Having laid the framework for characterizing the SOI detector prototype and conducting preliminary experiments, we are in a position to make some statements about the viability of this technique as an alternative to other optical vibration measurements. This section quantifies those comparisons and indicates certain applications to which speckle monitoring with photoconductor arrays is best suited. Numerical values are reported as rough estimates

After establishing that measurement with such a system is indeed possible, the most obvious question to ask about a particular measurement scheme for a given application is "how sensitive is it?" To that end, this section will attempt to answer that question for the direct speckle-monitoring method and other comparable techniques under similar

Since we have already established that speckle-monitoring is most effective for in-plane measurement, we will focus on that application. As a convenient metric for the lower limits of sensitivity, let us define the quantity *δmin* to be the minimum detectable displacement that produces a signal with amplitude equal to the noise for a given system output. Since we have seen that sensitivity generally scales in proportion to optical power and inversely to bandwidth, *δmin* will be given units of m Hz*-*1*/*2 W, where the power is the total available laser

The minimum detectable photocurrent is equal to the noise current, which from experiments is roughly 20 nA at 1 MHz bandwidth with an overall gain of 20 across a load resistor *RL* = 50Ω. Since noise scales with the square root of the bandwidth, we can estimate a noise figure of *σn* = 20 pAHz1*/*2. A gain of 20 corresponds to a responsivity of about = 10 A/W at 633 nm. This means that the minimum detectable change in collected power per

The amount of speckle translation required to achieve this change in photocurrent depends on the optical system and how much optical power is collected. For a small scattering spot whose width is *S,* illuminated with a total power *PS*, the speckle size in the detector plane is approximately ε = *λB/S*, where *B* is the optical matrix element. Assuming the light is scattered equally in all directions and the scattering spot size *S << B*, the average speckle intensity is approximately *I*0 = *PSλ*2/(2*π*ε2S2) as shown in (Dainty, 1975, Bessette, 2010). For a small translation *δs* of the speckle pattern, this means the detector will see on average (considering the ensemble of all possible random speckle patterns) a change in collected power given by ∆P = Io ε2 (δS/ε) = PSλ2δS/(2*π*εS2), where we have assumed that the speckle width is matched to the detector width. Setting ∆*P =* to ∆*Pmin* and solving for *δs*, we

power thermoelastic regime, such as by using current modulated CW laser diodes.

**5. SOI speckle monitoring: detection limits compared to alternatives**

constructed from the data gathered in the previous chapters.

**5.2 SOI Detectors, single element detection limit**

find that the minimum measurable motion is

**5.1 Pure in-plane-motion**

element is *Pmin* = 2 pWHz1*/*2.

circumstances.

power.

undamaged strip, the signal is disrupted by some other vibrations. Without analyzing the precise differences in the impulse response signature between the healthy and the damaged strips, it is fair enough to say that there is a clearly discernible difference. Such a distinction could be used to detect the presence of damage.

Fig. 23. Response of three identically sized aluminum foil strips under equal tension to a 500 μs rectangular pulse from a diode laser. a) Excitation diode current (arbitrary vertical scale), b) strip 1, no defect, c) strip 2, no defect, and c) strip 3, a tear introduced between points of acoustic generation and detection.

Although useful, this low-frequency, resonance technique is not perfect for every solution. Some applications are best served by a different approach to acoustic imaging that requires temporally and spectrally resolved pulse information such as time-of-flight and dispersion data. This generally precipitates the use of higher-bandwidth acoustic regimes.

The obvious drawback to operating in the thermoelastic regime is the decrease in acoustic power delivered to the object of interest. This problem can be addressed by using selective filtration methods on the detector signal, such as lock-in amplification and input-output signal correlation techniques.

#### **4.4 Summary**

In this section it was shown that speckle-monitoring is a viable technique for the remote detection of acoustic waves, in particular ultrasonic waveforms that are useful for material characterization and structural examination. The possibility of coupling speckle-monitoring with optical ultrasound generation is particularly attractive, as it allows for a completely non-contact system. Speckle-monitoring is especially appealing for use in measuring inplane ultrasonic waves, such as the S0 Lamb wave mode measured in the edge-excitation experiment, since it is selectively sensitive to that type of motion, unlike most other optical vibration detection schemes. There is an incentive to restrict optical excitation to the lowerpower thermoelastic regime, such as by using current modulated CW laser diodes.

### **5. SOI speckle monitoring: detection limits compared to alternatives**

Having laid the framework for characterizing the SOI detector prototype and conducting preliminary experiments, we are in a position to make some statements about the viability of this technique as an alternative to other optical vibration measurements. This section quantifies those comparisons and indicates certain applications to which speckle monitoring with photoconductor arrays is best suited. Numerical values are reported as rough estimates constructed from the data gathered in the previous chapters.

### **5.1 Pure in-plane-motion**

334 Photodetectors

undamaged strip, the signal is disrupted by some other vibrations. Without analyzing the precise differences in the impulse response signature between the healthy and the damaged strips, it is fair enough to say that there is a clearly discernible difference. Such a distinction

Fig. 23. Response of three identically sized aluminum foil strips under equal tension to a 500 μs rectangular pulse from a diode laser. a) Excitation diode current (arbitrary vertical scale), b) strip 1, no defect, c) strip 2, no defect, and c) strip 3, a tear introduced between points of

Although useful, this low-frequency, resonance technique is not perfect for every solution. Some applications are best served by a different approach to acoustic imaging that requires temporally and spectrally resolved pulse information such as time-of-flight and dispersion

The obvious drawback to operating in the thermoelastic regime is the decrease in acoustic power delivered to the object of interest. This problem can be addressed by using selective filtration methods on the detector signal, such as lock-in amplification and input-output

In this section it was shown that speckle-monitoring is a viable technique for the remote detection of acoustic waves, in particular ultrasonic waveforms that are useful for material characterization and structural examination. The possibility of coupling speckle-monitoring with optical ultrasound generation is particularly attractive, as it allows for a completely non-contact system. Speckle-monitoring is especially appealing for use in measuring inplane ultrasonic waves, such as the S0 Lamb wave mode measured in the edge-excitation experiment, since it is selectively sensitive to that type of motion, unlike most other optical

data. This generally precipitates the use of higher-bandwidth acoustic regimes.

could be used to detect the presence of damage.

acoustic generation and detection.

signal correlation techniques.

**4.4 Summary**

After establishing that measurement with such a system is indeed possible, the most obvious question to ask about a particular measurement scheme for a given application is "how sensitive is it?" To that end, this section will attempt to answer that question for the direct speckle-monitoring method and other comparable techniques under similar circumstances.

Since we have already established that speckle-monitoring is most effective for in-plane measurement, we will focus on that application. As a convenient metric for the lower limits of sensitivity, let us define the quantity *δmin* to be the minimum detectable displacement that produces a signal with amplitude equal to the noise for a given system output. Since we have seen that sensitivity generally scales in proportion to optical power and inversely to bandwidth, *δmin* will be given units of m Hz*-*1*/*2 W, where the power is the total available laser power.

### **5.2 SOI Detectors, single element detection limit**

The minimum detectable photocurrent is equal to the noise current, which from experiments is roughly 20 nA at 1 MHz bandwidth with an overall gain of 20 across a load resistor *RL* = 50Ω. Since noise scales with the square root of the bandwidth, we can estimate a noise figure of *σn* = 20 pAHz1*/*2. A gain of 20 corresponds to a responsivity of about = 10 A/W at 633 nm. This means that the minimum detectable change in collected power per element is *Pmin* = 2 pWHz1*/*2.

The amount of speckle translation required to achieve this change in photocurrent depends on the optical system and how much optical power is collected. For a small scattering spot whose width is *S,* illuminated with a total power *PS*, the speckle size in the detector plane is approximately ε = *λB/S*, where *B* is the optical matrix element. Assuming the light is scattered equally in all directions and the scattering spot size *S << B*, the average speckle intensity is approximately *I*0 = *PSλ*2/(2*π*ε2S2) as shown in (Dainty, 1975, Bessette, 2010). For a small translation *δs* of the speckle pattern, this means the detector will see on average (considering the ensemble of all possible random speckle patterns) a change in collected power given by ∆P = Io ε2 (δS/ε) = PSλ2δS/(2*π*εS2), where we have assumed that the speckle width is matched to the detector width. Setting ∆*P =* to ∆*Pmin* and solving for *δs*, we find that the minimum measurable motion is

Photoconductors for Measuring Speckle Motion to Monitor Ultrasonic Vibrations 337

grating spacing (Kamshilin, 2009), the detection limit is approximately 10 μm. This method is not nearly as sensitive to small displacements as the other methods discussed; however it

We have shown that photoconductive mesas directly monitoring speckle motion have an important place among applications requiring higher bandwidth detection of in-plane motion. The detection limit for a single element monitoring in-plane motion is on the order

The performance of speckle monitoring systems can be dramatically increased by using multiple detectors to make simultaneous measurements of a single speckle pattern displacement. An array configuration of photodetectors can be used to enhance the signal to noise ratio of displacement measurements. In addition to a lower noise floor, uncertainty in absolute displacement measurements can be mitigated. Additionally, the adverse effects owing to harmonic distortion can be lessened by the use of arrays, all allowing for better

The signals from multiple array elements can be combined in a way that reduces noise in the same spirit as simple averaging. The contribution of each element to the average, however, must be weighted according to the particular speckle that element is monitoring. The primary challenge is the ambiguity in both the sign and magnitude of the slope in photointensity as a function of speckle displacement. If the signals are corrected so that every element has the same phase (i.e., all elements are made to have a positive slope), then the weighted average shows an increase in signal to noise ratio as the number of elements is

A demonstration of the power of arrays for improving signal-to-noise was made by comparing the signals of individual detector elements with a phase-corrected average of 18 combined elements. The 18 independent signals were obtained by using a three-element array to monitor six different random speckle patterns, each undergoing the same displacement. The SNR of the phase-corrected average was superior by approximately 10 dB over the typical single element signal. In this demonstration case, the phases were sorted

This chapter has shown how photoconductive detectors tens of microns in size can provide more sensitivity than photo-diodes. Very small photo-diodes have very small areas, which limits their sensitivity at high bandwidth. They are useful in applications such as singlemode fiber optics telecommunications, because the light is concentrated to a very small area. When signals are as small as a nanowatts, photo-multipliers are often used. However, in applications where spatial resolution at high bandwidth is required, such as monitoring mechanical vibrations, photoconductive elements can have the best useful figure of merit.

We have demonstrated a minimum detectable power of *Pmin* is 2 pWHz1*/*2.

works well for high in-plane velocities when the object is moving a large distance.

**5.4 Summary**

of *δmin* = 16 pm Hz-1/2 W.

overall system calibration.

manually during post processing.

**7. Conclusions**

increased.

**6. Arrays of photoconductive mesas**

$$\delta\_{\rm S,min} = \, \mathcal{D}\pi \varepsilon S^2 \Delta P\_{\rm min} / \lambda^2 P\_{\rm S}$$

Using a typical scattering spot size of 0.5 mm and a detector-matched speckle size of 20 *μ*m, this gives *δS,min* = 160 pm Hz1*/*2 W. This is the minimum detectable translation of the speckle pattern. Typically, there will be optical magnification in this system with a value ~10, so this yields a minimum detectable displacement on the order of *δmin* = 16 pm Hz1*/*2 W.

For example, with a 1 W laser source and a bandwidth of 1 MHz and using a 50Ω load resistor, the detection limit is approximately *δmin* = 16 nm for a single element. This value can be brought down even more by using a larger load resistor, as large as the bandwidth limitations will allow.

#### **5.3 Other methods**

Classical interferometry can be used to detect in-plane motion under certain conditions. For instance, rather than using one beam to illuminate the object and one reference beam, the differential interferometry approach uses two beams to illuminate the object (Monchalin, 1986). If the input aperture of the system and the illuminating spot are sufficiently small, then what forms in the detector plane is essentially a single speckle. The phase of that one speckle is sensitive to motion parallel to the difference in wave-vectors of the two illuminating beams. If the two beams illuminate the surface at equal but opposite angles with respect to the normal, the setup is sensitive purely to motion in the plane of the object. Using a 0.5 mm spot size and a HeNe laser with a wavelength of 633 nm gives a detection limit *δmin* = 16 pm Hz-1/2 W*.* With a 1 W laser and 1 MHz bandwidth, a sensor with these parameters has a detection limit on the order of *δmin* = 16 nm. Again, this can be reduced by increasing the load resistor to the maximum the bandwidth requirements will allow. We see that the detection limit for the direct speckle monitoring method with SOI detectors is roughly equal to the interferometric method. The speckle monitoring method has the greatly added advantage that it does not require two coherent beams to be precisely overlapped at a specific angle in a small spot.

Speckle photography relies on tracking individual speckle features as they move across the detection plane with an electronic detector array. These methods are generally used in low speed applications -- CCD arrays with low read-out rates and long integration times (on the order of milliseconds). A recent report on different algorithms (Cofaru, 2010) finds that resolution is on the order of 10*-*3 pixel widths, generally speaking around *δmin* = 10 nm. Although this is on the same order as the detection limit of the SOI sensor, the SOI photoconductive mesa sensor described here does so at 1 MHz bandwidth.

Spatial filtering of moving speckles is a subcategory of speckle photography in which image processing of the speckle pattern is done in real-time by spatially filtering the speckle pattern with a periodic mask (for example a simple Ronchi grating) and integrating the filtered light (Komastu, 1976). As the speckle pattern translates across the grating, the integrated signal is periodic with a period equal to the time it takes an individual speckle to translate a distance equal to the grating spacing. The frequency of the integrated signal is proportional to velocity, so this technique is generally used to measure surface velocity. The measurement depends on determining a temporal frequency, so there must be at least one full period observed. This means that the speckle must travel at least the distance of a grating spacing. Since signal-to-noise is best when the speckle is about the same width as the grating spacing (Kamshilin, 2009), the detection limit is approximately 10 μm. This method is not nearly as sensitive to small displacements as the other methods discussed; however it works well for high in-plane velocities when the object is moving a large distance.

### **5.4 Summary**

336 Photodetectors

Using a typical scattering spot size of 0.5 mm and a detector-matched speckle size of 20 *μ*m, this gives *δS,min* = 160 pm Hz1*/*2 W. This is the minimum detectable translation of the speckle pattern. Typically, there will be optical magnification in this system with a value ~10, so this

For example, with a 1 W laser source and a bandwidth of 1 MHz and using a 50Ω load resistor, the detection limit is approximately *δmin* = 16 nm for a single element. This value can be brought down even more by using a larger load resistor, as large as the bandwidth

Classical interferometry can be used to detect in-plane motion under certain conditions. For instance, rather than using one beam to illuminate the object and one reference beam, the differential interferometry approach uses two beams to illuminate the object (Monchalin, 1986). If the input aperture of the system and the illuminating spot are sufficiently small, then what forms in the detector plane is essentially a single speckle. The phase of that one speckle is sensitive to motion parallel to the difference in wave-vectors of the two illuminating beams. If the two beams illuminate the surface at equal but opposite angles with respect to the normal, the setup is sensitive purely to motion in the plane of the object. Using a 0.5 mm spot size and a HeNe laser with a wavelength of 633 nm gives a detection limit *δmin* = 16 pm Hz-1/2 W*.* With a 1 W laser and 1 MHz bandwidth, a sensor with these parameters has a detection limit on the order of *δmin* = 16 nm. Again, this can be reduced by increasing the load resistor to the maximum the bandwidth requirements will allow. We see that the detection limit for the direct speckle monitoring method with SOI detectors is roughly equal to the interferometric method. The speckle monitoring method has the greatly added advantage that it does not require two coherent beams to be precisely overlapped at a

Speckle photography relies on tracking individual speckle features as they move across the detection plane with an electronic detector array. These methods are generally used in low speed applications -- CCD arrays with low read-out rates and long integration times (on the order of milliseconds). A recent report on different algorithms (Cofaru, 2010) finds that resolution is on the order of 10*-*3 pixel widths, generally speaking around *δmin* = 10 nm. Although this is on the same order as the detection limit of the SOI sensor, the SOI

Spatial filtering of moving speckles is a subcategory of speckle photography in which image processing of the speckle pattern is done in real-time by spatially filtering the speckle pattern with a periodic mask (for example a simple Ronchi grating) and integrating the filtered light (Komastu, 1976). As the speckle pattern translates across the grating, the integrated signal is periodic with a period equal to the time it takes an individual speckle to translate a distance equal to the grating spacing. The frequency of the integrated signal is proportional to velocity, so this technique is generally used to measure surface velocity. The measurement depends on determining a temporal frequency, so there must be at least one full period observed. This means that the speckle must travel at least the distance of a grating spacing. Since signal-to-noise is best when the speckle is about the same width as the

photoconductive mesa sensor described here does so at 1 MHz bandwidth.

yields a minimum detectable displacement on the order of *δmin* = 16 pm Hz1*/*2 W.

limitations will allow.

**5.3 Other methods**

specific angle in a small spot.

*δS,min* = 2*π*ε*S*2∆*Pmin*/λ2*PS* .

We have shown that photoconductive mesas directly monitoring speckle motion have an important place among applications requiring higher bandwidth detection of in-plane motion. The detection limit for a single element monitoring in-plane motion is on the order of *δmin* = 16 pm Hz-1/2 W.

### **6. Arrays of photoconductive mesas**

The performance of speckle monitoring systems can be dramatically increased by using multiple detectors to make simultaneous measurements of a single speckle pattern displacement. An array configuration of photodetectors can be used to enhance the signal to noise ratio of displacement measurements. In addition to a lower noise floor, uncertainty in absolute displacement measurements can be mitigated. Additionally, the adverse effects owing to harmonic distortion can be lessened by the use of arrays, all allowing for better overall system calibration.

The signals from multiple array elements can be combined in a way that reduces noise in the same spirit as simple averaging. The contribution of each element to the average, however, must be weighted according to the particular speckle that element is monitoring. The primary challenge is the ambiguity in both the sign and magnitude of the slope in photointensity as a function of speckle displacement. If the signals are corrected so that every element has the same phase (i.e., all elements are made to have a positive slope), then the weighted average shows an increase in signal to noise ratio as the number of elements is increased.

A demonstration of the power of arrays for improving signal-to-noise was made by comparing the signals of individual detector elements with a phase-corrected average of 18 combined elements. The 18 independent signals were obtained by using a three-element array to monitor six different random speckle patterns, each undergoing the same displacement. The SNR of the phase-corrected average was superior by approximately 10 dB over the typical single element signal. In this demonstration case, the phases were sorted manually during post processing.

### **7. Conclusions**

This chapter has shown how photoconductive detectors tens of microns in size can provide more sensitivity than photo-diodes. Very small photo-diodes have very small areas, which limits their sensitivity at high bandwidth. They are useful in applications such as singlemode fiber optics telecommunications, because the light is concentrated to a very small area. When signals are as small as a nanowatts, photo-multipliers are often used. However, in applications where spatial resolution at high bandwidth is required, such as monitoring mechanical vibrations, photoconductive elements can have the best useful figure of merit. We have demonstrated a minimum detectable power of *Pmin* is 2 pWHz1*/*2.

Photoconductors for Measuring Speckle Motion to Monitor Ultrasonic Vibrations 339

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Experiments showed the most sensitivity with photoconductive GaAs mesas, but speed was limited to tens of kHz because of slow space-charge buildup in regions of sharp transition between light and dark. Reducing the shadows with transparent ITO contacts and reducing the background resistivity with continuous illumination with an LED, provided a response time of about 10 μs, three times faster than without these special measures. Nonetheless, GaAs mesas were unable to respond fast enough to measure ultrasonic waves.

The solution was to use photoconductive mesas in silicon, which does not suffer from space charge buildup. Because carriers can diffuse very far in silicon, it is important to use siliconon-insulator (SOI) technology, to remove any diffusion into the substrate. It is also necessary to electrically isolate mesas by etching all the way through the silicon layer. With careful etching, mesas 15 μm wide were etched and contacted on either side. These photoconducting mesas were demonstrated to respond in 2 MHz with a 10V bias. Analysis of speckle motion measured with these SOI mesas indicated a sensitivity of *δmin* = 16 pm Hz1*/*2 W.

Preliminary experiments were carried out to demonstrate improvements when arrays of photo-conducting mesas are used, as long as the signals are properly combined. Further work on arrays will enable system calibration as well as measurement. All results, both experimental and theoretical, indicate chapter that this technology is certainly amenable to further development in practical applications of vibration and motion sensing, particularly at high speeds and small distances.

#### **8. References**

Experiments showed the most sensitivity with photoconductive GaAs mesas, but speed was limited to tens of kHz because of slow space-charge buildup in regions of sharp transition between light and dark. Reducing the shadows with transparent ITO contacts and reducing the background resistivity with continuous illumination with an LED, provided a response time of about 10 μs, three times faster than without these special measures. Nonetheless,

The solution was to use photoconductive mesas in silicon, which does not suffer from space charge buildup. Because carriers can diffuse very far in silicon, it is important to use siliconon-insulator (SOI) technology, to remove any diffusion into the substrate. It is also necessary to electrically isolate mesas by etching all the way through the silicon layer. With careful etching, mesas 15 μm wide were etched and contacted on either side. These photoconducting mesas were demonstrated to respond in 2 MHz with a 10V bias. Analysis of speckle motion measured with these SOI mesas indicated a sensitivity of *δmin* = 16 pm

Preliminary experiments were carried out to demonstrate improvements when arrays of photo-conducting mesas are used, as long as the signals are properly combined. Further work on arrays will enable system calibration as well as measurement. All results, both experimental and theoretical, indicate chapter that this technology is certainly amenable to further development in practical applications of vibration and motion sensing, particularly

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**15**

*Russia*

Olga Kudryashova et al.\*

**Remote Optical Diagnostics of Nonstationary**

*Institute for Problems of Chemical and Energetic Technologies SB RAS*

**Aerosol Media in a Wide Range of Particle Sizes**

Polydisperse gas flows with condensed particles suspended therein are widely spread in nature and play an important role in many branches of the modern engineering and technology. There is a necessity for estimating parameters of disperse media in the manufacturing processes, scientific research, and atmosphere sounding. In industry, the result of this estimation can be employed as the quality measure of a product. When designing and distributing multi-phase systems to study the processes of controlling their parameters, it is also needed to determine values of the quantities characterizing such

Technological processes exploit submicron media alongside with those of large and medium sizes. In such a system the particle sizes vary from tenths of nanometers to tenths of micrometers, and the resultant product may possess significantly different properties, even at small size variations of the condensed phase of a substance used. In this case, it becomes necessary to restore the particle size distribution function taking into account the contribution of all the particles present in the medium. Such functionality may be required from a measuring device when selecting modes of operation or designing sprayer units, controlling the quality of various micro- and nanopowders, monitoring the ecological situation of an area, and studying the dustiness of shop floors. Furthermore, the experimental information on disperse aerosol parameters is needed for evaluating the adequacy degree of a mathematical model accepted to describe an actual process and serves as initial data to calculate working processes in certain devices (Zuyev et al., 1986). The overview of the existing devices to investigate aerosol media has however shown that it

The available techniques of determining the condensed phase dispersiveness can conditionally be divided into two main groups based on: (i) estimating the particle size in samples selected from a medium under examination and (ii) direct noncontact determination of sizes of the particles present in the medium. The sampling methods have gained a wide distribution, but the essential drawbacks of such methods are both the

Anatoly Pavlenko, Boris Vorozhtsov, Sergey Titov, Vladimir Arkhipov, Sergey Bondarchuk,

**1. Introduction**

systems.

 \*

cannot be realized using the known instruments.

Eugeny Maksimenko, Igor Akhmadeev and Eugeny Muravlev

*Institute for Problems of Chemical and Energetic Technologies SB RAS, Russia*


4. Process and procedure of microwave-assisted extraction

There are two mechanisms in microwave heating: dielectric conduction and ionic conduction. The principle in dielectric heating is to rearrange the molecules in the dipolar structure by rotation in the presence of electrical changes. The dipoles are arranged at 2450 MHz and distributed randomly at 4.9109 times in the second. As a result of this motion, heat is generated by the vibration. In the ionic conduction mechanism, the magnetic field is generated by the movement of ions at the end of the application. The solvent heats up by friction resulting from the resistance of the solvent to the ion flux. In most applications, two mechanisms occur simultaneously.

The frequency, microwave power and heating speed, temperature, mass of food, water content, density, physical geometry, thermal properties, electrical conductivity, and dielectric properties of food are affected by microwave heating. Microwave, which has been used for various applications in food for many years, is also a preferred method for increasing extraction efficiency (Table 2). Contrary to classical heating, in the case of microwave heating, for example, all are heated uniformly and quickly at the same time. The cells are heated by microwave radiation by the moisture in them and apply pressure to the cell wall as a result of evaporation. The cell wall is disrupted by this high pressure, and the passage of the components to the solvent is ensured. One of the most important parameters in extraction process is solvent choice. Solvents with a larger dipole moment will heat up faster. According to the solvent dipole moments, acetonitrile, methanol, acetone, ethyl acetate, water, ethyl alcohol and hexane are the most commonly used solvents for phenolic substance extraction from plant source. The nonpolar solvent hexane (dipole moment <0.1) will not heat up in the microwave.

Microwave extraction is performed with two different systems (Figure 3). The most common system is the closed system extrusion made in a closed container that can control temperature and pressure. The other method is carried out in an open container under atmospheric pressure. The advantage of this method is that the amount of extraction and the amount of solvent used are small at large. The principle of heating using microwave energy is based on the direct effect of the microwave on the molecule through ion conduction and dipole rotation (rotation). In most applications, these two mechanisms come into play simultaneously. Ionic conduction is the electrophoretic migration of ions when a magnetic field is applied. The solution results in

Table 2. Microwave applications in the food industry.

heat production, the electrical conductivity of the food, the composition of the food, the

Preliminary treatment of rice brass raw materials and extraction of oils by solvent extraction are examples of studies in which ohmic heating systems are used as pretreatment in extraction processes. For food samples pretreated using ohmic heating method, the yield varied between

Pulsed electric field (PEF) technology is a food preservation process that is based on the use of the electric field to eliminate food-borne pathogenic microorganisms and to control microorganisms that destroy food, is nonthermal, and does not adversely affect food quality. A typical PEF unit consists of a high-voltage boost generator, application chamber, flow control system, control, and monitor device. The most important one of these is the application chamber, and it

The successful results of PEF technology in liquid foods (especially fruit juice, milk, etc.) are noteworthy and lead to the production of high-capacity systems. In the near future, it is known that in developed countries, high-quality products will be obtained by processing liquid and semiliquid foods with PEF technology. In this context, it will be inevitable that many countries will follow these developments as the result of the conjunctural changes that will take place in the competition of international food trade. For this purpose, attention should be paid to other disciplines (such as machinery, industry, electrical-electronics, materials, and food engineering) to produce PEF technology in our country. Initiation of studies on PEF technology, transmission, and dissemination of this technology in the industry will enable the technology to be

The use of microwave technology, which has been in use since the Second World War, in the analytical laboratory was at the end of the 1970s. Microwaves are electromagnetic radiation that varies in the range of 0.3–300 GHz and are usually extracted at 2.5–75 GHz in natural products. The efficiency of microwave energy depends largely on the content of the solvent, the plant material, and the applied microwave power. The advantage of microwave heating is the decomposition of oxidized weak hydrogen bonds at the poles of the molecules. Extraction with microwave is realized with two different systems. The most common system is the closed system extrusion made in a closed container that can control temperature and pressure. The other method is carried out in an open container under atmospheric pressure. The advantage of this method is that the amount of extraction and the amount of solvent used are largely small.

There are two mechanisms in microwave heating: dielectric conduction and ionic conduction. The principle in dielectric heating is to rearrange the molecules in the dipolar structure by

4. Process and procedure of microwave-assisted extraction

intensity of the electric field, and the duration of the waiting period.

168 Emerging Microwave Technologies in Industrial, Agricultural, Medical and Food Processing

49 and 92%, while for non-pretreated samples, the yield was 53%.

3.2.4. Pulsed electric field extraction

must be treated with care during design.

produced by the domestic industry.

3.2.5. Microwave-assisted extraction

appropriate if it is a fugitive compound. However, in closed containers it is necessary to wait for the temperature to fall before opening the container after extraction. The main difference is the heat. The extraction process begins with the extraction cell being loaded, for example. Microwave radiation is applied, and the pre-extraction step is started to heat the solvent to the set values. Normally, heating takes less than 2 min. The specimen is then irradiated and

Microwave-Assisted Green Extraction Technology for Sustainable Food Processing

http://dx.doi.org/10.5772/intechopen.76140

171

The advantages of microwave-assisted extraction are reduction in solvent consumption, increase in extraction efficiency, high recovery, ease of use and low cost, wide solvent, and solvent blends. The disadvantages are that if the target component or solvent is polar or volatile, the efficiency of the microwave is too weak, and it is applied at high temperatures such as 110–150C, which requires additional filtration or centrifugation to separate the solids

5. Applications of MAE as a green extraction technique in food industry

MAE is usually used in environmental analyses. Extraction of polyaromatic hydrocarbons, polychlorobiphenyls, and organochlorine pesticides from matrices such as sediment and soil has been studied. MAE has also been applied to food analyses. Extraction of 16 organochlorine insecticides in sesame seeds, use of microwave confinement in drug extraction from seed, and extraction and testing of lidocaine, methadone, diazepam, nordiazepam, proxifene, and norproxifen materials as model medicines are the main applications in which the MAE technique is used. At the same time, extraction of natural products such as drugs (pentobarbital, ketamine, diazepam); extraction from skeletal tissues; extraction of natural products such as terpenes, alkaloids, volatile oils, and carotenoids; extraction of color pigments in the paprika; identification of pesticide residues in sunflower seeds; discovery of a rapid method using

generally extracted at intervals of 10–30 min [17–19].

Figure 4. MAE apparatus.

as compared to supercritical fluid extraction.

Figure 3. Schematic view of the microwave-assisted extraction system (closed system and open system).

a resistive friction (friction) to this ion flow, and thus the solution heats up. Extraction is usually carried out in a closed container. In this case the pressure increases, and the solvent can be heated to higher temperatures than the boiling point. For most solvents (such as acetone, acetone hexane, acetone), the temperature in the container is two to three times the boiling point of the solvent. Two main types of MAE systems can be used: closed-vessel system (under controlled temperature and pressure) and open-vessel system (atmospheric temperature). Both systems and MAE apparatus (DKSH Australia Pty. Ltd.) are shown in Figures 3 and 4, respectively.

While the cells are being irradiated simultaneously in the closed-vessel system, the vessels are successively irradiated in the open system. In open vessels the temperature is limited by the atmospheric pressure boiling point of the solvent, while in closed vessels, the temperature can be increased by the applied pressure. The closed container system seems to be most Microwave-Assisted Green Extraction Technology for Sustainable Food Processing http://dx.doi.org/10.5772/intechopen.76140 171

Figure 4. MAE apparatus.

a resistive friction (friction) to this ion flow, and thus the solution heats up. Extraction is usually carried out in a closed container. In this case the pressure increases, and the solvent can be heated to higher temperatures than the boiling point. For most solvents (such as acetone, acetone hexane, acetone), the temperature in the container is two to three times the boiling point of the solvent. Two main types of MAE systems can be used: closed-vessel system (under controlled temperature and pressure) and open-vessel system (atmospheric temperature). Both systems and MAE apparatus (DKSH Australia Pty. Ltd.) are shown in Figures 3

Figure 3. Schematic view of the microwave-assisted extraction system (closed system and open system).

170 Emerging Microwave Technologies in Industrial, Agricultural, Medical and Food Processing

While the cells are being irradiated simultaneously in the closed-vessel system, the vessels are successively irradiated in the open system. In open vessels the temperature is limited by the atmospheric pressure boiling point of the solvent, while in closed vessels, the temperature can be increased by the applied pressure. The closed container system seems to be most

and 4, respectively.

appropriate if it is a fugitive compound. However, in closed containers it is necessary to wait for the temperature to fall before opening the container after extraction. The main difference is the heat. The extraction process begins with the extraction cell being loaded, for example. Microwave radiation is applied, and the pre-extraction step is started to heat the solvent to the set values. Normally, heating takes less than 2 min. The specimen is then irradiated and generally extracted at intervals of 10–30 min [17–19].

The advantages of microwave-assisted extraction are reduction in solvent consumption, increase in extraction efficiency, high recovery, ease of use and low cost, wide solvent, and solvent blends. The disadvantages are that if the target component or solvent is polar or volatile, the efficiency of the microwave is too weak, and it is applied at high temperatures such as 110–150C, which requires additional filtration or centrifugation to separate the solids as compared to supercritical fluid extraction.


**Chemically Induced Hepatocellular Carcinoma and Stages of Development with Biochemical and Genetic Modulation: A Special Reference to Insulin-Like-Growth Factor II and Raf Gene Signaling**

Biswajit Mukherjee\*, Miltu Kumar Ghosh and Chowdhury Mobaswar Hossain *Department of Pharmaceutical Technology, Jadavpur University, Kolkata India*

#### **1. Introduction**

200 Hepatocellular Carcinoma – Basic Research

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#### **1.1 Liver and its physiology**

The liver, the largest organ in the body, predominantly functions as a biochemical laboratory where metabolism takes place. It has both endocrine and exocrine functions; and is also involved in numerous metabolic activities and acting as a storage depot. Once the nutritional substances and other chemicals such as drugs, carcinogens etc. reach in liver, they are metabolized by hepatic enzymes. The organ is located on the right side of the abdomen just beneath the diaphragm in human. Liver is a solid organ consisting of several lobes. Each lobe is constituted with numerous lobules which are in general hexagonal in shape (Figure 1). The center of each lobule is occupied by the central vein and the periphery of the lobule is delineated by a close arrangement of hepatic artery, portal vein, and bile duct; called "portal triads". The portal triads appear at the vertices of the hexagonal lobules. The vessels generated from the portal triads ramify and distribute along the sides of the lobule, and open into the sinusoids which have thin epithelial lining, a discontinuous layer of fenestrated endothelial cells. The liver has different types of cells. Oval cells are generally found near the portal triad. This rare cell-type has been claimed as hepatic stem cells by some researchers (Zamule et al., 2011). However, the major cell-type in liver is the polygonal hepatic parenchymal cells (hepatocytes). Hepatic lobules are made up of more than 80% hepatocytes which have an average size of 25 and occupy 70-90% of liver mass, depending on the species. They have clear cell membrane; sometimes with two nuclei. They have large deposits of glycogen, often with lipid droplets and basophilic materials. They also contain other cellular organelles such as mitochondria, rough endoplasmic reticulum (granular) and smooth endoplasmic reticulum (agranular), golgi apparatus, and lysosomes. The hepatocytes are arranged in stacks of anastomosing plates, separated by an anastomosing

<sup>\*</sup> Corresponding Author

Chemically Induced Hepatocellular Carcinoma and Stages of Development with Biochemical and Genetic Modulation: A Special Reference to Insulin-Like-Growth Factor II and Raf Gene Signaling 203

appear. Normal hepatic function suffers and hepatic homeostasis decreases.

cirrhosis and cirrhosis associated with genetic liver diseases.

**<IP_ADDRESS> Hepatocellular carcinoma (also called hepatoma)**

cause primary liver cancer. Primary liver cancer has different types, too.

children and teenagers, similar pattern of the disease is called hepatoblastoma.

When primary liver cancer occurs in bile ducts it is called cholangiocarcinoma.

**1.3 Liver cancer and its types**

**1.3.1 Primary liver cancer**

**<IP_ADDRESS> Cholangiocarcinoma**

**<IP_ADDRESS> Angiosarcoma**

vessel of the liver.

**<IP_ADDRESS> Angiosarcoma and hemangiosarcoma**  These are fast growing rare type liver cancers.

increase in hepatic cells by rapid cell division (hyperplasia) or increase in cell-size (hypertrophy) primarily causes enlargement of the tissue (Michalopoulos & DeFrances, 1997). Exposure to high levels of chemical toxicants causes increase in liver-size about 2-3 times of its normal size to combat the enhanced metabolic pressure exerted by chemical exposure (Michalopoulos & DeFrances, 1997). By regeneration process liver replaces the necrotic/ dead cells or the cells damaged due to toxicity. During hepatic continual regeneration process, the increased collagen synthesis and deposition result in fibrosis. This alongwith further continual hepatic cell damage due to various stress conditions cause liver cirrhosis (Lv et al., 2006). In cirrhosis, liver morphology alters. Scarring and nodularity

Cancer is the uncontrolled proliferation of cells that is caused by the multi-genetic defects. When the phenomenon occurs in liver cells, this is called hepatic cancer or liver cancer. The cancerous process begins in a few cells and the cells become immortal. They invade adjacent cells, intrude other nearby tissues (sometimes tissues at a distance) and metastasize. Uncontrolled proliferation of the cells causes solid mass formation in liver, resulting hepatic tumor. The predominant risk factors and etiological agents responsible for hepatocellular carcinoma in humans have been identified as chronic infection with hepatitis B virus or hepatitis C virus, exposure to aflatoxin B1 or other chemical carcinogens, and alcoholic

Primary liver cancer means that the cancerous process occurs and develops primarily in liver and does not start from the spread of cancerous cells located outside the liver. It is the most common type of hepatic cancer. There may be several causes of primary liver cancer. Chronic viral infections such as hepatitis B or C, some toxins, chemical induced hepatic damage, radiation-induced hepatic damage, and chronic liver diseases such as cirrhosis can

Hepatocellular carcinoma is the incidence of primary liver cancer in liver parenchyma cells or hepatocytes. People suffering from liver chronic diseases such as cirrhosis of liver are more prone to hepatocellular carcinoma. This is common to adult patients. However, in

This rare form of rapidly growing fatal tumor develops in the endothelial cells of blood

system of sinusoids. One or two cells thick hepatocytes appear radiating from central vein towards the periphery. They metabolize and excrete into sinusoids or bile canaliculi. They can undergo cell division to produce more hepatocytes. Other than the endothelial cells, the liver sinusoids contain phagocytic cells derived from monocytes, known as Kupffer cells. These macrophages phagocytize particulates and cell debris. Another hepatic cell-type is known as the ito cell. These are adipose or stellate cells.

Liver undertakes several important functions in our body. It

#### **1.2 Hepatic regenerative capability**

Under normal condition in an adult, liver maintains its size. However, liver has the regenerative capacity. Under various stress conditions, liver-size may increase. Numerical

Fig. 1. Histological structure of a liver lobule

increase in hepatic cells by rapid cell division (hyperplasia) or increase in cell-size (hypertrophy) primarily causes enlargement of the tissue (Michalopoulos & DeFrances, 1997). Exposure to high levels of chemical toxicants causes increase in liver-size about 2-3 times of its normal size to combat the enhanced metabolic pressure exerted by chemical exposure (Michalopoulos & DeFrances, 1997). By regeneration process liver replaces the necrotic/ dead cells or the cells damaged due to toxicity. During hepatic continual regeneration process, the increased collagen synthesis and deposition result in fibrosis. This alongwith further continual hepatic cell damage due to various stress conditions cause liver cirrhosis (Lv et al., 2006). In cirrhosis, liver morphology alters. Scarring and nodularity appear. Normal hepatic function suffers and hepatic homeostasis decreases.


*4.2. Self-Attention Using Dependency Graph: BERTGAT*

In this section, we describe BERTGAT to encode the syntactic structure with graphattention network (GAT) [15]. It leverages the overall graph structure to learn complex relationships between entities, enabling the classification of various types of relationships. In general, dependency trees provide a rich structure to be exploited in relation extraction. Parse trees can have varying structures depending on the input sentences, which may differ in terms of length, complexity, and syntactic construction. Thus, organizing these trees into a fixed-size batch can be difficult. Unlike linear sequences, where tokens can be easily aligned and padded, the hierarchical structure of parse trees complicates this process. In sequence models, padding is used to create equal-length inputs for efficient batch processing. However, for parse trees, padding is not straightforward, as it involves adding artificial tree nodes that might disrupt the tree's structure and introduce noise to the model. Due to these difficulties, it is usually hard to parallelize neural models working on parse trees.

On the contrary, models based on the SDP (shortest dependency path) between two entities are computationally more efficient, but they might exclude crucial information by removing tokens outside the path. In addition, some studies stated that not all tokens in the dependency tree are needed to express the relation of the target entity pair. They have utilized SDP [37] or subtree rooted at the lowest common ancestor (LCA) of the two entities [14] to remove irrelevant information. However, SDP can lead to loss of crucial information and easily hurt robustness. For instance, according to the research by Zhang et al. [14], in the sentence "She was diagnosed with cancer last year, and succumbed this June", the dependency path 'She←diagnosed→cancer' is not sufficient to establish that cancer is the cause of death for the subject unless the conjunction dependency to succumbed is also present. In order to incorporate crucial information off the dependency path, they proposed a path-centric pruning strategy to keep nodes that are directly attached to the dependency path.

To address the issue, we here adapt the graph attention network to consider syntactic dependency tree structure by converting each tree into corresponding adjacency matrix. The graph attention [15] is jointly considered in self-attention sublayer to encode the

dependency structure between tokens into vector representations. That helps to capture relevant local structures of dependency edge patterns that are informative for classifying relations by considering the relationships between each node and its neighbors, assigning greater weights to more important neighboring nodes. This approach allows for more effective learning of node representations of graph data, ultimately helping to represent node features more accurately.

For this, the Stanford dependency parser [38] is utilized to retrieve universal dependencies for each sentence. A dependency tree is a type of directed graph where nodes correspond to words and edges indicate the syntactic relations between the head and dependent words. In this work, if there is a dependency between node *i* and node *j,* then its opposite direction of dependency, node *j* and node *i* is also included. The dependency types of edge such as 'subj' and 'obj' are not considered. A self-loop is also considered for each node in the tree. Since BERT takes as subword units generated by tokenizer instead of word-based linguistic tokens of a parse tree, we introduce additional edges to handle unit differences. Figure 6a shows the architecture of BERTGAT.

**Figure 6.** BERTGAT and T5slim\_dec architecture.

Given a graph with *n* nodes, we can represent the graph with an *n* × *n* adjacency matrix A, where *Aij* is 1 if there is a direct edge going from node *i* to node *j*. The encoder consists of two sublayers: multi-head self-attention layer and multi-head self-graph attention layer. The final hidden layer of the encoder is fed into a linear classification layer to predict a relation type, which is followed by a softmax operation. That is, the output layer is one-layer task-specific feed-forward network for relation classification.

The output of the BERT model is a contextualized representation for each word in the given text, which is expressed as the hidden state vector of each word. This output vector contains contextual information about the corresponding word. The input to GAT consists of a set of the hidden state vectors obtained from BERT, h = {*h*1, *h*2,..., *hV*}, which serve as the initial feature vectors for each token in the text.

The GAT layer in Figure 6a produces a new set of node features, h = {*h* 1, *h* 2,..., *h V*}, as its output and *V* is the number of nodes. The Equations (5) and (6) are used to obtain GAT representation. In this study, we follow the formulation of the Graph Attention Network (GAT) as proposed in the original paper by Veliˇckovi´c et al. (2018) [15]. The GAT model is defined by Equations (5) and (6).

In the beginning, a shared linear transformation, parameterized by weight matrix **w** is applied to each node to transform the input features into higher-level features. Here, **w** is a learnable linear projection matrix. Subsequently, a self-attention mechanism *a* is

performed on the nodes and attention coefficients *e* are computed for every pair of nodes. To calculate the connection importance of node *j* to node *i*, the masked attention coefficient *ei*,*j* is computed according to Equation (5) only when *j* is a neighbor of node *i* in the graph. N*i* represents the set of *i* 's one-hop neighbors, including the *i* node itself, as a self-loops are permitted.

While the multi-head self-attention layer in Figure 6a uses a scaled dot product function as a similarity function, the GAT layer uses a one-layer feedforward neural network denoted as *a* after concatenating the key and query. The scoring function *e* computes a score for every edge (*j,i*), which indicates the importance of the neighbor *j* to the node *i*. It assigns negative value if there is no connection and then the resulting *αi*,*j* is normalized with softmax, as shown in Equation (5). It makes the coefficients easily comparable across different nodes. In the equation, the attention mechanism *a* is a singlelayer FFNN, parametrized by a weight vector **a** and LeakyReLU nonlinearity activation function is applied where T represents transposition and || is the concatenation operation.

$$\begin{aligned} e\_{i,j} &= a \left( \mathbf{w}h\_{l}, \mathbf{w}h\_{j} \right), j \in \mathcal{N}\_{i} \\ a: &\text{LeakyReLU}(\text{Linear}\left(\text{concat}\left(\mathbf{w}h\_{l}, \mathbf{w}h\_{j}\right)\right)) \\ a\_{i,j} &= \text{softmax}\left(e\_{i,j}\right) = \frac{\exp\left(e\_{i,j}\right)}{\sum\_{k \in \mathcal{N}\_{l}} \exp\left(e\_{i,k}\right)} = \frac{\exp\left(\text{LeakyReLU}\left(\mathbf{a}^{\text{T}} \left|\mathbf{w}h\_{l}\right| \left|\mathbf{w}h\_{j}\right|\right)\right)}{\sum\_{k \in \mathcal{N}\_{l}} \exp\left(\text{LeakyReLU}\left(\mathbf{a}^{\text{T}} \left|\mathbf{w}h\_{l}\right| \left|\mathbf{w}h\_{k}\right|\right)\right)} \end{aligned} \tag{5}$$

The normalized attention coefficients *α* are used to compute a weighted sum of the corresponding neighbors and to select its most relevant neighbors, as shown in Equation (6). It utilizes the attention mechanism to aggregate neighborhood representations with different weights. That is, each node gathers and summarizes information from its neighboring nodes in the graph. The aggregated information and value is combined and serves as the final output representation for every node. In this way, a node iteratively aggregates the information from its neighbors and updates the representation. To perform multi-head attention, *K* heads are used. Here, *σ* refers to the ReLU activation function and *αi*,*j k* means normalized attention coefficients computed by the *k*-th attention mechanism. Finally, we use averaging and activation function and then add a linear classifier to predict for the relation type.

$$\begin{aligned} h'\_i = \sigma \Big( \sum\_{j \in \mathcal{N}\_i} a\_{i,j} \mathbf{W} \mathbf{h}\_j \Big) h'\_i = ||\_{k=1}^{\mathcal{K}} \sigma \Big( \sum\_{j \in \mathcal{N}\_i} a\_{i,j} \mathbf{}^k\_i \mathbf{W}^k h\_j \Big) (multi\\_head) \\\ h'\_i = \text{LeakyReLU} \left( \frac{1}{\mathcal{K}} \sum\_{k=1}^{\mathcal{K}} \sum\_{j \in \mathcal{N}\_i} a\_{i,j}^k \mathbf{W}^k h\_j \right) \end{aligned} \tag{6}$$

Figure 7 visualizes an example of graph self-attention for an entity node "Sympathomimetic Agents" in the sentence, "Concomitant administration of other Sympathomimetic Agents may potentiate the undesirable effects of FORADL." The interaction type between the two entities, Sympathomimetic Agents" and "FORDAL" is classified as "DDI-effect". In the Figure, (a) displays the sentence's dependency structure, (b) shows the same dependency structure in the form of a graph, (c) presents the adjacency table reflecting the dependency relationships among words, and (d) illustrates the transformation of the vector representation of node 5, "sympathomimetic agents" through graph attention. In addition, this model can incorporate off-connection but useful information by employing a residual connection around each of the two sub-layers, followed by layer normalization. That is, the output of GAT sublayer is LayerNorm(x + GAT\_Sublayer(x)), where x is the output of BERT's self-attention sublayer.

Thus, this model reflects both contextual relatedness and syntactic relatedness between tokens. In addition, the GAT model applies attention to the features of each node's neighbors to combine them and create a new representation of the node. Therefore, by utilizing attention weights that reflect the importance of edge connections, the neighbor information includes not only directly connected nodes but also indirectly connected nodes, effectively capturing local substructures within the graph.

**Figure 7.** An example of multi-head graph-attention network.


**Abstract**

At the initial stage of tunnel design, the tunnel stability can be assessed by different design techniques which are broadly classified into three categories i.e. Mathematical Analysis, Empirical Methods and Numerical Analysis. Mathematical methods or closed form solutions are more precise methods; however, its use is limited to simple geometries and almost impossible for complex geometries due to complex and tedious calculations involved. In practice, Empirical and Numerical Methods are usually used for stability analysis of tunnels. It should be noted that it is not the replacement of final design. Empirical design methods use information about the structural geology and other rock mass properties as input that can be easily obtained at the initial stage of a project. Numerical Methods commonly require mechanical properties, especially strength and deformation of rocks. Numerical methods are also considered as precise due to provision of allowance for variable inputs and geometry and having ability for sensitivity analysis. It is good practice to evaluate the stability of tunnels using at least two Empirical methods and validated through Numerical methods.

**Keywords:** tunnel design, design techniques, stability, sensitivity, RMR


**Abstract**

Interferometric analysis methods for measuring plasma properties are presented with emphasis on emerging trends in 2D phase reconstruction. Using recent imagery from exploded-wire experiments the relative phase profiles from independent interferograms are reconstructed. The well-known Fourier Transform Method is presented and discussed. Then, the electron and atom densities are recovered from the phase by solving a linear system of equations in the form of line-integrated density profiles. The mathematical models of the line density and phase function are described and elucidate why interferograms of plasma suffer from low contrast, high signal-to-noise ratio and poorly defined fringes. Although these effects pose challenges for phase reconstruction, the interferometric diagnostic continues to advance the plasma science.

**Keywords:** Abel transform, density measurement, Fourier transform method, optical interferometry, plasma properties


**3. Discussion**

To date, the BTBRob mouse has been understood as a model of severe T2D secondary to β-cell failure, with a consistent, full disease penetrance by 16 weeks of age in both sexes and earlier in males [4,6]. In this study, we encountered a unique cohort of male BTBRob mice for which environmental factors, including but not limited to diet, completely prevented hyperglycemia without further intervention. We have exploited these findings to study physiological changes as they pertain to islet function in obese, insulinresistant, normoglycemic animals compared to their equally obese and insulin-resistant T2D littermates.

The gut microbiome is of great interest in the T2D field and is well-known to be related to diet and other environmental conditions. Previous reports on mice and humans have documented alterations in the gut microbiome of T2D individuals compared to healthy controls [8,24]. While changes in the ratio of the dominant phyla, *Firmicutes* and *Bacteroidetes*, have been previously linked with T2D, we found no differences in *Firmicutes* or *Bacteroidetes* composition or relative abundance among groups [24,25]. Instead, the most defining characteristic of the NGOB gut microbiome fingerprint compared to both of the other groups was a change in nondominant phyla composition and abundance driven primarily by *Proteobacteria*. The elevations in *Proteobacteria* in NGOB mouse cecal matter are perplexing, as many reports indicate a higher disease incidence when *Proteobacteria* is elevated [26]. On the other hand, a richer overall microbial diversity has been associated with positive outcomes regarding glucose homeostasis [8,27].

A distinct endocrine profile was observed in the NGOB mouse compared to WT, and this profile was further altered in T2D. Elevated insulin and glucagon are hallmarks of β-cell stress and insulin resistance, and NGOB mice showed clear β-cell compensation, with a robust increase in IGR that was lost in the T2D cohort. The gut-islet relationship, of great interest in the T2D field, is thought to be modulated in part by the microbial composition of the GI tract. The incretin hormones, GLP-1 and GIP, are primarily secreted from gut enteroendocrine cells and act on the β-cell to promote insulin secretion. GLP-1 and GIP were both elevated in NGOB mice compared to WT. Surprisingly, they were even further elevated in plasma from T2D mice, even though loss of the incretin response is a known pathophysiological defect in T2D [27]. However, gut microbiota alterations have been shown to influence incretin sensitivity in obesity, insulin resistance, and T2D [28,29]; therefore, elevated GLP-1 and GIP levels may be reflective of a compensatory response that ultimately fails to promote β-cell function.

Changes in gut microbiota are also associated with alterations in circulating factors associated with T2D pathology [28,30,31]. This relationship with microbiota dysbiosis is further supported by elevated levels of resistin and PAI-1, adipokines associated with obesity and T2D [30,32,33]. Resistin and PAI-1 production and secretion are stimulated during innate immune response and, therefore, can reflect alterations in gut endothelial cell integrity that impact the systemic metabolic profile of the organism [30,33–36]. In obesity, bacterial byproducts, such as lipo-polysaccharides (LPS), enter circulation as the endothelial

wall of the intestine is degraded: a common consequence of obesity-associated intestinal inflammation [37,38]. LPS induces the release of resistin and PAI-1 from adipocytes, and LPS has also been shown to impair β-cell function via downregulation of the mature β-cell gene expression profile [39,40]. Therefore, it is possible that resistin and PAI-1 may serve as markers for β-cell dysfunction of T2D, independent of any direct influence on the β-cell themselves. More work would be necessary to tease apart this relationship.

The effects of elevated plasma AA and its precursor linoleic acid have been previously implicated in diabetes pathology and correlate with elevated HBA1c levels and hyperglycemia in human subjects [41]. In this work, we confirmed that elevated levels in circulating AA (or its isomers) correlate directly with elevated PGE metabolite levels. However, it has long been known that PGE2 is synthesized and excreted from pancreatic β-cells themselves and that this synthesis is a factor of both substrate availability and the expression of key synthetic enzymes, many of which are upregulated by the pro-inflammatory cytokine IL-1β [3,4,20–22]. In this work, we confirmed that this direct IL-1β-associated effect is of biological consequence, as the PGE2 analog, sulprostone, only influences the GSIS response of islets from T2D BTBROb mice, which already exhibits a prominent secretion defect compared to their nondiabetic controls. However, even though essential PUFAs must be obtained from the diet, diet alone cannot explain the altered ratios of omega-6 and omega-3 PUFAs downstream of linoleic acid and linolenic acid (including the most important to our work, AA and EPA) as equal numbers of T2D mice had also been fed an identical diet. Still, when considered in the context of our previous work demonstrating that BTBROb islets cultured in EPA-enriched media or nonobese diabetic mice fed an EPA-enriched diet significantly improved ex vivo or in vivo β-cell function, respectively [3], our findings certainly support the continued study of PUFA-based dietary interventions for T2D prevention or therapy.

In summary, we found that a host of systemic metabolic alterations are associated with the T2D phenotype of a strong genetic model of the disease and that, at least for the AA-to-PGE2 pathway, have directly implicated a specific class of metabolites in β-cell dysfunction of the disease. These findings have strong implications in the management of T2D, as many foods in the Western diet are enriched with AA. Therefore, pharmacological strategies to reduce gut/adipose inflammation in combination with a diet focused on limiting circulating AA may help to facilitate better β-cell function, either alone or in concert with T2D medications, promoting more effective blood glucose control even in the face of chronic obesity and insulin resistance.

#### **4. Materials and Methods**

#### *4.1. Animal Care and Husbandry*

BTBR mice heterozygous for the *Leptinob* mutation were purchased from The Jackson Laboratory and bred in house at the UW-Madison Breeding Core Facility to generate homozygous OB mice or wild-type (WT) controls. Experimental mice were singly housed in temperature- and humidity-controlled environments and maintained on a 12:12 h day/light cycle with free access to acidified water (InnoVive, San Diego, CA, USA) and one of two standard mouse chows of nearly identical macronutrient composition and energy density. A direct comparison of the nutrient content and ingredients of the Teklad global soy protein-free extruded 2920X diet (Envigo, Indianapolis, IN, USA) or the Rodent Laboratory Chow 5001 diet (Purina, Neenah, WI, USA) has been previously published [7]. Both wild-type control mice (*n* = 9; 3-Purina and 6-teklad) and OB mice (*n* = 17; 11-Purina and 6-teklad) were given either Purina or Teklad diets. At 10 weeks of age, OB mice that developed hyperglycemia were grouped as T2D/HGOB (*n* = 12). A subset of OB mice was able to maintain normoglycemia, which was grouped as NGOB (*n* = 5). All protocols were approved by the Institutional Animal Care and Use Committees of the University of Wisconsin-Madison and by the William S. Middleton Memorial Veterans Hospital, which are both accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (Project ID: G005181-R01-A03). All animals were treated in accordance with the standards set forth by the National Institutes of Health Office of Animal Care and Use.

#### *4.2. Blood Glucose Measurements and Insulin Tolerance Tests*

Insulin tolerance tests (ITTs) were performed essentially as previously described [42]. Briefly, mice were fasted for 4–6 h and 0.75 U/kg short-acting recombinant human insulin (Humulin® R; Eli Lilly, Indianapolis, IN, USA) was injected intraperitoneally. Blood glucose readings were taken by tail nick with an AlphaTRACK glucometer (Zoetis, Parsippany-Troy Hills, NJ, USA) at baseline and the indicated times after insulin administration. Percent blood glucose change from baseline was determined by normalizing the blood glucose reading at each timepoint to that at baseline for each mouse independently. The blood glucose percent change from baseline was averaged within genotypes at each time point, giving the means ± SEM.

#### *4.3. Ex Vivo Islet Glucose Stimulated Insulin Secretion Assays*

Islets were isolated from experimental mice at 10 weeks of age utilizing a collagenase digestion protocol as previously described [43]. Glucose-stimulated insulin secretion (GSIS) assays were performed as previously described [44] after the indicated treatments. Briefly, islets were washed and preincubated for 45 min in a 0.5% Bovine Serum Albumin (BSA) Krebs Ringer Bicarbonate Buffer (KRBB) containing 1.7 mM glucose. Islets were then incubated for an additional 45 min in either low glucose (1.7mM) or stimulatory glucose (16.7 mM) ± the EP3-selective agonist sulprostone (10 nM). Secretion media was collected, and islets were lysed in equal volume to determine insulin content. Insulin was measured via ELISA as previously described [44].

#### *4.4. Quantitative PCR for Gene Expression Analyses*

RNA isolation, cDNA synthesis, and quantitative PCR using SYBR Green reagent (Bio-Rad) to determine relative mRNA abundance among groups were all performed as previously described [45]. Data were normalized to that of β-actin to calculate ΔCT values. Primer sequences are available upon request.

#### *4.5. Terminal Blood Collection and Plasma Hormone/Metabolite Assays*

Terminal blood collection for plasma samples was performed by retroorbital puncture under anesthesia. Briefly, mice were anesthetized using 2,2,2-tribromoethanol (Sigma-Aldrich, St. Louis, MO, USA; #T48402). Blood was collected retro-orbitally using a heparincoated glass capillary tube and mixed with 5 μM EDTA, 10 nM DDP-4 inhibitor, and 20 nM aprotinin. Plasma was isolated via centrifugation and stored at −80 ◦C until needed. Plasma hormones were measured utilizing the Bio-Plex ProTM Diabetes Assay (Bio-Rad Laboratories, Hercules, CA, USA) following the manufacturer's protocol. The plasma prostaglandin E metabolite was measured utilizing a PGEM ELISA (Cayman Chemical Company, Ann Arbor, MI, USA; no. 514531) following the manufacturer's protocol as previously described [42].

#### *4.6. Gut Microbial DNA Preparation, Sequencing, and Analysis*

Microbial 16s sequencing of cecal matter was performed as previously described [46]. Briefly, 20–50 mg of the cecal matter was collected from WT, NGOB, and T2D mice, and genomic DNA was extracted and cleaned by using the Macherey-Nagel PCR Clean-up kit according to manufacturer's protocol (ThermoFisher Scientific, Waltham, MA, USA). Purified genomic DNA was submitted to the University of Wisconsin-Madison Biotechnology Center. DNA concentration was verified fluorometrically, and samples were prepared and amplified according to Illumina's 16s Metagenomic Sequencing Library Preparation Protocol with few modifications as described before [46]. Following PCR, reactions were cleaned using 0.7× volume of AxyPrep Mag PCR clean-up beads (Corning, Corning, NY, USA). Quality and quantity of the finished libraries were assessed using an Agilent DNA

1000 kit (Agilent Technologies, Santa Clara, CA, USA) and Qubit® dsDNA HS Assay Kit (ThermoFisher Scientific), respectively. In an equimolar fashion, libraries were pooled and appropriately diluted prior to sequencing. After sequencing, images were analyzed using the standard Illumina Pipeline, version 1.8.2. OTU assignments (Illumina, Inc., San Diego, CA, USA). Diversity plots were created using the QIIME (Ver. 1.9.1) analysis pipeline [46,47].

For the pilot experiment using fecal pellets from NGOB and pre-T2D mice, cryopreserved fecal pellets were shipped to Argonne National Labs where sequencing and data analysis were performed according to their standard protocols. Sequencing from the pilot experiment only went to the order level and not the species level.

#### *4.7. FIE-FTCIR MS for Unbiased Plasma Metabolomics*

Plasma samples were collected as described in Section 4.5. Samples were prepared using a methanol extraction procedure, and unbiased FIE-FTICR MS experiments were performed as previously described [7]. Statistical analysis was performed using MetaboScape 4.0 (Bruker, Billerica, MA, USA) and the online software MetaboAnalyst [48]. Putative metabolites were further annotated by METLIN with a 2-ppm mass error cutoff. Details about data analysis were described previously [7].

## *4.8. Statistical Analyses*

Data from all experiments excluding that previously described for microbiome and metabolomics analyses were analyzed using GraphPad Prism v.9 (GraphPad Software Inc., San Diego, CA, USA). Data were analyzed by *t*-test, one-way ANOVA, or two-way ANOVA as described in the figure legends. *p* < 0.05 was considered statistically significant.

## *4.9. Data Availability*

All data contained within this manuscript are available upon reasonable request of the corresponding author. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [49] partner repository with the dataset identifier PXD022624.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/2218-198 9/11/1/58/s1, Figure S1: Workflow of the FIE-FTICR MS-based platform for metabolomics. Table S1: Diet Composition, Table S2: Pilot 16s rRNA sequencing, Table S3: 16S rRNA analysis, Table S4: FIE-FTICR MS Mass List, Table S5: WT vs NGOB metabolites, Table S6: NGOB vs T2D metabolites.

**Author Contributions:** Conceptualization, M.D.S., Y.Z., J.C.N., B.T.L., D.W.L., Y.G. and M.E.K.; data curation, Y.Z., Y.G. and M.E.K.; formal analysis, M.D.S., Y.Z., N.E.R., C.P., I.M.O., R.J.F., J.C.N., E.G., A.R., H.K.S. and M.E.K.; funding acquisition, I.M.O., E.D.C., D.B.D., B.T.L., A.R.B., D.W.L., Y.G. and M.E.K.; investigation, M.D.S., Y.Z., N.E.R., I.M.O., R.J.F., J.C.N., E.G., A.R., H.K.S., M.H.F. and M.E.K.; methodology, M.D.S., Y.Z., N.E.R., D.W.L., Y.G. and M.E.K.; project administration, E.D.C., D.B.D. and M.E.K.; supervision, M.D.S., B.T.L., A.R.B., D.W.L., Y.G. and M.E.K.; visualization, M.D.S., Y.Z., N.E.R. and C.P.; writing—original draft, M.D.S., N.E.R., C.P. and J.C.N.; writing—review and editing, M.D.S., Y.Z., C.P., R.J.F., A.R., D.B.D., B.T.L., D.W.L., Y.G. and M.E.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported in part by Merit Review Awards I01 BX003700 (to M.E.K.), I01 BX004031 (to D.W.L.), and I01 BX003382 (to B.T.L.) from the United States (U.S.) Department of Veterans Affairs Biomedical Laboratory Research and Development (BLR&D) Service. This work was also supported in part by National Institutes of Health (NIH) grants K01 DK080845 (to M.E.K.), R01 DK102598 (to M.E.K.), R01 AG056771 (to D.W.L.), UL1 TR002373 (to A.R.B.), R01 DK104927 (to B.T.L.), R01 DK111848 (to B.T.L.), U01 DK127378 (to B.T.L.), P30 DK020595 (to B.T.L.), and P30 CA014520 (to I.M.O.); American Diabetes Association Grant 1-14-BS-115 (to M.E.K.); and a UW2020 grant from the Wisconsin Alumni Research Foundation (to M.E.K., E.D.C., and D.B.D). This research was conducted while D.W.L. was an AFAR Research Grant recipient from the American Federation for Aging Research. N.E.R. and J.C.N. were supported in part by training grants from the UW Institute on Aging (NIA T32 AG000213).

**Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Animal Care and Use Committees of the University of Wiscon-sin-Madison and by the William S. Middleton Memorial Veterans Hospital (protocol code: G005181-R01-A03 and date of approval: 6/13/2018).

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All data are contained within the article or supplementary material.

**Conflicts of Interest:** M.D.S., Y.Z., N.E.R., C.P., I.M.O., R.J.F., E.G., H.K.S., M.H.F., E.D.C., D.B.D., B.T.L., A.R.B., Y.G. and M.E.K. declare that they have no conflicts of interest with the contents of this article. J.C.N. is now employed by Novo Nordisk (800 Scudders Mill Rd, Plainsboro, NJ 08536). This work was completed in full during his predoctoral training with M.E.K. and is not related to his current position. D.W.L. received funding from and is a scientific advisory board member of Aeovian Pharmaceuticals, which seeks to develop novel, selective mTOR inhibitors for the treatment of various diseases, including diabetes. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the U.S. Department of Veterans Affairs, or the United States Government. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.


**6. Conclusion**

The folkloric use of medicinal plants has been justified by their constituents of active principles. However, the safety attributable to these natural products is overestimated. There are harmful substances found in plant materials as a result of natural processes. Unfortunately, these processes are reflecting an ecosystem situation and exposure that is far from that of a pristine environment. However, these facts seem to be overshadowed by the views of the "naturalists" who understand nature as pristine, pure, and harmless. Exposure to these natural toxins through herbal preparations and its attendant risks to human health may surge higher under this assumption that if a compound is natural, it is automatically safe. Caution should, therefore, be taken in the use of medicinal plants for treatment of various health conditions against the backdrop of poor regulation of herbal medicines.

We recommend the scientific determination of the safety profile of medicinal plants before use in therapy. In addition, appropriate regulatory agencies should intensify the monitoring of these phytomedicines to ascertain that they meet set regulatory standards, as their being natural does not mean being safe.

### **Acknowledgements**

The authors acknowledge Nnamdi Azikiwe University, Awka, Nigeria, for making her library facilities accessible to the authors for the accomplishment of the book chapter.

*Natural Does Not Mean Safe DOI: http://dx.doi.org/10.5772/intechopen.104732*


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