Residual spectra levels, converter noise levels, and coherent bits for CH5–CH12 (8 NI 9239 channels) and CH17–CH24 (8 NI 9205 channels) for the three test signals.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"463",leadTitle:null,fullTitle:"A Comprehensive Book on Autism Spectrum Disorders",title:"A Comprehensive Book on Autism Spectrum Disorders",subtitle:null,reviewType:"peer-reviewed",abstract:'The aim of the book is to serve for clinical, practical, basic and scholarly practices. In twentyfive chapters it covers the most important topics related to Autism Spectrum Disorders in the efficient way and aims to be useful for health professionals in training or clinicians seeking an update. Different people with autism can have very different symptoms. Autism is considered to be a "spectrum" disorder, a group of disorders with similar features. Some people may experience merely mild disturbances, while the others have very serious symptoms. This book is aimed to be used as a textbook for child and adolescent psychiatry fellowship training and will serve as a reference for practicing psychologists, child and adolescent psychiatrists, general psychiatrists, pediatricians, child neurologists, nurses, social workers and family physicians. A free access to the full-text electronic version of the book via Intech reading platform at http://www.intechweb.org is a great bonus.',isbn:null,printIsbn:"978-953-307-494-8",pdfIsbn:"978-953-51-4434-2",doi:"10.5772/975",price:139,priceEur:155,priceUsd:179,slug:"a-comprehensive-book-on-autism-spectrum-disorders",numberOfPages:490,isOpenForSubmission:!1,isInWos:1,hash:null,bookSignature:"Mohammad-Reza Mohammadi",publishedDate:"September 15th 2011",coverURL:"https://cdn.intechopen.com/books/images_new/463.jpg",numberOfDownloads:113886,numberOfWosCitations:19,numberOfCrossrefCitations:39,numberOfDimensionsCitations:128,hasAltmetrics:1,numberOfTotalCitations:186,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 20th 2010",dateEndSecondStepPublish:"November 17th 2010",dateEndThirdStepPublish:"March 24th 2011",dateEndFourthStepPublish:"April 23rd 2011",dateEndFifthStepPublish:"June 22nd 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,8",editedByType:"Edited by",kuFlag:!1,editors:[{id:"62755",title:"Dr.",name:"Mohammad-Reza",middleName:null,surname:"Mohammadi",slug:"mohammad-reza-mohammadi",fullName:"Mohammad-Reza Mohammadi",profilePictureURL:"https://mts.intechopen.com/storage/users/62755/images/1662_n.jpg",biography:"Professor Mohammad-Reza Mohammadi works as a child and adolescent psychiatrist at Tehran University of Medical Sciences (TUMS), and is the chair of Psychiatry and Psychology Research Centre of Iran. 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Kumavath",coverURL:"https://cdn.intechopen.com/books/images_new/5867.jpg",editedByType:"Edited by",editors:[{id:"163692",title:"Dr.",name:"Ranjith",surname:"Kumavath",slug:"ranjith-kumavath",fullName:"Ranjith Kumavath"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"554",title:"Bacterial Artificial Chromosomes",subtitle:null,isOpenForSubmission:!1,hash:"3092adcfb46acf538c9ef38530f92d8f",slug:"bacterial-artificial-chromosomes",bookSignature:"Pradeep Chatterjee",coverURL:"https://cdn.intechopen.com/books/images_new/554.jpg",editedByType:"Edited by",editors:[{id:"91537",title:"Dr.",name:"Pradeep",surname:"Chatterjee",slug:"pradeep-chatterjee",fullName:"Pradeep Chatterjee"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6764",title:"Cyanobacteria",subtitle:null,isOpenForSubmission:!1,hash:"87c7d8f86f7c1185aa4dd47c6492951a",slug:"cyanobacteria",bookSignature:"Archana Tiwari",coverURL:"https://cdn.intechopen.com/books/images_new/6764.jpg",editedByType:"Edited by",editors:[{id:"186791",title:"Dr.",name:"Archana",surname:"Tiwari",slug:"archana-tiwari",fullName:"Archana Tiwari"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"38512",title:"Dynamic Testing of Data Acquisition Channels Using the Multiple Coherence Function",doi:"10.5772/48539",slug:"dynamic-testing-of-data-acquisition-channels-using-the-multiple-coherence-function",body:'The use of the Fast Fourier Transform (FFT) has revolutionized digital signal processing in many ways; and one of its principle uses continues to be the calculation of power spectral densities that are then used to estimate system transfer functions. When performing transfer function measurements, best practise dictates that the coherence between the input and output also be computed to provide a measure of the confidence in the measurement.
Many researchers, however, have turned the FFT based calculation of system transfer functions into a means to identify and remove coherent noise present in sensor measurements. Based on power spectral densities calculated using the FFT, the coherent noise between signals can be determined and then subtracted to reduce the noise floor of the sensor data acquisition channel.
To achieve good coherence between the input and output signals at all frequencies of interest it is necessary to ensure that those frequencies are present in the input signal. Poor coherence between the input and output can identify frequencies where external signals are being picked up, or it can indicate that the input or output signals are reduced or not present. Good coherence at all frequencies of interest can only be achieved with the use of white or wideband input noise signals.
When the dynamic range of the device under test exceeds that of the measuring device, over the frequency range of interest, maintaining good coherence becomes increasingly difficult. In these cases, it becomes necessary to use band-limited inputs, or sine wave inputs, where the signal gains can be optimized to improve the dynamic range and the coherence.
Manufacturers and end-users alike require methods to characterize the performance or quality of the data acquisition channels they either produce or use. The study of quality, however, is actually devoted to understanding the noise of the devices under test. Often manufacturers report zero-input noise levels for their devices, however, those levels may not be achieved when the device is performing during actual use. The coherent removal procedure, to be presented, provides a method to dynamically test amplifiers, filters, or analog-to-digital (A/D) converters, and to compute their noise levels when using typical input signals.
The idea of inputting a white noise signal into two A/D converters and computing the residual spectra from the ordinary coherence function, in order to characterize the noise of the devices was first reported in [19], and that concept was extended to testing multiple A/D converters using the multiple coherence function in [20]. In this chapter, those methods are expanded to encompass the entire data acquisition channel, so that dynamic testing of multiple amplifiers, filters or A/D converters can be performed.
Bendat and Piersol’s texts [1, 2] provide much of the theoretical background to the work to be presented here. Their treatment of the subject matter walks the reader through power spectral density estimation with discrete Fourier transforms and introduces data windowing and averaging periodograms, to arrive at estimates of the auto power spectral density based on the method popularized by Welch [21]. The foundation for both single-input single-output systems, as well as multiple-input single-output systems is also established, as is the concept of the coherent estimate of the output signal, and the residual spectrum.
The removal of coherent background signals is particularly effective at improving detection ranges of electromagnetic (EM) sensing systems, where incoming background micro-pulsation EM signals arrive virtually instantaneously on remote sensors (known as reference sensors), and can be (coherently) subtracted from signals monitoring areas of interest [6, 10, 17]. The removal of coherent background signals lowers the noise floor and therefore increases the detection range of the EM sensing system.
In the early efforts of using the multiple coherent removal procedure to enhance array performance, the number of reference sensors was limited to two or three channels and the required equations were solved for the given case. Recognizing that the system of equations for the optimum system transfer functions gave rise to a positive definite matrix which could be solved using the Cholesky algorithm was first reported in [20]. (Although, the conditioned spectral densities discussed by Bendat and Piersol bare close resemblance to the Cholesky decomposition into a lower triangle matrix, known as the square root of the matrix, followed by the back substitution procedure to yield the solution.) This approach then allowed any number of channels to be easily and efficiently programmed and solved. In that same work, the level of the residual spectrum was interpreted in terms of the noise of the individual A/D converters and a procedure was given to compute the individual A/D converter noise levels.
The IEEE has recently approved a standard [8] on the terminology and testing of A/D converters to provide a framework on the reporting on the dynamic testing of A/D converters. All of the principle methods discussed; the frequency domain method, the curve-fit method and the histogram method, use sine waves for their input signal. As the quality of the devices under test has improved, so too have the requirements for the spectral purity of the sine wave and the requirement to synchronize the sine wave frequency with the sampling rate. The frequency domain method has emerged as the most commonly used technique, however with the increasing resolution of A/D converters this approach will be limited by spectral leakage even for the best data windows. For end users who wish to verify the advertised specifications of high quality devices, the requirements of the test signal can be difficult and expensive to attain, in that, a standard off-the-shelf function generator is insufficient to perform the testing.
The relationship between coherence and time delay has been extensively studied, and the selected IEEE reprint volume [5] is an excellent reference on the subject (the single-input single-output treatment given here parallels page 1 of that work very closely). One of the most significant results in the volume is provided by Carter [4] where it is shown that the coherence function, as calculated using the FFT, has a bias error proportional to the delay between the signals.
It is, precisely the reason, that the coherence function as estimated using the FFT method, is biased by the time delay between those signals, which results in rather poor performance when attempting to remove coherent background acoustic signals. Sound waves travel much slower then EM waves and, therefore, there can be appreciable delay between the arrival of background acoustic signals.
The coherent removal technique is, however, well suited to testing multiple data acquisition channels because, generally, it is a simple matter to synchronize signals under user control. Simultaneously sampling the inputs is one of the basic principles behind the success of FFT-based spectrum analyzers.
The chapter begins with a brief review of Welch’s procedure for estimating the auto and cross power spectral densities of signals, and introduces the concept of determining the root mean square (rms) level of a signal in the frequency domain. Next the single-input single-output system and optimum system transfer function is introduced, and the concept of the coherent output and residual spectrum is explained. These results are then generalized for the multiple-input single-output system. Procedures for computing the cross spectral densities of a general number of signals are then discussed and the solution of the optimum system transfer functions using the Cholesky decomposition is presented, to establish the background theoretical material for the remainder of the chapter.
Next a general model for a data acquisition channel is introduced, which includes both amplifier and A/D converter noise sources, along with an noiseless gain and filter stage. To interpret the residual spectrum where any number of channels are tested, each channel of a multiple-input single-output system is represented by the data acquisition channel model, and the optimum system transfer functions and residual spectrum are determined in terms of the data acquisition model parameters. These results are then generalized, to allow any of the channels to be the output and the remaining channels the inputs. Assuming the input signal is large and the channel characteristics are matched leads to simple expressions for the optimum system transfer functions and the residual spectra, in terms of the data acquisition channel model parameters. It is then demonstrated how to test for either the amplifier noise or the A/D converter noise of the acquisition channel by adjusting the channel gain.
Measurement examples are then given demonstrating the technique with a set of analog amplifiers and filters, measured with simultaneously-sampled, 24-bit, sigma-delta (
Firstly, the well-accepted procedure of using the FFT to compute the (single-input single-output) transfer functions is demonstrated by computing the transfer functions for each of the amplifiers and filters under test. Next, the multiple coherent removal procedure is used to calculate the noise of the high resolution
Generalizing the procedure made popular by Welch [21] for computing the power spectral density of one signal, the cross spectral density of two signals can be estimated by averaging the product of the FFT of segments of the two signals. If
where
is a constant which accounts for the spectral weighting of the data window
The auto (or self) spectral density, can be determine by setting
As is evident by the above expressions the auto spectral densities are real valued, whereas, cross spectral densities are complex-valued functions.
Using Parseval’s theorem, the root mean square (rms) level of the signal
where
Some references explicitly define both a one-sided and two-sided spectral density, while the definitions used here are not specific to either definition, when plotting spectral densities we shall use the accepted practise of plotting the one-sided spectral density, so that the spectral density values are doubled and one side of the spectra plotted. By this approach, summing the
The use of the Welch FFT-based method for computing spectral densities is well documented in the literature, the texts by Kay [9] and Marple [11], and their review paper [12], as well as the text by Oppenheim and Schafer [16], all have sections devoted to the Welch method. In MATLAB, the Signal Processing Toolbox includes the functions pwelch and cpsd for calculating auto and cross spectral densities [14]. In Section 2.5 of this chapter the Welch procedure is generalized for any number of signals.
A common use of spectral densities estimated by the Welch method is to compute the transfer function of an amplifier or filter under test. Digitally recorded representations of the input signal
The coherence between the two signals, is defined as
and is a measure of how well the two signals are linearly related to each other. The coherence function is normalized such that
Single-input single-output system where Hxy is the optimum transfer functions which linearly relate the input xto the system system outputy.
To understand the concept of coherent removal it is useful to refer to the single-input, single-output model in Fig. 1, and to define
and is recognized as the portion of
Also, as a consequence of the optimization procedure, the error signal is uncorrelated with
The error, or residual spectral density, is then given as
and represents the portion of
On rearrangement of Equation (9), the signal to residual noise ratio can be determined as
This ratio is often termed the degree of cancellation and for coherent removal applications provides a useful measure of the coherence and is an important indicator of the quality of the measurement. Since larger input signals, increase the value of the signal to residual noise ratio, it should be quoted along with the input signal level. For the spectral density plots presented later, one can easily estimate the degree of cancellation in dB by subtracting the level of the input signal from the residual spectrum.
The residual spectrum is computed directly from the recorded data and can always be calculated regardless of the data set. In a worse case scenario, where the input signal is completely uncorrelated with the output, the residual spectra would be equal to the output spectrum and
To expand the single channel coherent removal concept to the multiple channel case, consider the system shown in Fig. 2, where the
where
Multiple-input single-output system where {Hiy,i=1…q} are the optimum system transfer functions which linearly relate the inputs {xi,i=1…q} to the system system outputy.
The optimum system transfer functions which minimize the error, can be determined in a least squares or an expected value sense, and are given by the solution of the following equations [2, Eq.(8.12)],
where
Noting that
The Cholesky decomposition separates a positive definite matrix into the product of a lower triangle matrix and its conjugate transpose. The resulting lower triangle matrix is sometimes referred to as the square root of the matrix, and once determined allows the equations to be solved using the back substitution method. Numerical procedures for the Cholesky decomposition are discussed in [18]. With an FFT size of
Once the optimum transfer functions are determined, the power spectral density of the best linear predictor due to the
Similar to the single input case, the error spectral density is given as
and represents the portion of
For the dynamic testing of acquisition channels, the primary interest is actually the residual spectrum, and therefore the multiple coherence function often remains uncalculated.
To obtain the required cross spectral densities used to calculate the optimum system transfer functions, and ultimately the residual spectrum, the Welch spectral estimates must be computed for all auto and cross spectral densities of the inputs and the output.
To accomplish this task, each time segment of the
where
To construct the cross, and auto, spectra of all channels, each row of
To explicitly recognize the frequency dependence each row of
Where
With the ability to remove the coherent or linear portion of a signal, it is now possible to suppress coherent noise signals, and this is how the technique is used to improve the detection ranges of EM sensing systems. It is also possible to use the procedure to dynamically test sensors, amplifiers and A/D converters by applying the same signal to multiple devices under test and remove the coherent portion based on multiple channel recordings. The difficulty arises in interpreting what the residual error means in terms of the noise of the devices under test. To answer this question, one must first define a noise model for a single data acquisition channel, and then determine the optimum system transfer functions and residual spectrum, in terms of the data acquisition model parameters of each channel.
In the study of noise in electronic components, it is usual to lump together all noise sources into equivalent noise sources for the whole device [15]. Assuming that multiple acquisition channels are operating, Fig. 3 considers the
Equivalent noise model of a data acquisition channel, where xi is the digitally recorded representation of the true input signalui. The additive noise sources, miand ni represent the amplifier noise and converter noise, respectively, and the ideal linear transfer function Gi takes into account gain and anti-aliasing requirements.
Assuming that
If we now consider a second channel
When the input signal is zero, the auto spectral density of the output becomes the zero-input noise level and is given as
This term represents all the noise present in the acquisition channel and is sometimes referred to as the combined noise. As a rule, when performing noise measurements the gain of the channel is adjusted to test for the noise of the amplifier or the converter, separately.
In [20] the residual spectrum was interpreted for the case where the A/D converter noise is dominate over the gained amplifier noise. To determine a more general result, in terms of both the amplifier and converter noise sources, we can substitute the expressions given in Equations (22) and (23) into Equation (12), to obtain the following general expression for the optimum system transfer functions
where the output channel
Electronic devices which are manufactured and packaged together will have similar noise characteristics. Nevertheless, when physically isolated from one another or when operating independent of each other, no linear relationship will exist between the instantaneous noise values and therefore, the noise, although similar, will be uncorrelated. When multiple data acquisition channels are used in a common operating environment some correlation will undoubtedly exist between the equivalent noise sources, however, very often it is possible to assume with little error, that the noise sources under consideration are uncorrelated [15, p.24].
Applying this basic underlying assumption of the study of noise to our example, if the noise sources are uncorrelated with each other then
To proceed, it becomes necessary at this point to enforce that all the inputs are the same signal, so that
The structure of these equations is, perhaps, better demonstrated in matrix notation as:
where it is now observed that the solution requires the inversion of a matrix which is the sum of a diagonal matrix and an outer product.[1] - Computing the inverse of this matrix, one arrives at the solution for the optimum system transfer functions in terms of the data acquisition channel parameters, given as
Substitution of this result into Equation (15), and then into Equation (16), leads to an expression for the residual error in terms of the data acquisition channel parameters, which is given as
Since any of the
Similarly, the residual spectra in Equation (30) now becomes the residual of the
Since each channel can be selected as the output
If the anti-aliasing filters are well matched (or can be calibrated to be so) then
and
respectively.
Since the applied signal
from which it is observed that the optimum system transfer function for each channel is inversely proportional to the combined noise of that channel. Similarly, the residual noise expression now becomes
from which it is seen that the residual error is the noise of the output channel, plus the parallel combination of the noise of the input channels.
Note that the residual spectrum is always greater than the noise of the present output channel, and that, since the optimum system transfer function for each input channel is inversely proportional to the noise of that input channel, nosier channels are suppressed in the prediction of the output channel, while quieter channels are enhanced.
If the acquisition channel gain is large, such that
and
and the amplifier noise sources are dominate in the optimum system transfer function and residual expressions.
Conversely, if the acquisition channel gain is small, so that
and
and the converter noise dominates in the optimum system transfer function and residual expressions.
To determine the individual converter noise of each channel in terms of the directly measurable residual error signals a solution to Equation (36) is required. Assuming that the residual spectrum of each channel has been calculated, with the remaining channels composing the inputs, it is possible to establish a set of nonlinear equations which can be solved for the individual channel noise using a constrained nonlinear least squares optimization procedure.
To demonstrate the situation, consider the case where the gain is small and the converter noise is dominate and four A/D converters sample the same signal, so that
This nonlinear set of equations can be iteratively solved to determine (
The most intuitive measure of the quality of an A/D converter is the effective number of bits (). An A/D converter may provide
where
Determining
When the multiple channel coherent removal method is applied to A/D converter testing, it is possible to calculate the residual spectra of each channel, compute the converter noise spectra using the approach described in Section 3.3, and then compute the rms level of the noise
To avoid confusion with the ENOB definition, we shall define the number of coherent bits (for an A/D converter) as CB where
which is analogous to ENOB but based on the noise estimated using the coherent removal process.
To investigate how the multiple coherence function can be used to dynamically test multiple amplifiers and A/D converters a 4-channel data acquisition system was created using two series of amplifiers as shown in Fig. 4. The Series A amplifiers provide 60dB of gain and 0.1 ac coupling, while the Series B amplifiers have unity gain, 0.1 ac coupling and a 8-th order Chebyshev low pass filter with a 512 corner frequency.
The amplifiers are a general purpose post amplifier known as the RPA designed (in the early 2000’s) for measuring EM signals in the sub-Hertz to 1 kilo-Hertz frequency range. Each channel of the RPA has a differential input which is based on a standard three op-amp instrumentation amplifier design. The RPA is unique in that it uses LTC1150 [13] chopper-stabilized operational amplifiers (op-amps) to eliminate the
To dynamically test the system a pseudo-random noise source (PRNS) was connected to the input of each of the Series A amplifiers. The PRNS is based on the output of a Linear Feedback Shift Register (LFSR) as described by [7] which is then filtered with a programable raised cosine 10-th order low pass filter. The signal level, cycle rate and clock frequency can be adjusted.
The A/D converters under test are based on an National Instruments (NI) CompactRIO data acquisition system and include four NI 9239 4-channel DAQ modules and one NI 9205 32-channel DAQ module. The NI 9239 modules use 24-bit
The total of 24 channels, that are recorded for each measurement example, are arranged such that the first sixteen channels are recorded with the 4 NI 9239 modules and the last eight channels are from the NI 9205. Referring again to Fig. 4, the first NI 9239 module records the input signal (CH1) and three of the Series A amplifier outputs (CH2–CH4). The next two NI 9239 modules (8 channels) record eight copies of the final Series A amplifier output (CH5–CH12). The fourth NI 9239 module records the four outputs of the Series B amplifiers (CH13–CH16), and the NI 9205 records eight copies of the final Series B amplifier (CH17–CH24). Note that the Series B amplifiers provide the anti-aliasing filtering required for the NI 9205 and that the NI 9239 use
The four NI 9239 modules share a common sampling clock and the inputs (even between modules) can be considered to be sampled simultaneously. The result of the
Amplifier configuration and channel assignment for the measurement examples.
The sampling rate for all the measurement examples is set to 10 and, unless stated otherwise, the following parameters are used for the Welch cross spectral density estimates:
segment size = 16384
overlap = 50%
averages = 256
Hann data window.
To gain insight into the interpretation of the residual spectra given in Section 3, the 24 channels were recorded for the following three input signals:
16 noise,
500 noise,
250 sine wave,
The precise level of the input signal is not specifically required for the coherent removal process and is given for information purposes. Note that once gained by the Series A amplifiers these levels will be levels at the A/D converters (other than the raw input measurement of CH1). A sample of the data from each channel recorded with the 16 input noise signal is shown in Fig. 5.
The first results to be presented are the transfer functions between the input signal and the output of the Series A and Series B amplifiers. These results are based on the single-input single-output relationships given in Section 2.2, calculated with the wide band noise data set, and include plots of the ordinary coherence function between the input and each of the outputs.
Next the coherent removal process is applied to CH5–CH12 and CH17–CH24 (separately) to calculate the residual spectra of the NI 9239 and the NI 9205 A/D converters; and ultimately the coherent number of bits of both devices. The process is repeated for both noise inputs to investigate how the two different input noise signals effect the two A/D converter types.
Next, to investigate the noise of the RPA amplifier the coherent removal process is applied to channels (CH2–CH5) and (CH13–CH16), again for both input noise signals in order to investigate the effect on the RPA amplifier noise (which is based on a chopper-stabilized instrumentation amplifier design).
The last results presented look at the coherent removal process of CH5–CH12 and CH17–CH24 with the sine wave data described above, processed with a Hann window and no window.
A one second snippet of the raw data recorded with the 16 kHz noise source. The prime on CH1 indicates it has been scaled by 1000, refer to Fig. 4 for channel details.
To confirm that each of the amplifiers are operating as specified, transfer function measurements can be calculated using either of the listed noise inputs. The single-input single-output transfer functions of the four Series A amplifiers, namely
The magnitude and phase of each of the transfer functions along with the coherence is shown in Fig. 6 for the data collected with the 16 input noise signal. From the transfer functions plots, the gain of the Series A amplifiers is confirmed to be 60dB, and the 8-th order roll-off and phase response of the Series B Chebyshev filter is evident. As is the custom,
Measured transfer functions (magnitude and phase) and coherence.
We begin the coherent removal examples looking at the residual spectra of CH5–CH12 and CH17–CH24 for the two input noise signals. Fig. 7 shows the input spectra and residual spectra for each channel with the 16 input noise signal, while Fig. 8 shows the results for the 500 input noise signal. For reference the noise floor of an ideal 24-bit and ideal 16-bit device are shown as dashed lines in both figures. We adopt the practice of plotting the input and residual spectra with the same line type and colour, and note that where the curves interact that the input spectra is always greater than the residual spectra.
To compute the individual converter noise spectral densities, the procedure discussed in Section 3.3 is implemented using MATLAB’s fmincon subroutine, which allows the converter noise of each channel to be calculated at each frequency value. These results are then integrated to obtain the rms level of the converter noise (
Turning the discussion first towards CH5–CH12, which are the two NI 9239 modules sampling the same Series A amplifier, we observe as expected that all eight input spectra are nearly identical and that the residual spectra of each channel are approximately equal. It is also noted that the residual spectra are virtually unchanged when the band width of the input noise is changed.
Consistent with the fact that the residuals are unchanged, we see for the 16 input noise signal tabulated in columns 2–4, or the 500 input noise signal tabulated in columns 5–7, nearly identical values for the rms level of the residual, the rms level of the converter noise and the coherent bits. From the table, the NI 9239 achieves about 17.0 coherent bits, at a sample rate of 10. The consistency of the coherent removal results, for a variety of inputs signals, are a good indicating of the dynamic range of the NI 9239, and its ability to reject signals beyond the Nyquist frequency.
input | 16 kHz noise σu =1.6 V | 500 Hz noise σu =3.6 V | 250 Hz sine wave Hann window | 250 Hz sine wave no window | ||||||||
Chan. | σe (µV) | σn (µV) | CB | σe (µV) | σn (µV) | CB | σe (µV) | σn (µV) | CB | σe (µV) | σn (µV) | CB |
CH5 CH6 CH7 CH8 CH9 CH10 CH11 CH12 | 45.66 45.97 47.06 46.61 46.06 46.48 46.46 46.74 | 42.61 42.96 44.15 43.65 43.06 43.51 43.49 43.80 | 17.05 17.04 17.00 17.01 17.03 17.02 17.02 17.01 | 45.78 46.47 47.32 46.40 45.99 45.86 46.65 46.72 | 42.74 43.50 44.42 43.42 42.97 42.83 43.70 43.77 | 17.04 17.02 16.99 17.02 17.04 17.04 17.01 17.01 | 46.83 46.69 47.13 47.11 47.04 46.70 47.25 47.44 | 43.79 43.63 44.12 44.09 44.02 43.64 44.25 44.45 | 17.01 17.01 17.00 17.00 17.00 17.01 16.99 16.99 | 46.69 46.60 47.69 47.87 47.10 47.68 47.47 47.59 | 43.59 43.49 44.68 44.88 44.03 44.67 44.44 44.57 | 17.02 17.02 16.98 16.97 17.00 16.98 16.99 16.98 |
CH17 CH18 CH19 CH20 CH21 CH22 CH23 CH24 | 459.6 460.4 458.7 458.1 457.5 458.8 458.1 459.6 | 430.1 431.1 429.2 428.5 427.9 429.3 428.5 430.2 | 13.71 13.71 13.72 13.72 13.72 13.72 13.72 13.71 | 1003.0 993.4 988.5 986.8 991.0 981.0 994.0 1007.8 | 940.0 929.6 924.3 922.4 927.0 916.1 930.3 945.3 | 12.58 12.60 12.61 12.61 12.60 12.62 12.60 12.58 | 459.4 459.1 458.7 460.2 460.2 458.0 457.8 459.1 | 429.9 429.6 429.2 430.8 430.7 428.3 428.1 429.6 | 13.71 13.71 13.72 13.71 13.71 13.72 13.72 13.71 | 711.6 631.2 589.9 561.3 568.5 592.9 620.6 716.3 | 679.3 593.3 548.2 516.4 524.5 551.5 581.9 684.2 | 13.05 13.25 13.36 13.45 13.43 13.35 13.28 13.04 |
Residual spectra levels, converter noise levels, and coherent bits for CH5–CH12 (8 NI 9239 channels) and CH17–CH24 (8 NI 9205 channels) for the three test signals.
Input and residual spectra for CH5–CH12 and CH17–CH24 with the 16 noise input.
Closer inspection of the input signal spectrum reveal some interesting facts about the chopper-stabilized instrumentation amplifier design used in the RPA. In order to achieve its improved noise at ultra low frequencies the input signal is chopped (or modulated) at a fixed frequency, which in the case of the RPA is 2.048. Note that the modulation frequency and its first harmonic are clearly visible in the input spectral densities of Fig. 7.
The RPA design assumes that the input signal is band-limited such that Nyquist theory is satisfied. With the wideband noise source we are clearly violating this requirement, and the input spectrum is an ensemble of frequencies mixing together due to the chopping process. This mixing is greatly exaggerated when the 16 noise source is used. The net outcome, however, as seen in Fig. 7 is that the spectrum is still flat and harmonics are present at the chopping frequency. When the input noise bandwidth is reduced to 500, the noise floor of the RPA amplifier (after gain) is now visible for frequencies above
In terms of the residual spectra computed for CH5–CH12, which are all measuring the same amplifier output, whatever input signal is used, the residual spectra is limited by the noise of the individual A/D converters and not the amplifier noise present in the signal. We will look at the coherent removal process with the amplifier outputs after discussing the results for the NI 9205, which uses a SAR A/D converter.
The importance of using simultaneous sampling to maintain the phase relationship between channels is essential to the coherent removal process. In [19] the introduction of a delay in one of the channels was investigated and was shown to increase the residual spectrum, a result which was consistent with the finding in [4] that the coherence function (as calculated using FFT-based block data methods) has a bias error proportional to the delay between the signals.
How to process the NI 9205 channels presented a bit of dilemma, since absolute synchronization with the NI 9239 modules is achieved by applying a fractional sample delay filter to the NI 9205 channels in order to match the input delay of the
Noting that the bias error in the coherence is proportional to the amount delay, and that the delay between any adjacent channel of the NI 9205 (based on 10 sampling and an 8 inter channel delay) represents a delay of only 0.08 of a sample, the decision was made to process the eight NI
To simultaneously sample the NI 9205 channels and yield residual spectra that are unbiased by the inter-channel delay, analog sample-hold circuitry could be developed to sample the signal and then hold the level until the SAR A/D converter is able to read the value. This is in fact how the early coherent removal systems, developed for EM sensing systems, were designed in order to maintain the phase.
Processing data channels CH17–CH24 as a set for the two input noise signals allows us to investigate the residual spectra of the NI 9205 channels for these two test cases. The spectra of the input signals as well as the residual spectra of each channel, are plotted in Fig. 7 and Fig. 8 for the 16 and 500 PRNS tests respectively (alongside the results for the NI 9239). The rms levels of the residuals and converter noise, and the coherent bit values are given in Table 1.
Input and residual spectra for CH5–CH12 and CH17–CH24 with the 500 noise input.
Firstly, it is evident that the residual spectra of the NI 9205 increases when the 500 PRNS source is used. This increase is a result of the fact that the input signal with the narrower band source has a larger signal level resulting in more signal in both the pass band as well as the stop band. It is, however, the increase in the signal level in the stop band that is eventually aliased backed that results in the increase of the residual spectra noise floor. Part of the issue arises from the aliasing artifacts that occur in the input signal due to the chopping process of the RPA, which are not adequately removed before the NI 9205 samples the signal. Also, note that the residual spectra of the NI 9205 is flat for frequencies above 1, indicating that the aliasing artifacts are coherent between channels and removed.
The coherent removal measurements presented here for the NI 9205 are some of the first for a SAR based A/D converter with inter-channel delay. While results are not considered definitive due to the inter-channel delay it does appear that the technique is at least viable as a means to assure a certain level of residual is being achieved. Increasing the number of channels would also help to reduce the residual spectra levels. It is of interest to observe that the first and last channels in the channel list (which are recorded first and last) have the highest residual spectra consistent with the fact that these channels are time-wise the furthest from the other channels.
To test the coherent removal procedure with amplifier signals we processed channels CH2–CH5 and channels CH13–CH16 each as a separate channel set, for both the input noise signals listed above. Recall that these channels are recorded using the NI 9239 modules and are therefore simultaneously sampled.
The results for each channel set, with the 16 input noise source is shown in Fig. 9. For both of the channel sets, we observe that the noise floor is significantly above the noise level expected for the RPA. The cause is that the chopping process of the RPA is folding incoherent noise back into the frequencies of interest.
Input and residual spectra for CH2–CH5 and CH13–CH16 with the 16 noise input.
When the 500 input noise signal is used, the input spectrum contains much less out-of-band signal and the RPA residual, as seen in Fig. 10, is closer to the expected level for the RPA.
While the RPA chopping process has complicated the analysis of the amplifier testing presented here, if linear amplifiers had been used, far less variation in the residual spectrum would have been observed with changes in the input signal level and spectrum content.
Similar to the process used to determine the individual converter noise sources, the individual amplifier noise can also be calculated from the residual spectra and if desired the rms level computed. As these are some of the first examples presented for the testing of amplifiers, we conclude the discussion at the calculation of the residual spectra.
In terms of the residual spectra at the output of the Series A amplifiers and the Series B amplifiers the spectra levels are consisted with the operation of the amplifiers and filters for those devices. As an interesting note on the performance improvement with the NI 9239, compare the aliasing artifacts that arose in CH17–CH24, to the spectra for CH13–CH16 which clearly show the chopping harmonics still present in the stop band.
Input and residual spectra for CH2–CH5 and CH13–CH16 with the 500 noise input.
As sine wave inputs are often the test signal of choice for many dynamic testing procedures, it is of interest to test the coherent removal process with a sine wave input and to investigate how changing the data window effects the results. The input and residual spectra for CH5–CH12 and CH17–CH24 with the sine wave signal listed above are shown in Fig. 11, for both the Hann window and no window case, and the rms levels of the residual and converter noise, and the coherent bits are given in Table 1.
For the NI 9239 we observe once again that the residual spectra is unchanged from the white noise case, and changing the data window has little effect on this result. For the NI 9205 channels the residual spectra increase slightly when no data window is used, and this is reflected in a 0.7 bit decrease in coherent bits. Why the results are better with the Hann window are not entirely clear, but it appears related to the aliasing artifacts that have been introduced due to the RPA chopping process. Exactly how the window shapes the residual spectra also appears to be influencing the residual calculation.
Input and residual spectra for CH5–CH12 and CH17–CH24 with the 250 sine wave input signal processed with no window and the Hann window.
The sine wave used for this test was of poor quality and with the Hann data window the spectrum of the sine wave can be seen to be contaminated with the noise of the RPA amplifier. This noise accounts for some spectral energy in the input throughout the frequency band and assures the coherent removal process is successful. When there is no (or close to no) signal present in a given frequency band the calculation of the coherence can become numerically unstable. This is, in fact, the case for an ultra pure sine wave processed with a data window to highly suppress the sidelobe energy. In this case, applying no data window actually helps to ensure that signal input is present throughout the frequency band because applying no window causes the spectrum to leak significantly into the sidelobes.
Using sine waves to measure the residual spectrum is somewhat analogous to using a single sine wave to measure the entire transfer function frequency response. As with transfer function measurements, the user should use wideband noise signals to try to maximize the coherence between the signals under test for all frequencies of interest.
To implement the measurement examples presented, a LabVIEW application was developed to record and analyze the data in real-time. One of the reasons for implementing the entire processing in LabVIEW was to carry out the calculations using extended precision arithmetic. For the results presented, all of the spectral processing was implemented using MATLAB in double precision arithmetic.
Earlier simulations had suggested the use of extended precision would be necessary when using the coherent removal procedure with large channel counts. These same simulations also showed loss of numerical accuracy when the optimum system transfer functions equations are solved using matrix inversion procedures other than the Cholesky decomposition. While this result is still true, under closer scrutiny, and taking care to simulate bit noise with uniformly distributed variables and the amplifier noise sources with gaussian distributed variables, it was in fact possible to simulate results for as many as thirty-two 24-bit channels with 1 bit of noise using double precision arithmetic. Another important aspect, in order to match simulation results, was to use a large number of spectral averages. While extensive simulations have not been performed, a good rule of thumb is to use about ten times the number of channels, for the number of the averages. For the measurement examples presented no more than eight channels are actually processed using the coherent removal process at one time, so the use of double precision arithmetic and 256 averages is sufficient.
One of the perhaps overlooked aspects of the residual spectra interpretation is that the analysis verifies the practise of paralleling acquisition channels to improve noise performance. When the noise of each of the channels are approximately equal, the residual spectral density will simplify to
The ability of modern
The material presented here is derived from well-accepted procedures for computing cross power spectral densities, the optimum system transfer functions, and the residual spectrum. For any combination of channel data, the residual spectra can always be calculated, and from this perspective presentation of the residual spectra is provided similar to that of any other accepted measurement, such as the transfer function or the power spectral density. The approach taken here has been not to present the individual channel noise spectra as these quantities are derived from the residuals and are based on interpretation, certainly, however, the temptation exists to do so, because results become specific to a single device, and this is desirable for marketing purposes.
The use of the multiple coherence function for the dynamic testing of data acquisition channels greatly simplifies the setup, and test signal requirements for dynamic testing of data acquisition channels because any input signal can be used, and depending on the gain, testing can be performed to measure either the amplifier or the A/D converter noise.
Oxidoreductases, which includes oxidase, oxygenase, peroxidase, dehydrogenase, and others, are enzymes that catalyze redox reaction in living organisms and in the laboratory [1]. Interestingly, oxidoreductases catalyze reaction involving oxygen insertion, hydride transfer, proton extraction, and other essential steps. The substrate that is oxidized is considered as hydrogen or electron donor, whereas the substrate that is reduced during reaction as hydrogen/electrons acceptor. Most commonly, oxidoreductase enzymes use NAD, FAD, or NADP as a cofactor [2]. Organisms use this group of enzymes for synthesis of biomolecules, degradation and removal of molecules, metabolism of exogenous molecules like drugs, and so on [3, 4, 5]. Their biochemical property such as efficiency, specificity, good biodegradability, and being studied well make it fit well for industrial purposes. As a result, oxidoreductases are being utilized in nutrition, food processing, medicine, and other chemical synthesis. In the near future, oxidoreductase may be utilized as the best biocatalyst in pharmaceutical, food processing, and other industries [6, 7].
Enzymes like oxidoreductase play great and significant function in the field of disease diagnosis, prognosis, and treatment [8]. By analyzing the activities of enzymes and changes of certain substances in the body fluids, a number of disease conditions can be diagnosed [9, 10]. The determination of the activity of the oxidoreductases is helpful in understanding the metabolic activity of different organs [8, 11]. For example, the activity of oxidoreductase enzymes in Krebs cycle is significantly increased during skin infection [12].
There are different disease conditions resulting from deficiency (quantitative and qualitative) and excess of oxidoreductase, which may contribute to the metabolic abnormalities and decreased normal performance of life [13, 14]. For example, relative decreases in the activities of NADH dehydrogenase and ubiquinol-cytochrome c oxidoreductase are highly associated with the developments of peripheral arterial disease. Another best example is mutation of p450 oxidoreductase (POR) gene, which leads to insufficiency of P450 enzymes characterized by defective steroidogenesis. Similarly, deficiency of mitochondrial acetaldehyde dehydrogenase disturbs normal metabolism of alcohol and leads to accumulation of acetaldehyde [8, 15, 16]. These conditions in turn affect the normal development and reproduction.
Oxidoreductases are a family of enzymes that catalyze redox reactions. Oxidoreductases catalyze the transfer of electrons from oxidant to reductant [4]. Generally, oxidoreductases catalyze reactions which are similar to A– + B → A + B– where A is the oxidant and B is the reductant [17]. Oxidoreductases can be oxidases where a molecular oxygen acts as an acceptor of hydrogen or electrons and dehydrogenases which are enzymes that oxidize a substrate by transferring hydrogen to an acceptor that is either NAD+/NADP+ or a flavin enzyme. Other classes are oxidoreductases enzymes, peroxidases which are localized in peroxisomes and catalyze the reduction of hydrogen peroxide. Hydroxylases are involved in the addition of hydroxyl groups to their substrates, and oxygenases are key in the incorporation of oxygen from molecular oxygen into organic substrates. And reductase enzymes are involved in the catalysis of reduction reaction [2, 3, 18]. In general, oxidoreductase enzymes play an important role in both aerobic and anaerobic metabolism. They are involved in glycolysis, TCA cycle, oxidative phosphorylation, fatty acid, and amino acid metabolism [5, 19, 20].
In glycolysis, the enzyme glyceraldehydes-3-phosphate dehydrogenase catalyzes the reduction of NAD + to NADH. In order to maintain the redox state of the cell, this NADH must be re-oxidized to NAD+, which occurs in the oxidative phosphorylation pathway [21].
A high number of NADH molecules are produced in the TCA cycle. The product of glycolysis, pyruvate, enters the TCA cycle in the form of acetyl-CoA. Except leucine and lysine, all twenty of the amino acids can be degraded to TCA cycle intermediates. And most of the fatty acids are oxidized into acetyl coA through beta oxidation that enter TCA cycle [19, 22].
The precursor for the TCA cycle comes from lipids and carbohydrates, both of which produce the molecule acetyl-CoA. This acetyl-CoA enters the eight-step sequence of reactions that comprise the Krebs cycle, all of which occur inside mitochondria of eukaryotic cells. TCA or Krebs cycle produces NADH and FADH, and the reactions are catalyzed by classes of oxidoreductase enzymes [23].
Living cells use electron transport chain to transfer electrons stepwise from substrates (NADH & FADH2) to a molecular oxygen. The proton gradient which is generated through electron transport chain runs downhill to drive the synthesis of ATP. Electron transport chain and oxidative phosphorylation take place in the matrix of mitochondria, and there are oxidoreductase enzymes impregnated in the inner mitochondrial membrane, which catalyze these reactions and are engaged in energy production. NADH:quinone oxidoreductase, also called NADH dehydrogenase (complex I), is responsible for the transfer of electrons from NADH to quinones, coupled with proton translocation across the membrane. Succinate:quinone oxidoreductase, or succinate dehydrogenase (complex II), is an enzyme of the Krebs cycle, which oxidizes succinate and reduces quinones, in the absence of proton translocation. Quilon:cytochrome c oxidoreductase (complex III), which transfers electrons from quinols to cytochrome c and cytochrome c:oxygen oxidoreductase, an aa3-type enzyme (complex IV), which receives these electrons and transfers it to oxygen are both oxidoreductase enzymes involved in electron transport chain and oxidative phosphorylation [19, 24, 25] (Figure 1).
Oxidoreductase enzymes involved in electron transport chain and oxidative phosphorylation [18].
Liver is the principal organ for drug metabolism. The body uses different strategies to metabolize drugs like oxidation, reduction, hydrolysis, hydration, conjugation, condensation, or isomerization. The main goal of drug metabolism is to make the drug more hydrophilic and excrete easily. Enzymes involved in drug metabolism are found in many tissues and organs but are more concentrated in the liver. Rates of drug metabolism may vary among individuals. Some individuals metabolize a drug so rapidly; in others, metabolism may be so slow and have different effects. Genetic factors, coexisting disorders (particularly chronic liver disorders and advanced heart failure), and drug interactions are responsible factors for variation of rate of drug metabolism among individuals [26].
Generally, drug metabolism can be in three phases. In phase I drug metabolism, oxidoreductase enzymes such as cytochrome P450 oxidases add polar or reactive groups into drugs (xenobiotics). In phase I reaction, drugs are introduced into new or modified functional group through oxidation, reduction, and hydrolysis. In Phase II reactions, modified compounds are in conjugation with an endogenous substance, e.g., glucuronic acid, sulfate, and glycine. Phase II reactions are synthetic, and compounds become more polar and thus, more readily excreted by the kidneys (in urine) and the liver (in bile) than those formed in nonsynthetic reactions. At the end, in phase III reaction, the conjugated drugs (xenobiotics) may be further processed, before being recognized by efflux transporters and pumped out of cells. The metabolism of drug often converts hydrophobic compounds into hydrophilic products that are more readily excreted [27].
In normal cases, human body wants to remove or detoxify any compounds that cannot be metabolized otherwise utilized to serve the needs of the body. This removal process is carried out mainly by the liver. The liver has classes of oxidoreductase enzymes that are extremely effective at detoxification and removal of drugs from the body [5, 18].
Oxidation and metabolism of a high number of drugs and endogenous molecules are catalyzed by a class of oxidoreductase enzymes called cytochrome P450 monooxygenases. Even though they are distributed throughout the body, cytochrome P450 enzymes are primarily concentrated in liver cells. The CYP2D6 isozymes play a great role in metabolizing certain opioids, neuroleptics, antidepressants, and cardiac medications. Currently it is going to be understood that difference in the genes for CYP450 enzymes play to inter-individual differences in the serum concentrations of drug metabolites, resulting in interpatient variability in drug efficacy and safety [28].
Flavin-containing monooxygenases (FMOs) (EC 1.14.13.8) are a family of microsomal NADPH-dependent oxidoreductase, responsible for oxygenation of nucleophilic nitrogen, sulfur, phosphorus, other drugs, and endogenous molecules. Different variants of mammalian FMOs play a significant role in the oxygenation of nucleophilic xenobiotics. FMO utilizes NADPH as a cofactor and contains one FAD as a prosthetic group. FMOs have a broad substrate specificity and their activity is maximal at or above pH 8.4. FMO is a highly abundant enzyme in the liver endoplasmic reticulum and participates in drug metabolism (activation and detoxification) [29].
Before FMOs bind to a substrate, they activate molecular oxygen. First, flavin adenine dinucleotide (FAD), the prosthetic group of FMO, is reduced by NADPH to form FADH, then oxygen is added into the FAD, and hydro-peroxide FADH-4α-OOH is produced. And then, one oxygen atom is transferred to the substrate [30, 31].
Alcohol dehydrogenase (ADH) and mitochondrial aldehyde dehydrogenase (ALDH) are another family of oxidoreductase responsible for metabolizing ethanol. These enzymes are highly expressed in the liver but at lower levels in many tissues and play a great role in detoxification and easy removal of alcohols. Liver is the main organ for ethanol metabolism. Oxidation of ethanol with these enzymes can become a major energy source especially in the liver, and it can interfere metabolism of other nutrients [32].
The first step in ethanol metabolism is its oxidation to acetaldehyde, and this reaction is catalyzed by enzymes called alcohol dehydrogenases (ADHs). The second reaction in ethanol metabolism is oxidation of acetaldehyde into acetate catalyzed by aldehyde dehydrogenase (ALDH) enzymes. There are different ADH and ALDH enzymes encoded by different genes occurring in several alleles and enzymes that have different alcohol metabolizing capacity; thereby, they influence individuals’ alcoholism risk. These are either through rapid oxidation of ethanol to acetaldehyde where there is more active ADH or slower oxidation of acetaldehyde into acetate where there are less active ALDH enzymes. Excess accumulation of acetaldehyde is toxic, which results in different adverse reactions and produces nausea, skin rash, rapid heartbeat, etc. Most commonly, single-nucleotide polymorphisms (SNPs) are responsible for ADH and ALDH gene variants, and these may occur on both coding and non-coding regions of the gene [33, 34].
Monoamine oxidase is a very important oxidoreductase enzyme mainly responsible for degradation of amine neurotransmitters like norepinephrine, epinephrine, serotonin, and dopamine. Oxidation of different endogenous and exogenous biogenic amines may produce other active or inactive metabolites. Monoamine oxidase (MAO) is found in two isozyme forms: monoamine oxidase A (MAO-A) preferentially deaminates serotonin, norepinephrine, epinephrine, and dietary vasopressors such as tyramine, and MAO-B preferentially deaminates dopamine and phenethylamine. They are integral flavoproteins components of outer mitochondrial membranes in neurons and glia cell. The two isozymes of MAO differ based on substrate specificity and sensitivity to different inhibitors [35].
Monoamine oxidase enzymes catalyze the primary catabolic pathway for 5-HT oxidative deamination. Serotonin is converted into 5-hydroxy-indoleacetaldehyde, and this product is further oxidized by a NAD-dependent aldehyde dehydrogenase to form 5-hydroxyindoleacetic acid (5-HIAA). Immunohistochemical techniques and in situ hybridization histochemistry techniques are used to study the neuroanatomical localization and biochemical nature of the two forms of MAO [36].
Different antidepressant drugs like phenelzine and tranylcypromine inhibit the activity of monoamine oxidase. These are a result of MAO metabolizes biogenic amines such as 5-HT, DA, and NE. In addition, different dopaminergic neurotoxins such as 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) are metabolized by MAO [37].
Another essential class of oxidoreductase enzyme is NADPH-cytochrome P450 reductase (CPR). It is a membrane-bound protein localized in the ER membrane. PR involves in the detoxification and activation of a number of xenobiotics. CPR uses FAD and FMN as cofactors, and it transfers the hydride ion of NADPH to FAD, and then FAD transfers electrons to FMN and other oxidases. Finally, it reduces the P450 enzyme heme center to activate molecular oxygen. Thus, electrons transfer from NADPH to the P450 heme center by CPR, which is central for P450-catalyzed metabolism. Flow of electron can be expressed as follows:
Human cytochrome P450 reductase is encoded by the POR gene. It is a 78-kDa multi domain diflavin reductase that binds both FMN and FAD and is attached to the cytoplasmic side of the endoplasmic reticulum via a transmembrane segment at its N-terminus [5, 15, 38].
Several industries such as pharmaceutical, foods, biofuel production, natural gas conversion, and others have used enzyme catalysis at commercial scale [39]. Classes of oxidoreductase enzymes are becoming a target by a number of industries. The family of oxidoreductase like heme-containing peroxidases and peroxygenases, flavin-containing oxidases and dehydrogenases, and different copper-containing oxidoreductases is involved in synthesis and degradation of interested products by the above industries and they are biocatalysts of interest for establishing a bio-based economy. Oxidoreductase enzymes have the highest potential in the production of polymer building blocks, sustainable chemicals, and materials from plant biomass within lignocellulose biorefineries [6, 7, 40].
Enzymes are biological catalysts and have great specificity, efficiency, and selectivity in the reaction they catalyze [39]. Oxidoreductase enzymes have different redox-active centers for doing their functions. These unique features of oxidoreductase enzymes make it valuable targets of pharmaceutical and chemical industries. Advancement in recombinant DNA technology, protein engineering, and bioinformatics is a critical event in the application of enzymes in different industries. A number of dug synthesis processes require the involvement of oxidoreductase enzymes [6].
An oxidoreductase is involved in the synthesis of 3,4-dihydroxylphenyl alanine (DOPA), and 3,4-dihydroxylphenyl alanine is a drug used for treatment of Parkinson’s disease [41]. Similarly, a class of oxidoreductase called monoamine oxidase (MAO) catalyzes enantiomeric desymmetrization of bicyclic proline intermediate, which is an important precursor in the synthesis of boceprevir. Boceprevir is a NS3 protease inhibitor that is used for the treatment of chronic hepatitis C infections. Using MAO in this reaction reduces time and waste product generation and is economically cost-competitive and profitable [42]. Its coenzyme specificity makes oxidoreductase an effective biocatalyst in protein engineering [43]. In vitro different oxidoreductase enzymes are involved in regeneration of coenzymes, pyridine nucleotides, NAD(H) and NADP(H). Alcohol dehydrogenase and format dehydrogenase are frequently used enzymes for recycling of coenzymes, and the intermediate products are useful in the synthesis of pharmaceutical drugs such as mevinic acid [44, 45].
Enzymes are biological catalysts and have a number of applications in agricultural fields. Using enzymes has great efficacy and efficiency over chemical catalysts with respect to their productivity, time, cost, quality, and quantity products. There are different classes of oxidoreductase enzymes nowadays involved in fertilizer production, dairy processing, and other food processing in agricultural sector, and their cost-effectiveness and quality product were confirmed by a number of researches [3].
Manipulation of gene cod for different oxidoreductase in plants can also change the characters of plants in a way that it increases productivity and resists adverse effects of herbicide and environmental changes. For example, modification of DNA for glyphosate oxidoreductase (GOX) enzyme that catalyzes the oxidative cleavage of the C▬N bond on the carboxyl side of glyphosate, resulting in the formation of aminomethylphosphonic acid (AMPA) and glyoxylate thereby augmented expression of GOX plants, results in glyphosate herbicide side effect tolerance [46, 47]. Some families of oxidoreductase like xanthine dehydrogenase in plants are used to metabolize reactive oxygen species associated with plant-pathogen and protect plants from stress-induced oxidative damage. Upregulation of xanthine dehydrogenase expression in plants is helpful to increase productivity [48, 49].
Classes of oxidoreductase are also involved in dairy processing. Glucose oxidase produced by fungal species acts as preservatives in dairy products and other foods. The intermediate and end product of glucose oxidase have antimicrobial effect [50]. Isozyme of xanthine oxidoreductase in bovine milk, which catalyzes reduction of oxygen to generate reactive metabolite is used as an anti-microbial agent in the neonatal gastrointestinal tract [51]. Similarly, peroxidases which are a family of oxidoreductase found in higher plants catalyze the oxidation of many compounds including phenolics, in the presence of hydrogen peroxide responsible in browning or darkening of noodles and pasta and associated with a grain quality defect [52]. Protochlorophyllide oxidoreductase (POR), which exists in two isozymes POR A and POR B, plays a vital role in plant chlorophyll synthesis, and manipulation on these genes can induce plant development [53]. In general, there are a number of oxidoreductase enzymes found in plants, and their normal activity is crucial for qualitative and quantitative productivity of crops, and these were confirmed by a number of active researches. Different interventions are also going on at gene level to control the expression of oxidoreductase enzymes in plant as needed [3].
Oxidoreductase enzymes are involved in a number of valuable biochemical reactions in the living organism, and their qualitative and quantitative normality is essential. For example, one important class of oxidoreductase is xanthine oxidoreductase (XOR) that catalyzes oxidative hydroxylation of hypoxanthine to xanthine then to uric acid and over activity XOR leads to hyperuricemia and concomitant production of reactive oxygen species. In turn, hyperuricemia is confirmed as an independent risk factor for a number of clinical conditions such as gout, cardiovascular disease, hypertension, and others. Different urate-lowering drugs or XOR inhibitors are nowadays implemented to prevent and manage hyperuricemia disorder [9].
Another important class of oxidoreductase enzyme is cytochrome P450 oxidoreductase (POR) that is essential for multiple metabolic processes. Cytochrome P450 enzymes are involved in metabolism of steroid hormones, drugs, and xenobiotics. Nowadays, more than 200 different mutations and polymorphisms in POR gene have been identified and cause a complex set of disorders. Deficiency of cytochrome P450 oxidoreductase affects normal production of hormone; specifically, it affects steroid hormones, which are needed for normal development and reproduction. This is highly linked with the reproductive system, skeletal system, and other functions. Signs and symptoms can be seen from birth to adult age with different severities. Individuals with moderate cytochrome P450 oxidoreductase deficiency may have ambiguous external genitalia and have a high chance of infertility but a normal skeletal structure [5, 16, 18].
Aldehyde dehydrogenase 2 (ALDH2) deficiency known as Asian glow or alcohol flushing syndrome is a common genetic health problem that interferes with alcohol metabolism, and ALDH2 is a classical family of oxidoreductase enzymes. It was confirmed that ALDH2 deficiency results in the accumulation acetaldehyde, which is a toxic metabolite of alcohol metabolism and responsible for a number of health challenges like esophageal, head, and neck cancer. A number of researches conclude that acetaldehyde is a group 1 carcinogenic metabolite [33, 54]. Similarly, monoamine oxidase deficiency, which is a family oxidoreductase enzyme, affects the normal metabolism of serotonin and catecholamines. It is a rare X-linked disorder characterized by mild intellectual disability, and behavioral challenges appear at earlier age. Monoamine oxidase-A deficiency that occurs almost exclusively in males has episodes of skin flushing, excessive sweating, headaches, and diarrhea. Monoamine oxidase-A deficiency can be diagnosed by finding an elevated urinary concentration of the monoamine oxidase-A substrates in combination with reduced amounts of the monoamine oxidase products [36, 55].
Mitochondria generate huge amounts of energy (ATP) to eukaryotic cells through oxidation of fats and sugars; and fatty acid β-oxidation and oxidative phosphorylation are two metabolic pathways that are central to this process. Qualitative and quantitative normality of oxidoreductase enzymes involved in oxidative phosphorylation and fatty acid oxidations are essential to get sufficient energy (ATP) form metabolism. Deficiency of a complex I (NADH-CoQ oxidoreductase) is common, and a well-characterized mitochondrial problem causes reduced ATP production [56]. Complex I (NADH-CoQ oxidoreductase) is responsible for recycling of NADH to NAD+, and in turn, this is essential to sustain Krebs cycle and glycolysis. Mutations in both nuclear and mitochondrial DNA for Complex I gene are responsible for mitochondrial disease. Individuals with mitochondrial diseases suffer from an energy insufficiency characterized by myopathies, neuropathy, delayed development, cardiomyopathy, lactic acidosis, and others. Furthermore, since mitochondria are a hub of metabolism, mitochondrial dysfunctions are highly associated with metabolic diseases like hypertension, obesity, diabetes, neurodegenerative diseases, and even aging. Deficiency of complex I leads to elevation of NADH levels in the mitochondria that inhibit pyruvate dehydrogenase and α-ketoglutarate dehydrogenase. This condition completely inhibits Krebs cycle, and it is measured by CO2 evolution from [14C] labeled precursors. Similarly, complex II (succinate:ubiquinone oxidoreductase) deficiency affects both fatty acid oxidation and electron transport chain, and it induces retinopathies and encephalopathies [57, 58].
Deficiency of the pyruvate dehydrogenase complex (PDHC), another class of oxidoreductase enzymes, causes similar clinical and biochemical alteration in energy production with complex I (NADH-CoQ oxidoreductase) [59]. Both TCA cycle and respiratory chain can be affected by succinate dehydrogenase deficiency. Deficiency of oxidoreductase enzymes involved in Krebs cycle affects all carbohydrate, protein, fat, and nucleic acid metabolism as it is a common pathway for metabolism of the above macromolecules [60].
Oxidoreductase enzymes are also involved in bile acid synthesis. Classes of oxidoreductase enzymes called 3beta-hydroxy-Delta (5)-C (27)-steroid oxidoreductase catalyze an early step of bile acids synthesis from cholesterol and are encoded by HSD3B7 gene on chromosome 16p11.2-12. Mutations of HSD3B7 gene affect bile acids synthesis, cause development of progressive liver disease characterized by cholestatic jaundice, malabsorption of lipids, and lipid-soluble vitamins from the gastrointestinal tract, and finally progress to cirrhosis and liver failure [61].
One important biomolecule that acts as a precursor for other molecules and a component of cell membrane is cholesterol. Mammalian cells can get cholesterol from de novo biosynthesis or uptake of exogenously derived cholesterol associated with plasma low-density lipoprotein (LDL). 3-Hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase, which is a class of oxidoreductase, catalyzes the rate-limiting steps of de novo cholesterol biosynthetic pathway and target for manipulation pharmacologically. Under or over activity of HMG-CoA reductase can disturb cholesterol homeostasis and lead to either hypercholesterolemia or hypocholesterolemia. And disturbed cholesterol level associated with number serious clinical problem like atherosclerosis [62, 63].
The authors declare that they have no competing interests.
Mezgeu Legesse Habte drafted the paper and write the literature review.
Etsegenet Assefa assisted in guidance, critical assessment and peer review of the writing. Both authors have given their final approval of this version to be published. Both authors read and approved the final manuscript.
Availability of data and material: All necessary data and materials related to the article are included in the article.
Funding: This review article is not funded by any person or organization (not funded).
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\\n"}]'},components:[{type:"htmlEditorComponent",content:'Copyright is the term used to describe the rights related to the publication and distribution of original Works. Most importantly from a publisher's perspective, copyright governs how Authors, publishers and the general public can use, publish, and distribute publications.
\n\nIntechOpen only publishes manuscripts for which it has publishing rights. This is governed by a publication agreement between the Author and IntechOpen. This agreement is accepted by the Author when the manuscript is submitted and deals with both the rights of the publisher and Author, as well as any obligations concerning a particular manuscript. However, in accepting this agreement, Authors continue to retain significant rights to use and share their publications.
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