Pole placement of FBA‐23 in 50 Hz and 1 g
\r\n\tThis book will be very useful for a wide variety of readers, such as Chemists, Pharmaceuticals, Biochemists, Biotechnologists, Industrialists, Engineers, Researchers, Teachers and Students.
",isbn:"978-1-78984-072-8",printIsbn:"978-1-78984-071-1",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"ff1acef627b277c575a10b3259dd331b",bookSignature:"Dr. Martin Alberto Masuelli",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8504.jpg",keywords:"Extraction, Purification, Characterization, Acid Hydrolysis, Basic Hydrolysis, Freezing/Thawing, Separative Processes, Clarification, Leaching, Drying, Lyophilized, Applications",numberOfDownloads:1994,numberOfWosCitations:0,numberOfCrossrefCitations:2,numberOfDimensionsCitations:2,numberOfTotalCitations:4,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 5th 2018",dateEndSecondStepPublish:"November 26th 2018",dateEndThirdStepPublish:"January 25th 2019",dateEndFourthStepPublish:"April 15th 2019",dateEndFifthStepPublish:"June 14th 2019",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,editors:[{id:"99994",title:"Dr.",name:"Martin",middleName:"Alberto",surname:"Masuelli",slug:"martin-masuelli",fullName:"Martin Masuelli",profilePictureURL:"https://mts.intechopen.com/storage/users/99994/images/system/99994.jpg",biography:"Martin Masuelli is a Inv. Adj. of Instituto de Física Aplicada – CONICET – UNSL and Associate Professor-UNSL, San Luis, Argentina. He holds a master’s degree and a PhD and Master thesis in Membrane Technology from National University of San Luis (UNSL). He is Director of Physics Chemistry Service Laboratory, UNSL. He is expert in polysaccharides and physics chemistry of macromolecules. He is author or co-author of more than 26 peer-reviewed international publications, six book chapters, 70 communications in national and international congresses, editor of five books. He is a member of the Sociedad Argentina de Ciencia y Tecnología Ambiental and Asociación Argentina de Fisicoquímica y Química Inorgánica. He is Editor in Chief and Founder, in July 2013, of Journal of Polymer and Biopolymers Physics Chemistry, Science and Education Publishing. He is editorial board of numerous Journals.",institutionString:"Universidad Nacional de San Luis",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"National University of San Luis",institutionURL:null,country:{name:"Argentina"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"5",title:"Agricultural and Biological Sciences",slug:"agricultural-and-biological-sciences"}],chapters:[{id:"69850",title:"Extraction and Characterization of Pectins From Peels of Criolla Oranges (Citrus sinensis): Experimental Reviews",slug:"extraction-and-characterization-of-pectins-from-peels-of-criolla-oranges-citrus-sinensis-experimenta",totalDownloads:52,totalCrossrefCites:0,authors:[{id:"99994",title:"Dr.",name:"Martin",surname:"Masuelli",slug:"martin-masuelli",fullName:"Martin Masuelli"}]},{id:"65638",title:"Pectins as Emulsifying Agent on the Preparation, Characterization, and Photocatalysis of Nano-LaCrO3",slug:"pectins-as-emulsifying-agent-on-the-preparation-characterization-and-photocatalysis-of-nano-lacro3",totalDownloads:115,totalCrossrefCites:1,authors:[null]},{id:"66671",title:"Extraction and Purification of Pectin from Agro-Industrial Wastes",slug:"extraction-and-purification-of-pectin-from-agro-industrial-wastes",totalDownloads:651,totalCrossrefCites:0,authors:[null]},{id:"65895",title:"Flavonoids and Pectins",slug:"flavonoids-and-pectins",totalDownloads:157,totalCrossrefCites:0,authors:[null]},{id:"66458",title:"Pectin - Extraction, Purification, Characterization and Applications",slug:"pectin-extraction-purification-characterization-and-applications",totalDownloads:3,totalCrossrefCites:0,authors:[null]},{id:"65793",title:"Role of Pectin in Food Processing and Food Packaging",slug:"role-of-pectin-in-food-processing-and-food-packaging",totalDownloads:834,totalCrossrefCites:1,authors:[null]},{id:"66396",title:"Properties of Wine Polysaccharides",slug:"properties-of-wine-polysaccharides",totalDownloads:192,totalCrossrefCites:0,authors:[{id:"207851",title:"Dr.",name:"Zenaida",surname:"Guadalupe",slug:"zenaida-guadalupe",fullName:"Zenaida Guadalupe"},{id:"207853",title:"Dr.",name:"Belén",surname:"Ayestarán",slug:"belen-ayestaran",fullName:"Belén Ayestarán"},{id:"207854",title:"Dr.",name:"Leticia",surname:"Martínez-Lapuente",slug:"leticia-martinez-lapuente",fullName:"Leticia Martínez-Lapuente"}]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"278926",firstName:"Ivana",lastName:"Barac",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/278926/images/8058_n.jpg",email:"ivana.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"2828",title:"Fiber Reinforced Polymers",subtitle:"The Technology Applied for Concrete Repair",isOpenForSubmission:!1,hash:"4922c593466cc822b281fe7cc7d7fef6",slug:"fiber-reinforced-polymers-the-technology-applied-for-concrete-repair",bookSignature:"Martin Alberto Masuelli",coverURL:"https://cdn.intechopen.com/books/images_new/2828.jpg",editedByType:"Edited by",editors:[{id:"99994",title:"Dr.",name:"Martin",surname:"Masuelli",slug:"martin-masuelli",fullName:"Martin Masuelli"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6418",title:"Hyperspectral Imaging in Agriculture, Food and 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Chan and Manoj Kumar Tiwari",coverURL:"https://cdn.intechopen.com/books/images_new/3794.jpg",editedByType:"Edited by",editors:[{id:"252210",title:"Dr.",name:"Felix",surname:"Chan",slug:"felix-chan",fullName:"Felix Chan"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"53385",title:"Earthquake Instrumentation",doi:"10.5772/66215",slug:"earthquake-instrumentation",body:'\nEarthquake detection instrumentation is widely used in many industries to secure the system, structure and components. In addition, early warning of incident seismic wave is critical to safety of human lives. On 12 September 2016, South Korea experienced the most powerful earthquake ever recorded in the country since measurements began in 1978. A 5.8‐magnitude earthquake struck the historic city of Gyeongju and the people were subjected to a series of aftershocks affecting their daily lives [1]. Buildings built inside the historic district were not strong enough to sustain seismic incident despite its low destructive power. Luckily, since the frequency of the wave was higher than 16 Hz, a catastrophic falling down of buildings was not experienced. This tells the relationship among magnitude, wave frequency and the destructive power. Earthquake detection instrumentation has been improved to detect the magnitude and frequency effectively. Historically, the first seismic instrument was invented in Han dynasty. Modern instruments are based on electronic sensors which are able to detect tri‐axial acceleration.
\nIn order to investigate both theory and application of earthquake instruments, the mathematical expression of the instrument is defined by the motion of the mass‐spring‐damper system. Then the differential equation resulting from the equilibrium of forces is discussed. In addition, the transfer functions of both force balanced and servo balanced and feedback circuit are analysed. Finally, the application of earthquake instrument in the nuclear power plant is introduced.
\nLet us define a single seismic instrument having mass, spring and dashpot as shown in Figure 1. A dashpot is a device that provides viscous friction or damping [2]. The dashpot absorbs energy. This spring‐mass‐dashpot system can be expressed using Newton\'s second law as represented in Eq. (1):\n
The mathematical expression of the system above can be expressed as Eq. (1). The transfer function of Eq. (1) can be obtained as Eqs. (2) and (3) by taking Laplace transformation.
\nThe motion of the mass as a function of the ground displacement is given by a differential equation resulting from the equilibrium of forces as shown in Eq. (4) [3].\n
where Fs = -kx (k is the spring constant);
\nFr = -bx (b is the friction coefficient);
\nFg = -mu (m is the spring mass).
\nWhen the seismic instrument moves slowly, the acceleration and velocity become negligible, it records ground acceleration. If however, movement is fast enough, the acceleration of the mass dominates and the ground displacement is recorded.
\nFigure 2 shows the seismic instrument under ground motion. Where the ug is the ground displacement, xr is displacement of the seismometer mass and x0 is the mass equilibrium position.
\nSpring‐mass‐dashpot system.
Concept of seismic instrument under ground motion.
In general, the frequency of the seismic wave tends to be increased if the magnitude of the wave is getting smaller. The ground motion has very low‐frequency profile due to the movement of the earth.
\nThis study is focused on the plant industry application of the earthquake instrument. In this case, the detector covers DC to 50 Hz frequency components.
\nThe tri‐axial accelerometer has been widely used in industrial applications. An accelerometer is a sensor which can detect the earthquake‐induced motion. The tri‐axial accelerometer has a coil in the sensor that is suspended to it. When the accelerometer detects the earthquake‐induced motion, the coil is moved and the voltage is induced proportionally to the variation of magnetic force. This voltage is converted to acceleration.
\nThe tri‐axial accelerometer detects incident seismic waves from longitudinal (L), transverse (T) and vertical (V) direction using three identical sensors as illustrated in Figure 3. In general, each accelerometer has a feedback amplifier in consideration of the stability of the sensor output. Moreover, an added RC filter eliminates the high‐frequency component from the output. The accelerometers are installed at free‐field where the earthquake ground motion is detected by the surface motion.
\nConcept of tri‐axial accelerometer.
An accelerometer detects low‐frequency component from the earthquake ground motion. Two measurement principles are used to detect a strong motion earthquake requiring high sensitivity: open‐loop type or closed‐loop type with a feedback loop. In the plant industry, two sensor types have been used; force balanced acceleration using a pendulum, and servo acceleration with a feedback loop.
\nIn this section, the FBA‐23 (by Kinemetrics Inc.) accelerometer is analysed to provide a more detailed operational theory of pendulous type servo sensors. As seen in Figure 4, FBA‐23 consists of three‐axis FBAs (force balanced accelerators) for detecting incident seismic waves in (L), (T) and (V) directions. Each accelerometer is identical, however, the vertical one has an active oscillator circuit as an additional function.
\nSimplified circuit of FBA‐23 [4].
The electrical circuit of FBA‐23 has a non‐inverting amplifying circuit for adjusting the polarity of the output signal. The transfer function of the accelerometer can be expressed by Eq. (5).\n
where
The solution of Eq. (5) can be denoted by the second order equation having two poles (
The circuit has sensitivities of 0.25, 0.5, 1, 2 and 4 g full scale. This corresponds to normal absolute sensitivity at DC.
\nThe second order transfer function in Eq. (5) has natural frequencies of 50, 90 or 100 Hz. In addition, if we consider the damping factor of 0.707, then the two poles (
From Eq. (8), the natural frequency and damping factor of this instrument can be calculated as Eqs. (9) and (10) respectively.\n
The poles are then calculated as, P1 = -222.1 + j222.1 rad/s and P2 = -222.1 - j222.1 rad/s. Therefore, the resultant transfer function of this system becomes Eq. (11)\n
Figure 5 shows the Matlab Simulink results when the step input is applied. The corresponding plot described in Figure 6 shows overshoots and it needs relatively reduced settling time. In this simulation, the poles have 50 Hz of natural frequency and 1 g of absolute gain.
\nMatlab Simulink diagram (without RC filter).
Step response (without RC filter).
Since this transfer function may be affected by the zeros, therefore by adding low pass RC filter to the post amplifier, the stability and accuracy of this circuit would be improved. In this case, the RC filter adds one more pole as summarized in Table 1. This proves that the calculated poles are placed in the left half of the s‐plane as well.
\n | P1 | \nP2 | \nP3 | \n
---|---|---|---|
Real (rad/s) | \n−222.1 | \n−222.1 | \n−1000 | \n
Imaginary (rad/s) | \n222.1 | \n−222.1 | \n0 | \n
Pole placement of FBA‐23 in 50 Hz and 1 g
The full transfer function of the electrical circuit in 50 Hz has three poles as shown in Eq. (12).\n
Figure 7 shows the Matlab Simulink results when a step input is applied. Figure 8 also shows almost the same plot disregarding the existence of the RC filter.
\nMatlab Simulink diagram (with RC filter).
Step response (with RC filter).
In this section, the servo acceleration with a feedback loop type accelerometer (AC‐23 by Geosig), which consists of geophone and electrical circuits, is discussed. The geophone response can be seen from two viewpoints:\n
With a constant velocity, amplitude applied to the geophone.
With a constant acceleration, amplitude applied to the geophone.
Figure 9 represents the response curve of Geophone at 4.5 Hz.
\nGeophone response curve (4.5Hz) [5].
The relationship between these two points of view is a rotation when plotted with log‐log axis scales. In Figure 10, the principle of Geophone is drawn. When the motion on the x‐axis is incident then Geophone generates the open‐loop signal which has peak response of 4.5 Hz.
\nPrinciple of Geophone [6].
The geophone is a long coil travel version and the extra coil travel offers an advantage for higher tilt requirements where larger amplitude signals may be encountered. A range of natural frequencies is available from 4.5 Hz to 14 Hz, providing a choice of the correct geophone for a wide variety of applications. The geophone is suitable for detecting an extensive range of ground motion.
\nThe AC‐23 provides over‐damping the geophones with a feedback amplifier in a bridge circuit. The principle of over‐damping geophones is done by applying a voltage on the geophone, which has opposite polarity from the voltage, which is induced by the moving geophone coil. Since the voltage induced by the geophone coil is proportional to the velocity, the externally applied voltage is also proportional to it. This results in a current in the coil (∼force), which is also proportional to the velocity and therefore is a “damping” current, or additional damping. Increasing this damping further will lead to a resulting output that is proportional to acceleration.
\nThe function of the sensor, seen from a constant acceleration point of view is illustrated in Figure 11.
\nOperational principle of AC‐23 [6].
The geophone is connected in a resistor bridge, driven by a feedback amplifier and an inverter, which apply the amplified bridge differential signal in opposite polarity [6]. The amplifier has a fixed gain that will define the bandwidth of the accelerometer. The gain G and output Vo are represented by Eqs. (13) and (14). Figure 12 shows the equivalent circuit for the linear servo‐balanced accelerometer.\n
Equivalent circuit for AC‐23 (linear servo balanced accelerometer) [6].
In Figure 12, the test‐line shifts the voltage to one side of the bridge, which produces a current flow in the geophone, resulting in a displacement step of the seismic mass. The movement of the mass generates a voltage across the Geophone, which is detected by the differential amplifier and induces an output signal. The effect of the test signal on the bridge is cancelled by the differential input of the amplifier.
\nIn this part, the Geophone transfer function transfer is analysed in order to define how the sensor generates open‐loop transfer function as depicted in Eq. (13).\n
The transfer function in Eq. (15) has two poles and one zero. In this work, the MATLAB script of Geophone is used to illustrate the response curve under step input condition as shown in Figure 13.
\nOpen‐loop transfer function of Geophone.
The transfer function plotted as the frequency range vs. relative response to the acceleration is shown in Figure 14.
\nIf a unit step function is applied to the transfer function, the response as in Figure 15 is obtained.
\nIn this case, the magnitude and the phase can be drawn by the Bode plot as seen in Figure 16.
\nTransfer function plotted by the frequency vs. relative response to the acceleration.
Step response.
Bode plot.
In this chapter, two application examples for the NPP (nuclear power plant) are discussed with their designs. They are SMS (seismic monitoring system) and ASTS (automatic seismic trip system). SMS detects the seismic wave and provides alarms to the NPP operators. The operator then makes a decision whether or not to trip the nuclear reactor. ASTS has an automatic trip function, if the peak acceleration of the incident wave exceeds, the pre‐determined level trigger the reactor trip automatically. In the protection view‐point, SMS provides alarms for the manual trip while ASTS provides automatic protection.
\nThe SMS consists of tri‐axial accelerometers and a seismic monitoring cabinet which is located in an electrical equipment room in unit one (only one unit). The cabinet contains recorders, a playback and analysis unit, an annunciator, a seismic switch, test units and an uninterruptible power supply.
\nThe signal flow from the accelerometer to the seismic monitoring system is shown in Figure 17. From the accelerometer, the voltage signal is transmitted to the seismic monitoring cabinet and is converted by electronics into a signal proportional to the acceleration. The acceleration signal can activate recorders and/or local and MCR annunciators/alarms according to its magnitude.
\nWhen the acceleration of earthquake exceeds the seismic trigger or set‐points for OBE (Operating Basis Earthquake or Event) or SSE (Safe Shutdown Earthquake), the alarm is provided on the local annunciator panel of the seismic monitoring cabinet and that of large panel display system (LDPS) in main control room or alternatively in the remote shutdown room. That event also activates the time‐history recorder(s) in the seismic monitoring cabinet.
\nSMS block diagram.
ASTS continuously monitor PGA (peak ground acceleration) from the seismic wave and automatically generates the trip signal. As shown in Figure 18, the seismic signal detected by the accelerometer is filtered, rectified and converted to the current. The sensor module is to cut off the frequency ranges over 10 Hz in order to pick out the strong motion of the earthquake. Since the seismic signal has a positive and negative signal, a rectifier circuit is added. The rectifier converts the bipolar signal to the unipolar.
Conceptual diagram of sensor module for signal processing [7].
The overall scheme of ASTS is shown in Figure 19. The sensor output is interfaced with digital input card at the ASTS cabinet. Even the decision logic is set as two out of four but the system is composed of two independent channels. They are N1 and N2. For isolation between the channels, the digital input/output card is applied. The dotted line on the ASTS cabinet is the scope of trip logic channel N1 and N2. The ASTS cabinet is implemented by digitalized system such as PLC (programmable logic controller), FPGA (field programmable gate array), or DCS (distribute control system). The bi‐stable and decision logic are configured with software inside each channel.
\nOverall scheme of digitalized ASTS [7].
The operational principles are as follows. When the measured signal exceeds the set‐point, the edge triggering happens and the status is set to ‘0’ by comparator actuation. In this case, the latch is engaged due to the edge triggering, and keep the status for 10 s. The purpose of 10‐s latch is to synchronize the individual channel for the coincidence logic. If the signals are not properly synchronized, the trip initiation signal may not be actuated during a strong earthquake. ASTS consists of four diverse channel applications. The bi‐stable signal from each channel feeds into two‐out of‐four coincidence logic for generating trip initiation signal.
\nIn this work, two measurement principles are analysed to obtain the transfer function plotted by the frequency vs. relative response to the acceleration as well as the step responses. In order to define the sensitivity of open‐loop type or closed‐loop type with a feedback loop, two sensor types have been used; the force‐balanced acceleration and the servo acceleration. The open‐loop and closed‐loop response when the step input describes the transfer function by plotting it with amplitude and time. The open‐loop geophone shows exponentially decreasing function while the force balanced type illustrates overshoot before it is stabilized.
\nTwo application examples for the NPP are discussed with their designs in this work. They are SMS and ASTS. When the Gyeongju earthquake occurred, the main concern in the NPP, whether the peak ground acceleration exceeded in OBE or SSE, was highlighted. The second issue was the frequency range determining the manual or automatic reactor trip. ASTS eliminates frequency over 10 Hz for generating a trip signal. Through Gyeongju earthquake the adequacy of this theory has been proved. The third issue that arose in ASTS design was a latching time of 10 s to synchronize the individual channel for the coincidence logic. This issue needs to be resolved by analysing the seismic waves in Gyeongju.
\nSmartphone-based analysis has recently emerged as a useful tool, and it has been found to be promising in several fields including point-of-care analyses [1], chemical and biological sensing [2], microscopy and healthcare diagnostics [3], water quality sensing for environment [4, 5, 6], leaf color analysis for agriculture [7], pH [8, 9] and glucose [10] sensing, fluorescent imaging [11], imaging cytometry [12], electrochemical sensing [13], and immunoassays [14].
With recent advances on camera and sensor technologies, current smartphones are equipped with a low-power high-performance processor with up to 2.5 GHz operating frequencies; built-in high-resolution digital camera, generally above 5 Mpixels and up to 40 Mpixels; and built-in single or dual LEDs, which allow capturing an image even in low-light conditions [15]. Moreover, they are provided with advanced onboard sensors such as moisture sensors, proximity sensors, electromagnetic compasses, accelerometers, and gyroscopes [16]. These sensors generally provide fast response, being portable and of low cost, and the ability to be used in the field without extensive training [17]. Therefore, smartphones have become a tool as powerful as low-cost computers, which lead them to be valuable instruments in the analysis.
Over the last decade, smartphones have been increasingly used in a variety of scientific fields as spectrometers [18, 19, 20, 21, 22] and colorimeters [23, 24, 25]. Smartphone spectrometers use the wavelength components, which give spectral information, of the collimated light from the optical source which is dispersed after interaction with samples [18]. The color spectrum image is transformed into various color spaces for the extraction of quantitative data. The wavelength of the spectrum generally changes between 400 and 700 nm because of the optical filters set in front of the camera in the manufacturing process. Spectral information has been used in many applications including water monitoring [6], gas detection [26], and food quality control [27]. Smartphone colorimeters are commonly used instruments that quantify the concentration of the samples based on color changes due to concentration (like peroxide amount [28]) or time (methylene blue degradation [6]). In the colorimetric analysis, features of the referenced images need to be extracted for training the system, mostly created with machine learning or neural networks, which perform the quantitative analysis for the test images. Smartphone colorimeters have applications in both solid samples like paper-based test and liquid samples like colored solutions [29]. Both smartphone spectrometers and colorimeters are powerful tools for rapid qualitative and quantitative analyses due to the fact that they can be used in conditions where sophisticated tools or time-consuming steps cannot be used. They are rapid and low-cost tools that require less sample consumption and provide portability to perform the analysis in remote locations or locations with poor infrastructure [17].
While smartphones offer an attractive alternative to sophisticated tools for imaging and analysis in the field, there are some concerns about their suitability for quantitative analysis [1]. First, unlike scientific cameras, smartphone cameras have mostly limited control of camera parameters like exposure time, shutter speed, ISO, and color balance, and no access to raw image data which has a linear relationship with scene radiation. In addition, image processing algorithms, such as demosaicing, noise reduction, edge sharpening, white balance, and image compression, are applied automatically and vary significantly across smartphones. These methods corrupt the linearity of the pixel intensity values which causes loss of information that can be used in quantitative analysis. So, it is difficult to set and maintain imaging parameters to get accurate and repeatable information for analysis [30]. Second, ambient light conditions are hard to control during imaging in uncontrolled environments. Last, small color changes cannot be detected in an analysis as the red, green, and blue (RGB) intensity values may not be sufficient. These aforementioned concerns make smartphones questionable for quantitative analysis. Early studies in smartphone-based colorimetric analysis for medical and scientific applications pointed out these concerns and concluded that smartphones are incapable of pathology [31] or limited due to their image quality [32].
However, subsequent researchers have proposed a smartphone camera-based microscope which captures qualitatively relevant features of malaria and tuberculosis [33]. These counter conclusions and also advancements in camera and sensors together with the increasing capability in computer processing prove that smartphones are more portable, cost-efficient, and user-friendly platforms which make them alternatives to sophisticated and high-cost devices in quantitative analysis.
For instance, it was shown that a smartphone by itself is capable of quantifying colorimetric test strips without any external attachments [1, 34]. In [35], water monitoring was implemented with a simple holding attachment. With the help of 3D printing, housing and optical components were integrated into a colorimetric plate reader for enzyme-linked immunosorbent assay (ELISA) [36]. A 3D printed custom cradle including various optical components and an external broadband source, was demonstrated in [37] for biomarker absorption analysis. To detect miRNA sequences in a liquid-based assay, a smartphone-based fluorimeter system was designed using an external laser as the source [21]. Due to precise light confinement and flexible nature, optical fibers were integrated to smartphone spectrometer and applied for food quality monitoring [18]. To have more portable and cheap spectrometers, new designs were proposed without external electrical and optical components. A fiber optical bench was assembled on top of the smartphone without external LEDs to design a surface plasmon resonance-based refractive index sensor [38]. A spectrometer system was reported in [10] to detect glucose and troponin-I using built-in flash as a light source and a compact disk for the reflection grating.
In the colorimetric analysis, color information could be obtained with paper-based sensors to quantify the color variation in different color spaces such as RGB, HSV, and L*a*b* [24, 39, 40, 41]. In [25], alcohol concentration in saliva was detected using paper-based test converting images from RGB to HSV color space. This was the conventional approach which needs only a smartphone camera to capture and process an image. Besides the conventional approach, non-conventional approaches were applied on liquid samples in vials for detection of chlorine in water [42], and ripeness estimation of fruits [7]. Here, quantification was calculated using analytical formulas extracted from color space parameters. However, it is prone to deviation due to the disadvantages of JPEG images such as low bit depth and heavy post-processing (white balance, contrast, and brightness adjustment) [43, 44]. On the other hand, it was shown in [28, 41] that JPEG images could be used in the colorimetric analysis when advanced algorithms like machine learning were used to process the images. Unfortunately, advanced algorithms need more computational power as they need much larger datasets for training and testing the images. To address this issue, it was reported to use local database referenced with a single image for the quantification of the concentration level of solutions [45]. The proposed design was applied on nitrite, phosphate, chromium, and phenol solutions to quantify the concentration value using a single reference image which was captured and processed initially.
In literature, there are many lab-on-a-chip designs proposed for various applications [17, 46]. Bisphenol-A (BPA) detection in distilled and commercial water samples was demonstrated in [29] where a plastic fiber-based smartphone spectrometer with a custom-designed immersion probe and a cradle were used. The smartphone spectrometer was converted to a reflection probe spectrometer working within the visible spectrum. Explosive types were detected in [17] with the paper-based test using hierarchical clustering analysis and principal component analysis (PCA) regarding the color discrimination of the explosives. A closed chamber was used to eliminate ambient light conditions during imaging. Linear correlation and PCA methods were employed, respectively, for univariate and multivariate analyses [46]. It was reported that univariate analysis did not give statistically significant results due to ambient light conditions as imaging was not performed in a controlled environment. However, multivariate analysis gave promising results running PCA methods on eight different color spaces and clustering methods including red, green, blue, hue, saturation, value, lightness, and intensity parameters. A smartphone-based colorimetric reader for ELISA was proposed in [47] where imaging was performed in a controlled environment illuminated from the bottom. Ozcan and his research group [12, 14, 48, 49, 50, 51] had many designs for specific applications such as food allergen testing [48], urine [14] and blood [12] analysis, immunoassays [49], microscopy [50], and cytometry [51].
The rest of this chapter is organized as follows. The next section introduces the hardware designs for smartphone-based spectrometer and colorimetry. Section 3 presents the mobile apps and image processing algorithms to be used in smartphones. Section 4 describes assay preparation to be used in testing the performance of both spectrometer and colorimeter. Section 5 details the metric to evaluate the performance of the proposed designs. Closing remarks are given in Section 6.
In this section, a brief review of hardware designs of smartphone spectrometer and colorimetry is presented. Because of space constraints, only two examples of spectrometer and colorimetry are demonstrated in Section 2.1 and 2.2, respectively.
Recently, there has been a growing variety of spectrometer designs for specific applications. In [19], a compact imaging spectrometer was reported equipped with motorized selfie stick for remote sensing. Another spectrometer was designed to calculate spectra and quantify analytes by the assembly of medium-density-fiberboard, a DVD slice for diffraction grid, and mini incandescent lamps [20]. A flexible fiber bundle probe was integrated with a custom-designed cradle to convey spectra data to smartphone camera for food quality monitoring [18].
The next design [6] is illustrated in Figure 1a, which is a low-cost, portable, plastic fiber-based spectrometric smartphone to analyze dye adsorption for field-deployable environmental and wastewater management. In the design, the rear camera of LG G4 (1/2.6″ sensor size with 5312 × 2988 resolution, 1.12 μm pixel size) was used to collect the spectral data of the assays.
(a) The smartphone spectrometer with the inset (top-left) of a plastic fiber assembled into the built-in flash. (b) The spectral images from different-sized plastic fibers are shown.
The smartphone was fitted into a custom-designed cradle assembled with hot-plug apparatus toting a diffraction grating, and the whole part was connected with smartphone case and cuvette holder manufactured from Acrylonitrile Butadiene Styrene polymer using a 3D printer (Zortrax M200) with a 150-g polymer. Two pieces of plastic fiber cables were used in the design. The first 1.5-mm-diameter fiber carried the light from the smartphone flash to the cuvette while the second fiber cable with a diameter of 0.25 mm transmitted the light from cuvette to the camera which passed through the assay. The diameter of the second cable was critical as the light for spectral data was carried with this cable. Therefore, the effect of diameter on spectral data was analyzed using 0.25-, 0.5-, and 1.0-mm cables as given in Figure 1b, and 0.25-mm diameter was found to be adequate based on this experiment. A custom cradle was specially designed to align plastic optical fibers with smartphone optical components. As the cradle was solid, the solution could be placed into the cuvette slot. In order to simplify the spectrometer system design, no collector lens or mirrored components were placed in the light path. Besides cost, the most important factor in choosing plastic optical fibers instead of glass-based fiber optics was the ability to use plastic optical fibers without special tools for stripping and cutting.
To test the performance of the system, methylene blue (MB) solutions were prepared with different amounts and their respective spectral views are given in Figure 2. At the top row, the concentration of the solution varies from 0 ppm (most left), which corresponds to distilled water (DW), to 10 ppm (most right). The spectrum views are given at the bottom row where the reduction in red intensity with the concentration is quite visible.
MB solutions with a spectral view from 0.1 to 10 ppm.
This design was further improved to make it compatible with immersion probe, which made it more practical and user-friendly as illustrated in Figure 3 [29]. Schematic diagram of the 3D printed cradle is described in Figure 3a. The immersion probe was attached to the fiber-coupled smartphone flashlight and the reflection caused by radiation is carried to the camera via the grating. Plastic (PMMA)-based bifurcated fiber bundle was used to manufacture the probe with the diameter of 0.5 mm (also known as Y-cable) as shown in Figure 3b. The overall design is illustrated in Figure 3c. It was reported that no additional optical components were used in the reflection-based smartphone spectrometer system. The spectrum views of blank solution (left) and 5-ppm BPA solution (right) are given in Figure 3d to demonstrate color variation can be detected with the spectrometer system.
(a) The custom-designed cradle for smartphone-based spectrometer. Immersion probe for absorbance measurement is given in (b) and overall design is illustrated in (c). (d) The spectral images obtained from smartphone spectrometer.
As an alternative to spectrometric analysis, colorimetry is also widely used in many applications including food allergen testing [48], albumin testing in urine analysis [14], blood analysis [12], pH quantification [41], and water monitoring [45].
A digital tube reader designed in the 3D printer was equipped with two interchangeable LEDs to illuminate the test and control tubes so that the absorption spectrum of the colorimetric assay could be analyzed [48]. An albumin tester platform was proposed in [14] using an optomechanical attachment aligned with a smartphone camera. The 3D printed cradle was integrated to a compact laser diode, two AA batteries, a plastic lens, and an emission interference filter. An albumin-based fluorescent signal was obtained from the test tube by a digital fluorescent tube reader to calculate the albumin concentration values after comparison with a control tube. In [12], blood analysis was implemented with an integration of red blood cell counting, white blood cell counting, and hemoglobin measurement devices to smartphone cradle.
Smartphone-based colorimetric detection of pH, which varies between 0 and 14.0, was investigated with paper-based test [41]. The performance of the system was tested under two conditions: controlled and ambient illumination environments. To create controlled illumination settings, 3D printed cradle was equipped with apparatus which eliminates the interference of the present light as shown in Figure 4a. Four strips of same pH level were located side by side for imaging with an apparatus, then color calibration and white balancing were performed for those strips with the X-Rite ColorChecker Passport. The imaging was continued with replacing the strips in six different orientations as in Figure 4b.
The overall smartphone-based colorimetry with apparatus and X-rite ColorChecker passport for color correction are shown in (a). The pH strips with various orientations used in imaging are given in (b), and (c) shows random orientations and positions of the test strips inside the smartphone field of view for dual-illumination tests.
Later, random orientations as shown in Figure 4c were used to mimic scenarios that could happen when untrained users take a picture. The reason for using a group of four strips is to see the effect of luminance variation due to their positioning with respect to the camera flash.
For the ambient illumination environments, no apparatus was used, and instead of using the smartphone flash as a light source, sunlight and fluorescent and halogen sources were used. To test the system under challenging conditions, the light sources were used in solo and in dual and triple combinations. The images captured in both controlled and ambient illumination environments with a different replacement of the strips were used to train the machine learning algorithm which was designed to quantify the pH values accurately. Since the training set was enriched with the images captured in various and complicated scenarios, it was reported that pH values were detected with 100% accuracy.
One possible drawback of the colorimetry method in [41] is its computational cost due to its large training dataset. To address this issue, single-image referenced colorimeter was proposed in [45]. The system was simplified in the sense of both hardware and software design. Instead of using machine learning algorithm which needs a large dataset, it used local dataset created by a user with a single reference image. In addition, images from the local dataset were compared with test images using color-matching algorithms computationally cheaper than machine learning algorithms. The hardware design was also simplified into a cardboard box as shown in Figure 5. It was painted white, and white light-emitting diodes were mounted to the box ceiling to minimize the ambient illumination effects. A holder platform with the same height as the camera was placed for the assays to maintain the same distance for imaging. The system was tested on four different (nitrite, phosphate, chromium, and phenol) assays, and it was reported that the performance accuracy was between 76 and 100% depending on the assay types.
Experimental setup proposed in [45].
The previous section described the hardware designs for smartphone-based spectrometry and colorimetry. This section presents mobile applications and algorithms proposed for these designs.
Software applications are necessary tools due to complementary characteristics for the hardware designs of spectrometer and colorimetry. Mobile apps were therefore developed to make the overall system user-friendly [14, 15, 25, 28, 36, 45, 46, 52].
Albumin Tester [14] application was developed for Android phones to let the user determine the albumin concentration in the urine sample. To test alcohol in saliva, SPAQ [15, 25] application was developed which estimated the alcohol level based on the histogram distribution. Colorimetric Plate Reader app [36] was proposed for qualitative and quantitative ELISA test. PhotoMetrix [46] application was introduced, which runs the univariate and multivariate analyses to quantify the analytes in the samples. Colorimetric Test Reader app was presented in [52], which determines pH, protein, and glucose values in the assay.
Solmaz et al. [28] developed ChemTrainer app which quantifies the peroxide content running machine learning algorithm on the remote server as shown in Figure 6a. After capturing a photograph of colorimetric test strips, the app sends mean RGB values for a region of interest to the remoter server as machine learning algorithm needs only mean values for the classification. A message queue service was employed to enable multiple users to reach the server simultaneously.
The communication between the smartphone and a remote server is illustrated in (a) and the developed ChemTrainer app is presented in (b).
Screenshots of the ChemTrainer are given in Figure 6b. In the opening page, there are two options for the user: either capturing a new image with “Experiment” button or using existing image from the phone with the “Load From Gallery” button. After an image is captured or loaded from the gallery, the user may proceed or retake a new image. Next, a region of interest needs to be cropped with adjustable crop box. The app calculates the average red, green, and blue values of the cropped image and sends to the server, which runs a classification algorithm that decides the class of image. In the meantime, the app displays a progress animation until the result comes back from the server.
The ChemTrainer app was further improved to be able to work with single-image reference (SIR) and named as ChemTrainerSIR [45]. In addition, it gained additional features like saving location and time data for the previous experiments, which help the user to analyze the past results when needed. The ChemTrainerSIR app is described in Figure 7 with screenshots. There are “train” and “experiment” options on the home page as shown in Figure 7a. Training steps are introduced in Figure 7 from b to g (top row), while testing steps are described at the bottom row from h to m. If the user selects the train option, some initial information needs to be entered such as the name of the chemical compound as the name of the model (e.g., phosphate), the units of measurement (e.g., ppm), and the number of samples with known concentration levels. The user either captures an image of samples or loads from the gallery. Then, the user enters the reference values for each sample which will be used later in the testing phase. All these information are stored in a designated folder in internal storage. In the testing phase, the user first selects the model, which was used in training. Then, a new image is taken to quantify the concentration value based on comparison with the reference model.
Opening page of the ChemTrainerSIR app is given in (a). The top row (b–g) shows training steps, while the bottom row (h–m) presents the implementation of the app on the sample.
A digital image can be acquired either as monochrome (black and white) or color image using electronic equipment utilizing charge-coupled device (CCD) or complementary metal oxide semiconductor (CMOS) sensors.
These sensors use a two-dimensional array of millions of tiny light pixels to capture an image. These pixels collect photons and store them as an electrical signal after the shutter button of the camera is pressed which leads to the beginning of the exposure. The pixels are closed after the exposure finishes, and intensity value in the pixel is quantified as digital values by measuring the strength of the electrical signal, which is directly related with the number of photons stored in the pixel. However, this approach would only create a monochrome or gray scale image as the pixels are unable to distinguish photons in terms of color. A color filter needs to be placed over the pixel to capture a color image. This filter allows only one of the primary colors, that is red (R), green (G), or blue (B), to pass into the pixel, so that it stores only filtered photons for the respective color. In other words, the intensity of each pixel gives single color information which leads to a RAW image. Here, each pixel has only one of R, G, or B information while all R, G, and B values need to be known for each pixel. Therefore, demosaicing is applied to determine other two missing color values by interpolating from nearby pixels where those colors are known. After demosaicing, other methods such as white-balance, gamma correction, color space correction, and compression are applied to convert the image from RAW to a common format like JPEG.
JPEG images have a small size and they can be displayable instantly. However, there are some concerns because of the methods that are applied to convert an image from RAW to JPEG. In the conversion process, the image is compressed resulting in providing a non-linear RGB color space with only 8-bit color depth [43, 53]. However, RAW images contain original image data with 10–14 bits of color information. The conversion process corrupts the linearity of the image. A linear image conserves the relation between the intensity value and the number of photons which maintains the linearity with scene radiance. This linearity is required for quantitative scientific data acquisition in many applications [6, 30, 36, 37, 54]. As a linear image, the RAW format is therefore generally chosen. The main issue is how to reach a RAW image in smartphones. Although most semi-professional and professional cameras have access to reach the images in a RAW format, it is unconventional for smartphones. With recent developments, the latest smartphones offer access to images in the RAW format [55]. The RAW images could be found in three extensions such as “.NEF” (Nikon), “CR2” (Canon), and “.DNG.” The most used format is “.DNG” as it has a common open format. Currently, no app is available to process the “.DNG” images in a smartphone yet. Therefore, free DCRAW software [18] can be used to convert “.DNG” image to tagged image file format (TIFF) for easier extraction of the R, G, and B values of the image.
RGB is the most commonly used color model in image processing. However, it can be converted to other models such as hue, saturation, and value (HSV); hue, saturation, and lightness (HSL); hue, saturation, and intensity (HSI); and lightness, green-red, and blue-yellow (L*a*b*). Hue is defined with the color portion of the color model and described with a number from 0 to 360°. Saturation is defined with the amount of gray in the color, from 0 to 100%. Value, lightness, or intensity is the brightness or intensity of the color, from 0 to 100% where black is represented with 0 while 100 is the brightest.
After the image acquisition, numerous image processing methods can be employed to improve the image visualization so that better features can be extracted from the image. The feature extraction plays a critical role in some methods like PCA, convolutional neural network, and machine learning, which interpret multiple types of information contained in an image using these features [46].
The performance of these methods was investigated with RAW and JPEG image formats with different color spaces such as RGB, HSV, and L*a*b*. RAW and JPEG image formats were studied in [6, 29] after converting images from RGB to HSV. Absorbance experiments were employed based on V components of HSV and it was reported that RAW format outperforms the JPEG formats in absorbance measurements. On the other hand, [41] showed that JPEG images gave a similar performance with RAM image if least-squares support-vector machine (LS-SVM) was employed in creating the learning model. Based on this conclusion, RGB, HSV, and L*a*b* color spaces were investigated using JPEG formats for quantifying peroxide content based on machine learning classifiers [28]. JPEG images were also used in [45] where the images were converted from RGB to L*a*b* color space. Instead of machine learning algorithms, color matching algorithms such as deltaE and color correlation methods were employed due to their simplicity. It was reported that deltaE showed superior performance with L*a*b color space for colorimetric water quality detection.
In previous sections, various hardware and software designs were introduced for smartphone-based spectrometer and colorimetry. These designs need to be tested under the conditions that users may encounter in real life. In this section, strip and assay preparations are introduced, which are commonly used for water quality and field tests.
Colorimetric detection of pH values was studied in [41], which used pH strips to test their proposed system. First, solutions were prepared by mixing deionized water with sodium hydroxide (NaOH) and nitric acid (HNO3) to ensure the pH values in the range of 0–14.0. During the preparations, pH values were checked with a pH meter (HI 2223, Hanna Instruments, RI, USA) calibrated with standard buffers, pH 4.0 (HI 7004) and 7.0 (HI 7007) prior to using pH indicator strips (Merck, Germany). In addition, dual-illumination tests were performed with buffer solutions (4.0–9.0, Sigma-Aldrich, USA). Before imaging pH strips, they were immersed into the pH solutions for 5 s and wiped gently with tissue paper, so that light refraction caused by the liquid drops could be minimized.
Peroxide quantification with colorimetric tests was investigated in [28] and hydrogen peroxide (H2O2) solutions were prepared for the peroxide test strips (Quantofix Peroxide 100). First, a stock solution with 500 ppm concentration of H2O2 (Sigma-Aldrich) was prepared in distilled water. The stock solution was later diluted to prepare the initial concentrations such as 1, 3, 10, 30, and 100 ppm. The peroxide test strips were dipped into these solutions for 1 s, and images were taken by smartphone after they were dried on tissue paper for 5 s.
Bisphenol-A (BPA) detection with smartphone spectrometer was demonstrated in [29]. The BPA concentration was determined with absorbance measurements using an immersion probe. The phenolic compound was put into reaction with 4-Aminoantipyrine (4-AAP) (Sigma-Aldrich, >98%) and potassium ferricyanide (Carlo Erba) for colorimetric quantification. Around 200 ppm of BPA stock solution was prepared in ethanol and then test solutions ranging from 0.1 to 10.0 ppm were prepared by serial dilution from the stock solution. The pH of all solutions was set to 8.0 using 0.25 M sodium bicarbonate (NaHCO3) (Sigma-Aldrich, ≤99.7%) and distilled water. To finalize the solutions, 1.5 mL of 20.8 mM 4-AAP and 1.5 mL of 83.4 mM potassium ferricyanide solutions were mixed into 12 mL of BPA solutions. The solutions were ready for the absorbance measurement after 10 min of incubation.
Single-image-referenced colorimetric water quality detection in [45] was performed using four different analyte solutions. The first solution is nitrite
Quantitative performance evaluations of smartphone-based spectrometer and colorimeter are an important factor in the development of new algorithms and designs. Standard metrics for regression and classification problems can be used to assess smartphone-based system performance. The importance of metrics varies for each sensing scheme.
In a spectrometer, the absorbance spectrum needs to be calculated using multicolored images. RGB images are mostly converted to HSV images and value (V of HSV) is used to calculate the absorbance (A) using the Beer-Lambert law [56],
where I0 is the transmitted light intensity of reference solution (mostly distilled water), and I is the transmitted light intensity of the other solutions. After the absorbance graph is plotted with respect to wavelength, the reference wavelength point which gives the maximum absorbance of the reference solution is selected. Then, the calibration curve, which is basically the linear regression line, is plotted with respect to the reference wavelength point to calculate R2 (the coefficient of determination). R2 is the first metric to evaluate to assess the performance of the model. R2 values greater than 0.9 are acceptable values, although a larger coefficient is accepted as a more successful result. Next, evaluation term is the limit of detection (LOD) defined as the lowest quantity or concentration of an analyte that can be reliably detected with a given analytical method. It is calculated as three standard deviations above the reference solution. The slope of the calibration curve is the sensitivity of the spectrometer.
In classification-based colorimetry, the following metrics are available: classification accuracy, sensitivity (recall), specificity, precision, and f1-score. These metrics are the same in traditional machine learning classification tasks and can be extracted from the confusion matrix. Classification accuracy is detection accuracy in the case of analytical detection. For binary classification problems with only two classes, the receiver operation characteristic (ROC) curve and area under curve (AUC) are additional metrics. In a confusion matrix, rows represent the instances in an actual (true) class while columns represent the instances in a predicted class. To calculate the detection accuracy, diagonal elements of the confusion matrix are summed and divided by the total number of data points. Precision is calculated by the ratio of true positive events to the sum of true and false positive events as given below:
The sensitivity (recall) is the ratio of true positive to the sum of true positive and false negative:
Lastly, f1 score is the harmonic average of precision and the recall and is equal to 1 for perfect precision and recall:
Regression and classification metrics should be chosen based on the colorimetric detection scheme. Spectrometric detection requires the use of regression metrics while the detection of discrete color change should be assessed with classification metrics.
In this chapter, a review of smartphone-based colorimetric determination of chromogenic assays has been provided on color spaces, existing color matching and detection techniques, hardware and software designs, and performance metrics that have been developed over the past few decades.
After a broad survey of the smartphone spectrometers, a technical background of the methods for image acquisition system, image analysis, and measurement procedure, which are commonly used as baseline methods in the literature, was introduced with their basic mathematical, statistical concepts and definitions, which are required for understanding the mathematics and techniques behind the proposed colorimetric detection methods.
In addition, portable hardware designs compatible with smartphones and their Android applications were introduced with fundamental differences including physical setup, interfaces, and challenges.
Moreover, performance metrics were analyzed in order to see which aspects are considered more in the evaluation and impacts of these perspectives on the evaluation results.
The authors thank Dr. Gazihan Alankuş for mobile apps and the “Ekosfer Laboratory and Research Services” for their assistance with the colorimetric assays. This research was supported by the scientific research projects coordination unit of Izmir Katip Celebi University (project no. 2018-ÖDL-MÜMF-0021).
The authors declare no conflict of interest.
Edited by Jan Oxholm Gordeladze, ISBN 978-953-51-3020-8, Print ISBN 978-953-51-3019-2, 336 pages,
\nPublisher: IntechOpen
\nChapters published March 22, 2017 under CC BY 3.0 license
\nDOI: 10.5772/61430
\nEdited Volume
This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\\n\\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\\n\\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\\n\\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\\n\\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\\n\\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\\n\\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\\n\\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\\n\\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\\n\\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\\n\\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\\n\\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
\\n"}]'},components:[{type:"htmlEditorComponent",content:'This book serves as a comprehensive survey of the impact of vitamin K2 on cellular functions and organ systems, indicating that vitamin K2 plays an important role in the differentiation/preservation of various cell phenotypes and as a stimulator and/or mediator of interorgan cross talk. Vitamin K2 binds to the transcription factor SXR/PXR, thus acting like a hormone (very much in the same manner as vitamin A and vitamin D). Therefore, vitamin K2 affects a multitude of organ systems, and it is reckoned to be one positive factor in bringing about "longevity" to the human body, e.g., supporting the functions/health of different organ systems, as well as correcting the functioning or even "curing" ailments striking several organs in our body.
\n\nChapter 1 Introductory Chapter: Vitamin K2 by Jan Oxholm Gordeladze
\n\nChapter 2 Vitamin K, SXR, and GGCX by Kotaro Azuma and Satoshi Inoue
\n\nChapter 3 Vitamin K2 Rich Food Products by Muhammad Yasin, Masood Sadiq Butt and Aurang Zeb
\n\nChapter 4 Menaquinones, Bacteria, and Foods: Vitamin K2 in the Diet by Barbara Walther and Magali Chollet
\n\nChapter 5 The Impact of Vitamin K2 on Energy Metabolism by Mona Møller, Serena Tonstad, Tone Bathen and Jan Oxholm Gordeladze
\n\nChapter 6 Vitamin K2 and Bone Health by Niels Erik Frandsen and Jan Oxholm Gordeladze
\n\nChapter 7 Vitamin K2 and its Impact on Tooth Epigenetics by Jan Oxholm Gordeladze, Maria A. Landin, Gaute Floer Johnsen, Håvard Jostein Haugen and Harald Osmundsen
\n\nChapter 8 Anti-Inflammatory Actions of Vitamin K by Stephen J. Hodges, Andrew A. Pitsillides, Lars M. Ytrebø and Robin Soper
\n\nChapter 9 Vitamin K2: Implications for Cardiovascular Health in the Context of Plant-Based Diets, with Applications for Prostate Health by Michael S. Donaldson
\n\nChapter 11 Vitamin K2 Facilitating Inter-Organ Cross-Talk by Jan O. Gordeladze, Håvard J. Haugen, Gaute Floer Johnsen and Mona Møller
\n\nChapter 13 Medicinal Chemistry of Vitamin K Derivatives and Metabolites by Shinya Fujii and Hiroyuki Kagechika
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