The characteristics of Chaenomeles japonica cultivars. Different letters (a, b, and c) in the same row indicate significant differences between samples (p < 0.05).
\r\n\tHence, this book is targeted to deliver the bundled characteristics and features of MXenes to transfer the various scopes and virtues to the research community.
",isbn:"978-1-83768-120-4",printIsbn:"978-1-83768-119-8",pdfIsbn:"978-1-83768-121-1",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"184e1a0c9b5e62ebb3c7ebc53103db9f",bookSignature:"Prof. Dhanasekaran Vikraman",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11994.jpg",keywords:"Energy Devices, Semiconducting Devices, MXene Formulation, Supercapacitors, Batteries, Water Electrolysis, Li-Ion, FET, Photodetectors, Solar Cells, Perovskites, W2C",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 19th 2022",dateEndSecondStepPublish:"July 22nd 2022",dateEndThirdStepPublish:"September 20th 2022",dateEndFourthStepPublish:"December 9th 2022",dateEndFifthStepPublish:"February 7th 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"18 days",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Dhanasekaran Vikraman is an Assistant Professor at Dongguk University-Seoul, Seoul, Korea. His research interests are centered around the cost-effective fabrication of low-dimensional materials for electronics and energy devices. He has authored more than 175 international peer-reviewed research articles and numerous various forms of research publications.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"199404",title:"Prof.",name:"Dhanasekaran",middleName:null,surname:"Vikraman",slug:"dhanasekaran-vikraman",fullName:"Dhanasekaran Vikraman",profilePictureURL:"https://mts.intechopen.com/storage/users/199404/images/system/199404.png",biography:"Dr. Dhanasekaran Vikraman is an assistant professor in the Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea. He received his bachelor’s degree from Mannai Rajagopalaswamy Government Arts College, Mannargudi, affiliated with Bharathidasan University, India. He completed his master’s degree and Ph.D. at the Department of Physics, Alagappa University, India. Later, he received a visiting scientist position at KIST, Korea; a Marie-Curie Experienced Researcher fellowship at the Department of Physics, Aristotle University of Thessaloniki, Greece; and post-doc positions at Sejong University and Ajou University, Korea. He has authored more than 180 international journal articles and 3 book chapters and edited several books. 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Churchill, Maja Dutour Sikirić, Božana Čolović and Helga Füredi Milhofer",coverURL:"https://cdn.intechopen.com/books/images_new/8812.jpg",editedByType:"Edited by",editors:[{id:"219335",title:"Dr.",name:"David",surname:"Churchill",slug:"david-churchill",fullName:"David Churchill"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"59946",title:"A New Statistical Tool Focused on Metrological Tasks",doi:"10.5772/intechopen.74872",slug:"a-new-statistical-tool-focused-on-metrological-tasks",body:'\nThe key point of the text is the principle of the probability of the origin of the data. We believe that it is useful before exposition of this principle and its consequences spell out some general speculations about the situation in metrology. Metrology as a technology needs a simple, well-established and understandable procedure for implementing its tasks. Metrology as a business tries to canonize and protect its methods from strangers. These peculiars prevent the use of new mathematical tools. Metrology in a narrow sense begins with the creation of the standard, continues by the construction of a calibration hierarchy, and ends with the calibration of the working instrument of measurement. Metrology in a broad sense is a component of the experiment everywhere where its main tools are used, namely, traceability to the standard and an estimation of uncertainty.
\nUncertainty is estimated using statistical tools. The peculiarity of statistical instruments applicable in metrology is the essential role of a priori information in their work. The best way to obtain a priori information is a specially performed calibration experiment. In an ideal metrological experiment, the values of all model parameters are known and controlled except for one single parameter whose value is estimated.
\nStatistics without a priori information cannot be used as the metrological tool. But the origin of the a priori information can be different. For example, certain object does not in any way depend on the will of the observer, and, consequently, a calibration experiment is impossible. But it is possible to collect a lot of different data about this object and similar ones. Data can only be used to classify them and to monitor the evolution of the object. On the other hand, if we reformulate the accumulated database as a priori information for identifying an object class from new data, then this is already a metrological formulation of the problem. The estimation of the absolute value characterizing the object is difficult because there is no direct traceability to the standard. But recognizing an object and estimating the magnitude of relative changes from a small amount of data can be formulated as a metrological task.
\nUsually, the data of the working experiment on the subject of observation are not numerous, but there is a priori information obtained in the calibration experiment. It is assumed that by the time of the working experiment this information is still relevant. Comparing the data and the model, we can estimate the observed state of the object.
\nAn effective method—to compare the model used and the available data—is to estimate the probability that the data is generated by a source corresponding to the model. This probability is interpreted, in particular, as an estimate of the reliability of a particular value of the investigated quantity, described in the a priori model as an adjustable parameter. In other words, as an argument for the criterion to choose, one of the many variants of the measurement model provides a description of the object under study.
\nIn this text, an analysis of the features of traditional statistical tools [1] and some new tools to replace them is proposed. The dignity of new tools (in particular the rank measure) is significantly a better universality, but its disadvantage is a large computing expenses.
\nThe rank measure was first proposed and intuitively grounded in [2]. In paper [3], it was formally justified. Some aspects of its application were discussed in Ref. [4]. Paper [5] describes the main tools and their applications for the method of converting the densities (MCD). In paper [6] the application of a rank measure to the type of experiment rarely used by metrology but widespread in technical disciplines is discussed. This is a simple interpretation of dynamic experiment. Its main features are as follows: enough data is collected, and a minimum number of observable factors are required to evaluate the values of many parameters of the model.
\nHabitual models of the measurement experiment are constructed from the principal \n
The stochastic component is a description of the random (or considered to be) influence on the result of the experiment. Often, this description consists of a system of equivalent noise sources with some specified characteristics.
\nThe components of the model are formalized as headings of procedures whose variables are divided into two parts—the variable values of which must be determined quite accurately by the time of the working experiment, and the variable \n
The main purpose of the model is to formulate a prediction. For metrological tasks, we set the value of the controlled parameters of the model, and from it we obtain a data structure modeling experimental data. Two modeling methods that can be compared with the definitions of probability have been distributed. The Monte Carlo method (MCM) is comparable to a countable probability, and the method of converting the densities (MCD) is comparable with the axiomatic probability.
\nIn metrological statistics the most widespread one is the simple additive noise model (additive random error model) \n
It is important that it is a priori known about a random component. It is usually assumed that only the form of distribution of probability of the source of chance is known. It is necessary to estimate the value of the constant component (as a shift parameter of a known distribution) over a small number of data affected by a random error with zero shift (for simplicity of interpretation) but with a scattering magnitude of unknown magnitude. It is also assumed that the time between measurements is so large that the data sampling elements are statistically independent.
\nA trivial model with an unknown scattering parameter in accordance with mathematical statistics and normative documents of metrology is identified according to the formula (we call it the sectoral formula) \n
The property of the formula is illustrated in Figure 1. In this figure, by MCM the cloud of possible results of a multiple experiment is calculated and is delineated by means of a formula. The formula is linear, therefore divides the cloud of estimates into two regions by oblique boundaries.
\nClouds of scattering of results of estimates. The number of tests is 106, the multiplicity of the experiment is 5, the source of chance has the normal distribution with μ = 1 and σ = 1 and 2 (the notations in the figure by different transparency) and the color markings for the confidence probability of 0.95 are blue (erroneous estimates) and red (correct
The change in the coefficient of coverage will lead to a shift in the boundaries of the blue and red sectors, and a corresponding change in the confidence probability is due to a change in the ratio of the shares of estimates within and outside the confidence interval.
\nThe advantage of the formula is that whatever the dispersion of the source of chance, you will still get your 95% of correct estimates. This is illustrated by the superposition of clouds with different dispersions.
\nThe disadvantage is the strong dependence of the error probability on the standard deviation. If by will of chance the data is close, then the probability of error is large, greater than the confidence probability. If the data is very scattered, then the confidence interval is too wide, with that the actual probability of making a mistake is negligible. The confidence interval is located at the level value of statistics from the border blue/red to the border red/blue. But in the statistical limit, the confidence probability will be met. Intuitively, it is believed that, namely, the extreme values of the cloud of estimates are discarded, but in reality, it is not so. The paradox is that the probability of error is more there when the data seem better and vice versa.
\nThe illustration is given for normal distribution and normative statistics. For other distributions and for other statistics, the scattering clouds of the results are different, sometimes quite bizarre. Coefficient of coverage should also have its own value different from Student; however, it is quite simple to calculate. Here are just several simple illustrations. Let us replace the normal distribution to a very important uniform distribution. First, we apply to it normative statistics [Figure 2 (left and central)] and then more suitable statistics of extrema \n
Clouds of scattering of results of estimates. For normal
Without going into numerical details, we give a few qualitative remarks on the illustrations given. Although the scale of both the distributions and clouds of assessments is comparable, coverage coefficients are distinctly different. It can be judged from the tilt of the colored borders.
\nClouds differ not only in form but also in size. The most compact cloud gives set of a normal distribution with of normative statisticians [Figure 2 (left)] because this combination is optimal. The combination of a uniform distribution and normative statistics (central) is not optimal; hence, the cloud is scattered more. This loss of efficiency is not catastrophic, so this combination is used in practice. Normative statistics provide acceptable estimates for many finite distributions and many distributions with light tails, but there are such distributions where the efficiency is too small, for example, distributions with heavy tails. The combination of uniform distribution and statistics of extrema (right figure), although not optimally but somewhat more efficient than in the previous example. But in practice this combination is not used because the sectoral formula of the cloud cross section leads to an unacceptably overestimation of the confidence interval value. The reason is that the maximum cloud density of this example is at the vertex, when, as in the previous examples, the maximum density is closer to the centres of the clouds. An effective algorithm for estimating the distribution of the scattering parameter could help, but because of the variability of the distribution form, mathematical statistic could not offer such an algorithm.
\nDe facto, the distribution form and both statistics are used as a single set. The situation can be interpreted in two ways. On the one hand, having the form of distribution, we can choose or synthesize statistics more or less effectively. On the other hand, selecting statistics from a certain set of tools, we actually choose a class of distribution forms for which the statistics are still effective. However, neither the value of efficiency nor the form of distribution can be precisely determined.
\nThe normative tool has yet a problem that we call a mysterious amendment to deviation. Deviation is recommended to be used not in a pure form, but with a correction coefficient (the so-called standard deviation). It is explained that this amendment allegedly eliminated deviation from the dispersion of the normal random source. But very few noticed that this is not quite true.
\nFirstly, the distribution of deviation is asymmetric, its form changes, and is especially strongly at small amounts of repeated experiments. And only to an infinite number of experiments it approximates to normality and, accordingly, to symmetry.
\nSecondly, because of the nonsymmetric form of deviation distribution, it is not entirely clear in which its characteristic should be adjusted. It is customary to correct the mode, but with the same success, it is possible to correct a centre of gravity or some kind of composite criterion composed of the moments of this distribution.
\nThirdly, even for the mode, the recommended corrections only partially eliminate the problem. The reason lies in the desire to describe the correction factor by a simple formula. While its magnitude is simply calculated, the result does not fit into any of the proposed theoretical constructions (Figure 3). The reason is the complex and contradictory changes in the form and position of the cloud of estimates as the number of repeated experiments is changing.
\nEstimates of the scattering parameter and the effect of corrections as a function of the number of repetitions of the experiment. The source of randomness is the normal distribution with μ = 2 and σ = 0.5. The statistics for estimating the scattering parameter is the deviation. MCM is used for obtaining data by two 107 tests. Each point is the result of an independent experiment. Legend on the figure field:
The idea of the correction is that, a priori knowing its magnitude, we correct the estimate made by the statistics that measures the scattering parameter so that in the statistical limit the estimate coincides with the value of the dispersion. The question arises: what for? The quality of the estimate of the measured quantity is determined by the sectoral formula, and the coefficient of coverage of which is calculated even more easily than the correction. A reasonable way is to abandon the amendments and the coefficients of coverage numerically computed, but this will no longer be the coefficients of the Student.
\nThe sectoral formula is useful, but the rank measure copes with similar tasks of metrology better.
\nThe principle says that the important instrument of metrological research should allow to estimate the probability of obtaining a certain sample of data from the selected model.
\nAccording to the principle—using the model and experimental data—the joint probability distribution for all values of each of the estimated variables is calculated. Each point of this distribution is interpreted as the probability that the data is obtained in accordance with the model and, moreover, with specific values of its parameters. Evaluation of the result of the experiment is given as \n
The task of constructing the estimation algorithm is solved in the general form of both MCM and CDM. The results are comparable, although the algorithms are different. To solve this, we need a consistency of the numerical model and also a metric for the data structures that model the results of the experiment.
\nFormally, this sequence of operations must be performed: \n
The numerical consistency of the model is understood as the ability of the model (if all the adjustable variables are given) in a numerical experiment to generate model data indistinguishable (quite similar) from the data obtained in the experiment.
\nThe metric should evaluate the magnitude of the difference between the same type of data in both experimental and simulation origin. The metric is constructed based on the modeling method and also on features of the application where it is used.
\nWhen using MCM, the ‘natural’ metric consists of counting the (approximate) matches of the data set to be checked and the extensive database generated for the given parameter values. In order to estimate the probability to the value of the parameter being evaluated, the model is launched many times (at example
When using MCD, the estimation algorithm solves the deconvolution problem in the general formulation \n
Obviously, the solution in general form, without taking into account the structure of the model and data, is very labour-consuming by both methods. But for simple models and data, the situation is so simplified that it leads to simple algorithms.
\nThe concept of a rank measure was proposed years ago and analyzed from both the intuitive and the formal points of view. Here, we propose an approach which can be regarded as justification as rationale in constructive style.
\nIn fact, suppose that for two data samples of the same length, all elements are the same. Should the metric distinguish them? It is obvious enough that it is not necessary to distinguish and there is no possibility to do this.
\nNow, in each sample, one element by element of a different but identical value and in the same position is replaced. As before, the samples are indistinguishable.
\nNow, in one of the data samples, we change the positions of any two elements. If the data elements are equal, then the samples are indistinguishable. If the data elements are different, then the samples can be distinguished, but should this be done?
\nIf the data is independent, then any position of each element is equally probable. Thus, the probability of origin is unchanged. The metric must be such that a simple permutation of data elements within one of the samples does not change the value of the metric. Consequently, neither the number nor the step of internal permutations on the value of the metric is affected.
\nThis creates an equivalence class for data samples formally different as records of the data acquisition process, but within the class, those samples are indistinguishable by the metric. Data sample after simple sorting in ascending order (rank statistics) is a natural representative of each of these classes and can be used instead.
\nEach of the data sample elements \n
The probability of the origin of the value of each data element \n
An important feature of the algorithm for identifying a trivial statistical model with the assumptions made is that there is no need to explicitly define the metric. You can immediately go to the estimation of the demanded probability of origin by comparing the prediction of the model in the form of the densities of the distributions of each of the data elements and the sorted experimental data. The formula of a rank measure can be dissected to three factors:
\nTheir interpretation is obvious: the latter is the formula of the likelihood method, the second is the correction to the likelihood method and the first is the normalizing factor. For this reason, the rank measure can be considered as a corrected likelihood method.
\nThe rank measure is the simplest solution of the identification problem for the simplest model that can be obtained within the framework of calculating the probability of origin. The reason is in the availability of an analytical formula for calculating the model’s prediction. For more complex models, there is no such formula. At least we need to compute the prediction of the model numerically. Studies were conducted and it was revealed that for two important particular models’ explicit formulation of a metric is not required too. It is multifactor expansion of the trivial model and model where the parameters of the dynamic deterministic function are identified against the background of noise.
\nIn this section we give examples of the application of a rank measure in some basic types of experiments. Let us compare the results obtained by algorithms using a rank measure and the results of normative algorithms. In this section, several varieties of direct measurement experiment and one generalization are considered.
\nCalibration experiment is main type of experiments in metrology. There is no means of measurement which one way or another would not undergo calibration. The purpose of the calibration experiment is to compare the measuring instrument with the standard, collect the data and describe a correction function that will be used as a priori information in the working measurement experiment.
\nIn the calibration experiment, the values of the standard and the readings of the measuring instrument are juxtaposed. In this case, the measuring means is used to estimate the value of the standard used. The results are collected and form a data structure, for example, as in Figure 4 (left).
\nThe structure of the calibration data in graphical form (left) and the correction function (right) as the regression of these data
The correction function is constructed as a regression at the calibration data. The obvious representation is the density stretched over the whole measurement range and accumulating all the calibration information [Figure 4 (right)]. The more calibration data and the more carefully the regression, the more reliable the results. The replacement of the abscissa axis from the value of the reference value to the unknown means that the probability of the value of the standard corresponding to the experimental data is estimated.
\nThe quantity and quality of the information collected in the calibration experiment and the information stored in the correction function largely determine the capabilities of the working measurement experiment. Although modern regulatory documents allow the use of a correction function in this form, for example, IEEE 1451, historically, the systematic error is eliminated separately, and the uncertainty of the measurement tool is described as an interval approximation of the density function in the form of a two-term formula or its simplifications.
\nThe correction function is used in a working experiment to fully evaluate the result of the experiment. If the data comes in the form of a point estimate (number), then the corrected measurement result is calculated as cross section of correction function, which is interpreted as the distribution density of the possible values of the measured value. That is, the systematic error is eliminated, and an estimate of the uncertainty of the values of the measurand is given (Figure 5).
\nThe transition from point experimental data
On the other hand, the data may already contain a description of the uncertainty, for example, in the form of a probability density \n
The transition from experimental data with uncertainty
Measuring the same physical quantity repeatedly, in principle, we get the opportunity to deal with errors and thereby improve the accuracy of the evaluation of the result. The problem of normative statistical tools is that it was far from always possible to use data efficiently, and sometimes efficiency was reduced to zero. From this point of view, since the rank measure uses the form of a specific distribution, it will always be optimal in efficiency with respect to this distribution.
\nThe greatest effect of using the rank measure as statistics for estimating the distribution parameters is observed in a multiple experiment with unknown scattering. According to the principle of probability of origin, the probability of obtaining experimental data from a random process model with a known form of the distribution density is estimated, but the parameters of the shift
For example, we estimate the shift parameter from the data for normal and uniform distributions \n
Uncertainty functions
The uncertainty functions for different distributions differ in varying degrees by form but mainly by the scattering estimate. The distributions used in the example are both symmetric for this reason, and the difference in the estimation of the shift parameter is small.
\nNow, it became possible to move from a joint estimation of parameters to only an estimate of the shift parameter (usually interpreted as an estimate of the measured quantity). At this stage, it is possible to take into account a priori information about the scattering parameter. This information can be different. One of the polar cases is its complete absence; the scattering can be any \n
The uncertainty functions of estimating the shift parameter (left) and their difference (right). The notations on the left figure are a red line for the normal distribution and blue for the uniform, respectively. For a correct comparison, the uncertainty functions are normalized, which is interpreted as an assumption of the validity of both models simultaneously.
If, for joint uncertainty function, the influence of the form of the model distribution is obvious, then the integral estimates of only the shift parameter differ insignificantly. Small differences can be interpreted as evidence of the prevalent thesis ‘if there is a small number of data the form of the distribution is unimportant’. More precisely, when identifying only the shift parameter for a small number of data, the form of the distribution has no important significance and does not introduce significant errors in addition for a wide class of distributions. However, it is possible to construct counterexamples that show that this is not always so, for example, using distributions having a significant displacement.
\nThe form of the uncertainty function of the result for a number of reasons has heavier tails than the original distribution. Briefly, there are two main reasons. There is still a high probability of obtaining compact data from the distribution with a large value of the scattering parameter, which heavies the tails of the uncertainty function. On the contrary, the probability of compact distributions is concentrated in a small space, which leads to a high probability density near the vertex of the uncertainty function and sharpens it.
\nNow, we can write an interval estimate of the measurement result as a quantile of the uncertainty function. For the confidence probability of 0.95 by the normal distribution model, result estimation with uncertainty is 0.153 ± 0.869 and by the uniform distribution model is 0.149 ± 0.94. Uncertainty function has less scattering than the original distribution (at example for normal distribution ±1.96 and for uniform ±2.0), which is actually the goal of increasing the multiplicity of the experiment. The recording of the result by the form is the same as the normative one, but in fact it has a more rigorous meaning. Tails of joint distributions (as well as clouds of estimates) are cut vertically, but not by the sector as in the normative case.
\nThere are many cases when the scattering parameter is known a priori with greater or lesser accuracy. The direct way to take into account information about the value of the scattering parameter is to solve the estimation problem for an unknown parameter and only then to use a priori information \n
The most often known is the range of possible values of the scattering parameter \n
Illustration of the use of a priori information on the scattering parameter in order to convert joint uncertainty to uncertainty function of the shift parameter. Legend on the right picture field: The green line is the exact knowledge of the scattering parameter
Under favorable conditions, instead of the joint uncertainty function of the parameters, one can use the fact that the correction function itself is a distribution. Consequently, one complete correction function can be replaced by a set of ordinal correction functions with the same external characteristics. This is done either experimentally in a calibration experiment or analytically from the formulas of the densities of ordinal distributions for each value of the measured quantity in the entire measurement range. We obtain a family of correction functions passing along and partially overlapping \n
Illustration of the use of the set of ordinal correction functions. On the left is a set of correction functions for a triple experiment, and on the right is an example of estimating the value of the measured parameter for data {0.4, 0.41, 0.44} each of the three ordinal estimations (color lines) and resultant estimation (black line).
This tool is more refined because it can take into account the change in the form of the distribution of the correction function for different elements from the data set. But it is more vulnerable because it does not provide for any additional sources of randomness that cannot be the taken into account in the calibration experiment.
\nThe situation where the scattering parameter is known sufficiently accurately is not so rare, although it is hidden inside the measuring instrument. At best, the user can adjust the ‘accumulation time’. If the accumulation of information is made in digital form, then this is a direct analogue to the number of repeated measurements, but in the analogue form, the accumulation is not fundamentally different from the effect of repeated measurements.
\nThe abstraction of point data is very useful from a practical point of view. Its application seriously simplifies both calculations and their interpretation, and the results are of quite satisfactory quality. In most cases, it should be used. However, in the strict approach, each data element must be assigned to its own individual uncertainty. For many applications, including the case of multiple measurement experiments, an adequate form of describing the uncertainty of the experimental data is the probability density of the obtained value \n
Normative documents including GUM solve this problem taking into account uncertainties apart, for example, preliminarily dividing the uncertainties into type A and type B and then combining them in a specific way. The method is simple but strictly adequate only for normal distribution and simple models. For distributions similar to normal distribution, the deterioration in the result still is quite acceptable.
\nTo strictly take into account the uncertainty of the measuring instrument, it is sufficient to slightly upgrade the rank measure to.
\n\n\n
The formula is interpreted as an n-fold integral of a rank measure from deviation to point data with their joint probability. The complexity of applying the formula is the multiplicity of the integral and the need to constantly check the order of the data if the density of the data distribution overlaps. When the distribution density of data is reduced to the delta function, the upgraded measure reduces to the original measure. The delta function is the model of point data. From this point of view, uncertainty function for point data is the most likely, but for data deviations it is a less likely alternative.
\nIn a more general case, all sources of uncertainty are taken into account in a natural way when calculating the model’s predictions and when a comparison of the prediction and an adequate data model is made.
\nLet us explain this with an example (Figure 11). The data is the same as for Figure 10. We will supplement the data with uncertainty ±0.05. The uncertainty is the same for all data elements, but it can also have an individual value. The law of distribution of uncertainty will be assumed to be uniform. The model of the measurement experiment being studied differs from the trivial model only in the presence of two sources of randomness. One source has a normal distribution law, for example, the error of manufacturing samples from the same material whose property is being investigated. Another source has a uniform distribution of, for example, uncertainty of a digit measuring instrument.
\nIllustration of identification of a trivial model with two different sources of randomness. The left figure is obtained for point data, and the central figure is obtained for data with uncertainty. The right figure shows the uncertainty functions of the estimates for the two preceding figures:
The work of the algorithm can be interpreted as the creation of a film. Each frame is an estimate of the parameters from a given set of point data \n
The uncertainty is large compared to the distance between data; hence, the probability of accidental coincidence of data is large, which leads to a touch of the uncertainty function of the estimates to the abscissa axis [Figure 11 (centre)]. The uncertainty of the data, as it was, ‘smears out’ the uncertainty function of the estimate. Uncertainty is greater in all respects but especially strongly affects the top of the uncertainty function of the estimate of the measured parameter and often changes the form of the evaluation function.
\nThis allows us to build a logical chain from the interpretation of data by interpreting possible estimates to the final estimate of the uncertainty of the measurand. For example, \n
The purpose of the multifactorial experiment is to estimate the value of several quantities in the form of a joint uncertainty function by factors. The number of factors considered varies easily, so in the examples we confine ourselves to two. And so, \n
Another solution is obtained if the experimental data are obtained synchronously \n
For example, if the multiplicity of experiment is 3, the number of factors is 2, \n
Initial joint distribution and set of ordinal joint distributions.
The rank measure is constructed as follows. The data structure (in the example this is three data pairs) is ordered by one of the factors, for example, by \n
For example, let’s use the model whose distribution is shown in Figure 12. The received data is \n
In the event that the statistical links between the factors are significant, the task is solved only numerically. For MCM, this is a direct numerical experiment. MCD is a search for direct and inverse transformations of such that make the distribution of the model independent by factors.
\nIn order to pass from the model of direct measurement to the model of the indirect measurement experiment, it is necessary to replace the measurand of trivial model by a more complex measurement principle model \n
Although in the natural sciences and in technology one can find very complex principal models of the experiment, metrology strives to avoid indirect experiments. This is achieved through the creation of new standards and the construction of suitable calibration schemes (calibration hierarchy). Even if the measurement tool uses inside the complex indirect model but being calibrated in the target units, then it realizes direct experiment. All that metrology can afford is the use of an indirect experiment as a temporary means in cases where a direct reference to the standard is not yet possible. Of course, one can complicate the formulation of the problem of indirect experiment in different ways, for example, in the analogy of Section 6.3.5, complicating the data structure, but it is unlikely that metrologists will be interested in this.
\nThe tools that metrology now uses have been created by statisticians at the beginning of the last century. By the middle of the century, metrology had mastered them. Over the years, the goals and circumstances of their creation and some of the properties have been forgotten. This creates some misunderstandings when interpreting the results of their application. Attempting to implement the GUM has been useful by simplifying and standardizing their application, but the tools themselves remained the same.
\nAs a result of the application of new tools, a direct and obvious chain of information gathering and use is built up in the performance of metrology tasks from calibration to the final result.
\nThe
Properties of JQ fruit such as firmness, and a high amount of juice and fibers characterized them as a potential crop in horticulture [7, 8, 9]. The knowledge about the biochemical characteristics of the JQ fruit recently increased, and their bioactive compound quantity and composition highlighted them as promising raw materials with a positive influence on human health [3, 10, 11]. The abundance of compounds with strong antioxidant properties, such as phenols, triterpenes, and vitamin C, can contribute to chronic disease prevention [2, 12, 13, 14]. Phenols as natural antioxidants are well-known with a positive effect on human health as anti-inflammatory [15], neuroprotective [16], anticancer [17], antiviral [18], improving cardiovascular activity [19], antidiabetic [20], and blood cholesterol and triglyceride-lowering [21, 22].
JQ fruits are also rich in fibers, as well organic acids, mainly malic and citric acids at levels of 10 times that of apple fruits [8, 23]. However, due to their characteristics, such as firmness, sourness, and astringency they are not suitable for fresh consumption, so mostly used for the production of juices, jams, syrups, alcoholic and non-alcoholic beverages, etc. [7, 24, 25, 26, 27]. If JQ fruits are not immediately processed or to be frozen, and appropriate storage should be considered to ensure minimum losses in quality parameters and biochemical composition. Several studies have shown that JQ fruits could be stored without significant losses in quality under a controlled atmosphere system or at 1°C and 85% relative humidity [9, 28]. Moreover, an unavoidable big amount of waste is generated during processing, mostly consisting of seeds [7, 29]. Even so, recent studies showed that seeds left after industrial manufacturing can be used for oil recovery, mucilage extraction, and as a source of organic acids, polyphenols, triterpenes, and microelements [29, 30, 31, 32]. Another residual, such as pomace, also is suitable for secondary uses, for example, pectin and fiber extraction [33, 34].
Significant amounts of biologically active compounds, including phenols and triterpenes, were detected also in JQ leaves [11, 35, 36]. Since the branches of the wild JQ plant are mostly prickly, collecting their leaves is a hard and complicated process.
The new cultivars Darius, Rondo, and Rasa are needleless, which can enrich the use-value of the whole plant. In order to expand the utilization of these new cultivars, it was necessary to determine their biochemical value. Moreover, the optimization of process parameters will help increase the extraction efficiency, as high temperature, oxidation, and other decomposition processes have negative effects on sensitive bio-compounds. Adjusting extraction parameters such as polarity of the solvent and its ratio to water, time, temperature, additional energy source (ultrasound, microwave, etc.), have a crucial impact on the efficiency of phenols extraction [37, 38, 39, 40]. This chapter aims to determine the impact of genotype and extraction methods on quince
All the solvents, reagents, and standards were used of analytical grade. The following substances were used in the study: Ethanol 96% (
Fruits, leaves of the Japanese quince cultivars, Darius, Rondo, and Rasa were collected from test gardens belonging to the Institute of Horticulture, Lithuanian Research Centre for Agriculture and Forestry, Babtai (55° 60´ N, 23° 48′ E). The fruits were harvested from late August to mid-September, depending on the technical maturity, for each cultivar randomly from five shrubs in September. Leaves were collected for each cultivar randomly at different seasons, spring (May), summer (August), and autumn (October). Fruits and leaves were frozen (at
Different fractions of grounded Japanese quince seeds.
First, the influence of different solvents on the extraction efficiency was initially determined, 0.5 g of the seeds or leaves powder with 10 mL solvent in different concentrations (ratio 1:20, w/v) were mixed and left at room temperature 22°C in the dark for 24 h. After extraction, the samples were centrifuged and filtered through a Whatman filter paper. For phenolic compounds extraction efficiency analysis, three solvents (ethanol, methanol, and acetone) and three concentrations (100%, 70%, and 50%) were chosen. After selecting the most efficient solvent system, ultrasound extraction (UE) of phenolic compounds was carried out with Sonorex Digital 10 P ultrasonic bath (Bandelin Electronic GmbH & Co. KG, Berlin, Germany). Response surface methodology (RSM) was used to examine the influence of UE processing variables on phenols extraction, and three parameters were selected for optimization—temperature, extraction time, and ultrasonic power regarding methodology described by Urbanavičiūte et al. [10].
Dry matter content was determined after forced air convention drying at 105°C to a constant weight. The total soluble solids were determined using a digital refractometer (ATAGO PR-32, Atago Co., Ltd., Tokyo, Japan). Fruit firmness was determined by the texture analyzer TA.XTPlus (Stable Micro Systems, UK) using the P/2 probe. Total sugar content was determined using the Bertrand method. Fiber content was determined using.
Fiber analyzer Ankom 2000 (Ankom Technologies, Madison NY, USA) and expressed as a percentage.
Spectrophotometric measurements were performed using a Genesys-10 UV/Vis spectrophotometer (Thermo Spectronic, Rochester, NY, USA). The total amount of phenols was assessed using Folin–Ciocalteu method according [41] at 765 nm wavelength, and was expressed in mg 100 g−1, the equivalent of gallic acid. The antiradical activity was determined using two methods—the DPPH (515 nm) method described by Brand-Williams et al. [42], and ABTS (734 nm) assay was applied according to the methodology described by Re et al. [43]. Antiradical activities were expressed in μmol of Trolox equivalents in g−1 dry extracts. Total proanthocyanidins (640 nm) were determined using the technique described by Heil et al. [44]. According to the methodology described by Liaudanskas et al. [45], the high-performance liquid chromatography (HPLC) method was used for the determination of phenolic compounds. The antimicrobial activity against three Gram-positive and three Gram-negative bacteria was evaluated by the agar well diffusion method described by Urbanavičiūte et al. [10].
Data collected are expressed as mean ± standard deviation. The results of three replicates were presented and univariate analysis of variance (ANOVA) was applied. Tukey’s HSD (honest significant difference test) was used for multiple mean comparisons. Statistical significant differences were considered at p < 0,05. The statistical analysis was performed using Statistica 10 software (StatSoft, Inc., Tulsa, OK, USA).
The cultivars Darius, Rondo, and Rasa are characterized by the absence of needles, their leaves are resistant to blemishes, the whole plant is resistant to frost and the fruit to rot. The fruits finish ripening evenly in early September. Rondo is characterized by large shrubs with orange flowers. Shrubs of Darius are small, with several main branches, blooming in orange-dense inflorescences. Shrubs of the Rasa are of medium size, the flowers are pink and not dense. The characteristics of the fruit listed in Table 1, may help for better adoption of quince processing techniques, and for farmers to select cultivars with desired properties.
Characteristic | |||
---|---|---|---|
Darius | Rondo | Rasa | |
Average yield, kg/shrub | 9 ± 0.6a | 10 ± 0.5a | 8 ± 0.8a |
Fruit weight, g | 46.2 ± 8.3a | 75.7 ± 13.1b | 97.7 ± 21.2b |
Diameter of fruit, mm | 44.4 ± 2.1a | 53.0 ± 2.7b | 58.7 ± 3.4b |
Seeds weight, g | 2.5 ± 0.8a | 3.4 ± 0.9a | 7.8 ± 1.9b |
Number of seeds | 59.2 ± 19.9a | 62.0 ± 15.6a | 127.2 ± 36.3b |
Flesh thickness, mm | 11.6 ± 1.3a | 13.5 ± 0.6a | 12.0 ± 1.9a |
Diameter of the core, mm | 22.6 ± 1.7a | 24.8 ± 2.9a | 39.6 ± 5.0b |
Dry Matter of fruit, % | 9.2 ± 0.1a | 9.6 ± 02a | 9.2 ± 0.1a |
Dry Matter of leaves, % | 46.1 ± 0.1a | 43.1 ± 0.1a | 43.7 ± 0.1a |
Dry Matter of seeds, % | 60.2 ± 0.1a | 53.8 ± 0.1b | 54.1 ± 0.3b |
Skin firmness, N cm−2 | 329.3 ± 31.1a | 315.5 ± 14.3a | 324.3 ± 38.6a |
Flesh firmness, N cm−2 | 180.6 ± 13.3a | 150.8 ± 12.2a | 171.2 ± 20.1a |
Total sugar content, % | 3.45 ± 0.02a | 4.0 ± 0.34a | 2.69 ± 0.22b |
Sucrose, % | 1.08 ± 0.07a | 1.05 ± 0.04a | 0.77 ± 0.03b |
Fiber content, % | 19.3 ± 2.3a | 17.9 ± 3.1a | 21.1 ± 2.1a |
Dry soluble solids, % | 9.9 ± 0.2a | 9.4 ± 0.3a | 8.1 ± 0.1b |
The characteristics of Chaenomeles japonica cultivars. Different letters (a, b, and c) in the same row indicate significant differences between samples (p < 0.05).
Significant differences were found between the three cultivars in fruit size and their number of seeds. Rasa had the largest fruit and highest number of ripening seeds, almost twice as much as others (Table 1). The skin and flesh firmness were not significantly different between cultivars, but the fruits of the Rondo were slightly softer. Moreover, Rasa had the lowest amount of sugars and sucrose content.
Our previous studies have shown that the biochemical composition significantly varied between genotypes. Rasa had the highest amount of rutin and lowest vitamin C, while Rondo and Darius had higher catechin and chlorogenic acid, respectively [10]. Genotypic differences in the biochemical composition of JQ juices were also reported [46].
Moreover, it was found that different solvents and their ratio to water had a significant impact on phenols extraction efficiency using the simple maceration method from freeze-dried JQ fruit powder [10]. Besides, using ultrasound power decreased extraction time while increasing phenol yield from JQ fruit by 14.5% [10]. Five phenols (−)-epicatechin, (+)-catechin, chlorogenic acid, rutin, and isoquercitrin were identified in all the three cultivars Rasa, Darius, and Rondo, using the HPLC method [10]. The predominant phenols in all cultivars were flavan-3-ols (catechin and epicatechin), which account for around 94% of the total amount [10]. It was also reported that all cultivars have accumulated high levels of proanthocyanidins [10]. Similar results were demonstrated where 11 phenols were determined with a distribution of procyanidins (57.8%), (−)-epicatechin (33%), and chlorogenic acid (4.4%) [47]. In addition, 24 phenols were identified in the study of five
Moreover, phenols’ quantity and their biological activity expand the uses of JQ fruit as a promising substitute for chemical preservatives in the food and cosmetic industry due to demonstrated antibacterial activity [48]. The cultivars Rasa, Darius, and Rondo showed antimicrobial activity against three Gram-positive and three Gram-negative bacteria, in a concentration-dependent manner [10]. However, they have not shown antifungal activity against
Nevertheless, it was mentioned that phenol-rich extracts obtained from JQ fruit could replace aggressive synthetic drugs with side effects through demonstrated anticancer activity [13, 14, 49, 50]. Also, JQ fruit extracts can be used as an antioxidant drug for the prevention of diseases caused by inflammation or oxidative stress [47, 51]. Moreover, JQ phenols-rich extracts as modulators of carbohydrates metabolism showed a promising hypoglycemic effect and decreased intracellular ROS accumulation [52]. Despite all these health benefits and pharmaceutical properties of JQ fruits, till now it is mostly used for the food industry. Moreover, exploiting significant unavoidable amounts of waste left after industrial processing can offer potential economic and ecological benefits.
Due to the firmness, sourness, and astringency of JQ fruit, they are not suitable for fresh consumption, mostly used for syrup and candied production. The JQ by-products after processing were received from Puree/juice manufacturer, collected after three different processing methods—syrup and candied fruits, puree, and juice (Figure 3). The seeds separation and preparation for analysis were performed following methods described by Urbanavičiūtė et al. [29]. The amount of by-products after JQ fruits processing depended on the manufacturing technology, ranged from 20–40%, and consisted mostly of pomace and seeds [29].
General scheme of quince processing.
Most of the by-products remain after juicing (Figure 2C), range 40–60% of fresh fruit weight [7, 29]. The pomace left after juicing showed a significant amount of phenols, even 13 times more than juice (Table 2). Juicing is a rapid process that releases only a fraction of the biologically active compounds from the fruit, so most of them remain in the pomace. The JQ pomace also showed strong antioxidant activity and high amounts of proanthocyanidins.
Parameters | Juice | Pomace |
---|---|---|
Total Phenols, mg 100 g−1 | 488 ± 11b | 6645.6 ± 211a |
RSA (DPPH), μmol TE 100 g−1 | 14.7 ± 1.1b | 152.2 ± 13a |
RSA (ABTS), μmol TE 100 g−1 | 69.7 ± 2.1b | 938.2 ± 33a |
Content of proanthocyanidins, mg 100 g−1 | 218.5 ± 7.1b | 1368.7 ± 55.4a |
The total phenols, proanthocyanidins, and RSA - Radical scavenging activity DPPH, and ABTS of JQ juice and pomace. Different letters (a, b, and c) in the same row indicate significant differences between samples (p < 0.05).
Numerous studies have reported an important role of phenols in inhibiting the growth of microorganisms [53, 54, 55]. Our results showed that JQ pomace extracts had antimicrobial activity against three Gram-positive and three Gram-negative bacteria in a concentration-dependent manner, where the strongest inhibition effect was obtained using 5% concentration pomace extracts (Figure 4).
Antibacterial activity of
The greatest inhibitory effect of pomace extracts was found on the gram-positive
In the food industry, JQ seeds are mostly discarded as waste, while new utilization can reduce losses for producers, especially when the seeds accounted for more than 30% of the total waste [29]. Moreover, it was reported that seeds from different species of the
Before determining the content and diversity of phenolic compounds in Rasa, Darius, and Rondo seeds, the impact of different extraction parameters on their efficiency was performed. To determine the most efficient solvent system for phenols extraction, three solvents in different concentrations were used. After maceration with methanol, the highest content of phenolic compounds was extracted with a solvent concentration of 70% (Figure 5).
Impact of methanol concentration and seeds particle size on the total content of total phenols compounds in quince seeds.
Pure ethanol has been shown to be the wrong choice for extracting phenols from quince seeds, and no significant differences between fractions were found (Figure 6). Extraction efficiency with ethanol was the highest when the ratio of water/alcohol was 1:1 (Figure 6).
Impact of ethanol concentration and seeds particle size on the total content of phenolic compounds in quince seeds.
The extraction of crushed JQ seeds with pure acetone also was not appropriate, while 70% concentration showed the highest amount of extracted phenols, five and six times more than using ethanol and methanol, respectively (Figure 7).
Impact of acetone concentration and seeds particle size on the total content of phenolic compounds in quince seeds.
Our results showed that solvent type and its concentration had significant effects on phenol extraction efficiency from JQ seeds. Moreover, our results agreed with previously reported studies that dual solvent systems are more efficient for phenols extraction than with pure solvents [37, 57, 58]. Besides, phenols dissolved differently regarding the solvent system, for example, it was reported that methanol is the best for extraction of catechin, epicatechin, and epigallocatechin, then 70% acetone provides the highest content of proanthocyanidins and total phenolic compounds, while the highest content of gallic acid was extracted with 75% ethanol [59].
The most efficient solvent system (70% acetone) was used for ultrasound extraction of phenolic compounds with an ultrasonic bath. Response surface methodology (RSM) was used to examine the influence of ultrasound processing variables on phenols extraction, and three parameters were selected for optimization—temperature (15–50°C), extraction time (15 min–60 min), and ultrasonic power (48 W–480 W) following the methodology described by Urbanavičiūte et al. [10].
Using RSM, the highest amount of phenols was obtained when the samples were extracted for 60 min, at 50°C with 480 W ultrasonic power (Figure 8).
The influence of (a) – Time and ultrasound power, (b) – Temperature and time, (c) – Ultrasound power and temperature on extraction efficiency of phenols from JQ seeds.
The temperature had the highest impact on phenols extraction, the efficiency increased around 80%, and time was reduced to 23 hours in comparison with simple maceration. Such a strong effect may have been due to the ultrasound power ability to destroy complexes of mucus with phenols, which makes their extraction more difficult from seeds [60]. Using the previously determined optimal parameters for phenol extraction, qualitative and quantitative analysis for Rasa, Rondo, and Darius seeds extracts were performed. The highest total phenolic compounds and antioxidant activity in seed extracts were found in Rondo and the lowest in Darius (Table 3).
Cultivars | Total phenols mg/100 g | RSA (DPPH) μmol TE g−1 | RSA (ABTS) μmol TE g−1 |
---|---|---|---|
Rasa | 427.6 ± 13.2b | 20.4 ± 1.4b | 41 ± 2.1b |
Rondo | 499.6 ± 14.5a | 26.4 ± 1.2a | 50.9 ± 2.4a |
Darius | 283.5 ± 9.4c | 14.7 ± 0.8c | 27.7 ± 1.1c |
The antioxidant activity, and total phenols of JQ seeds cultivars cultivated in Lithuania. Different letters (a, b, and c) in the same column indicate significant differences between samples (p < 0.05).
Twelve phenolic compounds in Rasa, Rondo, and Darius seeds extracts using high-performance liquid chromatography (HPLC) were identified (Table 4).
Compound, μg g−1 DW | |||
---|---|---|---|
Darius | Rondo | Rasa | |
Rutin | 6.0 ± 0.3a | 6.7 ± 0.4a | 6.5 ± 0.3a |
(+)-Catechin | 4.2 ± 0.2b | 4.2 ± 0.1b | 7.5 ± 0.5a |
Chlorogenic acid | 20.7 ± 1.5b | 17.2 ± 1.3a | 24.2 ± 2.3b |
Caffeic acid | 9.7 ± 0.7a | 9.4 ± 0.5a | 9.3 ± 0.6a |
Syringic acid | 3.9 ± 0.2a | 3.9 ± 0.2a | 4.3 ± 0.5a |
Hyperoside | 5.3 ± 0.5a | 5.1 ± 0.4a | ND |
Procyanidin B2 | 22.5 ± 1.5b | 41.5 ± 3.5a | 45.0 ± 1.4a |
Procyanidin C1 | 29.1 ± 2.1b | 41.0 ± 3.2a | 37.4 ± 1.8c |
(−)-Epicatechin | 14.1 ± 0.7b | 19.8 ± 0.5a | 16.7 ± 1.3c |
Isoquercitrin | 10.4 ± 1.5a | 9.7 ± 1.2a | 9.7 ± 1.5a |
Quercitrin | 4.4 ± 0.2a | 4.6 ± 0.2a | 4.6 ± 0.1a |
p-Coumaric acid | 6.4 ± 0.5a | 5.8 ± 0.6a | 6.8 ± 0.7a |
Total | 136.8 ± 9.7b | 169.1 ± 4.5a | 172.1 ± 3.7a |
The quantitative composition (HPLC) of phenolic compound in JQ seeds. Different letters (a, b, and c) in the same row indicate significant differences between samples (p < 0.05).
The predominant compounds were procyanidin B2, procyanidin C1, and chlorogenic acid, their amounts varied between genotypes. Rasa and Darius had a higher amount of chlorogenic acid than Rondo, while Rasa with Rondo had a higher amount of procyanidins than Darius (Table 4).
Phenols quantitative analysis using HPLC was applied for seeds left after different industrial processing. Seeds, which were left after puree production had significantly the highest amount of phenols (Table 5). From 12 detected phenols, procyanidin B2 and C1, and (−)-epicatechin were predominant. Epicatechin levels could be explained as a consequence of the production method, as JQ fruit (reach in epicatechin) was treated without removing the seeds.
Compound, μg g−1 DW | Seeds left after different JQ processing | ||
---|---|---|---|
After juicing | After puree | After syrup | |
Rutin | 5.6 ± 0.3a | 8.4 ± 0.4a | 6.6 ± 0.3a |
(+)-Catechin | 13.9 ± 0.2a | 4.2 ± 0.1a | 7.5 ± 0.5b |
Chlorogenic acid | 20.7 ± 1.5a | 25.0 ± 1.3b | 8.8 ± 2.3b |
Caffeic acid | 9.1 ± 0.7a | 10.3 ± 0.5a | 9.6 ± 0.6a |
Syringic acid | 5.7 ± 0.2a | 8.2 ± 0.2b | 4.3 ± 0.5a |
Hyperoside | 5.0 ± 0.5a | 8.2 ± 0.4b | ND |
Procyanidin B2 | 24.6 ± 1.5a | 790.9 ± 43.5b | 62.0 ± 1.4c |
Procyanidin C1 | 27.6 ± 2.1a | 317.6 ± 23.2b | 65.8 ± 4.8c |
(−)-Epicatechin | 14.4 ± 0.7a | 709.8 ± 36.5b | 43.3 ± 2.3c |
Isoquercitrin | 9.7 ± 1.5a | 14.3 ± 1.2b | 10.0 ± 1.5a |
Quercitrin | 4.6 ± 0.2a | 7.6 ± 0.2b | 4.1 ± 0.1a |
p-Coumaric acid | 8.0 ± 0.5a | 21.9 ± 0.6a | 6.8 ± 0.7a |
Total | 146.2 ± 9.7a | 2041.4 ± 84.5b | 250.7 ± 13.7c |
Impact of JQ processing on the quantitative composition of phenols in seeds. Different letters (a, b, and c) in the same row indicate statistically significant differences between the individual compounds (p < 0.05).
To optimize the extraction of phenolic compounds from JQ leaves, three solvents and five concentrations were selected. The lowest concentration of phenolic compounds in quince leaves was determined by the maceration with pure solvents (Figure 9). The proportion of water in solvents had no significant effect on the extraction efficiency. Significant higher content of phenolic compounds was obtained by maceration of quince leaves with 50% acetone (Figure 9).
Impact of solvents and their concentration on the total content of phenolic compounds in quince leaf.
The following ranges of independent variables were chosen for the extraction of phenolic compounds from quince leaves in the ultrasonic bath—ultrasonic power 48 W–480 W, temperature 30–60°, and time 20–60 minutes (Figure 10).
The influence of (A) – time and ultrasound power, (B) – temperature and ultrasound power, (C) – time and temperature on extraction efficiency of phenols from JQ leaves.
Based on the developed model, the obtained results showed that the effect of all the three variables was not significant and had no effect on the extraction efficiency, except that the process was shortened by 23 hours compared to simple maceration. The highest extraction yield of phenolic compounds was obtained when the samples were extracted with 480 W ultrasonic power for 60 min, at 60°C temperature (Figure 10).
Our previous studies revealed that leaves of Rasa, Darius, and Rondo cultivated in both Latvia and Lithuania were rich in biologically active compounds [11, 35]. The highest amount of phenols were found in Darius and the lowest in Rondo leaves. The identified compounds belong to three main groups—hydroxycinnamic acid derivatives, flavonols, and flavan-3-ols. The chlorogenic acid was the most common in leaves of all three cultivars, which accounts for about 80% of the total phenolic compound [11, 35].
The environmental conditions in different seasons also significantly influenced the total amount of phenolic compounds and antiradical activity in leaves. They accumulate higher levels of phenolic compounds in spring and autumn in adverse weather conditions such as frosts and temperature fluctuations, and less levels in summer under more favorable conditions (Figure 11). To summarize, the development of new needleless cultivars Rasa, Darius, and Rondo has facilitated collecting promising and long-undervalued raw materials, such as
Effect of cultivar and climate conditions on the accumulation of phenolic compounds and radical scavenging activity in quince leaves.
Due to the significant amount of phenols in JQ leaves, the antimicrobial activity against three Gram-positive and three Gram-negative bacteria, and one yeast strain, in Rasa, Darius, and Rondo was identified. JQ leaves have shown antibacterial activity against microorganisms, the strongest inhibition effect was obtained for the
Microorganism | Extract Conc., % | ||||
---|---|---|---|---|---|
Rasa | Darius | Rondo | |||
Inhibition zone size, mm | |||||
Gram-positive | 0.5 | 10.0 ± 0.0a | 11.3 ± 0.5a | 11.0 ± 0.0a | |
1 | 15.0 ± 0.0a | 15.3 ± 0.5a | 12.0 ± 0.0b | ||
5 | 18.3 ± 0.5a | 17.0 ± 0.0a | 14.7 ± 0.5b | ||
0.5 | 17.0 ± 0.0b | 17.3 ± 0.5b | 20.0 ± 0.0a | ||
1 | 20.0 ± 0.0b | 20.3 ± 0.5b | 23.0 ± 0.0a | ||
5 | 25.3 ± 0.5a | 24.0 ± 0.0a | 25.7 ± 0.5a | ||
0.5 | 9.0 ± 0.0a | 9.3 ± 0.5a | 0.0 | ||
1 | 14.0 ± 0.0a | 10.3 ± 0.5b | 11.0 ± 0.0b | ||
5 | 16.3 ± 0.5b | 16.0 ± 0.0b | 17.7 ± 0.5a | ||
Gram-negative | 0.5 | 9.0 ± 0.0a | 9.3 ± 0.5a | 10.0 ± 0.0a | |
1 | 10.0 ± 0.0 | 10.3 ± 0.5 | 11.0 ± 0.0 | ||
5 | 15.3 ± 0.5 | 15.0 ± 0.0 | 15.6 ± 0.5 | ||
0.5 | 0.0 | 0.0 | 9.0 ± 0.0a | ||
1 | 11.7 ± 0.8a | 10.3 ± 0.5a | 11.0 ± 0.0a | ||
5 | 16.3 ± 0.5a | 15.0 ± 0.0a | 12.7 ± 0.5b | ||
0.5 | 9.0 ± 0.0a | 0.0 | 0.0 | ||
1 | 10.0 ± 0.0a | 9.3 ± 0.5a | 9.0 ± 0.0a | ||
5 | 14.3 ± 0.5a | 15.0 ± 0.0a | 12.7 ± 0.5b | ||
0.5 | 0 | 0 | 0 | ||
1 | 0 | 0 | 0 | ||
5 | 0 | 0 | 0 |
Antibacterial activity of Chaenomeles japonica cultivars leaves extracts. Different letters (a, b, and c) in the same row indicate statistically significant differences between the individual compounds (p < 0.05).
A previous study has been reported that
Although no significant differences were detected between cultivars in fiber content, leaves had a higher amount than their fruits (Figure 12).
The total fiber content in Japanese quince leaves and fruit.
Recent research on
Source | Bio compounds | Possible utilization | References |
---|---|---|---|
Phenols, triterpenes | Functional food, Cosmetic | [2, 10, 26] | |
Pharmaceutical (Anti-inflammation) | [47, 51] | ||
Pharmaceutical (Anticancer) | [14, 49, 50] | ||
Pharmaceutical (Antibacterial) | [10, 48] | ||
Pharmaceutical (hypoglycaemic) | [52] | ||
vitamin C | Functional food, cosmetic | [3, 7, 9] | |
Fibers | Functional food | [8, 23] | |
Phenols, fibers | Functional food, Cosmetic | (Table 2), [33] | |
Pharmaceutical (Antibacterial) | (Figure 4), [48] | ||
Phenols | Functional food, Cosmetic | (Tables 3,4,5), [56] | |
Tocopherols, carotenoids, squalene, phytosterols | Cosmetic | [29, 31, 61] | |
Mucilage | Food safety | [62, 63] | |
Pharmaceutical (wound healing) | [64, 65, 66, 67] | ||
Cosmetic (emulsion stabilization) | [68] | ||
Phenols, triterpenes | Functional food, Cosmetic | [11, 35] | |
Pharmaceutical (Anti-inflammation) | [69] | ||
Pharmaceutical (Anticancer) | [11, 70] | ||
Pharmaceutical (Antibacterial) | (Table 6) | ||
Fibers | Functional food | (Figure 12) |
Promising utilisation of quince Chaenomeles japonica plant.
JQ fruit extracts rich in proanthocyanidins showed proapoptotic activity in Caco-2 cells [13]. Their extracts rich in flavanols had an antiproliferative effect against various cancer cells, decreased their invasiveness by regulating several genes involved in apoptosis, angiogenesis, and metastasis [14, 49]. Their extracts also demonstrated an inhibitory effect on the MMP-2 and MMP-9 enzymes activity and can be used in cancer chemoprevention [50]. Besides, they showed the ability to protect biological membrane lipids from oxidation, and usage of those extracts as an antioxidant drug can be a prevention for diseases caused by inflammation or oxidative stress [47, 51]. Moreover, the human cells of hepatoma HepG2 pretreated with JQ phenols-rich extracts as modulators of carbohydrates metabolism showed a promising hypoglycemic effect and decreased intracellular ROS accumulation [52].
Due to the high content of bio-compounds with positive effects on human health, fruits, leaves, and by-products of cultivars Rasa, Darius, and Rondo are excellent raw materials for functional food, which allows increasing antioxidants in final products (Table 7). Moreover, extracts from fruit and their pomace, leaves, seeds, could be used as an antibacterial agent and a substitute for chemical preservatives in both the food and cosmetic industry. The importance of antioxidants for skincare is as essential as their internal consumption, so all JQ organs are a great source for cosmetic products as plant-based raw material.
Several studies showed that seeds of JQ are suitable for obtaining the oil rich in α-tocopherol, carotenoids, squalene, phytosterols, and phenols [29, 31, 61]. It was reported that seeds remaining after industrial manufacturing are still suitable for oil recovery. The yield and biochemical composition of oils depended on both the processing of seed pretreatment and the oil extraction method [29]. The biochemical composition varied between cultivars, especially the profile of fatty acids and accumulation of bioactive compounds, while oil yield was affected by the extraction technique [71]. The lowest oil yield was obtained using the cold-press method from Rondo seeds, and the largest from Rasa using ultrasonic extraction [71]. Despite the fact that JQ seed oils contain strong antioxidants it is not recommendable for the food industry regarding a very high omega-6/omega-3 ratio [29, 61, 72]. However, JQ seed oil is well suited for skincare products regarding the high content of linoleic acid and perfect fitness of linoleic acid/oleic acid ratio [73]. In addition, studies have shown that quince seeds are suitable for other products, such as mucilage preparation, which have various biological activities and possible applications [32, 64]. Quince seed mucilage has been used successfully for wounds and toxin-damaged skin treatment [64, 65, 66, 67]. Quince seed’s ability to form edible films together with oregano or thyme essential oil can be used to ensure food safety, and extend the shelf life of food products [62, 63]. Ethanol extract from quince seeds protected skin from allergen-induced Th2-type inflammation and reduced the effects of atopic dermatitis [74]. In addition, quince seed can be used for skincare products as an excellent emulsifier and stabilizer [68].
The same as fruits and seeds of these cultivars, leaves are promising raw material due to the high amount of chlorogenic acid that has shown many biological properties, including antibacterial, antioxidant, anticarcinogenic activities, hypoglycaemic and hypolipidemic effects [75, 76, 77]. The compounds with strong biological activity such as triterpenes have been detected in these three cultivar leaves as well [35]. Among the four identified triterpenes, ursolic and oleanolic acid were predominant, whose functions for human health have been investigated including cardioprotective [78], strong immunomodulatory [79], suppress tumorigenesis [80], valuable antimetastatic agent [81], and prevention or alleviation of glycation-associated renal diseases [82]. Finally, other
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Throughout the life cycle, many of them thrive in pathogen-rich environments, manage harsh weathers, exposed to a number of allochemicals, and adapt well to both terrestrial and marine ecosystems. Their remarkable ability to cope up with the enormous oxidative stress generated in all these circumstances, make them attractive models in this field of research. Endocrine control of oxidative stress in insects is recently emerging. Adipokinetic hormone, glucagon, ecdysteroids and juvenile hormone have been implicated in antioxidative protective role in insects. Drosophila and Caenorhabditis elegans have provided the largest body of evidence addressing the free radical theory of ageing. Oxidative stress is also induced by pesticides/insecticides. In mollusks, pesticides exert their biological effects via generation of ROS. Oxidative stress has been shown to be associated with exposure to several organophosphorous compounds and different classes of pyrethroids. Malathion is a potential hazard to the environment. Adverse effects induced by malathion in earthworms and insects have been reported. Information is now available in great detail on the role of ROS in modulating insect immunity during parasite invasion and bacterial infection. In Drosophila melanogaster ROS are actively produced in the midgut at a basal level in the presence of commensal microbiota and highly generated upon bacterial challenge. The involvement of reactive oxygen species (ROS) in mosquito immunity against bacteria and Plasmodium was investigated in the malaria vector Anopheles gambiae. The concentration of ROS increased in sand fly midguts after they fed on the insect pathogen Serratia marcescens. Elevated oxidative stress was previously reported for a mosquito line experimentally infected with Wolbachia, indicating that oxidative stress may be important for Wolbachia-mediated antiviral protection. In a nutshell, this chapter highlights the current advances of oxidative stress in invertebrate model systems and its implications.",book:{id:"5121",slug:"free-radicals-and-diseases",title:"Free Radicals and Diseases",fullTitle:"Free Radicals and Diseases"},signatures:"R.K. Chaitanya, K. Shashank and P. Sridevi",authors:[{id:"178087",title:"Dr.",name:"Rk",middleName:null,surname:"Chaitanya",slug:"rk-chaitanya",fullName:"Rk Chaitanya"}]},{id:"51874",doi:"10.5772/64700",title:"Ovarian Cancer Metastasis: A Unique Mechanism of Dissemination",slug:"ovarian-cancer-metastasis-a-unique-mechanism-of-dissemination",totalDownloads:3109,totalCrossrefCites:12,totalDimensionsCites:23,abstract:"Ovarian cancer is the most lethal of all gynecologic malignancies and has witnessed minimal improvements in patient outcomes in the past three decades. About 70% of ovarian cancer patients present with disseminated disease at the time of diagnosis. The standard of care remains a combination of debulking surgery and platinum‐ and taxanes‐based cytotoxic chemotherapy. Even though metastasis is the leading cause of ovarian cancer related fatalities, our understanding of the process remains limited. Ovarian cancer has a unique pattern of metastasis where the hematogenous spread is less common. Ovarian cancer cells mainly metastasize within the peritoneal cavity, which involves exfoliation from the primary tumor, survival, and transport in the peritoneal fluid followed by metastatic colonization of the organs within the peritoneal cavity. A key step for successful metastasis is their attachment and productive interactions with the mesothelial cells covering the metastatic organs for the establishment of metastatic tumors. This chapter provides an overview of ovarian cancer metastasis highlighting the unique dissemination and the underlying mechanisms of regulation of the steps involved. The role of the microenvironment in the process of metastasis will also be reviewed.",book:{id:"5267",slug:"tumor-metastasis",title:"Tumor Metastasis",fullTitle:"Tumor Metastasis"},signatures:"Anirban K. Mitra",authors:[{id:"185152",title:"Dr.",name:"Anirban",middleName:"Kumar",surname:"Mitra",slug:"anirban-mitra",fullName:"Anirban Mitra"}]},{id:"51903",doi:"10.5772/64787",title:"Role of Oxygen Free Radicals in Cancer Development and Treatment",slug:"role-of-oxygen-free-radicals-in-cancer-development-and-treatment",totalDownloads:3627,totalCrossrefCites:14,totalDimensionsCites:17,abstract:"It is well known that species derived from oxygen are cytotoxic and are involved in the etiology of cancer. Several carcinogens during metabolism exert their effect by producing reactive oxygen species (ROS). One of the consequences of oxidative damage to cellular DNA is mutated. It plays a vital role in the process of carcinogenesis (especially in the initiation and progression). The alters, including rearrangement of DNA sequence, base modification, DNA miscoding lesions, gene amplification, and the activation of oncogenes, could be implicated in the initiation stage of several cancers. Mitochondrial changes in the cancer cells are well known and as a result are respiratory injured. Mitochondrial dysfunction could lead to a low coupling efficiency of the mitochondrial electron transport chain (mETC), raising electron leakage and increased ROS formation. It has been documented that by reducing and inactivation of antioxidant system, the oxidative stress (OS) in cancer cells is higher. Cancer cells exhibit a higher oxidative stress level compared to normal cells, rendering tumor cells more vulnerable to raise ROS levels. Therefore, increasing ROS levels through redox modulation can be a strategy to selectively kill cancer cells but not normal cells. A promising anti-cancer method named “oxidation therapy” has been developed by causing cytotoxic oxidative stress for cancer therapy. In this chapter, we described the role of ROS as a double-edged sword in cancer development and treatment.",book:{id:"5121",slug:"free-radicals-and-diseases",title:"Free Radicals and Diseases",fullTitle:"Free Radicals and Diseases"},signatures:"Jalal Pourahmad, Ahmad Salimi and Enaytollah Seydi",authors:[{id:"172672",title:"Prof.",name:"Jalal",middleName:null,surname:"Pourahmad",slug:"jalal-pourahmad",fullName:"Jalal Pourahmad"}]},{id:"44689",doi:"10.5772/55415",title:"Drug Resistance and Molecular Cancer Therapy: Apoptosis Versus Autophagy",slug:"drug-resistance-and-molecular-cancer-therapy-apoptosis-versus-autophagy",totalDownloads:4010,totalCrossrefCites:2,totalDimensionsCites:14,abstract:null,book:{id:"2857",slug:"apoptosis",title:"Apoptosis",fullTitle:"Apoptosis"},signatures:"Rebecca T. Marquez, Bryan W. Tsao, Nicholas F. 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Autophagy is physiologic process of eukaryotic systems, which have significant role in adaptation to oxidative stress by degradation of metalloproteins and oxidatively damaged macromolecules. By oxidizing, membrane injuries allow the leakage of enzymes and contribute to cell damage. However, recent publications demonstrate the protecting role of lysosome system during excessive reactive oxygen species (ROS) production by the elimination of damaged proteins or organelles. Activation of autophagic or lysosomal system can eliminate the oxidizing components of cell in oxidative stress response. This chapter aims to provide the novel insight data for oxidative damage-mediated autophagy as well as their metabolic networks.",book:{id:"5121",slug:"free-radicals-and-diseases",title:"Free Radicals and Diseases",fullTitle:"Free Radicals and Diseases"},signatures:"Adem Kara, Semin Gedikli, Emin Sengul, Volkan Gelen and Seckin\nOzkanlar",authors:[{id:"177953",title:"Dr.",name:"Adem",middleName:null,surname:"Kara",slug:"adem-kara",fullName:"Adem Kara"},{id:"178363",title:"Dr.",name:"Emin",middleName:null,surname:"Sengul",slug:"emin-sengul",fullName:"Emin Sengul"},{id:"178365",title:"Dr.",name:"Semin",middleName:null,surname:"Gedikli",slug:"semin-gedikli",fullName:"Semin Gedikli"},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen"},{id:"178367",title:"Dr.",name:"Seckin",middleName:null,surname:"Ozkanlar",slug:"seckin-ozkanlar",fullName:"Seckin Ozkanlar"}]}],mostDownloadedChaptersLast30Days:[{id:"44699",title:"Apoptosis and Activation-Induced Cell Death",slug:"apoptosis-and-activation-induced-cell-death",totalDownloads:2952,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"2857",slug:"apoptosis",title:"Apoptosis",fullTitle:"Apoptosis"},signatures:"Joaquín H. Patarroyo S. and Marlene I. Vargas V",authors:[{id:"141183",title:"Prof.",name:"Joaquín",middleName:null,surname:"Patarroyo",slug:"joaquin-patarroyo",fullName:"Joaquín Patarroyo"},{id:"146188",title:"Prof.",name:"Marlene",middleName:null,surname:"Vargas",slug:"marlene-vargas",fullName:"Marlene Vargas"}]},{id:"44689",title:"Drug Resistance and Molecular Cancer Therapy: Apoptosis Versus Autophagy",slug:"drug-resistance-and-molecular-cancer-therapy-apoptosis-versus-autophagy",totalDownloads:4017,totalCrossrefCites:2,totalDimensionsCites:14,abstract:null,book:{id:"2857",slug:"apoptosis",title:"Apoptosis",fullTitle:"Apoptosis"},signatures:"Rebecca T. Marquez, Bryan W. Tsao, Nicholas F. Faust and Liang Xu",authors:[{id:"19713",title:"Dr.",name:"Liang",middleName:null,surname:"Xu",slug:"liang-xu",fullName:"Liang Xu"},{id:"149902",title:"Dr.",name:"Rebecca",middleName:null,surname:"Marquez",slug:"rebecca-marquez",fullName:"Rebecca Marquez"}]},{id:"51334",title:"Free Radicals and Biomarkers Related to the Diagnosis of Cardiorenal Syndrome",slug:"free-radicals-and-biomarkers-related-to-the-diagnosis-of-cardiorenal-syndrome",totalDownloads:3764,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"The National Heart, Lung, and Blood Institute Working Group has postulated the cardiorenal syndrome (CRS) as an interaction between the kidneys and the cardiovascular system in which therapy to relieve congestive heart failure (HF) symptoms is limited by the further worsening renal function. CRS is classified from type I to V, taking into account the progression of the symptoms in terms of mechanisms, clinical conditions, and biomarkers. Experimental and clinical studies have shown the kidney as both a trigger and a target to sympathetic nervous system (SNS) overactivity. Renal damage and ischemia, activation of the renin angiotensin aldosterone system (RAAS), and dysfunction of nitric oxide (NO) system are associated with kidney adrenergic activation. Indeed, the imbalances of RAAS and/or SNS share an important common process in CRS: the activation and production of free radicals, especially reactive oxygen species (ROS). The present chapter addresses connections of the free radicals as potential biomarkers as the imbalances in the RAAS and the SNS are developed. Understanding the involvement of free radicals in CRS may bring knowledge to design studies in order to develop accurate pharmacological interventions.",book:{id:"5121",slug:"free-radicals-and-diseases",title:"Free Radicals and Diseases",fullTitle:"Free Radicals and Diseases"},signatures:"Carolina B.A. Restini, Bruna F.M. Pereira and Tufik M. Geleilete",authors:[{id:"178144",title:"Dr.",name:"Carolina",middleName:null,surname:"Baraldi A. Restini",slug:"carolina-baraldi-a.-restini",fullName:"Carolina Baraldi A. Restini"},{id:"178387",title:"Ms.",name:"Bruna",middleName:null,surname:"Pereira",slug:"bruna-pereira",fullName:"Bruna Pereira"},{id:"184159",title:"Dr.",name:"Tufik",middleName:null,surname:"Geleilete",slug:"tufik-geleilete",fullName:"Tufik Geleilete"}]},{id:"52345",title:"Oxidative Stress in Invertebrate Systems",slug:"oxidative-stress-in-invertebrate-systems",totalDownloads:2477,totalCrossrefCites:9,totalDimensionsCites:23,abstract:"Invertebrates have been valuable research models in the discovery of many scientific principles owing to the numerous advantages they provide. Throughout the life cycle, many of them thrive in pathogen-rich environments, manage harsh weathers, exposed to a number of allochemicals, and adapt well to both terrestrial and marine ecosystems. Their remarkable ability to cope up with the enormous oxidative stress generated in all these circumstances, make them attractive models in this field of research. Endocrine control of oxidative stress in insects is recently emerging. Adipokinetic hormone, glucagon, ecdysteroids and juvenile hormone have been implicated in antioxidative protective role in insects. Drosophila and Caenorhabditis elegans have provided the largest body of evidence addressing the free radical theory of ageing. Oxidative stress is also induced by pesticides/insecticides. In mollusks, pesticides exert their biological effects via generation of ROS. Oxidative stress has been shown to be associated with exposure to several organophosphorous compounds and different classes of pyrethroids. Malathion is a potential hazard to the environment. Adverse effects induced by malathion in earthworms and insects have been reported. Information is now available in great detail on the role of ROS in modulating insect immunity during parasite invasion and bacterial infection. In Drosophila melanogaster ROS are actively produced in the midgut at a basal level in the presence of commensal microbiota and highly generated upon bacterial challenge. The involvement of reactive oxygen species (ROS) in mosquito immunity against bacteria and Plasmodium was investigated in the malaria vector Anopheles gambiae. The concentration of ROS increased in sand fly midguts after they fed on the insect pathogen Serratia marcescens. Elevated oxidative stress was previously reported for a mosquito line experimentally infected with Wolbachia, indicating that oxidative stress may be important for Wolbachia-mediated antiviral protection. In a nutshell, this chapter highlights the current advances of oxidative stress in invertebrate model systems and its implications.",book:{id:"5121",slug:"free-radicals-and-diseases",title:"Free Radicals and Diseases",fullTitle:"Free Radicals and Diseases"},signatures:"R.K. Chaitanya, K. Shashank and P. Sridevi",authors:[{id:"178087",title:"Dr.",name:"Rk",middleName:null,surname:"Chaitanya",slug:"rk-chaitanya",fullName:"Rk Chaitanya"}]},{id:"51782",title:"Is Extracellular Matrix a Castle Against to Invasion of Cancer Cells?",slug:"is-extracellular-matrix-a-castle-against-to-invasion-of-cancer-cells-",totalDownloads:2337,totalCrossrefCites:2,totalDimensionsCites:4,abstract:"Metastasis is a complicated course that involves the spread of a neoplasm to distant parts of the body from its original site. A cancer cell must complete a series of steps before it becomes a clinically detectable lesion for successful colonization in the body. These are separation from the primary tumor, invasion and penetration of their basement membranes, entry into the blood vessels and survival within blood, and entry into lymphatics. A major challenge in extracellular matrix (ECM) biology is to understand the roles of the ECM and how disruption of ECM dynamics may contribute to cancer. A noteworthy area of forthcoming cancer research will be to determine whether abnormal ECM could be an effective cancer therapeutic target. We should understand how ECM composition and organization are normally maintained and how they may be deregulated in cancer. So the aims of this chapter were to focus on extracellular matrix. Invasion and metastatic skills, properties and functions of the ECM, abnormal ECM dynamics, tumor microenvironment and ECM, details of ECM invasion, role of ECM and ECM‐associated proteins in metastasis, tumor dormant and metastatic process, essential component of the niches, role of the ECM in tumor angiogenesis and lymphangiogenesis are be briefly explained in this chapter.",book:{id:"5267",slug:"tumor-metastasis",title:"Tumor Metastasis",fullTitle:"Tumor Metastasis"},signatures:"Serdar Altınay",authors:[{id:"185324",title:"Associate Prof.",name:"Serdar",middleName:null,surname:"Altınay",slug:"serdar-altinay",fullName:"Serdar Altınay"}]}],onlineFirstChaptersFilter:{topicId:"411",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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