6 (Selle & Ravindran, 2007). Historically, phytates have been considered solely as antinutrients because they are known as strong chelators of divalent minerals such as Ca2+, Mg2+, Zn2+ and Fe2+. Moreover, phytates are also capable of binding with starch and proteins while preventing their assimilation through the digestive system (Afinah et al., 2010). The soybean meal present 0.34% of phytate the soybean has 0.34% of phytate which represents approximately 60% of the amount of total phosphorus (Kornegay, 2000). To improve the utilization of phosphorus and other nutrients complexed to phytate has been used in the enzyme phytase in diets for poultry and pigs. In Brazil more than 50% of poultry diets were formulated using phytase.

#### **2.2 Soybean processing**

Soybean processing is useful and necessary to destroy or remove undesirable constituents, make nutrients more accessible or to improve palatability. However, processing toward

Soybeans (*Glycine max*) and Soybean Products in Poultry and Swine Nutrition 181

Storage Craking

Wet soybean meal

Soybean meal

Inadequate heating fails to completely destroy the ANFs, which may have a detrimental impact on animal performance, while excessive heating reduces the availability of lysine via the Maillard reaction and possibly, to a lesser extent, of other amino acids (Caprita et al. 2010). While processed beans should be periodically tested for trypsin inhibitor or urease levels, a simple on-going test is to taste the beans. Under-heated beans have a characteristic 'nutty' taste, while over-heated beans have a much darker color and a burnt taste. The problem with overheating is potential destruction of lysine and other heat-sensitive amino

Fig. 1. The simple flowchart of soybean processing (Adapted of Butolo, 2010).

Desolventizing

Toasting

Drying and cooling

Conditioning (60 oC)

Dehulling

Flaking

Soybean flakes for expander or extrusion (25%)

Grinding

Full fat soybean

Soybean flakes for extraction (75%)

Triage

Cleaning

Drying

Extraction

Miscella

Destilation

Desolventizing and Degumming

acids (Leeson Summers, 2008).

Centrifuging and drying

**Soybean reception control**

**Soybean products for storage**

**Extraction**

**Preparation of soybean for oil extraction**

Soy oil

these ends also leads to changes in the composition of the various soybean materials compared with whole soybeans. These changes may be intentional, as in the case of heating to diminish trypsin inhibitor activity (Anderson et al.,1995). Although exist other options for reducing the antinutritional effects of soybean products, the cheaper and more efficient is heat-processing (Pusztai et al., 1997).

The most common procedures have involved a combination or extraction, cooking and fermentation. With soybeans, moist-heat treatment is particularly effective in reducing trypsin inhibitor activity below biological threshold levels, as determined by short-term animal bioassay. With present day manufacturing processes, residual trypsin inhibitor activity in edible-grade soy protein products is about 5-20% of the activity originally present in raw soybeans (Rackis & Gumbmann, 1981).

The heat necessary to destroy trypsin inhibitors and other hemagglutinins found in raw soybeans is dependent upon exposure time, and so high temperatures for a shorter time period are as effective as lower temperatures for longer times (Leeson & Summers, 2008).

In the 1930's, soybeans were mechanically processed using hydraulic or screw presses, which squeezed out the oil of the heated or cooked soybeans. In the late 1940's and early 1950's, most of the industry converted to the solvent-extraction process, which removes more oil from the soybean. Today, more than 99 percent of the U.S. processing capacity is using the solvent extraction process produced in large crushing facilities that produce meals of consistent high quality (Johnson Smith, sd).

According Soybean Meal INFOcenter, the soybean products appear with the initial processes that included cleaned and dehulled and other three processes is used to separate the soybean oil from the protein meal. The first is solvent extraction, which is the one used most commonly around the world, uses hexane to leach or wash (extract) the oil from flaked oilseeds. This method reduces the level of oil in the extracted flakes to one percent or less. After this continuous pressing is performed at elevated temperatures, using a screw press to express the oil from ground and properly conditioned soybeans. The pressed cake is reduced to between 4 percent and 6 percent oil content by this method. At the end, dydraulic or batch pressing, this is an intermittent pressing operation carried out at elevated temperatures in a mechanical or hydraulic press after the soybeans have been rolled into flakes and properly conditioned by heat treatment. It is the oldest known method of processing oilseeds. According Butolo (2010), the industrialization flow for obtaining soybean oil, soybean meal or full fat soybean is divided into four distinct phases, which are shown in Figure 1.

A great processing variety exists, in table 1 sowed the composition of soybean feed ingredient products.

The quality of soya meal is the result of many factors, including bean variety, origin and storage. The various processing steps employed from the time the bean is received can affect the quality of the resulting meal and oil obtained. Heat treatment of the meal is essential to optimize its protein quality. The variables of moisture, temperature and time are interrelated and are important to achieve proper cooking conditions. The magnitude of these variables must be determined for each plant, preferably using a biological assay for evaluation. Many in vitro tests designed to measure protein quality in soya have been proposed and evaluated (Wright, 1968). Simple crude protein or amino acid assays provide information on the protein, but do not provide useful information on the quality of the protein. Chemists have used trypsin inhibitor analyses, urease activity, protein solubility in potassium hydroxide, protein solubility in water and dye binding methods to assay for protein quality (Johnson Smith, nd).

these ends also leads to changes in the composition of the various soybean materials compared with whole soybeans. These changes may be intentional, as in the case of heating to diminish trypsin inhibitor activity (Anderson et al.,1995). Although exist other options for reducing the antinutritional effects of soybean products, the cheaper and more efficient is

The most common procedures have involved a combination or extraction, cooking and fermentation. With soybeans, moist-heat treatment is particularly effective in reducing trypsin inhibitor activity below biological threshold levels, as determined by short-term animal bioassay. With present day manufacturing processes, residual trypsin inhibitor activity in edible-grade soy protein products is about 5-20% of the activity originally present

The heat necessary to destroy trypsin inhibitors and other hemagglutinins found in raw soybeans is dependent upon exposure time, and so high temperatures for a shorter time period are as effective as lower temperatures for longer times (Leeson & Summers, 2008). In the 1930's, soybeans were mechanically processed using hydraulic or screw presses, which squeezed out the oil of the heated or cooked soybeans. In the late 1940's and early 1950's, most of the industry converted to the solvent-extraction process, which removes more oil from the soybean. Today, more than 99 percent of the U.S. processing capacity is using the solvent extraction process produced in large crushing facilities that produce meals

According Soybean Meal INFOcenter, the soybean products appear with the initial processes that included cleaned and dehulled and other three processes is used to separate the soybean oil from the protein meal. The first is solvent extraction, which is the one used most commonly around the world, uses hexane to leach or wash (extract) the oil from flaked oilseeds. This method reduces the level of oil in the extracted flakes to one percent or less. After this continuous pressing is performed at elevated temperatures, using a screw press to express the oil from ground and properly conditioned soybeans. The pressed cake is reduced to between 4 percent and 6 percent oil content by this method. At the end, dydraulic or batch pressing, this is an intermittent pressing operation carried out at elevated temperatures in a mechanical or hydraulic press after the soybeans have been rolled into flakes and properly conditioned by heat treatment. It is the oldest known method of processing oilseeds. According Butolo (2010), the industrialization flow for obtaining soybean oil, soybean meal or full fat soybean is divided into four distinct phases, which are

A great processing variety exists, in table 1 sowed the composition of soybean feed

The quality of soya meal is the result of many factors, including bean variety, origin and storage. The various processing steps employed from the time the bean is received can affect the quality of the resulting meal and oil obtained. Heat treatment of the meal is essential to optimize its protein quality. The variables of moisture, temperature and time are interrelated and are important to achieve proper cooking conditions. The magnitude of these variables must be determined for each plant, preferably using a biological assay for evaluation. Many in vitro tests designed to measure protein quality in soya have been proposed and evaluated (Wright, 1968). Simple crude protein or amino acid assays provide information on the protein, but do not provide useful information on the quality of the protein. Chemists have used trypsin inhibitor analyses, urease activity, protein solubility in potassium hydroxide, protein solubility in water and dye binding methods to assay for

heat-processing (Pusztai et al., 1997).

in raw soybeans (Rackis & Gumbmann, 1981).

of consistent high quality (Johnson Smith, sd).

shown in Figure 1.

ingredient products.

protein quality (Johnson Smith, nd).

Fig. 1. The simple flowchart of soybean processing (Adapted of Butolo, 2010).

Inadequate heating fails to completely destroy the ANFs, which may have a detrimental impact on animal performance, while excessive heating reduces the availability of lysine via the Maillard reaction and possibly, to a lesser extent, of other amino acids (Caprita et al. 2010). While processed beans should be periodically tested for trypsin inhibitor or urease levels, a simple on-going test is to taste the beans. Under-heated beans have a characteristic 'nutty' taste, while over-heated beans have a much darker color and a burnt taste. The problem with overheating is potential destruction of lysine and other heat-sensitive amino acids (Leeson Summers, 2008).

Soybeans (*Glycine max*) and Soybean Products in Poultry and Swine Nutrition 183

are no red particles showing, the mixture should stand another 30 minutes, and again if no red color is seen, it suggests overheating of the meal. If up to 25% of the surface is covered in red particles, it is an indication of acceptable urease activity, while 25 – 50% coverage suggest need for more detailed analysis. Over 50% incidence of red colored particles suggests an under-heated meal (Leeson Summers, 2008). But urease index is not useful to determine excessive heat treatment since additional heating has no effect on

The KOH protein solubility test is based on the solubility of soybean proteins in a dilute solution of potassium hydroxide. The procedure involves the incubation of a sample with a 0.2% KOH solution for 20 min at room temperature. Following this incubation, the sample is centrifuged and the supernatant is analyzed for the protein concentration. The solubility of the protein, expressed as a percentage, was calculatedby dividing the protein content of the KOH extracted solution by the protein content of the original soybean sample (Caprita et al. 2010). KOH protein solubility is a better indicator of overprocessing than underprocessing of soybeans (Batal et al., 2000). In table 2 are shown the levels of urease activity and protein

Protein Dispersibility Index (PDI) can be used to measure protein quality. According Butolo (2010) to determine the protein dispersibility index should be mixed 8g of soybean meal with 150ml water, then it is centrifuged to 8.500ppm for 10 minutes, filter and determining the soluble nitrogen by the Kjeldahl method. Batal et al. (2000) indicated that PDI demonstrates more consistent response to heating of soyflakes than did urease index or protein solubility in KOH, because the urease index is not linear and that it rapidly falls from approximately 2.0 units of pH change to near zero as SBM is heated contributes to the

processing Urease test (pH change) KOH Protein solubility (%)

Table 2. Globally accepted relation between the degree of soybean processing for urease activity and protein solubility in potassium hydroxide (from Palić et al. 2008; Butolo, 2010). Nitrogen Solubility Index (NSI) is other methods can be used for determines protein quality. It uses a slow stirring technique. Nitrogen is extracted from the ground flour by placing approximately 1.5 g into a 200 ml beaker and adding 75 ml of 0.5% KOH. The sample is stirred 20 minutes at 120 rpm. PDI and NSI are a more consistent and sensitive indicator for

It is very important whether the assessment process quality of soybeans for the animal is

The storage and genetic variation are factors that can alter the nutritional composition of soybean as well as the performance of monogastric animals. Narayan et al., (1988) found that chemical characteristics, moisture content, fat, water-soluble nitrogen (WSN), nitrogen solubility index (NSI), sugars, trypsin inhibitor activity, available lysine, pigment and

Under-processed > 0.20 90 Normal - 85 Adequately processed 0.05 - 0.20 77-80 Over-processed - < 77

monitoring both underheating and over-heating of SBM (Caprita et al. 2010).

solubility in potassium hydroxide acceptable in most soybeans processing.

difficulty in determining a precise maximum acceptable.

unlikely to have decreased performance.

**2.3 Storage and genetic variety**

Degree of soybean

the urease index (Caprita et al. 2010).

Table 1. Nutrient profile of soybean products for poultry and swine nutrition (From Rostagno et al., 2005).

The most widely adopted method more economical and faster is the measurement of urease activity (urease test or urease index), however difficult the correlation of the protein solubility in KOH. Levels of the enzyme urease are used as an indicator of trypsin inhibitor activity. Urease is much easier and cheap to measure than is trypsin inhibitor and both molecules show similar characteristics of heat sensitivity. Two analytical methods for urease test were described, the first is based on ph difference in which 200 mg of sample (soybean or soybean product) is incubated in 10.0 ml of phosphate buffered urea solution at 300C for 30 minutes, after which the increase in pH units (ΔpH) from pH 7.00 is recorded (Palić et al. 2008; Butolo, 2010). The second is based a simple colorimetric assay in which urea-phenol-red solution is brought to an amber color by using either 0.1 N HCl or 0.1 N NaOH. About 25 g of soybean meal is then added to 50 ml of indicator in a petri dish. After 5 minutes, the sample is viewed for the presence of red particles. If there

Dry matter (%) 90.47 88.21 89.88 88.80 99.60 Gross energy (Kcal kg-1) 4938 4164 4495 3854 9333 Starch (%) 6.70 3.00 - - - Fat (%) 17.64 1.40 0.43 2.86 99.60 Crude fibre (%) 6.24 4.27 2.64 33.00 - Crude protein(%) 37.00 47.90 62.92 13.50 - Arginine(%) 2.71 3.50 5.32 0.81 - Lysine (%) 2.23 2.92 4.07 0.89 - Methionine + cystine (%) 1.08 1.37 1.90 0.39 - Threonine (%) 1.47 1.86 2.60 0.51 - Triptophan (%) 0.47 0.64 0.87 0.14 - Calcium (%) 0.23 0.31 0.27 0.49 - Av. Phosphorus(%) 0.17 0.21 0.27 0.05 - Potassium(%) 1.67 2.11 2.18 - -

Metabolizable energy (Kcal kg-1) 3429 2302 2677 871 8790 Dig. Arginine(%) 2.54 3.31 5.12 0.64 - Dig. Lysine (%) 2.02 2.70 3.75 0.59 - Dig. Methionine + cystine (%) 0.93 1.21 1.67 0.21 - Dig. Threonine (%) 1.29 1.66 2.29 0.25 - Dig. Triptophan (%) 0.43 0.58 0.79 0.09 -

Digestible energy (Kcal kg-1) 4250 3540 4035 2370 8600 Dig. Arginine(%) 2.52 3.31 0.68 - Dig. Lysine (%) 1.99 2.66 0.53 - Dig. Methionine + cystine (%) 0.93 1.23 0.26 - Dig. Threonine (%) 1.28 1.62 0.31 - Dig. Triptophan (%) 0.40 0.57 0.09 -

The most widely adopted method more economical and faster is the measurement of urease activity (urease test or urease index), however difficult the correlation of the protein solubility in KOH. Levels of the enzyme urease are used as an indicator of trypsin inhibitor activity. Urease is much easier and cheap to measure than is trypsin inhibitor and both molecules show similar characteristics of heat sensitivity. Two analytical methods for urease test were described, the first is based on ph difference in which 200 mg of sample (soybean or soybean product) is incubated in 10.0 ml of phosphate buffered urea solution at 300C for 30 minutes, after which the increase in pH units (ΔpH) from pH 7.00 is recorded (Palić et al. 2008; Butolo, 2010). The second is based a simple colorimetric assay in which urea-phenol-red solution is brought to an amber color by using either 0.1 N HCl or 0.1 N NaOH. About 25 g of soybean meal is then added to 50 ml of indicator in a petri dish. After 5 minutes, the sample is viewed for the presence of red particles. If there

Table 1. Nutrient profile of soybean products for poultry and swine nutrition (From

**Soybean meal (48%)**

**Soybean protein concentrate**

**Soybean Hulls**

**Soy oil**

**Full-fat extruded soybean**

**Nutrient profile**

**Poultry**

**Swine**

Rostagno et al., 2005).

are no red particles showing, the mixture should stand another 30 minutes, and again if no red color is seen, it suggests overheating of the meal. If up to 25% of the surface is covered in red particles, it is an indication of acceptable urease activity, while 25 – 50% coverage suggest need for more detailed analysis. Over 50% incidence of red colored particles suggests an under-heated meal (Leeson Summers, 2008). But urease index is not useful to determine excessive heat treatment since additional heating has no effect on the urease index (Caprita et al. 2010).

The KOH protein solubility test is based on the solubility of soybean proteins in a dilute solution of potassium hydroxide. The procedure involves the incubation of a sample with a 0.2% KOH solution for 20 min at room temperature. Following this incubation, the sample is centrifuged and the supernatant is analyzed for the protein concentration. The solubility of the protein, expressed as a percentage, was calculatedby dividing the protein content of the KOH extracted solution by the protein content of the original soybean sample (Caprita et al. 2010). KOH protein solubility is a better indicator of overprocessing than underprocessing of soybeans (Batal et al., 2000). In table 2 are shown the levels of urease activity and protein solubility in potassium hydroxide acceptable in most soybeans processing.

Protein Dispersibility Index (PDI) can be used to measure protein quality. According Butolo (2010) to determine the protein dispersibility index should be mixed 8g of soybean meal with 150ml water, then it is centrifuged to 8.500ppm for 10 minutes, filter and determining the soluble nitrogen by the Kjeldahl method. Batal et al. (2000) indicated that PDI demonstrates more consistent response to heating of soyflakes than did urease index or protein solubility in KOH, because the urease index is not linear and that it rapidly falls from approximately 2.0 units of pH change to near zero as SBM is heated contributes to the difficulty in determining a precise maximum acceptable.

Table 2. Globally accepted relation between the degree of soybean processing for urease activity and protein solubility in potassium hydroxide (from Palić et al. 2008; Butolo, 2010).

Nitrogen Solubility Index (NSI) is other methods can be used for determines protein quality. It uses a slow stirring technique. Nitrogen is extracted from the ground flour by placing approximately 1.5 g into a 200 ml beaker and adding 75 ml of 0.5% KOH. The sample is stirred 20 minutes at 120 rpm. PDI and NSI are a more consistent and sensitive indicator for monitoring both underheating and over-heating of SBM (Caprita et al. 2010).

It is very important whether the assessment process quality of soybeans for the animal is unlikely to have decreased performance.

#### **2.3 Storage and genetic variety**

The storage and genetic variation are factors that can alter the nutritional composition of soybean as well as the performance of monogastric animals. Narayan et al., (1988) found that chemical characteristics, moisture content, fat, water-soluble nitrogen (WSN), nitrogen solubility index (NSI), sugars, trypsin inhibitor activity, available lysine, pigment and

Soybeans (*Glycine max*) and Soybean Products in Poultry and Swine Nutrition 185

Micronizing is the name given to a cooking process that uses infrared rays to cook cereals and pulses at lower temperatures and for shorter times than other heating methods. Gas burners are used to generate the infrared rays that are absorbed by the products. The raw materials are passed under the burners on variable speed belts to achieve the desired level of "cook". The product is then passed through a roller mill to create flakes. These flakes can be used whole or ground into a meal (MMfeeds). The increase available energy and improve digestibility are both achieve due to the gelatinisation of starch molecules during the cooking process. Trindade Neto et al. (2002) observed that pigs fed micronized soybean takes more days to reach 50 and 90 kg of body weight when compared with those fed

Soybean hulls, due to their high fiber contents, are known to be poorly digested by nonruminant animals. Recent studies, however, suggest that the hulls have potential as an alternative feed ingredient for swine and poultry. The soybean hulls can be included up to 10 and 12% for growing or finishing pig diets, respectively, replacing the wheat bran on a weight basis without any adverse effects on palatability of diets and animal performances (Chee et al., 2005). However, Moreira et al. (2009) not recommend the use of soybean hulls to piglets due reducing daily feed intake and daily weight gain for the animals fed feed containing soybean hull (15% inclusion in the diet) compared to the control feed without

Esonu et al. (2005) studing laying hens, found that inclusion of up to 20% soybean hulls, improves the Feed cost/dozen eggs, and when cellulolytic enzyme supplementation at 30% dietary level of soybean hull meal in layer diet could not significantly affect the performance

Currently, it is very common the use of soybean hulls in programmers of feed restriction

Soybean protein concentrate (or soy protein concentrate) is the product obtained by removing most of the oil and water-soluble non-protein constituents from selected, sound, cleaned, dehulled soybeans. The traded product among 650 to 900 g/kg CP on a moisturefree basis. Soybean protein isolate (or soy protein isolate) is the dried product obtained by

**Protein Source Million Metric Tons Percent**  Soybean meal 152.1 66.5 Rapeseed meal 30.7 13.5 Cottonseed meal 14.4 6.3 Sunflower meal 12.2 5.3 Palm kernel meal 6.1 2.7 Peanut meal 5.9 2.6 Fish meal 5.3 2.3 Copra meal 1.9 0.8

TOTAL 228.6

Table 3. World Protein Meal Consumption\*.

\* Soy Stats (2010)

soybean meal.

soybean hull.

of laying hens.

**3.3 Soy protein isolates**

and welfare of breeders and laying hens.

**3.2 Soybean hulls**

lipoxygenase activity of seeds decreased during storage whereas non-protein nitrogen (NPN), extent of browning, free fatty acid (FFA) content and peroxide value are increased.

According Cromwell et al. (2002) the rate and efficiency of weight gain, scanned backfat and longissimus area, and calculated carcass lean percentage were not different (P > 0.05) for pigs fed diets containing conventional or genetically modified, herbicide (glyphosate) tolerant soybean. For poultry, Taylor et al. (2007) concluded that the diets containing soybean meal produced from genetically modified (GM) glyphosate-tolerant were nutritionally equivalent to diets containing soybean meal produced from the control and conventional reference soybean varieties when fed to broilers.

Other genetically modified soybeans were study for Palacios et al. (2004) that compared the growth performance of chicks and pigs fed diets containing modified soybeans: Kunitz trypsin inhibitor-free (KF), lectin-free (LF), lectin and Kunitz trypsin inhibitor-free (LFKF), conventional soybeans (CSB), and commercially obtained, dehulled, solventextracted soybean meal (SBM). They verify that Chicks fed diets containing any of the raw soybean varieties gained less weight than did chicks and among the raw soybean treatments, there was a greater effect on growth performance by removing both lectins and Kunitz trypsin inhibitor (LFKF), than by removing each antinutritional factor separately. Feeding raw soybeans to chicks decreased average daily gain (ADG) by 49% for CSB, 37% for KF, 38% for LF, and 27% for LFKF compared with the ADG achieved by chicks fed SBM. For to pigs deprecreased ADG by 78% for CSB, 60% for LF, and 35% for LFKF compared with the ADG achieved by pigs fed the same variety but extruded. This results and others (Brune et al., 2010; Becker-Ritt et al., 2004; Vasconcelos et al., 1997) prove that variability in the amounts of these components (proteases inhibitors and lectins) can be affect by cultivars differences.

As observed, there seems great potential for reduction in content of anti-nutrients within GM soybeans, as studies have shown that the isogenic variant lacking the Kunitz trypsin inhibitor and other soybean variants low in Kunitz trypsin inhibitor are nutritionally superior to conventional raw soybeans but not as good as commercial soybean meal. Others genetic improvements in reducing the phytate-bound phosphorous, and reduction or elimination of oligosaccharide carbohydrates are the most important economical traits that are being researched